Imagine a future where artificial intelligence quietly shoulders the drudgery of software development: refactoring tangled code, migrating legacy systems, and hunting down race conditions, so that human engineers can devote themselves to architecture, design, and the genuinely novel problems still beyond a machine’s reach. Recent advances appear to have nudged that future tantalizingly close, but a new paper by researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and several collaborating institutions argues that this potential future reality demands a hard look at present-day challenges. 

Titled “Challenges and Paths Towards AI for Software Engineering,” the work maps the many software-engineering tasks beyond code generation, identifies current bottlenecks, and highlights research directions to overcome them, aiming to let humans focus on high-level design while routine work is automated. 

“Everyone is talking about how we don’t need programmers anymore, and there’s all this automation now available,” says Armando Solar‑Lezama, MIT professor of electrical engineering and computer science, CSAIL principal investigator, and senior author of the study. “On the one hand, the field has made tremendous progress. We have tools that are way more powerful than any we’ve seen before. But there’s also a long way to go toward really getting the full promise of automation that we would expect.”

Solar-Lezama argues that popular narratives often shrink software engineering to “the undergrad programming part: someone hands you a spec for a little function and you implement it, or solving LeetCode-style programming interviews.” Real practice is far broader. It includes everyday refactors that polish design, plus sweeping migrations that move millions of lines from COBOL to Java and reshape entire businesses. It requires nonstop testing and analysis — fuzzing, property-based testing, and other methods — to catch concurrency bugs, or patch zero-day flaws. And it involves the maintenance grind: documenting decade-old code, summarizing change histories for new teammates, and reviewing pull requests for style, performance, and security.

Industry-scale code optimization — think re-tuning GPU kernels or the relentless, multi-layered refinements behind Chrome’s V8 engine — remains stubbornly hard to evaluate. Today’s headline metrics were designed for short, self-contained problems, and while multiple-choice tests still dominate natural-language research, they were never the norm in AI-for-code. The field’s de facto yardstick, SWE-Bench, simply asks a model to patch a GitHub issue: useful, but still akin to the “undergrad programming exercise” paradigm. It touches only a few hundred lines of code, risks data leakage from public repositories, and ignores other real-world contexts — AI-assisted refactors, human–AI pair programming, or performance-critical rewrites that span millions of lines. Until benchmarks expand to capture those higher-stakes scenarios, measuring progress — and thus accelerating it — will remain an open challenge.

If measurement is one obstacle, human‑machine communication is another. First author Alex  Gu, an MIT graduate student in electrical engineering and computer science, sees today’s interaction as “a thin line of communication.” When he asks a system to generate code, he often receives a large, unstructured file and even a set of unit tests, yet those tests tend to be superficial. This gap extends to the AI’s ability to effectively use the wider suite of software engineering tools, from debuggers to static analyzers, that humans rely on for precise control and deeper understanding. “I don’t really have much control over what the model writes,” he says. “Without a channel for the AI to expose its own confidence — ‘this part’s correct … this part, maybe double‑check’ — developers risk blindly trusting hallucinated logic that compiles, but collapses in production. Another critical aspect is having the AI know when to defer to the user for clarification.” 

Scale compounds these difficulties. Current AI models struggle profoundly with large code bases, often spanning millions of lines. Foundation models learn from public GitHub, but “every company’s code base is kind of different and unique,” Gu says, making proprietary coding conventions and specification requirements fundamentally out of distribution. The result is code that looks plausible yet calls non‑existent functions, violates internal style rules, or fails continuous‑integration pipelines. This often leads to AI-generated code that “hallucinates,” meaning it creates content that looks plausible but doesn’t align with the specific internal conventions, helper functions, or architectural patterns of a given company. 

Models will also often retrieve incorrectly, because it retrieves code with a similar name (syntax) rather than functionality and logic, which is what a model might need to know how to write the function. “Standard retrieval techniques are very easily fooled by pieces of code that are doing the same thing but look different,” says Solar‑Lezama. 

The authors mention that since there is no silver bullet to these issues, they’re calling instead for community‑scale efforts: richer, having data that captures the process of developers writing code (for example, which code developers keep versus throw away, how code gets refactored over time, etc.), shared evaluation suites that measure progress on refactor quality, bug‑fix longevity, and migration correctness; and transparent tooling that lets models expose uncertainty and invite human steering rather than passive acceptance. Gu frames the agenda as a “call to action” for larger open‑source collaborations that no single lab could muster alone. Solar‑Lezama imagines incremental advances—“research results taking bites out of each one of these challenges separately”—that feed back into commercial tools and gradually move AI from autocomplete sidekick toward genuine engineering partner.

“Why does any of this matter? Software already underpins finance, transportation, health care, and the minutiae of daily life, and the human effort required to build and maintain it safely is becoming a bottleneck. An AI that can shoulder the grunt work — and do so without introducing hidden failures — would free developers to focus on creativity, strategy, and ethics” says Gu. “But that future depends on acknowledging that code completion is the easy part; the hard part is everything else. Our goal isn’t to replace programmers. It’s to amplify them. When AI can tackle the tedious and the terrifying, human engineers can finally spend their time on what only humans can do.”

“With so many new works emerging in AI for coding, and the community often chasing the latest trends, it can be hard to step back and reflect on which problems are most important to tackle,” says Baptiste Rozière, an AI scientist at Mistral AI, who wasn’t involved in the paper. “I enjoyed reading this paper because it offers a clear overview of the key tasks and challenges in AI for software engineering. It also outlines promising directions for future research in the field.”

Gu and Solar-Lezama wrote the paper with University of California at Berkeley Professor Koushik Sen and PhD students Naman Jain and Manish Shetty, Cornell University Assistant Professor Kevin Ellis and PhD student Wen-Ding Li, Stanford University Assistant Professor Diyi Yang and PhD student Yijia Shao, and incoming Johns Hopkins University assistant professor Ziyang Li. Their work was supported, in part, by the National Science Foundation (NSF), SKY Lab industrial sponsors and affiliates, Intel Corp. through an NSF grant, and the Office of Naval Research.

The researchers are presenting their work at the International Conference on Machine Learning (ICML). 

MIT researchers have developed a new theoretical framework for studying the mechanisms of treatment interactions. Their approach allows scientists to efficiently estimate how combinations of treatments will affect a group of units, such as cells, enabling a researcher to perform fewer costly experiments while gathering more accurate data.

As an example, to study how interconnected genes affect cancer cell growth, a biologist might need to use a combination of treatments to target multiple genes at once. But because there could be billions of potential combinations for each round of the experiment, choosing a subset of combinations to test might bias the data their experiment generates. 

In contrast, the new framework considers the scenario where the user can efficiently design an unbiased experiment by assigning all treatments in parallel, and can control the outcome by adjusting the rate of each treatment.

The MIT researchers theoretically proved a near-optimal strategy in this framework and performed a series of simulations to test it in a multiround experiment. Their method minimized the error rate in each instance.

This technique could someday help scientists better understand disease mechanisms and develop new medicines to treat cancer or genetic disorders.

“We’ve introduced a concept people can think more about as they study the optimal way to select combinatorial treatments at each round of an experiment. Our hope is this can someday be used to solve biologically relevant questions,” says graduate student Jiaqi Zhang, an Eric and Wendy Schmidt Center Fellow and co-lead author of a paper on this experimental design framework.

She is joined on the paper by co-lead author Divya Shyamal, an MIT undergraduate; and senior author Caroline Uhler, the Andrew and Erna Viterbi Professor of Engineering in EECS and the MIT Institute for Data, Systems, and Society (IDSS), who is also director of the Eric and Wendy Schmidt Center and a researcher at MIT’s Laboratory for Information and Decision Systems (LIDS). The research was recently presented at the International Conference on Machine Learning.

Simultaneous treatments

Treatments can interact with each other in complex ways. For instance, a scientist trying to determine whether a certain gene contributes to a particular disease symptom may have to target several genes simultaneously to study the effects.

To do this, scientists use what are known as combinatorial perturbations, where they apply multiple treatments at once to the same group of cells.

“Combinatorial perturbations will give you a high-level network of how different genes interact, which provides an understanding of how a cell functions,” Zhang explains.

Since genetic experiments are costly and time-consuming, the scientist aims to select the best subset of treatment combinations to test, which is a steep challenge due to the huge number of possibilities.

Picking a suboptimal subset can generate biased results by focusing only on combinations the user selected in advance.

The MIT researchers approached this problem differently by looking at a probabilistic framework. Instead of focusing on a selected subset, each unit randomly takes up combinations of treatments based on user-specified dosage levels for each treatment.

The user sets dosage levels based on the goal of their experiment — perhaps this scientist wants to study the effects of four different drugs on cell growth. The probabilistic approach generates less biased data because it does not restrict the experiment to a predetermined subset of treatments.

The dosage levels are like probabilities, and each cell receives a random combination of treatments. If the user sets a high dosage, it is more likely most of the cells will take up that treatment. A smaller subset of cells will take up that treatment if the dosage is low.

“From there, the question is how do we design the dosages so that we can estimate the outcomes as accurately as possible? This is where our theory comes in,” Shyamal adds.

Their theoretical framework shows the best way to design these dosages so one can learn the most about the characteristic or trait they are studying.

After each round of the experiment, the user collects the results and feeds those back into the experimental framework. It will output the ideal dosage strategy for the next round, and so on, actively adapting the strategy over multiple rounds.

Optimizing dosages, minimizing error

The researchers proved their theoretical approach generates optimal dosages, even when the dosage levels are affected by a limited supply of treatments or when noise in the experimental outcomes varies at each round.

In simulations, this new approach had the lowest error rate when comparing estimated and actual outcomes of multiround experiments, outperforming two baseline methods.

In the future, the researchers want to enhance their experimental framework to consider interference between units and the fact that certain treatments can lead to selection bias. They would also like to apply this technique in a real experimental setting.

“This is a new approach to a very interesting problem that is hard to solve. Now, with this new framework in hand, we can think more about the best way to design experiments for many different applications,” Zhang says.

This research is funded, in part, by the Advanced Undergraduate Research Opportunities Program at MIT, Apple, the National Institutes of Health, the Office of Naval Research, the Department of Energy, the Eric and Wendy Schmidt Center at the Broad Institute, and a Simons Investigator Award.

Scientists have long known that the brain’s visual system isn’t fully hardwired from the start — it becomes refined by what babies see — but the authors of a new MIT study still weren’t prepared for the degree of rewiring they observed when they took a first-ever look at the process in mice as it happened in real-time.

As the researchers in The Picower Institute for Learning and Memory tracked hundreds of “spine” structures housing individual network connections, or “synapses,” on the dendrite branches of neurons in the visual cortex over 10 days, they saw that only 40 percent of the ones that started the process survived. Refining binocular vision (integrating input from both eyes) required numerous additions and removals of spines along the dendrites to establish an eventual set of connections.

Former graduate student Katya Tsimring led the study, published this month in Nature Communications, which the team says is the first in which scientists tracked the same connections all the way through the “critical period,” when binocular vision becomes refined.

“What Katya was able to do is to image the same dendrites on the same neurons repeatedly over 10 days in the same live mouse through a critical period of development, to ask, what happens to the synapses or spines on them?,” says senior author Mriganka Sur, the Paul and Lilah Newton Professor in the Picower Institute and MIT’s Department of Brain and Cognitive Sciences. “We were surprised by how much change there is.”

Extensive turnover

In the experiments, young mice watched as black-and-white gratings with lines of specific orientations and directions of movement drifted across their field of view. At the same time, the scientists observed both the structure and activity of the neurons’ main body (or “soma”) and of the spines along their dendrites. By tracking the structure of 793 dendritic spines on 14 neurons at roughly Day 1, Day 5 and Day 10 of the critical period, they could quantify the addition and loss of the spines, and therefore the synaptic connections they housed. And by tracking their activity at the same time, they could quantify the visual information the neurons received at each synaptic connection. For example, a spine might respond to one specific orientation or direction of grating, several orientations, or might not respond at all. Finally, by relating a spine’s structural changes across the critical period to its activity, they sought to uncover the process by which synaptic turnover refined binocular vision.

Structurally, the researchers saw that 32 percent of the spines evident on Day 1 were gone by Day 5, and that 24 percent of the spines apparent on Day 5 had been added since Day 1. The period between Day 5 and Day 10 showed similar turnover: 27 percent were eliminated, but 24 percent were added. Overall, only 40 percent of the spines seen on Day 1 were still there on Day 10.

Meanwhile, only four of the 13 neurons they were tracking that responded to visual stimuli still responded on Day 10. The scientists don’t know for sure why the other nine stopped responding, at least to the stimuli they once responded to, but it’s likely they now served a different function.

What are the rules?

Having beheld this extensive wiring and rewiring, the scientists then asked what entitled some spines to survive over the 10-day critical period.

Previous studies have shown that the first inputs to reach binocular visual cortex neurons are from the “contralateral” eye on the opposite side of the head (so in the left hemisphere, the right eye’s inputs get there first), Sur says. These inputs drive a neuron’s soma to respond to specific visual properties such as the orientation of a line — for instance, a 45-degree diagonal. By the time the critical period starts, inputs from the “ipsilateral” eye on the same side of the head begin joining the race to visual cortex neurons, enabling some to become binocular.

It’s no accident that many visual cortex neurons are tuned to lines of different directions in the field of view, Sur says.

“The world is made up of oriented line segments,” Sur notes. “They may be long line segments; they may be short line segments. But the world is not just amorphous globs with hazy boundaries. Objects in the world — trees, the ground, horizons, blades of grass, tables, chairs — are bounded by little line segments.”

Because the researchers were tracking activity at the spines, they could see how often they were active and what orientation triggered that activity. As the data accumulated, they saw that spines were more likely to endure if (a) they were more active, and (b) they responded to the same orientation as the one the soma preferred. Notably, spines that responded to both eyes were more active than spines that responded to just one, meaning binocular spines were more likely to survive than non-binocular ones.

“This observation provides compelling evidence for the ‘use it or lose it’ hypothesis,” says Tsimring. “The more active a spine was, the more likely it was to be retained during development.”

The researchers also noticed another trend. Across the 10 days, clusters emerged along the dendrites in which neighboring spines were increasingly likely to be active at the same time. Other studies have shown that by clustering together, spines are able to combine their activity to be greater than they would be in isolation.

By these rules, over the course of the critical period, neurons apparently refined their role in binocular vision by selectively retaining inputs that reinforced their budding orientation preferences, both via their volume of activity (a synaptic property called “Hebbian plasticity”) and their correlation with their neighbors (a property called “heterosynaptic plasticity”). To confirm that these rules were enough to produce the outcomes they were seeing under the microscope, they built a computer model of a neuron, and indeed the model recapitulated the same trends as what they saw in the mice.

“Both mechanisms are necessary during the critical period to drive the turnover of spines that are misaligned to the soma and to neighboring spine pairs,” the researchers wrote, “which ultimately leads to refinement of [binocular] responses such as orientation matching between the two eyes.”

In addition to Tsimring and Sur, the paper’s other authors are Kyle Jenks, Claudia Cusseddu, Greggory Heller, Jacque Pak Kan Ip, and Julijana Gjorgjieva. Funding sources for the research came from the National Institutes of Health, The Picower Institute for Learning and Memory, and the Freedom Together Foundation.

In 1954, the world’s first successful organ transplant took place at Brigham and Women’s Hospital, in the form of a kidney donated from one twin to the other. At the time, a group of doctors and scientists had correctly theorized that the recipient’s antibodies were unlikely to reject an organ from an identical twin. One Nobel Prize and a few decades later, advancements in immune-suppressing drugs increased the viability of and demand for organ transplants. Today, over 1 million organ transplants have been performed in the United States, more than any other country in the world.

The impressive scale of this achievement was made possible due to advances in organ matching systems: The first computer-based organ matching system was released in 1977. Despite continued innovation in computing, medicine, and matching technology over the years, over 100,000 people in the U.S. are currently on the national transplant waiting list and 13 people die each day waiting for an organ transplant. 

Most computational research in organ allocation is focused on the initial stages, when waitlisted patients are being prioritized for organ transplants. In a new paper presented at ACM Conference on Fairness, Accountability, and Transparency (FAccT) in Athens, Greece, researchers from MIT and Massachusetts General Hospital focused on the final, less-studied stage: organ offer acceptance, when an offer is made and the physician at the transplant center decides on behalf of the patient whether to accept or reject the offered organ. 

“I don’t think we were terribly surprised, but we were obviously disappointed,” co-first author and MIT PhD student Hammaad Adam says. Using computational models to analyze transplantation data from over 160,000 transplant candidates in the Scientific Registry of Transplant Recipients (SRTR) between 2010 and 2020, the researchers found that physicians were overall less likely to accept liver and lung offers on behalf of Black candidates, resulting in additional barriers for Black patients in the organ offer acceptance process.  

For livers, Black patients had 7 percent lower odds of offer acceptance than white patients. When it came to lungs, the disparity became even larger, with 20 percent lower odds of having an offer acceptance than white patients with similar characteristics.

The data don’t necessarily point to clinician bias as the main influence. “The bigger takeaway is that even if there are factors that justify clinical decision-making, there could be clinical conditions that we didn’t control for, that are more common for Black patients,” Adam explains. If the wait-list fails to account for certain patterns in decision-making, they could create obstacles in the process even if the process itself is “unbiased.”

The researchers also point out that high variability in offer acceptance and risk tolerances among transplant centers is a potential factor complicating the decision-making process. Their FAccT paper references a 2020 paper published in JAMA Cardiology, which concluded that wait-list candidates listed at transplant centers with lower offer acceptance rates have a higher likelihood of mortality. 

Another key finding was that an offer was more likely to be accepted if the donor and candidate were of the same race. The paper describes this trend as “concerning,” given the historical inequities in organ procurement that have limited donation from racial and ethnic minority groups. 

Previous work from Adam and his collaborators has aimed to address this gap. Last year, they compiled and released Organ Retrieval and Collection of Health Information for Donation (ORCHID), the first multi-center dataset describing the performance of organ procurement organizations (OPOs). ORCHID contains 10 years’ worth of OPO data, and is intended to facilitate research that addresses bias in organ procurement.

“Being able to do good work in this field takes time,” says Adam, who notes that the entirety of the organ offer acceptance project took years to complete. To his knowledge, only one paper to date studies the association between offer acceptance and race. 

While the bureaucratic and highly interdisciplinary nature of clinical AI projects can dissuade computer science graduate students from pursuing them, Adam committed to the project for the duration of his PhD in the lab of associate professor of electrical engineering Marzyeh Ghassemi, an affiliate of the MIT Jameel Clinic and the Institute of Medical Engineering and Sciences.

To graduate students interested in pursuing clinical AI research projects, Adam recommends that they “free [themselves] from the cycle of publishing every four months.”

“I found it freeing, to be honest — it’s OK if these collaborations take a while,” he says. “It’s hard to avoid that. I made the conscious choice a few years ago and I was happy doing that work.”

This work was supported with funding from the MIT Jameel Clinic. It was also supported, in part, by Takeda Development Center Americas Inc. (successor in interest to Millennium Pharmaceuticals Inc.), an NIH Ruth L. Kirschstein National Research Service Award, a CIFAR AI Chair at the Vector Institute, and by the National Institutes of Health.

Researchers at MIT and other institutions have identified compounds that can fight off viral infection by activating a defense pathway inside host cells. These compounds, they believe, could be used as antiviral drugs that work against not just one but any kind of virus.

The researchers identified these compounds, which activate a host cell defense system known as the integrated stress response pathway, in a screen of nearly 400,000 molecules. In tests in human cells, the researchers showed that the compounds help cells fend off infection from RSV, herpes virus, and Zika virus. They also proved effective in combating herpes infection in a mouse model.

The research team now plans to test the compounds against additional viruses, in hopes of developing them for eventual clinical trials.

“We’re very excited about this work, which allows us to harness the stress response of the host cells to arrive at a means to identify and develop broad-spectrum antivirals,” says James Collins, the Termeer Professor of Medical Engineering and Science in MIT’s Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering.

Collins and Maxwell Wilson, an associate professor of molecular biology at the University of California, Santa Barbara and chief scientific officer of Integrated Biosciences, are the senior authors of the new study, which appears in Cell. Felix Wong, a former MIT postdoc and chief executive officer of Integrated Biosciences, is the lead author of the paper. In addition to MIT, UCSB, and Integrated Biosciences, the research team also includes scientists from Illumina Ventures and Princeton University.

Boosting cell defense

In human cells, the integrated stress response pathway is turned on in response to viral infection as well as other types of stress such as starvation. During viral infection, the pathway is triggered by double-stranded RNA, a molecule produced during the replication cycle of viruses. When that RNA is detected, the cell shuts down protein synthesis, which blocks the virus from producing the proteins it needs to replicate.

Compounds that boost this pathway, the researchers believe, could be good candidates for new antiviral drugs that could combat any type of virus.

“Typically, how antivirals are developed is that you develop one antiviral for one specific virus,” Wong says. “In this case, we hypothesized that being able to modulate the host cell stress response might give us a new class of broad-spectrum antivirals — compounds that directly act on the host cells to alter something fundamental about how all viruses replicate.”

To help them identify compounds that would enhance the activity of this pathway during viral infection, the researchers invented a novel optogenetic screen. Optogenetics is a bioengineering technique that allows researchers to insert light-sensitive proteins into the genome of a cell. In this case, the researchers engineered modifications to a protein called PKR, which turns on the stress pathway, so that they could turn it on with light.

Using this technique, the researchers screened a library of nearly 400,000 commercially available and proprietary chemical compounds. Each of these compounds was applied to human cells as the cells were also exposed to blue light, which simulated viral infection by activating PKR.

By measuring the cells’ survival rates, the researchers could determine which compounds boosted activation of the pathway and amplified the cells’ ability to shut down viral reproduction. This screen yielded about 3,500 compounds with potential antiviral activity, which were evaluated further.

“If the pathway were turned on in response to viral infection, what our compounds do is they turn it on full blast,” Wong says. “Even in the presence of a small amount of virus, if the pathway is triggered, then the antiviral response is also maximized.”

Fighting infection

The researchers then selected eight of the most promising compounds and screened them for their ability to kill viruses while avoiding harmful effects in human cells. Based on these tests, the researchers chose three top candidates, which they called IBX-200, IBX-202, and IBX-204.

In cells that were infected with either Zika virus, herpes virus, or RSV, treatment with these compounds significantly reduced the amount of virus in the cells. The researchers then tested one of the compounds, IBX-200, in mice infected with herpes virus, and found that it was able to reduce the viral load and improve symptoms.

Experiments showed that these compounds appear to turn on an enzyme that is involved in detecting stress. This activates the stress response pathway and primes the cells to become more responsive to viral infection. When applied to cells that are not already infected, the compounds have no effect.

The researchers now plan to evaluate their lead candidates against a broader range of viruses. They also aim to identify additional compounds that activate the integrated stress response, as well as other cellular stress pathways with the potential to clear viral or bacterial infections.

The research was funded by the Defense Threat Reduction Agency, the National Science Foundation, the U.S. Army Research Office, and Integrated Biosciences.

When ChatGPT or Gemini give what seems to be an expert response to your burning questions, you may not realize how much information it relies on to give that reply. Like other popular generative artificial intelligence (AI) models, these chatbots rely on backbone systems called foundation models that train on billions, or even trillions, of data points.

In a similar vein, engineers are hoping to build foundation models that train a range of robots on new skills like picking up, moving, and putting down objects in places like homes and factories. The problem is that it’s difficult to collect and transfer instructional data across robotic systems. You could teach your system by teleoperating the hardware step-by-step using technology like virtual reality (VR), but that can be time-consuming. Training on videos from the internet is less instructive, since the clips don’t provide a step-by-step, specialized task walk-through for particular robots.

A simulation-driven approach called “PhysicsGen” from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Robotics and AI Institute customizes robot training data to help robots find the most efficient movements for a task. The system can multiply a few dozen VR demonstrations into nearly 3,000 simulations per machine. These high-quality instructions are then mapped to the precise configurations of mechanical companions like robotic arms and hands. 

PhysicsGen creates data that generalize to specific robots and condition via a three-step process. First, a VR headset tracks how humans manipulate objects like blocks using their hands. These interactions are mapped in a 3D physics simulator at the same time, visualizing the key points of our hands as small spheres that mirror our gestures. For example, if you flipped a toy over, you’d see 3D shapes representing different parts of your hands rotating a virtual version of that object.

The pipeline then remaps these points to a 3D model of the setup of a specific machine (like a robotic arm), moving them to the precise “joints” where a system twists and turns. Finally, PhysicsGen uses trajectory optimization — essentially simulating the most efficient motions to complete a task — so the robot knows the best ways to do things like repositioning a box.

Each simulation is a detailed training data point that walks a robot through potential ways to handle objects. When implemented into a policy (or the action plan that the robot follows), the machine has a variety of ways to approach a task, and can try out different motions if one doesn’t work.

“We’re creating robot-specific data without needing humans to re-record specialized demonstrations for each machine,” says Lujie Yang, an MIT PhD student in electrical engineering and computer science and CSAIL affiliate who is the lead author of a new paper introducing the project. “We’re scaling up the data in an autonomous and efficient way, making task instructions useful to a wider range of machines.”

Generating so many instructional trajectories for robots could eventually help engineers build a massive dataset to guide machines like robotic arms and dexterous hands. For example, the pipeline might help two robotic arms collaborate on picking up warehouse items and placing them in the right boxes for deliveries. The system may also guide two robots to work together in a household on tasks like putting away cups.

PhysicsGen’s potential also extends to converting data designed for older robots or different environments into useful instructions for new machines. “Despite being collected for a specific type of robot, we can revive these prior datasets to make them more generally useful,” adds Yang.

Addition by multiplication

PhysicsGen turned just 24 human demonstrations into thousands of simulated ones, helping both digital and real-world robots reorient objects.

Yang and her colleagues first tested their pipeline in a virtual experiment where a floating robotic hand needed to rotate a block into a target position. The digital robot executed the task at a rate of 81 percent accuracy by training on PhysicGen’s massive dataset, a 60 percent improvement from a baseline that only learned from human demonstrations.

The researchers also found that PhysicsGen could improve how virtual robotic arms collaborate to manipulate objects. Their system created extra training data that helped two pairs of robots successfully accomplish tasks as much as 30 percent more often than a purely human-taught baseline.

In an experiment with a pair of real-world robotic arms, the researchers observed similar improvements as the machines teamed up to flip a large box into its designated position. When the robots deviated from the intended trajectory or mishandled the object, they were able to recover mid-task by referencing alternative trajectories from their library of instructional data.

Senior author Russ Tedrake, who is the Toyota Professor of Electrical Engineering and Computer Science, Aeronautics and Astronautics, and Mechanical Engineering at MIT, adds that this imitation-guided data generation technique combines the strengths of human demonstration with the power of robot motion planning algorithms.

“Even a single demonstration from a human can make the motion planning problem much easier,” says Tedrake, who is also a senior vice president of large behavior models at the Toyota Research Institute and CSAIL principal investigator. “In the future, perhaps the foundation models will be able to provide this information, and this type of data generation technique will provide a type of post-training recipe for that model.”

The future of PhysicsGen

Soon, PhysicsGen may be extended to a new frontier: diversifying the tasks a machine can execute.

“We’d like to use PhysicsGen to teach a robot to pour water when it’s only been trained to put away dishes, for example,” says Yang. “Our pipeline doesn’t just generate dynamically feasible motions for familiar tasks; it also has the potential of creating a diverse library of physical interactions that we believe can serve as building blocks for accomplishing entirely new tasks a human hasn’t demonstrated.”

Creating lots of widely applicable training data may eventually help build a foundation model for robots, though MIT researchers caution that this is a somewhat distant goal. The CSAIL-led team is investigating how PhysicsGen can harness vast, unstructured resources — like internet videos — as seeds for simulation. The goal: transform everyday visual content into rich, robot-ready data that could teach machines to perform tasks no one explicitly showed them.

Yang and her colleagues also aim to make PhysicsGen even more useful for robots with diverse shapes and configurations in the future. To make that happen, they plan to leverage datasets with demonstrations of real robots, capturing how robotic joints move instead of human ones.

The researchers also plan to incorporate reinforcement learning, where an AI system learns by trial and error, to make PhysicsGen expand its dataset beyond human-provided examples. They may augment their pipeline with advanced perception techniques to help a robot perceive and interpret their environment visually, allowing the machine to analyze and adapt to the complexities of the physical world.

For now, PhysicsGen shows how AI can help us teach different robots to manipulate objects within the same category, particularly rigid ones. The pipeline may soon help robots find the best ways to handle soft items (like fruits) and deformable ones (like clay), but those interactions aren’t easy to simulate yet.

Yang and Tedrake wrote the paper with two CSAIL colleagues: co-lead author and MIT PhD student Hyung Ju “Terry” Suh SM ’22 and MIT PhD student Bernhard Paus Græsdal. Robotics and AI Institute researchers Tong Zhao ’22, MEng ’23, Tarik Kelestemur, Jiuguang Wang, and Tao Pang PhD ’23 are also authors. Their work was supported by the Robotics and AI Institute and Amazon.

The researchers recently presented their work at the Robotics: Science and Systems conference.

In order to produce effective targeted therapies for cancer, scientists need to isolate the genetic and phenotypic characteristics of cancer cells, both within and across different tumors, because those differences impact how tumors respond to treatment.

Part of this work requires a deep understanding of the RNA or protein molecules each cancer cell expresses, where it is located in the tumor, and what it looks like under a microscope.

Traditionally, scientists have looked at one or more of these aspects separately, but now a new deep learning AI tool, CellLENS (Cell Local Environment and Neighborhood Scan), fuses all three domains together, using a combination of convolutional neural networks and graph neural networks to build a comprehensive digital profile for every single cell. This allows the system to group cells with similar biology — effectively separating even those that appear very similar in isolation, but behave differently depending on their surroundings.

The study, published recently in Nature Immunology, details the results of a collaboration between researchers from MIT, Harvard Medical School, Yale University, Stanford University, and University of Pennsylvania — an effort led by Bokai Zhu, an MIT postdoc and member of the Broad Institute of MIT and Harvard and the Ragon Institute of MGH, MIT, and Harvard.

Zhu explains the impact of this new tool: “Initially we would say, oh, I found a cell. This is called a T cell. Using the same dataset, by applying CellLENS, now I can say this is a T cell, and it is currently attacking a specific tumor boundary in a patient.

“I can use existing information to better define what a cell is, what is the subpopulation of that cell, what that cell is doing, and what is the potential functional readout of that cell. This method may be used to identify a new biomarker, which provides specific and detailed information about diseased cells, allowing for more targeted therapy development.”

This is a critical advance because current methodologies often miss critical molecular or contextual information — for example, immunotherapies may target cells that only exist at the boundary of a tumor, limiting efficacy. By using deep learning, the researchers can detect many different layers of information with CellLENS, including morphology and where the cell is spatially in a tissue.

When applied to samples from healthy tissue and several types of cancer, including lymphoma and liver cancer, CellLENS uncovered rare immune cell subtypes and revealed how their activity and location relate to disease processes — such as tumor infiltration or immune suppression.

These discoveries could help scientists better understand how the immune system interacts with tumors and pave the way for more precise cancer diagnostics and immunotherapies.

“I’m extremely excited by the potential of new AI tools, like CellLENS, to help us more holistically understand aberrant cellular behaviors within tissues,” says co-author Alex K. Shalek, the director of the Institute for Medical Engineering and Science (IMES), the J. W. Kieckhefer Professor in IMES and Chemistry, and an extramural member of the Koch Institute for Integrative Cancer Research at MIT, as well as an Institute member of the Broad Institute and a member of the Ragon Institute. “We can now measure a tremendous amount of information about individual cells and their tissue contexts with cutting-edge, multi-omic assays. Effectively leveraging that data to nominate new therapeutic leads is a critical step in developing improved interventions. When coupled with the right input data and careful downsteam validations, such tools promise to accelerate our ability to positively impact human health and wellness.”

For many years, health experts have been concerned about “food deserts,” places where residents lack good nutritional options. Now, an MIT-led study of three major global cities uses a new, granular method to examine the issue, and concludes that having fewer and less nutritional eating options nearby correlates with obesity and other health outcomes.

Rather than just mapping geographic areas, the researchers examined the dietary value of millions of food items on roughly 30,000 restaurant menus and derived a more precise assessment of the connection between neighborhoods and nutrition.

“We show that what is sold in a restaurant has a direct correlation to people’s health,” says MIT researcher Fabio Duarte, co-author of a newly published paper outlining the study’s results. “The food landscape matters.”

The open-access paper, “Data-driven nutritional assessment of urban food landscapes: insights from Boston, London, Dubai,” was published this week in Nature: Scientific Reports.

The co-authors are Michael Tufano, a PhD student at Wageningen University, in the Netherlands; Duarte, associate director of MIT’s Senseable City Lab, which uses data to study cities as dynamic systems; Martina Mazzarello, a postdoc at the Senseable City Lab; Javad Eshtiyagh, a research fellow at the Senseable City Lab; Carlo Ratti, professor of the practice and director of the Senseable City Lab; and Guido Camps, a senior researcher at Wageningen University.

Scanning the menu

To conduct the study, the researchers examined menus from Boston, Dubai, and London, in the summer of 2023, compiling a database of millions of items available through popular food-delivery platforms. The team then evaluated the food items as rated by the USDA’s FoodData Central database, an information bank with 375,000 kinds of food products listed. The study deployed two main metrics, the Meal Balance Index, and the Nutrient-Rich Foods Index.

The researchers examined about 222,000 menu items from over 2,000 restaurants in Boston, about 1.6 million menu items from roughly 9,000 restaurants in Dubai, and about 3.1 million menu items from about 18,000 restaurants in London. In Boston, about 71 percent of the items were in the USDA database; in Dubai and London, that figure was 42 percent and 56 percent, respectively.

The team then rated the nutritional value of the items appearing on menus, and correlated the food data with health-outcome data from Boston and London. In London, they found a clear correlation between neighborhood menu offerings and obesity, or the lack thereof; with a slightly less firm correlation in Boston. Areas with food options that include a lot of dietary fibers, sometimes along with fruits and vegetables, tend to have better health data.

In Dubai, the researchers did not have the same types of health data available but did observe a strong correlation between rental prices and the nutritional value of neighborhood-level food, suggesting that wealthier residents have better nourishment options.

“At the item level, when we have less nutritional food, we see more cases of obsesity,” Tufano says. “It’s true that not only do we have more fast food in poor neighborhoods, but the nutritional value is not the same.”

Re-mapping the food landscape

By conducting the study in this fashion, the scholars added a layer of analysis to past studies of food deserts. While past work has broken ground by identifying neighborhoods and areas lacking good food access, this research makes a more comprehensive assessment of what people consume. The research moves toward evaluating the complex mix of food available in any given area, which can be true even of areas with more limited options.

“We were not satisfied with this idea that if you only have fast food, it’s a food desert, but if you have a Whole Foods, it’s not,” Duarte says. “It’s not necessarily like that.”

For the Senseable City Lab researchers, the study is a new technique further enabling them to understand city dynamics and the effects of the urban environment on health. Past lab studies have often focused on issues such as urban mobility, while extending to matters such as mobility and air pollution, among other topics.

Being able to study food and health at the neighborhood level, though, is still another example of the ways that data-rich spheres of life can be studied in close detail.

“When we started working on cities and data, the data resolution was so low,” Ratti says. “Today the amount of data is so immense we see this great opportunity to look at cities and see the influence of the urban environment as a big determinant of health. We see this as one of the new frontiers of our lab. It’s amazing how we can now look at this very precisely in cities.”

MIT researchers have developed a new bionic knee that can help people with above-the-knee amputations walk faster, climb stairs, and avoid obstacles more easily than they could with a traditional prosthesis.

Unlike prostheses in which the residual limb sits within a socket, the new system is directly integrated with the user’s muscle and bone tissue. This enables greater stability and gives the user much more control over the movement of the prosthesis.

Participants in a small clinical study also reported that the limb felt more like a part of their own body, compared to people who had more traditional above-the-knee amputations.

“A prosthesis that's tissue-integrated — anchored to the bone and directly controlled by the nervous system — is not merely a lifeless, separate device, but rather a system that is carefully integrated into human physiology, offering a greater level of prosthetic embodiment. It’s not simply a tool that the human employs, but rather an integral part of self,” says Hugh Herr, a professor of media arts and sciences, co-director of the K. Lisa Yang Center for Bionics at MIT, an associate member of MIT’s McGovern Institute for Brain Research, and the senior author of the new study.

Tony Shu PhD ’24 is the lead author of the paper, which appears today in Science.

Better control

Over the past several years, Herr’s lab has been working on new prostheses that can extract neural information from muscles left behind after an amputation and use that information to help guide a prosthetic limb.

During a traditional amputation, pairs of muscles that take turns stretching and contracting are usually severed, disrupting the normal agonist-antagonist relationship of the muscles. This disruption makes it very difficult for the nervous system to sense the position of a muscle and how fast it’s contracting.

Using the new surgical approach developed by Herr and his colleagues, known as agonist-antagonist myoneuronal interface (AMI), muscle pairs are reconnected during surgery so that they still dynamically communicate with each other within the residual limb. This sensory feedback helps the wearer of the prosthesis to decide how to move the limb, and also generates electrical signals that can be used to control the prosthetic limb.

In a 2024 study, the researchers showed that people with amputations below the knee who received the AMI surgery were able to walk faster and navigate around obstacles much more naturally than people with traditional below-the-knee amputations.

In the new study, the researchers extended the approach to better serve people with amputations above the knee. They wanted to create a system that could not only read out signals from the muscles using AMI but also be integrated into the bone, offering more stability and better sensory feedback.

To achieve that, the researchers developed a procedure to insert a titanium rod into the residual femur bone at the amputation site. This implant allows for better mechanical control and load bearing than a traditional prosthesis. Additionally, the implant contains 16 wires that collect information from electrodes located on the AMI muscles inside the body, which enables more accurate transduction of the signals coming from the muscles.

This bone-integrated system, known as e-OPRA, transmits AMI signals to a new robotic controller developed specifically for this study. The controller uses this information to calculate the torque necessary to move the prosthesis the way that the user wants it to move.

“All parts work together to better get information into and out of the body and better interface mechanically with the device,” Shu says. “We’re directly loading the skeleton, which is the part of the body that’s supposed to be loaded, as opposed to using sockets, which is uncomfortable and can lead to frequent skin infections.”

In this study, two subjects received the combined AMI and e-OPRA system, known as an osseointegrated mechanoneural prosthesis (OMP). These users were compared with eight who had the AMI surgery but not the e-OPRA implant, and seven users who had neither AMI nor e-OPRA. All subjects took a turn at using an experimental powered knee prosthesis developed by the lab.

The researchers measured the participants’ ability to perform several types of tasks, including bending the knee to a specified angle, climbing stairs, and stepping over obstacles. In most of these tasks, users with the OMP system performed better than the subjects who had the AMI surgery but not the e-OPRA implant, and much better than users of traditional prostheses.

“This paper represents the fulfillment of a vision that the scientific community has had for a long time — the implementation and demonstration of a fully physiologically integrated, volitionally controlled robotic leg,” says Michael Goldfarb, a professor of mechanical engineering and director of the Center for Intelligent Mechatronics at Vanderbilt University, who was not involved in the research. “This is really difficult work, and the authors deserve tremendous credit for their efforts in realizing such a challenging goal.”

A sense of embodiment

In addition to testing gait and other movements, the researchers also asked questions designed to evaluate participants’ sense of embodiment — that is, to what extent their prosthetic limb felt like a part of their own body.

Questions included whether the patients felt as if they had two legs, if they felt as if the prosthesis was part of their body, and if they felt in control of the prosthesis. Each question was designed to evaluate the participants’ feelings of agency, ownership of device, and body representation.

The researchers found that as the study went on, the two participants with the OMP showed much greater increases in their feelings of agency and ownership than the other subjects.

“Another reason this paper is significant is that it looks into these embodiment questions and it shows large improvements in that sensation of embodiment,” Herr says. “No matter how sophisticated you make the AI systems of a robotic prosthesis, it’s still going to feel like a tool to the user, like an external device. But with this tissue-integrated approach, when you ask the human user what is their body, the more it’s integrated, the more they’re going to say the prosthesis is actually part of self.”

The AMI procedure is now done routinely on patients with below-the-knee amputations at Brigham and Women’s Hospital, and Herr expects it will soon become the standard for above-the-knee amputations as well. The combined OMP system will need larger clinical trials to receive FDA approval for commercial use, which Herr expects may take about five years.

The research was funded by the Yang Tan Collective and DARPA.

Marine scientists have long marveled at how animals like fish and seals swim so efficiently despite having different shapes. Their bodies are optimized for efficient, hydrodynamic aquatic navigation so they can exert minimal energy when traveling long distances.

Autonomous vehicles can drift through the ocean in a similar way, collecting data about vast underwater environments. However, the shapes of these gliding machines are less diverse than what we find in marine life — go-to designs often resemble tubes or torpedoes, since they’re fairly hydrodynamic as well. Plus, testing new builds requires lots of real-world trial-and-error.

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the University of Wisconsin at Madison propose that AI could help us explore uncharted glider designs more conveniently. Their method uses machine learning to test different 3D designs in a physics simulator, then molds them into more hydrodynamic shapes. The resulting model can be fabricated via a 3D printer using significantly less energy than hand-made ones.

The MIT scientists say that this design pipeline could create new, more efficient machines that help oceanographers measure water temperature and salt levels, gather more detailed insights about currents, and monitor the impacts of climate change. The team demonstrated this potential by producing two gliders roughly the size of a boogie board: a two-winged machine resembling an airplane, and a unique, four-winged object resembling a flat fish with four fins.

Peter Yichen Chen, MIT CSAIL postdoc and co-lead researcher on the project, notes that these designs are just a few of the novel shapes his team’s approach can generate. “We’ve developed a semi-automated process that can help us test unconventional designs that would be very taxing for humans to design,” he says. “This level of shape diversity hasn’t been explored previously, so most of these designs haven’t been tested in the real world.”

But how did AI come up with these ideas in the first place? First, the researchers found 3D models of over 20 conventional sea exploration shapes, such as submarines, whales, manta rays, and sharks. Then, they enclosed these models in “deformation cages” that map out different articulation points that the researchers pulled around to create new shapes.

The CSAIL-led team built a dataset of conventional and deformed shapes before simulating how they would perform at different “angles-of-attack” — the direction a vessel will tilt as it glides through the water. For example, a swimmer may want to dive at a -30 degree angle to retrieve an item from a pool.

These diverse shapes and angles of attack were then used as inputs for a neural network that essentially anticipates how efficiently a glider shape will perform at particular angles and optimizes it as needed.

Giving gliding robots a lift

The team’s neural network simulates how a particular glider would react to underwater physics, aiming to capture how it moves forward and the force that drags against it. The goal: find the best lift-to-drag ratio, representing how much the glider is being held up compared to how much it’s being held back. The higher the ratio, the more efficiently the vehicle travels; the lower it is, the more the glider will slow down during its voyage.

Lift-to-drag ratios are key for flying planes: At takeoff, you want to maximize lift to ensure it can glide well against wind currents, and when landing, you need sufficient force to drag it to a full stop.

Niklas Hagemann, an MIT graduate student in architecture and CSAIL affiliate, notes that this ratio is just as useful if you want a similar gliding motion in the ocean.

“Our pipeline modifies glider shapes to find the best lift-to-drag ratio, optimizing its performance underwater,” says Hagemann, who is also a co-lead author on a paper that was presented at the International Conference on Robotics and Automation in June. “You can then export the top-performing designs so they can be 3D-printed.”

Going for a quick glide

While their AI pipeline seemed realistic, the researchers needed to ensure its predictions about glider performance were accurate by experimenting in more lifelike environments.

They first fabricated their two-wing design as a scaled-down vehicle resembling a paper airplane. This glider was taken to MIT’s Wright Brothers Wind Tunnel, an indoor space with fans that simulate wind flow. Placed at different angles, the glider’s predicted lift-to-drag ratio was only about 5 percent higher on average than the ones recorded in the wind experiments — a small difference between simulation and reality.

A digital evaluation involving a visual, more complex physics simulator also supported the notion that the AI pipeline made fairly accurate predictions about how the gliders would move. It visualized how these machines would descend in 3D.

To truly evaluate these gliders in the real world, though, the team needed to see how their devices would fare underwater. They printed two designs that performed the best at specific points-of-attack for this test: a jet-like device at 9 degrees and the four-wing vehicle at 30 degrees.

Both shapes were fabricated in a 3D printer as hollow shells with small holes that flood when fully submerged. This lightweight design makes the vehicle easier to handle outside of the water and requires less material to be fabricated. The researchers placed a tube-like device inside these shell coverings, which housed a range of hardware, including a pump to change the glider’s buoyancy, a mass shifter (a device that controls the machine’s angle-of-attack), and electronic components.

Each design outperformed a handmade torpedo-shaped glider by moving more efficiently across a pool. With higher lift-to-drag ratios than their counterpart, both AI-driven machines exerted less energy, similar to the effortless ways marine animals navigate the oceans.

As much as the project is an encouraging step forward for glider design, the researchers are looking to narrow the gap between simulation and real-world performance. They are also hoping to develop machines that can react to sudden changes in currents, making the gliders more adaptable to seas and oceans.

Chen adds that the team is looking to explore new types of shapes, particularly thinner glider designs. They intend to make their framework faster, perhaps bolstering it with new features that enable more customization, maneuverability, or even the creation of miniature vehicles.

Chen and Hagemann co-led research on this project with OpenAI researcher Pingchuan Ma SM ’23, PhD ’25. They authored the paper with Wei Wang, a University of Wisconsin at Madison assistant professor and recent CSAIL postdoc; John Romanishin ’12, SM ’18, PhD ’23; and two MIT professors and CSAIL members: lab director Daniela Rus and senior author Wojciech Matusik. Their work was supported, in part, by a Defense Advanced Research Projects Agency (DARPA) grant and the MIT-GIST Program.

Common sense tells us to run from molten lava flowing from active volcanoes. But MIT professors J. Jih, Cristina Parreño Alonso, and Skylar Tibbits — faculty in the Department of Architecture at the School of Architecture and Planning — have their bags packed to head to southwest Iceland in anticipation of an imminent volcanic eruption. The Nordic island nation is currently experiencing a period of intense seismic activity; seven volcanic eruptions have taken place in its southern peninsula in under a year.

Earlier this year, the faculty built and placed a series of lightweight, easily deployable steel structures close to the volcano, where a few of the recent eruptions have taken place; several more structures are on trucks waiting to be delivered to sites where fissures open and lava oozes out. Cameras are in place to record what happens when the lava meets and hits these structures to help understand the lava flows.

This new research explores what type of shapes and materials can be used to interact with lava and successfully divert it from heading in the direction of habitats or critical infrastructure that lie in its path. Their work is supported by a Professor Amar. G. Bose Research Grant.

“We’re trying to imagine new ways of conceptualizing infrastructure when it relates to lava and volcanic eruptions,” says Jih, an associate professor of the practice. “Lovely for us as designers, physical prototyping is the only way you can test some of these ideas out.” 

Currently, the Icelandic Department of Civic Protection and Emergency Management and an engineering group, EFLA, are diverting the lava with massive berms (approximately 44 to 54 yards in length and 9 yards in height) made from earth and stone.

Berms protecting the town of Grindavik, a power plant, and the popular Blue Lagoon geothermal spa have met with mixed results. In November 2024, a volcano erupted for the seventh time in less than a year, forcing the evacuation of town residents and the Blue Lagoon’s guests and employees. The latter’s parking lot was consumed by lava.

Sigurdur Thorsteinsson, chief brand, design, and innovation officer of the Blue Lagoon, as well as a designer and a partner in Design Group Italia, was on site for this eruption and several others.

“Some magma went into the city of Grindavik and three or four houses were destroyed,” says Thorsteinsson. “One of our employees watched her house go under magma on television, which was an emotional moment.”

While staff at the Blue Lagoon have become very efficient at evacuating guests, says Thorsteinsson, each eruption forces the tourist destination to close and townspeople to evacuate, disrupting lives and livelihoods.

“You cannot really stop the magma,” says Thorsteinsson, who is working with the MIT faculty on this research project. “It’s too powerful.”

Tibbits, associate professor of design research and founder and co-director of the Self-Assembly Lab, agrees. His research explores how to guide or work with the forces of nature.

Last year, Tibbits and Jih were in Iceland on another research project when erupting volcanoes interrupted their work. The two started thinking about how the lava could be redirected.

“The question is: Can we find more strategic interventions in the field that could work with the lava, rather than fight it?” says Tibbits.

To investigate what kinds of materials would withstand this type of interaction, they invited Parreño Alonso, a senior lecturer in the Department of Architecture, to join them.

“Cristina, being the department authority on magma, was an obvious and important partner for us,” says Jih with a smile.

Parreño Alonso has been working with volcanic rock for years and taught a series of design studios exploring volcanic rock as an architectural material. She also has proposed designing structures to engage directly with lava flows and recently has been examining volcanic rock in a molten state and melting basalt in MIT’s foundry with Michael Tarkanian, a senior lecturer in MIT’s Department of Materials Science and Engineering, and Metals Lab director. For this project, she is exploring the potential of molten rock as a substitute for concrete, a widely used material because of its pliability.

“It’s exciting how this idea of working with volcanoes was taking shape in parallel, from different angles, within the same department,” says Parreño Alonso. “I love how these parallel interests have led to such a beautiful collaboration.”

She also sees other opportunities by collaborating with these forces of nature.

“We are interested in the potential of generating something out of the interaction with the lava,” she says. “Could it be a landscape that becomes a park? There are many possibilities.”

The steel structures were first tested at MIT’s Metals Lab with Tarkanian and then built onsite in Iceland. The team wanted to make the structures lightweight so they could be quickly set up in the field, but strong enough so they wouldn’t be easily destroyed. Various designs were created; this iteration of the design has V-shaped structures that can guide the lava to flow around them, or they can be reconfigured as ramps or tunnels.

“There is a road that has been hit by many of the recent eruptions and must keep being rebuilt,” says Tibbits. “We created two ramps that could in the future serve as tunnels, allowing the lava to flow over the road and create a type of lava cave where the cars could drive under the cooled lava.”

Tibbits says they see the structures in the field now as an initial intervention. After documenting and studying how they interact with the lava, the architects will develop new iterations of what they believe will eventually become critical infrastructure for locations around the world with active volcanoes.

“If we can show and prove what kinds of shapes and structures and what kinds of materials can divert magma flows, I think it’s incredibly valuable research,” says Thorsteinsson.

Thorsteinsson lives in Italy half of the year and says the volcanoes there — Mount Etna in Sicily and Mount Vesuvius in the Gulf of Naples — pose a greater danger than those in Iceland because of the densely populated neighborhoods nearby. Volcanoes in Hawaii and Japan are in similarly populated areas.

“Whatever information you can learn about diverting magma flows to other directions and what kinds of structures are needed — it would be priceless,” he says.

For people with Type 1 diabetes, developing hypoglycemia, or low blood sugar, is an ever-present threat. When glucose levels become extremely low, it creates a life-threatening situation for which the standard treatment of care is injecting a hormone called glucagon.

As an emergency backup, for cases where patients may not realize that their blood sugar is dropping to dangerous levels, MIT engineers have designed an implantable reservoir that can remain under the skin and be triggered to release glucagon when blood sugar levels get too low.

This approach could also help in cases where hypoglycemia occurs during sleep, or for diabetic children who are unable to administer injections on their own.

“This is a small, emergency-event device that can be placed under the skin, where it is ready to act if the patient’s blood sugar drops too low,” says Daniel Anderson, a professor in MIT’s Department of Chemical Engineering, a member of MIT’s Koch Institute for Integrative Cancer Research and Institute for Medical Engineering and Science (IMES), and the senior author of the study. “Our goal was to build a device that is always ready to protect patients from low blood sugar. We think this can also help relieve the fear of hypoglycemia that many patients, and their parents, suffer from.”

The researchers showed that this device could also be used to deliver emergency doses of epinephrine, a drug that is used to treat heart attacks and can also prevent severe allergic reactions, including anaphylactic shock.

Siddharth Krishnan, a former MIT research scientist who is now an assistant professor of electrical engineering at Stanford University, is the lead author of the study, which appears today in Nature Biomedical Engineering.

Emergency response

Most patients with type 1 diabetes use daily insulin injections to help their body absorb sugar and prevent their blood sugar levels from getting too high. However, if their blood sugar levels get too low, they develop hypoglycemia, which can lead to confusion and seizures, and may be fatal if it goes untreated.

To combat hypoglycemia, some patients carry preloaded syringes of glucagon, a hormone that stimulates the liver to release glucose into the bloodstream. However, it isn’t always easy for people, especially children, to know when they are becoming hypoglycemic.

“Some patients can sense when they’re getting low blood sugar, and go eat something or give themselves glucagon,” Anderson says. “But some are unaware that they’re hypoglycemic, and they can just slip into confusion and coma. This is also a problem when patients sleep, as they are reliant on glucose sensor alarms to wake them when sugar drops dangerously low.”

To make it easier to counteract hypoglycemia, the MIT team set out to design an emergency device that could be triggered either by the person using it, or automatically by a sensor.

The device, which is about the size of a quarter, contains a small drug reservoir made of a 3D-printed polymer. The reservoir is sealed with a special material known as a shape-memory alloy, which can be programmed to change its shape when heated. In this case, the researcher used a nickel-titanium alloy that is programmed to curl from a flat slab into a U-shape when heated to 40 degrees Celsius.

Like many other protein or peptide drugs, glucagon tends to break down quickly, so the liquid form can’t be stored long-term in the body. Instead, the MIT team created a powdered version of the drug, which remains stable for much longer and stays in the reservoir until released.

Each device can carry either one or four doses of glucagon, and it also includes an antenna tuned to respond to a specific frequency in the radiofrequency range. That allows it to be remotely triggered to turn on a small electrical current, which is used to heat the shape-memory alloy. When the temperature reaches the 40-degree threshold, the slab bends into a U shape, releasing the contents of the reservoir.

Because the device can receive wireless signals, it could also be designed so that drug release is triggered by a glucose monitor when the wearer’s blood sugar drops below a certain level.

“One of the key features of this type of digital drug delivery system is that you can have it talk to sensors,” Krishnan says. “In this case, the continuous glucose-monitoring technology that a lot of patients use is something that would be easy for these types of devices to interface with.”

Reversing hypoglycemia

After implanting the device in diabetic mice, the researchers used it to trigger glucagon release as the animals’ blood sugar levels were dropping. Within less than 10 minutes of activating the drug release, blood sugar levels began to level off, allowing them to remain within the normal range and avert hypoglycemia.

The researchers also tested the device with a powdered version of epinephrine. They found that within 10 minutes of drug release, epinephrine levels in the bloodstream became elevated and heart rate increased.

In this study, the researchers kept the devices implanted for up to four weeks, but they now plan to see if they can extend that time up to at least a year.

“The idea is you would have enough doses that can provide this therapeutic rescue event over a significant period of time. We don’t know exactly what that is — maybe a year, maybe a few years, and we’re currently working on establishing what the optimal lifetime is. But then after that, it would need to be replaced,” Krishnan says.

Typically, when a medical device is implanted in the body, scar tissue develops around the device, which can interfere with its function. However, in this study, the researchers showed that even after fibrotic tissue formed around the implant, they were able to successfully trigger the drug release.

The researchers are now planning for additional animal studies and hope to begin testing the device in clinical trials within the next three years.

“It’s really exciting to see our team accomplish this, which I hope will someday help diabetic patients and could more broadly provide a new paradigm for delivering any emergency medicine,” says Robert Langer, the David H. Koch Institute Professor at MIT and an author of the paper.

Other authors of the paper include Laura O’Keeffe, Arnab Rudra, Derin Gumustop, Nima Khatib, Claudia Liu, Jiawei Yang, Athena Wang, Matthew Bochenek, Yen-Chun Lu, Suman Bose, and Kaelan Reed.

The research was funded by the Leona M. and Harry B. Helmsley Charitable Trust, the National Institutes of Health, a JDRF postdoctoral fellowship, and the National Institute of Biomedical Imaging and Bioengineering.

This work was carried out, in part, through the use of MIT.nano’s facilities.

The first time Steve Jobs held a public demo of the Apple Macintosh, in early 1984, scripted jokes were part of the rollout. First, Jobs pulled the machine out of a bag. Then, using speech technology from Samsung, the Macintosh made a quip about rival IBM’s mainframes: “Never trust a computer you can’t lift.”

There’s a reason Jobs was doing that. For the first few decades that computing became part of cultural life, starting in the 1950s, computers seemed unfriendly, grim, and liable to work against human interests. Take the 1968 film “2001: A Space Odyssey,” in which the onboard computer, HAL, turns against the expedition’s astronauts. It’s a famous cultural touchstone. Jobs, in selling the idea of a personal computer, was using humor to ease concerns about the machines.

“Against the sense of computing as cold and numbers-driven, the fact that this computer was using voice technology to deliver jokes made it seem less forbidding, less evil,” says MIT scholar Benjamin Mangrum.

In fact, this dynamic turns up throughout modern culture, in movies, television, fiction, and the theater. We often deal with our doubts and fears about computing through humor, whether reconciling ourselves to machines or critiquing them. Now, Mangrum analyzes this phenomenon in a new book, “The Comedy of Computation: Or, How I Learned to Stop Worrying and Love Obsolescence,” published this month by Stanford University Press.

“Comedy has been a form for making this technology seem ordinary,” says Mangrum, an associate professor in MIT’s literature program. “Where in other circumstances computing might seem inhuman or impersonal, comedy allows us to incorporate it into our lives in a way that makes it make sense.”

Reversals of fortune

Mangrum’s interest in the subject was sparked partly by William Marchant’s 1955 play, “The Desk Set” — a romantic comedy later turned into a film starring Katharine Hepburn and Spencer Tracy — which queries, among other things, how office workers will co-exist alongside computers.

Perhaps against expectations, romantic comedies have turned out to be one of the most prominent contemporary forms of culture that grapple with technology and its effects on us. Mangrum, in the book, explains why: Their plot structure often involves reversals, which sometimes are extended to technology, too. Computing might seem forbidding, but it might also pull people together.

“One of the common tropes about romantic comedies is that there are characters or factors in the drama that obstruct the happy union of two people,” Mangrum observes. “And often across the arc of the drama, the obstruction or obstructive character is transformed into a partner, or collaborator, and assimilated within the happy couple’s union. That provides a template for how some cultural producers want to present the experience of computing. It begins as an obstruction and ends as a partner.”

That plot structure, Mangrum notes, dates to antiquity and was common in Shakespeare’s day. Still, as he writes in the book, there is “no timeless reality called Comedy,” as the vehicles and forms of it change over time. Beyond that, specific jokes about computing can quickly become outmoded. Steve Jobs made fun of mainframes, and the 1998 Nora Ephron comedy “You’ve Got Mail” got laughs out of dial-up modems, but those jokes might leave most people puzzled today.

“Comedy is not a fixed resource,” Mangrum says. “It’s an ever-changing toolbox.”

Continuing this evolution into the 21st century, Mangrum observes that a lot of computational comedy centers on an entire category of commentary he calls “the Great Tech-Industrial Joke.” This focuses on the gap between noble-sounding declared aspirations of technology and the sometimes-dismal outcomes it creates.

Social media, for instance, promised new worlds of connectivity and social exploration, and has benefits people enjoy — but it has also generated polarization, misinformation, and toxicity. Technology’s social effects are complex. Whole televisions shows, such as “Silicon Valley,” have dug into this terrain.

“The tech industry announces that some of its products have revolutionary or utopian aims, but the achievements of many of them fall far short of that,” Mangrum says. “It’s a funny setup for a joke. People have been claiming we’re saving the world, when actually we’re just processing emails faster. But it’s a mode of criticism aimed at big tech, since its products are more complicated.”

A complicated, messy picture

“The Comedy of Computation” digs into several other facets of modern culture and technology. The notion of personal authenticity, as Mangrum observes, is a fairly recent and modern construct in society — and it’s another sphere of life that collides with computing, since social media is full of charges of inauthenticity.

“That ethics of authenticity connects to comedy, as we make jokes about people not being authentic,” Mangrum says.

“The Comedy of Computation” has received praise from other scholars. Mark Goble, a professor of English at the University of California at Berkeley, has called it “essential for understanding the technological world in its complexity, absurdity, and vibrancy.”

For his part, Mangrum emphasizes that his book is an exploration of the full complexity of technology, culture, and society.

“There’s this really complicated, messy picture,” Mangrum says. “And comedy sometimes finds a way of experiencing and finding pleasure in that messiness, and other times it neatly wraps it up in a lesson that can make things neater than they actually are.”

Mangrum adds that the book focuses on “the combination of the threat and pleasure that’s involved across the history of the computer, in the ways it’s been assimilated and shaped society, with real advances and benefits, along with real threats, for instance to employment. I’m interested in the duality, the simultaneous and seemingly conflicting features of that experience.”

For all their impressive capabilities, large language models (LLMs) often fall short when given challenging new tasks that require complex reasoning skills.

While an accounting firm’s LLM might excel at summarizing financial reports, that same model could fail unexpectedly if tasked with predicting market trends or identifying fraudulent transactions.

To make LLMs more adaptable, MIT researchers investigated how a certain training technique can be strategically deployed to boost a model’s performance on unfamiliar, difficult problems.

They show that test-time training, a method that involves temporarily updating some of a model’s inner workings during deployment, can lead to a sixfold improvement in accuracy. The researchers developed a framework for implementing a test-time training strategy that uses examples of the new task to maximize these gains.

Their work could improve a model’s flexibility, enabling an off-the-shelf LLM to adapt to complex tasks that require planning or abstraction. This could lead to LLMs that would be more accurate in many applications that require logical deduction, from medical diagnostics to supply chain management.

“Genuine learning — what we did here with test-time training — is something these models can’t do on their own after they are shipped. They can’t gain new skills or get better at a task. But we have shown that if you push the model a little bit to do actual learning, you see that huge improvements in performance can happen,” says Ekin Akyürek PhD ’25, lead author of the study.

Akyürek is joined on the paper by graduate students Mehul Damani, Linlu Qiu, Han Guo, and Jyothish Pari; undergraduate Adam Zweiger; and senior authors Yoon Kim, an assistant professor of Electrical Engineering and Computer Science (EECS) and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL); and Jacob Andreas, an associate professor in EECS and a member of CSAIL. The research will be presented at the International Conference on Machine Learning.

Tackling hard domains

LLM users often try to improve the performance of their model on a new task using a technique called in-context learning. They feed the model a few examples of the new task as text prompts which guide the model’s outputs.

But in-context learning doesn’t always work for problems that require logic and reasoning.

The MIT researchers investigated how test-time training can be used in conjunction with in-context learning to boost performance on these challenging tasks. Test-time training involves updating some model parameters — the internal variables it uses to make predictions — using a small amount of new data specific to the task at hand.

The researchers explored how test-time training interacts with in-context learning. They studied design choices that maximize the performance improvements one can coax out of a general-purpose LLM.

“We find that test-time training is a much stronger form of learning. While simply providing examples can modestly boost accuracy, actually updating the model with those examples can lead to significantly better performance, particularly in challenging domains,” Damani says.

In-context learning requires a small set of task examples, including problems and their solutions. The researchers use these examples to create a task-specific dataset needed for test-time training.

To expand the size of this dataset, they create new inputs by slightly changing the problems and solutions in the examples, such as by horizontally flipping some input data. They find that training the model on the outputs of this new dataset leads to the best performance.

In addition, the researchers only update a small number of model parameters using a technique called low-rank adaption, which improves the efficiency of the test-time training process.

“This is important because our method needs to be efficient if it is going to be deployed in the real world. We find that you can get huge improvements in accuracy with a very small amount of parameter training,” Akyürek says.

Developing new skills

Streamlining the process is key, since test-time training is employed on a per-instance basis, meaning a user would need to do this for each individual task. The updates to the model are only temporary, and the model reverts to its original form after making a prediction.

A model that usually takes less than a minute to answer a query might take five or 10 minutes to provide an answer with test-time training, Akyürek adds.

“We wouldn’t want to do this for all user queries, but it is useful if you have a very hard task that you want to the model to solve well. There also might be tasks that are too challenging for an LLM to solve without this method,” he says.

The researchers tested their approach on two benchmark datasets of extremely complex problems, such as IQ puzzles. It boosted accuracy as much as sixfold over techniques that use only in-context learning.

Tasks that involved structured patterns or those which used completely unfamiliar types of data showed the largest performance improvements.

“For simpler tasks, in-context learning might be OK. But updating the parameters themselves might develop a new skill in the model,” Damani says.

In the future, the researchers want to use these insights toward the development of models that continually learn.

The long-term goal is an LLM that, given a query, can automatically determine if it needs to use test-time training to update parameters or if it can solve the task using in-context learning, and then implement the best test-time training strategy without the need for human intervention.

This work is supported, in part, by the MIT-IBM Watson AI Lab and the National Science Foundation.

During photosynthesis, an enzyme called rubisco catalyzes a key reaction — the incorporation of carbon dioxide into organic compounds to create sugars. However, rubisco, which is believed to be the most abundant enzyme on Earth, is very inefficient compared to the other enzymes involved in photosynthesis.

MIT chemists have now shown that they can greatly enhance a version of rubisco found in bacteria from a low-oxygen environment. Using a process known as directed evolution, they identified mutations that could boost rubisco’s catalytic efficiency by up to 25 percent.

The researchers now plan to apply their technique to forms of rubisco that could be used in plants to help boost their rates of photosynthesis, which could potentially improve crop yields.

“This is, I think, a compelling demonstration of successful improvement of a rubisco’s enzymatic properties, holding out a lot of hope for engineering other forms of rubisco,” says Matthew Shoulders, the Class of 1942 Professor of Chemistry at MIT.

Shoulders and Robert Wilson, a research scientist in the Department of Chemistry, are the senior authors of the new study, which appears this week in the Proceedings of the National Academy of Sciences. MIT graduate student Julie McDonald is the paper’s lead author.

Evolution of efficiency

When plants or photosynthetic bacteria absorb energy from the sun, they first convert it into energy-storing molecules such as ATP. In the next phase of photosynthesis, cells use that energy to transform a molecule known as ribulose bisphosphate into glucose, which requires several additional reactions. Rubisco catalyzes the first of those reactions, known as carboxylation. During that reaction, carbon from CO₂ is added to ribulose bisphosphate.

Compared to the other enzymes involved in photosynthesis, rubisco is very slow, catalyzing only one to 10 reactions per second. Additionally, rubisco can also interact with oxygen, leading to a competing reaction that incorporates oxygen instead of carbon — a process that wastes some of the energy absorbed from sunlight.

“For protein engineers, that’s a really attractive set of problems because those traits seem like things that you could hopefully make better by making changes to the enzyme’s amino acid sequence,” McDonald says.

Previous research has led to improvement in rubisco’s stability and solubility, which resulted in small gains in enzyme efficiency. Most of those studies used directed evolution — a technique in which a naturally occurring protein is randomly mutated and then screened for the emergence of new, desirable features.

This process is usually done using error-prone PCR, a technique that first generates mutations in vitro (outside of the cell), typically introducing only one or two mutations in the target gene. In past studies on rubisco, this library of mutations was then introduced into bacteria that grow at a rate relative to rubisco activity. Limitations in error-prone PCR and in the efficiency of introducing new genes restrict the total number of mutations that can be generated and screened using this approach. Manual mutagenesis and selection steps also add more time to the process over multiple rounds of evolution.

The MIT team instead used a newer mutagenesis technique that the Shoulders Lab previously developed, called MutaT7. This technique allows the researchers to perform both mutagenesis and screening in living cells, which dramatically speeds up the process. Their technique also enables them to mutate the target gene at a higher rate.

“Our continuous directed evolution technique allows you to look at a lot more mutations in the enzyme than has been done in the past,” McDonald says.

Better rubisco

For this study, the researchers began with a version of rubisco, isolated from a family of semi-anaerobic bacteria known as Gallionellaceae, that is one of the fastest rubisco found in nature. During the directed evolution experiments, which were conducted in E. coli, the researchers kept the microbes in an environment with atmospheric levels of oxygen, creating evolutionary pressure to adapt to oxygen.

After six rounds of directed evolution, the researchers identified three different mutations that improved the rubisco’s resistance to oxygen. Each of these mutations are located near the enzyme’s active site (where it performs carboxylation or oxygenation). The researchers believe that these mutations improve the enzyme’s ability to preferentially interact with carbon dioxide over oxygen, which leads to an overall increase in carboxylation efficiency.

“The underlying question here is: Can you alter and improve the kinetic properties of rubisco to operate better in environments where you want it to operate better?” Shoulders says. “What changed through the directed evolution process was that rubisco began to like to react with oxygen less. That allows this rubisco to function well in an oxygen-rich environment, where normally it would constantly get distracted and react with oxygen, which you don’t want it to do.”

In ongoing work, the researchers are applying this approach to other forms of rubisco, including rubisco from plants. Plants are believed to lose about 30 percent of the energy from the sunlight they absorb through a process called photorespiration, which occurs when rubisco acts on oxygen instead of carbon dioxide.

“This really opens the door to a lot of exciting new research, and it’s a step beyond the types of engineering that have dominated rubisco engineering in the past,” Wilson says. “There are definite benefits to agricultural productivity that could be leveraged through a better rubisco.”

The research was funded, in part, by the National Science Foundation, the National Institutes of Health, an Abdul Latif Jameel Water and Food Systems Lab Grand Challenge grant, and a Martin Family Society Fellowship for Sustainability.

The MIT Health and Life Sciences Collaborative (MIT HEALS) is launching the Biswas Postdoctoral Fellowship Program to advance the work of outstanding early-career researchers in health and life sciences. Supported by a gift from the Biswas Family Foundation, the program aims to help apply cutting-edge research to improve health care and the lives of millions.

The program will support exceptional postdocs dedicated to innovation in human health care through a full range of pathways, such as leveraging AI in health-related research, developing low-cost diagnostics, and the convergence of life sciences with such areas as economics, business, policy, or the humanities. With initial funding of $12 million, five four-year fellowships will be awarded for each of the next four years, starting in early 2026.

“An essential goal of MIT HEALS is to find new ways and opportunities to deliver health care solutions at scale, and the Biswas Family Foundation shares our commitment to scalable innovation and broad impact. MIT is also in the talent business, and the foundation’s gift allows us to bring exceptional scholars to campus to explore some of the most pressing issues in human health and build meaningful connections across academia and industry. We look forward to welcoming the first cohort of Biswas Fellows to MIT,” says MIT president Sally Kornbluth.

“We are deeply honored to launch this world-class postdoctoral fellows program,” adds Anantha P. Chandrakasan, MIT’s chief innovation and strategy officer and head of MIT HEALS. “We fully expect to attract top candidates from around the globe to lead innovative cross-cutting projects in AI and health, cancer therapies, diagnostics, and beyond. These fellows will be selected through a rigorous process overseen by a distinguished committee, and will have the opportunity to collaborate with our faculty on the most promising and impactful ideas.”

Angela Koehler, faculty lead of MIT HEALS, professor in MIT’s Department of Biological Engineering, and associate director of the Koch Institute for Integrative Cancer Research, emphasized that the objectives of MIT HEALS align well with a stated goal of the Biswas Family Foundation: to leverage “scientific and technological advancements to revolutionize health care and make a lasting impact on global public health.”

“Health care is a team sport,” Koehler says. “MIT HEALS seeks to create connections involving investigators with diverse expertise across the Institute to tackle the most transformative problems impacting human health. Members of the MIT community are well poised to participate in teams and make an impact.”

MIT HEALS also seeks to maximize its effectiveness by expanding collaboration with medical schools and hospitals, starting with defining important problems that can be approached through research, and continuing all the way to clinical studies, Koehler says.

The Biswas Family Foundation has already demonstrated a similar strategy.

“The Biswas family has a history of enabling connections and partnerships between institutions that each bring a piece to the puzzle,” Koehler says. “This could be a dataset, an algorithm, an agent, a technology platform, or patients.”

Hope Biswas, co-founder of the Biswas Family Foundation with her husband, MIT alumnus Sanjit Biswas SM ’05, also highlighted the synergies between the foundation and MIT.

“The Biswas Family Foundation is proud to support the MIT HEALS initiative, which reimagines how scientific discovery can translate into real-world health impact. Its focus on promoting interdisciplinary collaboration to find new solutions to challenges in health care aligns closely with our mission to advance science and technology to improve health outcomes at scale,” Biswas says.

“As part of this commitment,” Biswas adds, “we are especially proud to support outstanding postdoctoral scholars focused on high-impact cross-disciplinary work in fields such as computational biology, nanoscale therapeutics, women’s health, and fundamental, curiosity-driven life sciences research. We are excited to contribute to an effort that brings together cutting-edge science and a deep commitment to translating knowledge into action.”

AI and machine-learning systems present a new universe of opportunities to investigate disease, biological mechanisms, therapeutics, and health care delivery using huge datasets.

“AI and computational systems biology can improve the accuracy of diagnostic approaches, enable the development of precision medicines, improve choices related to individualized treatment strategy, and improve operational efficiency within health care systems,” says Koehler. “Sanjit and Hope’s support of broad initiatives in AI and computational systems biology will help MIT researchers explore a variety of paths to impact human health on a large scale.”

Frontiers in health-related research are increasingly found where diverse fields converge, and Koehler provides the example of how advances in high-throughput experimentation to develop large datasets “may couple well with the development of new computation or AI tools.” She adds that the four-year funding term provided by the postdoctoral fellowship is “long enough to enable fellows to think big and take on projects at interfaces, emerging as bilingual researchers at the end of the program.”

Chandrakasan sees potential in the program for the Biswas Fellows to make revolutionary progress in health research.

“I’m incredibly grateful to the Biswas Family Foundation for their generous support in enabling transformative research at MIT,” Chandrakasan says.

Lanthanides are a class of rare earth elements that in many countries are added to fertilizer as micronutrients to stimulate plant growth. But little is known about how they are absorbed by plants or influence photosynthesis, potentially leaving their benefits untapped.

Now, researchers from MIT have shed light on how lanthanides move through and operate within plants. These insights could help farmers optimize their use to grow some of the world’s most popular crops.

Published today in the Journal of the American Chemical Society, the study shows that a single nanoscale dose of lanthanides applied to seeds can make some of the world’s most common crops more resilient to UV stress. The researchers also uncovered the chemical processes by which lanthanides interact with the chlorophyll pigments that drive photosynthesis, showing that different lanthanide elements strengthen chlorophyll by replacing the magnesium at its center.

“This is a first step to better understand how these elements work in plants, and to provide an example of how they could be better delivered to plants, compared to simply applying them in the soil,” says Associate Professor Benedetto Marelli, who conducted the research with postdoc Giorgio Rizzo. “This is the first example of a thorough study showing the effects of lanthanides on chlorophyll, and their beneficial effects to protect plants from UV stress.”

Inside plant connections

Certain lanthanides are used as contrast agents in MRI and for applications including light-emitting diodes, solar cells, and lasers. Over the last 50 years, lanthanides have become increasingly used in agriculture to enhance crop yields, with China alone applying lanthanide-based fertilizers to nearly 4 million hectares of land each year.

“Lanthanides have been considered for a long time to be biologically irrelevant, but that’s changed in agriculture, especially in China,” says Rizzo, the paper’s first author. “But we largely don’t know how lanthanides work to benefit plants — nor do we understand their uptake mechanisms from plant tissues.”

Recent studies have shown that low concentrations of lanthanides can promote plant growth, root elongation, hormone synthesis, and stress tolerance, but higher doses can cause harm to plants. Striking the right balance has been hard because of our lack of understanding around how lanthanides are absorbed by plants or how they interact with root soil.

For the study, the researchers leveraged seed coating and treatment technologies they previously developed to investigate the way the plant pigment chlorophyll interacts with lanthanides, both inside and outside of plants. Up until now, researchers haven’t been sure whether chlorophyll interacts with lanthanide ions at all.

Chlorophyll drives photosynthesis, but the pigments lose their ability to efficiently absorb light when the magnesium ion at their core is removed. The researchers discovered that lanthanides can fill that void, helping chlorophyll pigments partially recover some of their optical properties in a process known as re-greening.

“We found that lanthanides can boost several parameters of plant health,” Marelli says. “They mostly accumulate in the roots, but a small amount also makes its way to the leaves, and some of the new chlorophyll molecules made in leaves have lanthanides incorporated in their structure.”

This study also offers the first experimental evidence that lanthanides can increase plant resilience to UV stress, something the researchers say was completely unexpected.

“Chlorophylls are very sensitive pigments,” Rizzo says. “They can convert light to energy in plants, but when they are isolated from the cell structure, they rapidly hydrolyze and degrade. However, in the form with lanthanides at their center, they are pretty stable, even after extracting them from plant cells.”

The researchers, using different spectroscopic techniques, found the benefits held across a range of staple crops, including chickpea, barley, corn, and soybeans.

The findings could be used to boost crop yield and increase the resilience of some of the world’s most popular crops to extreme weather.

“As we move into an environment where extreme heat and extreme climate events are more common, and particularly where we can have prolonged periods of sun in the field, we want to provide new ways to protect our plants,” Marelli says. “There are existing agrochemicals that can be applied to leaves for protecting plants from stressors such as UV, but they can be toxic, increase microplastics, and can require multiple applications. This could be a complementary way to protect plants from UV stress.”

Identifying new applications

The researchers also found that larger lanthanide elements like lanthanum were more effective at strengthening chlorophyll pigments than smaller ones. Lanthanum is considered a low-value byproduct of rare earths mining, and can become a burden to the rare earth element (REE) supply chain due to the need to separate it from more desirable rare earths. Increasing the demand for lanthanum could diversify the economics of REEs and improve the stability of their supply chain, the scientists suggest.

“This study shows what we could do with these lower-value metals,” Marelli says. “We know lanthanides are extremely useful in electronics, magnets, and energy. In the U.S., there’s a big push to recycle them. That’s why for the plant studies, we focused on lanthanum, being the most abundant, cheapest lanthanide ion.”

Moving forward, the team plans to explore how lanthanides work with other biological molecules, including proteins in the human body.

In agriculture, the team hopes to scale up its research to include field and greenhouse studies to continue testing the results of UV resilience on different crop types and in experimental farm conditions.

“Lanthanides are already widely used in agriculture,” Rizzo says. “We hope this study provides evidence that allows more conscious use of them and also a new way to apply them through seed treatments.”

The research was supported by the MIT Climate Grand Challenge and the Office for Naval Research.

Scientists are striving to discover new semiconductor materials that could boost the efficiency of solar cells and other electronics. But the pace of innovation is bottlenecked by the speed at which researchers can manually measure important material properties.

A fully autonomous robotic system developed by MIT researchers could speed things up.

Their system utilizes a robotic probe to measure an important electrical property known as photoconductance, which is how electrically responsive a material is to the presence of light.

The researchers inject materials-science-domain knowledge from human experts into the machine-learning model that guides the robot’s decision making. This enables the robot to identify the best places to contact a material with the probe to gain the most information about its photoconductance, while a specialized planning procedure finds the fastest way to move between contact points.

During a 24-hour test, the fully autonomous robotic probe took more than 125 unique measurements per hour, with more precision and reliability than other artificial intelligence-based methods.

By dramatically increasing the speed at which scientists can characterize important properties of new semiconductor materials, this method could spur the development of solar panels that produce more electricity.

“I find this paper to be incredibly exciting because it provides a pathway for autonomous, contact-based characterization methods. Not every important property of a material can be measured in a contactless way. If you need to make contact with your sample, you want it to be fast and you want to maximize the amount of information that you gain,” says Tonio Buonassisi, professor of mechanical engineering and senior author of a paper on the autonomous system.

His co-authors include lead author Alexander (Aleks) Siemenn, a graduate student; postdocs Basita Das and Kangyu Ji; and graduate student Fang Sheng. The work appears today in Science Advances.

Making contact

Since 2018, researchers in Buonassisi’s laboratory have been working toward a fully autonomous materials discovery laboratory. They’ve recently focused on discovering new perovskites, which are a class of semiconductor materials used in photovoltaics like solar panels.

In prior work, they developed techniques to rapidly synthesize and print unique combinations of perovskite material. They also designed imaging-based methods to determine some important material properties.

But photoconductance is most accurately characterized by placing a probe onto the material, shining a light, and measuring the electrical response.

“To allow our experimental laboratory to operate as quickly and accurately as possible, we had to come up with a solution that would produce the best measurements while minimizing the time it takes to run the whole procedure,” says Siemenn.

Doing so required the integration of machine learning, robotics, and material science into one autonomous system.

To begin, the robotic system uses its onboard camera to take an image of a slide with perovskite material printed on it.

Then it uses computer vision to cut that image into segments, which are fed into a neural network model that has been specially designed to incorporate domain expertise from chemists and materials scientists.

“These robots can improve the repeatability and precision of our operations, but it is important to still have a human in the loop. If we don’t have a good way to implement the rich knowledge from these chemical experts into our robots, we are not going to be able to discover new materials,” Siemenn adds.

The model uses this domain knowledge to determine the optimal points for the probe to contact based on the shape of the sample and its material composition. These contact points are fed into a path planner that finds the most efficient way for the probe to reach all points.

The adaptability of this machine-learning approach is especially important because the printed samples have unique shapes, from circular drops to jellybean-like structures.

“It is almost like measuring snowflakes — it is difficult to get two that are identical,” Buonassisi says.

Once the path planner finds the shortest path, it sends signals to the robot’s motors, which manipulate the probe and take measurements at each contact point in rapid succession.

Key to the speed of this approach is the self-supervised nature of the neural network model. The model determines optimal contact points directly on a sample image — without the need for labeled training data.

The researchers also accelerated the system by enhancing the path planning procedure. They found that adding a small amount of noise, or randomness, to the algorithm helped it find the shortest path.

“As we progress in this age of autonomous labs, you really do need all three of these expertise — hardware building, software, and an understanding of materials science — coming together into the same team to be able to innovate quickly. And that is part of the secret sauce here,” Buonassisi says.

Rich data, rapid results

Once they had built the system from the ground up, the researchers tested each component. Their results showed that the neural network model found better contact points with less computation time than seven other AI-based methods. In addition, the path planning algorithm consistently found shorter path plans than other methods.

When they put all the pieces together to conduct a 24-hour fully autonomous experiment, the robotic system conducted more than 3,000 unique photoconductance measurements at a rate exceeding 125 per hour.

In addition, the level of detail provided by this precise measurement approach enabled the researchers to identify hotspots with higher photoconductance as well as areas of material degradation.

“Being able to gather such rich data that can be captured at such fast rates, without the need for human guidance, starts to open up doors to be able to discover and develop new high-performance semiconductors, especially for sustainability applications like solar panels,” Siemenn says.

The researchers want to continue building on this robotic system as they strive to create a fully autonomous lab for materials discovery.

This work is supported, in part, by First Solar, Eni through the MIT Energy Initiative, MathWorks, the University of Toronto’s Acceleration Consortium, the U.S. Department of Energy, and the U.S. National Science Foundation.

In 1954, the world’s first successful organ transplant took place at Brigham and Women’s Hospital, in the form of a kidney donated from one twin to the other. At the time, a group of doctors and scientists had correctly theorized that the recipient’s antibodies were unlikely to reject an organ from an identical twin. One Nobel Prize and a few decades later, advancements in immune-suppressing drugs increased the viability of and demand for organ transplants. Today, over 1 million organ transplants have been performed in the United States, more than any other country in the world.

The impressive scale of this achievement was made possible due to advances in organ matching systems: The first computer-based organ matching system was released in 1977. Despite continued innovation in computing, medicine, and matching technology over the years, over 100,000 people in the U.S. are currently on the national transplant waiting list and 13 people die each day waiting for an organ transplant. 

Most computational research in organ allocation is focused on the initial stages, when waitlisted patients are being prioritized for organ transplants. In a new paper presented at ACM Conference on Fairness, Accountability, and Transparency (FAccT) in Athens, Greece, researchers from MIT and Massachusetts General Hospital focused on the final, less-studied stage: organ offer acceptance, when an offer is made and the physician at the transplant center decides on behalf of the patient whether to accept or reject the offered organ. 

“I don’t think we were terribly surprised, but we were obviously disappointed,” co-first author and MIT PhD student Hammaad Adam says. Using computational models to analyze transplantation data from over 160,000 transplant candidates in the Scientific Registry of Transplant Recipients (SRTR) between 2010 and 2020, the researchers found that physicians were overall less likely to accept liver and lung offers on behalf of Black candidates, resulting in additional barriers for Black patients in the organ offer acceptance process.  

For livers, Black patients had 7 percent lower odds of offer acceptance than white patients. When it came to lungs, the disparity became even larger, with 20 percent lower odds of having an offer acceptance than white patients with similar characteristics.

The data don’t necessarily point to clinician bias as the main influence. “The bigger takeaway is that even if there are factors that justify clinical decision-making, there could be clinical conditions that we didn’t control for, that are more common for Black patients,” Adam explains. If the wait-list fails to account for certain patterns in decision-making, they could create obstacles in the process even if the process itself is “unbiased.”

The researchers also point out that high variability in offer acceptance and risk tolerances among transplant centers is a potential factor complicating the decision-making process. Their FAccT paper references a 2020 paper published in JAMA Cardiology, which concluded that wait-list candidates listed at transplant centers with lower offer acceptance rates have a higher likelihood of mortality. 

Another key finding was that an offer was more likely to be accepted if the donor and candidate were of the same race. The paper describes this trend as “concerning,” given the historical inequities in organ procurement that have limited donation from racial and ethnic minority groups. 

Previous work from Adam and his collaborators has aimed to address this gap. Last year, they compiled and released Organ Retrieval and Collection of Health Information for Donation (ORCHID), the first multi-center dataset describing the performance of organ procurement organizations (OPOs). ORCHID contains 10 years’ worth of OPO data, and is intended to facilitate research that addresses bias in organ procurement.

“Being able to do good work in this field takes time,” says Adam, who notes that the entirety of the organ offer acceptance project took years to complete. To his knowledge, only one paper to date studies the association between offer acceptance and race. 

While the bureaucratic and highly interdisciplinary nature of clinical AI projects can dissuade computer science graduate students from pursuing them, Adam committed to the project for the duration of his PhD in the lab of associate professor of electrical engineering Marzyeh Ghassemi, an affiliate of the MIT Jameel Clinic and the Institute of Medical Engineering and Sciences.

To graduate students interested in pursuing clinical AI research projects, Adam recommends that they “free [themselves] from the cycle of publishing every four months.”

“I found it freeing, to be honest — it’s OK if these collaborations take a while,” he says. “It’s hard to avoid that. I made the conscious choice a few years ago and I was happy doing that work.”

This work was supported with funding from the MIT Jameel Clinic. It was also supported, in part, by Takeda Development Center Americas Inc. (successor in interest to Millennium Pharmaceuticals Inc.), an NIH Ruth L. Kirschstein National Research Service Award, a CIFAR AI Chair at the Vector Institute, and by the National Institutes of Health.

Incoming information from the retina is channeled into two pathways in the brain’s visual system: one that’s responsible for processing color and fine spatial detail, and another that’s involved in spatial localization and detecting high temporal frequencies. A new study from MIT provides an account for how these two pathways may be shaped by developmental factors.

Newborns typically have poor visual acuity and poor color vision because their retinal cone cells are not well-developed at birth. This means that early in life, they are seeing blurry, color-reduced imagery. The MIT team proposes that such blurry, color-limited vision may result in some brain cells specializing in low spatial frequencies and low color tuning, corresponding to the so-called magnocellular system. Later, with improved vision, cells may tune to finer details and richer color, consistent with the other pathway, known as the parvocellular system.

To test their hypothesis, the researchers trained computational models of vision on a trajectory of input similar to what human babies receive early in life — low-quality images early on, followed by full-color, sharper images later. They found that these models developed processing units with receptive fields exhibiting some similarity to the division of magnocellular and parvocellular pathways in the human visual system. Vision models trained on only high-quality images did not develop such distinct characteristics.

“The findings potentially suggest a mechanistic account of the emergence of the parvo/magno distinction, which is one of the key organizing principles of the visual pathway in the mammalian brain,” says Pawan Sinha, an MIT professor of brain and cognitive sciences and the senior author of the study.

MIT postdocs Marin Vogelsang and Lukas Vogelsang are the lead authors of the study, which appears today in the journal Communications Biology. Sidney Diamond, an MIT research affiliate, and Gordon Pipa, a professor of neuroinformatics at the University of Osnabrueck, are also authors of the paper.

Sensory input

The idea that low-quality visual input might be beneficial for development grew out of studies of children who were born blind but later had their sight restored. An effort from Sinha’s laboratory, Project Prakash, has screened and treated thousands of children in India, where reversible forms of vision loss such as cataracts are relatively common. After their sight is restored, many of these children volunteer to participate in studies in which Sinha and his colleagues track their visual development.

In one of these studies, the researchers found that children who had cataracts removed exhibited a marked drop in object-recognition performance when the children were presented with black and white images, compared to colored ones. Those findings led the researchers to hypothesize that reduced color input characteristic of early typical development, far from being a hindrance, allows the brain to learn to recognize objects even in images that have impoverished or shifted colors.

“Denying access to rich color at the outset seems to be a powerful strategy to build in resilience to color changes and make the system more robust against color loss in images,” Sinha says.

In that study, the researchers also found that when computational models of vision were initially trained on grayscale images, followed by color images, their ability to recognize objects was more robust than that of models trained only on color images. Similarly, another study from the lab found that models performed better when they were trained first on blurry images, followed by sharper images.

To build on those findings, the MIT team wanted to explore what might be the consequences of both of those features — color and visual acuity — being limited at the outset of development. They hypothesized that these limitations might contribute to the development of the magnocellular and parvocellular pathways.

In addition to being highly attuned to color, cells in the parvocellular pathway have small receptive fields, meaning that they receive input from more compact clusters of retinal ganglion cells. This helps them to process fine detail. Cells in the magnocellular pathway pool information across larger areas, allowing them to process more global spatial information.

To test their hypothesis that developmental progressions could contribute to the magno and parvo cell selectivities, the researchers trained models on two different sets of images. One model was presented with a standard dataset of images that are used to train models to categorize objects. The other dataset was designed to roughly mimic the input that the human visual system receives from birth. This “biomimetic” data consists of low-resolution, grayscale images in the first half of the training, followed by high-resolution, colorful images in the second half.

After the models were trained, the researchers analyzed the models’ processing units — nodes within the network that bear some resemblance to the clusters of cells that process visual information in the brain. They found that the models trained on the biomimetic data developed a distinct subset of units that are jointly responsive to low-color and low-spatial-frequency inputs, similar to the magnocellular pathway. Additionally, these biomimetic models exhibited groups of more heterogenous parvocellular-like units tuned predominantly to higher spatial frequencies or richer color signals. Such distinction did not emerge in the models trained on full color, high-resolution images from the start.

“This provides some support for the idea that the ‘correlation’ we see in the biological system could be a consequence of the types of inputs that are available at the same time in normal development,” Lukas Vogelsang says.

Object recognition

The researchers also performed additional tests to reveal what strategies the differently trained models were using for object recognition tasks. In one, they asked the models to categorize images of objects where the shape and texture did not match — for example, an animal with the shape of cat but the texture of an elephant.

This is a technique several researchers in the field have employed to determine which image attributes a model is using to categorize objects: the overall shape or the fine-grained textures. The MIT team found that models trained on biomimetic input were markedly more likely to use an object’s shape to make those decisions, just as humans usually do. Moreover, when the researchers systematically removed the magnocellular-like units from the models, the models quickly lost their tendency to use shape to make categorizations.

In another set of experiments, the researchers trained the models on videos instead of images, which introduces a temporal dimension. In addition to low spatial resolution and color sensitivity, the magnocellular pathway responds to high temporal frequencies, allowing it to quickly detect changes in the position of an object. When models were trained on biomimetic video input, the units most tuned to high temporal frequencies were indeed the ones that also exhibited magnocellular-like properties in the spatial domain.

Overall, the results support the idea that low-quality sensory input early in life may contribute to the organization of sensory processing pathways of the brain, the researchers say. The findings do not rule out innate specification of the magno and parvo pathways, but provide a proof of principle that visual experience over the course of development could also play a role.

“The general theme that seems to be emerging is that the developmental progression that we go through is very carefully structured in order to give us certain kinds of perceptual proficiencies, and it may also have consequences in terms of the very organization of the brain,” Sinha says.

The research was funded by the National Institutes of Health, the Simons Center for the Social Brain, the Japan Society for the Promotion of Science, and the Yamada Science Foundation.

Being placed in foster care is a necessary intervention for some children. But many advocates worry that kids can languish in foster care too long, with harmful effects for children who are temporarily unattached from a permanent family.

A new study co-authored by an MIT economist shows that an innovative Chilean program providing legal aid to children shortens the length of foster-care stays, returning them to families faster. In the process, it improves long-term social outcomes for kids and even reduces government spending on the foster care system.

“It was amazingly successful because the program got kids out of foster care about 30 percent faster,” says Joseph Doyle, an economist at the MIT Sloan School of Management, who helped lead the research. “Because foster care is expensive, that paid for the program by itself about four times over. If you improve the case management of kids in foster care, you can improve a child’s well-being and save money.”

The paper, “Effects of Enhanced Legal Aid in Child Welfare: Evidence from a Randomized Trial of Mi Abogado,” is published in the American Economic Review.

The authors are Ryan Cooper, a professor and director of government innovation at the University of Chicago; Doyle, who is the Erwin H. Schell Professor of Management at MIT Sloan; and Andrés P. Hojman, a professor at the Pontifical Catholic University of Chile.

Rigorous design

To conduct the study, the scholars examined the Chilean government’s new program “Mi Abogado” — meaning, “My Lawyer” — which provided enhanced legal support to children in foster care, as well as access to psychologists and social workers. Legal advocates in the program were given a reduced caseload, for one thing, to help them focus further on each individual case.

Chile introduced Mi Abogado in 2017, with a feature that made it ripe for careful study: The program randomizes most of the participants selected, as part of how it was rolled out. From the pool of children in the foster care system, randomly being part of the program makes it easier to identify its causal impact on later outcomes.

“Very few foster-care redesigns are evaluated in such a rigorous way, and we need more of this innovative approach to policy improvement,” Doyle notes.

The experiment included 1,781 children who were in Chile’s foster care program in 2019, with 581 selected for the Mi Abogado services; it tracked their trajectories over more than two years. Almost all the participants were in group foster-care homes.

In addition to reduced time spent in foster care, the Chilean data showed that children in the Mi Abogado program had a subsequent 30 percent reduction in terms of contact with the criminal justice system and a 5 percent increase in school attendance, compared to children in foster care who did not participate in the program.

“They were getting involved with crime less and attending school more,” Doyle says.

As powerful as the results appear, Doyle acknowledges that he would like to be able to analyze further which elements of the Mi Abogado program had the biggest impact — legal help, counseling and therapy, or other factors.

“We would like to see more about what exactly they are doing for children to speed their exit from care,” Doyle says. “Is it mostly about therapy? Is it working with judges and cutting through red tape? We think the lawyer is a very important part. But the results suggest it is not just the lawyer that improves outcomes.”

More programs in other places?

The current paper is one of many studies Doyle has developed during his career that relate to foster care and related issues. In another forthcoming paper, Doyle and some co-authors find that about 5 percent of U.S. children spend some time in foster care — a number that appears to be fairly common internationally, too.

“People don’t appreciate how common child protective services and foster care are,” Doyle says. Moreover, he adds, “Children involved in these systems are particularly vulnerable.”

With a variety of U.S. jurisdictions running their own foster-care systems, Doyle notes that many people have the opportunity to usefully learn about the Mi Abogado program and consider if its principles might be worth testing. And while that requires some political will, Doyle expresses optimism that policymakers might be open to new ideas.

“It’s not really a partisan issue,” Doyle says. “Most people want to help protect kids, and, if an intervention is needed for kids, have an interest in making the intervention run well.”

After all, he notes, the impact of the Mi Abogado program appears to be both substantial and lasting, making it an interesting example to consider.

“Here we have a case where the child outcomes are improved and the government saved money,” Doyle observes. “I’d like to see more experimentation with programs like this in other places.”

Support for the research was provided in part by the MIT Sloan Latin America Office. Chile’s Studies Department of the Ministry of Education made data available from the education system.

Often when we listen to music, we just instinctually enjoy it. Sometimes, though, it’s worth dissecting a song or other composition to figure out how it’s built.

Take the 1953 jazz standard “Satin Doll,” written by Duke Ellington and Billy Strayhorn, whose subtle structure rewards a close listening. As it happens, MIT Professor Emeritus Samuel Jay Keyser, a distinguished linguist and an avid trombonist on the side, has given the song careful scrutiny.

To Keyser, “Satin Doll” is a glittering example of what he calls the “same/except” construction in art. A basic rhyme, like “rent” and “tent,” is another example of this construction, given the shared rhyming sound and the different starting consonants.

In “Satin Doll,” Keyser observes, both the music and words feature a “same/except” structure. For instance, the rhythm of the first two bars of “Satin Doll” is the same as the second two bars, but the pitch goes up a step in bars three and four. An intricate pattern of this prevails throughout the entire body of “Satin Doll,” which Keyser calls “a musical rhyme scheme.”

When lyricist Johnny Mercer wrote words for “Satin Doll,” he matched the musical rhyme scheme. One lyric for the first four bars is, “Cigarette holder / which wigs me / Over her shoulder / she digs me.” Other verses follow the same pattern.

“Both the lyrics and the melody have the same rhyme scheme in their separate mediums, words and music, namely, A-B-A-B,” says Keyser. “That’s how you write lyrics. If you understand the musical rhyme scheme, and write lyrics to match that, you are introducing a whole new level of repetition, one that enhances the experience.”

Now, Keyser has a new book out about repetition in art and its cognitive impact on us, scrutinizing “Satin Doll” along with many other works of music, poetry, painting, and photography. The volume, “Play It Again, Sam: Repetition in the Arts,” is published by the MIT Press. The title is partly a play on Keyser’s name.

Inspired by the Margulis experiment

The genesis of “Play It Again, Sam” dates back several years, when Keyser encountered an experiment conducted by musicologist Elizabeth Margulis, described in her 2014 book, “On Repeat.” Margulis found that when she altered modern atonal compositions to add repetition to them, audiences ranging from ordinary listeners to music theorists preferred these edited versions to the original works.

“The Margulis experiment really caused the ideas to materialize,” Keyser says. He then examined repetition across art forms that featured research on associated cognitive activity, especially music, poetry, and the visual arts. For instance, the brain has distinct locations dedicated to the recognition of faces, places, and bodies. Keyser suggests this is why, prior to the advent of modernism, painting was overwhelmingly mimetic.

Ideally, he suggests, it will be possible to more comprehensively study how our brains process art — to see if encountering repetition triggers an endorphin release, say. For now, Keyser postulates that repetition involves what he calls the 4 Ps: priming, parallelism, prediction, and pleasure. Essentially, hearing or seeing a motif sets the stage for it to be repeated, providing audiences with satisfaction when they discover the repetition.

With remarkable range, Keyser vigorously analyzes how artists deploy repetition and have thought about it, from “Beowulf” to Leonard Bernstein, from Gustave Caillebotte to Italo Calvino. Some artworks do deploy identical repetition of elements, such as the Homeric epics; others use the “same/except” technique.

Keyser is deeply interested in visual art displaying the “same/except” concept, such as Andy Warhol’s famous “Campbell Soup Cans” painting. It features four rows of eight soup cans, which are all the same — except for the kind of soup on each can.

“Discovering this ‘same/except’ repetition in a work of art brings pleasure,” Keyser says.

But why is this? Multiple experimental studies, Keyser notes, suggest that repeated exposure of a subject to an image — such as an infant’s exposure to its mother’s face — helps create a bond of affection. This is the “mere exposure” phenomenon, posited by social psychologist Robert Zajonc, who as Keyser notes in the book, studied in detail “the repetition of an arbitrary stimulus and the mild affection that people eventually have for it.”

This tendency also helps explain why product manufacturers create ads with just the name of their products in ads: Seen often enough, the viewer bonds with the name. However the mechanism connecting repetition with pleasure works, and whatever its original function, Keyser argues that many artists have successfully tapped into it, grasping that audiences like repetition in poetry, painting, and music.

A shadow dog in Albuquerque

In the book, Keyser’s emphasis on repetition generates some distinctive interpretive positions. In one chapter, he digs into Lee Friendlander’s well-known photo, “Albuquerque, New Mexico,” a street scene with a jumble of signs, wires, and buildings, often interpreted in symbolic terms: It’s the American West frontier being submerged under postwar concrete and commerce.

Keyser, however, has a really different view of the Friendlander photo. There is a dog sitting near the middle of it; to the right is the shadow of a street sign. Keyser believes the shadow resembles the dog, and thinks it creates playful repetition in the photo.

“This particular photograph is really two photographs that rhyme,” Keyser says.“They’re the same, except one is the dog and one is the shadow. And that’s why that photograph is pleasurable, because you see that, even if you may not be fully aware of it. Sensing repetition in a work of art brings pleasure.”

“Play It Again, Sam” has received praise from arts practitioners, among others. George Darrah, principal drummer and arranger of the Boston Pops Orchestra, has called the book “extraordinary” in its “demonstration of the ways that poetry, music, painting, and photography engender pleasure in their audiences by exploiting the ability of the brain to detect repetition.” He adds that “Keyser has an uncanny ability to simplify complex ideas so that difficult material is easily understandable.”

In certain ways “Play It Again, Sam” contains the classic intellectual outlook of an MIT linguist. For decades, MIT-linked linguistics research has identified the universal structures of human language, revealing important similarities despite the seemingly wild variation of global languages. And here too, Keyser finds patterns that help organize an apparently boundless world of art. “Play It Again, Sam” is a hunt for structure.

Asked about this, Keyser acknowledges the influence of his longtime field on his current intellectual explorations, while noting that his insights about art are part of a greater investigation into our works and minds.

“I’m bringing a linguistic habit of mind to art,” Keyser says. “But I’m also pointing an analytical lens in the direction of natural predilections of the brain. The idea is to investigate how our aesthetic sense depends on the way the mind works. I’m trying to show how art can exploit the brain’s capacity to produce pleasure from non-art related functions.”

Using an inexpensive electrode coated with DNA, MIT researchers have designed disposable diagnostics that could be adapted to detect a variety of diseases, including cancer or infectious diseases such as influenza and HIV.

These electrochemical sensors make use of a DNA-chopping enzyme found in the CRISPR gene-editing system. When a target such as a cancerous gene is detected by the enzyme, it begins shearing DNA from the electrode nonspecifically, like a lawnmower cutting grass, altering the electrical signal produced.

One of the main limitations of this type of sensing technology is that the DNA that coats the electrode breaks down quickly, so the sensors can’t be stored for very long and their storage conditions must be tightly controlled, limiting where they can be used. In a new study, MIT researchers stabilized the DNA with a polymer coating, allowing the sensors to be stored for up to two months, even at high temperatures. After storage, the sensors were able to detect a prostate cancer gene that is often used to diagnose the disease.

The DNA-based sensors, which cost only about 50 cents to make, could offer a cheaper way to diagnose many diseases in low-resource regions, says Ariel Furst, the Paul M. Cook Career Development Assistant Professor of Chemical Engineering at MIT and the senior author of the study.

“Our focus is on diagnostics that many people have limited access to, and our goal is to create a point-of-use sensor. People wouldn’t even need to be in a clinic to use it. You could do it at home,” Furst says.

MIT graduate student Xingcheng Zhou is the lead author of the paper, published June 30 in the journal ACS Sensors. Other authors of the paper are MIT undergraduate Jessica Slaughter, Smah Riki ’24, and graduate student Chao Chi Kuo.

An inexpensive sensor

Electrochemical sensors work by measuring changes in the flow of an electric current when a target molecule interacts with an enzyme. This is the same technology that glucose meters use to detect concentrations of glucose in a blood sample.

The electrochemical sensors developed in Furst’s lab consist of DNA adhered to an inexpensive gold leaf electrode, which is laminated onto a sheet of plastic. The DNA is attached to the electrode using a sulfur-containing molecule known as a thiol.

In a 2021 study, Furst’s lab showed that they could use these sensors to detect genetic material from HIV and human papillomavirus (HPV). The sensors detect their targets using a guide RNA strand, which can be designed to bind to nearly any DNA or RNA sequence. The guide RNA is linked to an enzyme called Cas12, which cleaves DNA nonspecifically when it is turned on and is in the same family of proteins as the Cas9 enzyme used for CRISPR genome editing.

If the target is present, it binds to the guide RNA and activates Cas12, which then cuts the DNA adhered to the electrode. That alters the current produced by the electrode, which can be measured using a potentiostat (the same technology used in handheld glucose meters).

“If Cas12 is on, it’s like a lawnmower that cuts off all the DNA on your electrode, and that turns off your signal,” Furst says.

In previous versions of the device, the DNA had to be added to the electrode just before it was used, because DNA doesn’t remain stable for very long. In the new study, the researchers found that they could increase the stability of the DNA by coating it with a polymer called polyvinyl alcohol (PVA).

This polymer, which costs less than 1 cent per coating, acts like a tarp that protects the DNA below it. Once deposited onto the electrode, the polymer dries to form a protective thin film.

“Once it’s dried, it seems to make a very strong barrier against the main things that can harm DNA, such as reactive oxygen species that can either damage the DNA itself or break the thiol bond with the gold and strip your DNA off the electrode,” Furst says.

Successful detection

The researchers showed that this coating could protect DNA on the sensors for at least two months, and it could also withstand temperatures up to about 150 degrees Fahrenheit. After two months, they rinsed off the polymer and demonstrated that the sensors could still detect PCA3, a prostate cancer gene that can be found in urine.

This type of test could be used with a variety of samples, including urine, saliva, or nasal swabs. The researchers hope to use this approach to develop cheaper diagnostics for infectious diseases, such as HPV or HIV, that could be used in a doctor’s office or at home. This approach could also be used to develop tests for emerging infectious diseases, the researchers say.

A group of researchers from Furst’s lab was recently accepted into delta v, MIT’s student venture accelerator, where they hope to launch a startup to further develop this technology. Now that the researchers can create tests with a much longer shelf-life, they hope to begin shipping them to locations where they could be tested with patient samples.

“Our goal is to continue to test with patient samples against different diseases in real world environments,” Furst says. “Our limitation before was that we had to make the sensors on site, but now that we can protect them, we can ship them. We don’t have to use refrigeration. That allows us to access a lot more rugged or non-ideal environments for testing.”

The research was funded, in part, by the MIT Research Support Committee and a MathWorks Fellowship.

A new imaging technique developed by MIT researchers could enable quality-control robots in a warehouse to peer through a cardboard shipping box and see that the handle of a mug buried under packing peanuts is broken.

Their approach leverages millimeter wave (mmWave) signals, the same type of signals used in Wi-Fi, to create accurate 3D reconstructions of objects that are blocked from view.

The waves can travel through common obstacles like plastic containers or interior walls, and reflect off hidden objects. The system, called mmNorm, collects those reflections and feeds them into an algorithm that estimates the shape of the object’s surface.

This new approach achieved 96 percent reconstruction accuracy on a range of everyday objects with complex, curvy shapes, like silverware and a power drill. State-of-the-art baseline methods achieved only 78 percent accuracy.

In addition, mmNorm does not require additional bandwidth to achieve such high accuracy. This efficiency could allow the method to be utilized in a wide range of settings, from factories to assisted living facilities.

For instance, mmNorm could enable robots working in a factory or home to distinguish between tools hidden in a drawer and identify their handles, so they could more efficiently grasp and manipulate the objects without causing damage.

“We’ve been interested in this problem for quite a while, but we’ve been hitting a wall because past methods, while they were mathematically elegant, weren’t getting us where we needed to go. We needed to come up with a very different way of using these signals than what has been used for more than half a century to unlock new types of applications,” says Fadel Adib, associate professor in the Department of Electrical Engineering and Computer Science, director of the Signal Kinetics group in the MIT Media Lab, and senior author of a paper on mmNorm.

Adib is joined on the paper by research assistants Laura Dodds, the lead author, and Tara Boroushaki, and former postdoc Kaichen Zhou. The research was recently presented at the Annual International Conference on Mobile Systems, Applications and Services.

Reflecting on reflections

Traditional radar techniques send mmWave signals and receive reflections from the environment to detect hidden or distant objects, a technique called back projection.

This method works well for large objects, like an airplane obscured by clouds, but the image resolution is too coarse for small items like kitchen gadgets that a robot might need to identify.

In studying this problem, the MIT researchers realized that existing back projection techniques ignore an important property known as specularity. When a radar system transmits mmWaves, almost every surface the waves strike acts like a mirror, generating specular reflections.

If a surface is pointed toward the antenna, the signal will reflect off the object to the antenna, but if the surface is pointed in a different direction, the reflection will travel away from the radar and won’t be received.

“Relying on specularity, our idea is to try to estimate not just the location of a reflection in the environment, but also the direction of the surface at that point,” Dodds says.

They developed mmNorm to estimate what is called a surface normal, which is the direction of a surface at a particular point in space, and use these estimations to reconstruct the curvature of the surface at that point.

Combining surface normal estimations at each point in space, mmNorm uses a special mathematical formulation to reconstruct the 3D object.

The researchers created an mmNorm prototype by attaching a radar to a robotic arm, which continually takes measurements as it moves around a hidden item. The system compares the strength of the signals it receives at different locations to estimate the curvature of the object’s surface.

For instance, the antenna will receive the strongest reflections from a surface pointed directly at it and weaker signals from surfaces that don’t directly face the antenna.

Because multiple antennas on the radar receive some amount of reflection, each antenna “votes” on the direction of the surface normal based on the strength of the signal it received.

“Some antennas might have a very strong vote, some might have a very weak vote, and we can combine all votes together to produce one surface normal that is agreed upon by all antenna locations,” Dodds says.

In addition, because mmNorm estimates the surface normal from all points in space, it generates many possible surfaces. To zero in on the right one, the researchers borrowed techniques from computer graphics, creating a 3D function that chooses the surface most representative of the signals received. They use this to generate a final 3D reconstruction.

Finer details

The team tested mmNorm’s ability to reconstruct more than 60 objects with complex shapes, like the handle and curve of a mug. It generated reconstructions with about 40 percent less error than state-of-the-art approaches, while also estimating the position of an object more accurately.

Their new technique can also distinguish between multiple objects, like a fork, knife, and spoon hidden in the same box. It also performed well for objects made from a range of materials, including wood, metal, plastic, rubber, and glass, as well as combinations of materials, but it does not work for objects hidden behind metal or very thick walls.

“Our qualitative results really speak for themselves. And the amount of improvement you see makes it easier to develop applications that use these high-resolution 3D reconstructions for new tasks,” Boroushaki says.

For instance, a robot can distinguish between multiple tools in a box, determine the precise shape and location of a hammer’s handle, and then plan to pick it up and use it for a task. One could also use mmNorm with an augmented reality headset, enabling a factory worker to see lifelike images of fully occluded objects.

It could also be incorporated into existing security and defense applications, generating more accurate reconstructions of concealed objects in airport security scanners or during military reconnaissance.

The researchers want to explore these and other potential applications in future work. They also want to improve the resolution of their technique, boost its performance for less reflective objects, and enable the mmWaves to effectively image  through thicker occlusions.

“This work really represents a paradigm shift in the way we are thinking about these signals and this 3D reconstruction process. We’re excited to see how the insights that we’ve gained here can have a broad impact,” Dodds says.

This work is supported, in part, by the National Science Foundation, the MIT Media Lab, and Microsoft.

Imagine that you want to know the plot of a movie, but you only have access to either the visuals or the sound. With visuals alone, you’ll miss all the dialogue. With sound alone, you will miss the action. Understanding our biology can be similar. Measuring one kind of data — such as which genes are being expressed — can be informative, but it only captures one facet of a multifaceted story. For many biological processes and disease mechanisms, the entire “plot” can’t be fully understood without combining data types.

However, capturing both the “visuals and sound” of biological data, such as gene expression and cell structure data, from the same cells requires researchers to develop new approaches. They also have to make sure that the data they capture accurately reflects what happens in living organisms, including how cells interact with each other and their environments.

Whitehead Institute for Biomedical Research and Harvard University researchers have taken on these challenges and developed Perturb-Multimodal (Perturb-Multi), a powerful new approach that simultaneously measures how genetic changes such as turning off individual genes affect both gene expression and cell structure in intact liver tissue. The method, described in Cell on June 12, aims to accelerate discovery of how genes control organ function and disease.

The research team, led by Whitehead Institute Member Jonathan Weissman and then-graduate student in his lab Reuben Saunders, along with Xiaowei Zhuang, the David B. Arnold Professor of Science at Harvard University, and then-postdoc in her lab Will Allen, created a system that can test hundreds of different genetic modifications within a single mouse liver while capturing multiple types of data from the same cells.

“Understanding how our organs work requires looking at many different aspects of cell biology at once,” Saunders says. “With Perturb-Multi, we can see how turning off specific genes changes not just what other genes are active, but also how proteins are distributed within cells, how cellular structures are organized, and where cells are located in the tissue. It’s like having multiple specialized microscopes all focused on the same experiment.”

“This approach accelerates discovery by both allowing us to test the functions of many different genes at once, and then for each gene, allowing us to measure many different functional outputs or cell properties at once — and we do that in intact tissue from animals,” says Zhuang, who is also a Howard Hughes Medical Institute (HHMI) investigator.

A more efficient approach to genetic studies

Traditional genetic studies in mice often turn off one gene and then observe what changes in that gene’s absence to learn about what the gene does. The researchers designed their approach to turn off hundreds of different genes across a single liver, while still only turning off one gene per cell — using what is known as a mosaic approach. This allowed them to study the roles of hundreds of individual genes at once in a single individual. The researchers then collected diverse types of data from cells across the same liver to get a full picture of the consequences of turning off the genes.

“Each cell serves as its own experiment, and because all the cells are in the same animal, we eliminate the variability that comes from comparing different mice,” Saunders says. “Every cell experiences the same physiological conditions, diet, and environment, making our comparisons much more precise.”

“The challenge we faced was that tissues, to perform their functions, rely on thousands of genes, expressed in many different cells, working together. Each gene, in turn, can control many aspects of a cell’s function. Testing these hundreds of genes in mice using current methods would be extremely slow and expensive — near impossible, in practice.” Allen says.

Revealing new biology through combined measurements

The team applied Perturb-Multi to study genetic controls of liver physiology and function. Their study led to discoveries in three important aspects of liver biology: fat accumulation in liver cells — a precursor to liver disease; stress responses; and hepatocyte zonation (how liver cells specialize, assuming different traits and functions, based on their location within the liver).

One striking finding emerged from studying genes that, when disrupted, cause fat accumulation in liver cells. The imaging data revealed that four different genes all led to similar fat droplet accumulation, but the sequencing data showed they did so through three completely different mechanisms.

“Without combining imaging and sequencing, we would have missed this complexity entirely,” Saunders says. “The imaging told us which genes affect fat accumulation, while the sequencing revealed whether this was due to increased fat production, cellular stress, or other pathways. This kind of mechanistic insight could be crucial for developing targeted therapies for fatty liver disease.”

The researchers also discovered new regulators of liver cell zonation. Unexpectedly, the newly discovered regulators include genes involved in modifying the extracellular matrix — the scaffolding between cells. “We found that cells can change their specialized functions without physically moving to a different zone,” Saunders says. “This suggests that liver cell identity is more flexible than previously thought.”

Technical innovation enables new science

Developing Perturb-Multi required solving several technical challenges. The team created new methods for preserving the content of interest in cells — RNA and proteins — during tissue processing, for collecting many types of imaging data and single-cell gene expression data from tissue samples that have been fixed with a preservative, and for integrating multiple types of data from the same cells.

“Overcoming the inherent complexity of biology in living animals required developing new tools that bridge multiple disciplines — including, in this case, genomics, imaging, and AI,” Allen says.

The two components of Perturb-Multi — the imaging and sequencing assays — together, applied to the same tissue, provide insights that are unattainable through either assay alone.

“Each component had to work perfectly while not interfering with the others,” says Weissman, who is also a professor of biology at MIT and an HHMI investigator. “The technical development took considerable effort, but the payoff is a system that can reveal biology we simply couldn’t see before.”

Expanding to new organs and other contexts

The researchers plan to expand Perturb-Multi to other organs, including the brain, and to study how genetic changes affect organ function under different conditions like disease states or dietary changes.

“We’re also excited about using the data we generate to train machine learning models,” adds Saunders. “With enough examples of how genetic changes affect cells, we could eventually predict the effects of mutations without having to test them experimentally — a ‘virtual cell’ that could accelerate both research and drug development.”

“Perturbation data are critical for training such AI models and the paucity of existing perturbation data represents a major hindrance in such ‘virtual cell’ efforts,” Zhuang says. “We hope Perturb-Multi will fill this gap by accelerating the collection of perturbation data.”

The approach is designed to be scalable, with the potential for genome-wide studies that test thousands of genes simultaneously. As sequencing and imaging technologies continue to improve, the researchers anticipate that Perturb-Multi will become even more powerful and accessible to the broader research community.

“Our goal is to keep scaling up. We plan to do genome-wide perturbations, study different physiological conditions, and look at different organs,” says Weissman. “That we can now collect so many types of data from so many cells, at speed, is going to be critical for building AI models like virtual cells, and I think it’s going to help us answer previously unsolvable questions about health and disease.”

Several researchers have taken a broad view of scientific progress over the last 50 years and come to the same troubling conclusion: Scientific productivity is declining. It’s taking more time, more funding, and larger teams to make discoveries that once came faster and cheaper. Although a variety of explanations have been offered for the slowdown, one is that, as research becomes more complex and specialized, scientists must spend more time reviewing publications, designing sophisticated experiments, and analyzing data.

Now, the philanthropically funded research lab FutureHouse is seeking to accelerate scientific research with an AI platform designed to automate many of the critical steps on the path toward scientific progress. The platform is made up of a series of AI agents specialized for tasks including information retrieval, information synthesis, chemical synthesis design, and data analysis.

FutureHouse founders Sam Rodriques PhD ’19 and Andrew White believe that by giving every scientist access to their AI agents, they can break through the biggest bottlenecks in science and help solve some of humanity’s most pressing problems.

“Natural language is the real language of science,” Rodriques says. “Other people are building foundation models for biology, where machine learning models speak the language of DNA or proteins, and that’s powerful. But discoveries aren’t represented in DNA or proteins. The only way we know how to represent discoveries, hypothesize, and reason is with natural language.”

Finding big problems

For his PhD research at MIT, Rodriques sought to understand the inner workings of the brain in the lab of Professor Ed Boyden.

“The entire idea behind FutureHouse was inspired by this impression I got during my PhD at MIT that even if we had all the information we needed to know about how the brain works, we wouldn’t know it because nobody has time to read all the literature,” Rodriques explains. “Even if they could read it all, they wouldn’t be able to assemble it into a comprehensive theory. That was a foundational piece of the FutureHouse puzzle.”

Rodriques wrote about the need for new kinds of large research collaborations as the last chapter of his PhD thesis in 2019, and though he spent some time running a lab at the Francis Crick Institute in London after graduation, he found himself gravitating toward broad problems in science that no single lab could take on.

“I was interested in how to automate or scale up science and what kinds of new organizational structures or technologies would unlock higher scientific productivity,” Rodriques says.

When Chat-GPT 3.5 was released in November 2022, Rodriques saw a path toward more powerful models that could generate scientific insights on their own. Around that time, he also met Andrew White, a computational chemist at the University of Rochester who had been granted early access to Chat-GPT 4. White had built the first large language agent for science, and the researchers joined forces to start FutureHouse.

The founders started out wanting to create distinct AI tools for tasks like literature searches, data analysis, and hypothesis generation. They began with data collection, eventually releasing PaperQA in September 2024, which Rodriques calls the best AI agent in the world for retrieving and summarizing information in scientific literature. Around the same time, they released Has Anyone, a tool that lets scientists determine if anyone has conducted specific experiments or explored specific hypotheses.

“We were just sitting around asking, ‘What are the kinds of questions that we as scientists ask all the time?’” Rodriques recalls.

When FutureHouse officially launched its platform on May 1 of this year, it rebranded some of its tools. Paper QA is now Crow, and Has Anyone is now called Owl. Falcon is an agent capable of compiling and reviewing more sources than Crow. Another new agent, Phoenix, can use specialized tools to help researchers plan chemistry experiments. And Finch is an agent designed to automate data driven discovery in biology.

On May 20, the company demonstrated a multi-agent scientific discovery workflow to automate key steps of the scientific process and identify a new therapeutic candidate for dry age-related macular degeneration (dAMD), a leading cause of irreversible blindness worldwide. In June, FutureHouse released ether0, a 24B open-weights reasoning model for chemistry.

“You really have to think of these agents as part of a larger system,” Rodriques says. “Soon, the literature search agents will be integrated with the data analysis agent, the hypothesis generation agent, an experiment planning agent, and they will all be engineered to work together seamlessly.”

Agents for everyone

Today anyone can access FutureHouse’s agents at platform.futurehouse.org. The company’s platform launch generated excitement in the industry, and stories have started to come in about scientists using the agents to accelerate research.

One of FutureHouse’s scientists used the agents to identify a gene that could be associated with polycystic ovary syndrome and come up with a new treatment hypothesis for the disease. Another researcher at the Lawrence Berkeley National Laboratory used Crow to create an AI assistant capable of searching the PubMed research database for information related to Alzheimer’s disease.

Scientists at another research institution have used the agents to conduct systematic reviews of genes relevant to Parkinson’s disease, finding FutureHouse’s agents performed better than general agents.

Rodriques says scientists who think of the agents less like Google Scholar and more like a smart assistant scientist get the most out of the platform.

“People who are looking for speculation tend to get more mileage out of Chat-GPT o3 deep research, while people who are looking for really faithful literature reviews tend to get more out of our agents,” Rodriques explains.

Rodriques also thinks FutureHouse will soon get to a point where its agents can use the raw data from research papers to test the reproducibility of its results and verify conclusions.

In the longer run, to keep scientific progress marching forward, Rodriques says FutureHouse is working on embedding its agents with tacit knowledge to be able to perform more sophisticated analyses while also giving the agents the ability to use computational tools to explore hypotheses.

“There have been so many advances around foundation models for science and around language models for proteins and DNA, that we now need to give our agents access to those models and all of the other tools people commonly use to do science,” Rodriques says. “Building the infrastructure to allow agents to use more specialized tools for science is going to be critical.”

Leveraging the strengths of two world-class research institutions, MIT and Mass General Brigham (MGB) recently celebrated the launch of the MIT-MGB Seed Program. The new initiative, which is supported by Analog Devices Inc. (ADI), will fund joint research projects led by researchers at MIT and Mass General Brigham. These collaborative projects will advance research in human health, with the goal of developing next-generation therapies, diagnostics, and digital tools that can improve lives at scale. 

The program represents a unique opportunity to dramatically accelerate innovations that address some of the most urgent challenges in human health. By supporting interdisciplinary teams from MIT and Mass General Brigham, including both researchers and clinicians, the seed program will foster groundbreaking work that brings together expertise in artificial intelligence, machine learning, and measurement and sensing technologies with pioneering clinical research and patient care.

“The power of this program is that it combines MIT’s strength in science, engineering, and innovation with Mass General Brigham’s world-class scientific and clinical research. With the support and incentive to work together, researchers and clinicians will have the freedom to tackle compelling problems and find novel ways to overcome them to achieve transformative changes in patient care,” says Sally Kornbluth, president of MIT.

“The MIT-MGB Seed Program will enable cross-disciplinary collaboration to advance transformative research and breakthrough science. By combining the collective strengths and expertise of our great institutions, we can transform medical care and drive innovation and discovery with speed,” says Anne Klibanski, president and CEO of Mass General Brigham.

The initiative is funded by a gift from ADI. Over the next three years, the ADI Fund for Health and Life Sciences will support approximately six joint projects annually, with funding split between the two institutions. 

“The converging domains of biology, medicine, and computing promise a new era of health-care efficacy, efficiency, and access. ADI has enjoyed a long and fruitful history of collaboration with MIT and Mass General Brigham, and we are excited by this new initiative’s potential to transform the future of patient care,” adds Vincent Roche, CEO and chair of the board of directors at ADI.

In addition to funding, teams selected for the program will have access to entrepreneurial workshops, including some hosted by The Engine — an MIT-built venture firm focused on tough tech. These sessions will connect researchers with company founders, investors, and industry leaders, helping them chart a path from breakthrough discoveries in the lab to real-world impact.

The program will launch an open call for proposals to researchers at MIT and Mass General Brigham. The first cohort of funded projects is expected to launch in fall 2025. Awardees will be selected by a joint review committee composed of MIT and Mass General Brigham experts.

According to MIT’s faculty lead for the MIT-MGB Seed Program, Alex K. Shalek, building collaborative research teams with leaders from both institutions could help fill critical gaps that often impede innovation in health and life sciences. Shalek also serves as director of the Institute for Medical Engineering & Science (IMES), the J. W. Kieckhefer Professor in IMES and Chemistry, and an extramural member of the Koch Institute for Integrative Cancer Research.

 “Clinicians often see where current interventions fall short, but may lack the scientific tools or engineering expertise needed to develop new ones. Conversely, MIT researchers may not fully grasp these clinical challenges or have access to the right patient data and samples,” explains Shalek, who is also a member of the Ragon Institute of Mass General Brigham, MIT, and Harvard. “By supporting bilateral collaborations and building a community across disciplines, this program is poised to drive critical advances in diagnostics, therapeutics, and AI-driven health applications.”

Emery Brown, a practicing anesthesiologist at Massachusetts General Hospital, will serve alongside Shalek as Mass General Brigham’s faculty lead for the program.

“The MIT-MGB Seed Program creates a perfect storm. The program will provide an opportunity for MIT faculty to bring novel science and engineering to attack and solve important clinical problems,” adds Brown, who is also the Edward Hood Taplin Professor of Medical Engineering and Computational Neuroscience at MIT. “The pursuit of solutions to important and challenging clinical problems by Mass General Brigham physicians and scientists will no doubt spur MIT scientists and engineers to develop new technologies, or find novel applications of existing technologies.”

The MIT-MGB Seed Program is a flagship initiative in the MIT Health and Life Sciences Collaborative (MIT HEALS). It reflects MIT HEALS’ core mission to establish MIT as a central hub for health and life sciences innovation and translation, and to leverage connections with other world-class research institutions in the Boston area.

“This program exemplifies the power of interdisciplinary research,” says Anantha Chandrakasan, MIT’s chief innovation and strategy officer, dean of engineering, and head of MIT HEALS. “It creates a critical bridge between clinical practice and technological innovation — two areas that must be deeply connected to advance real-world solutions.”

The program’s launch was celebrated at a special event at MIT’s Samberg Conference Center on March 31.

Diffusion models like OpenAI’s DALL-E are becoming increasingly useful in helping brainstorm new designs. Humans can prompt these systems to generate an image, create a video, or refine a blueprint, and come back with ideas they hadn’t considered before.

But did you know that generative artificial intelligence (GenAI) models are also making headway in creating working robots? Recent diffusion-based approaches have generated structures and the systems that control them from scratch. With or without a user’s input, these models can make new designs and then evaluate them in simulation before they’re fabricated.

A new approach from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) applies this generative know-how toward improving humans’ robotic designs. Users can draft a 3D model of a robot and specify which parts they’d like to see a diffusion model modify, providing its dimensions beforehand. GenAI then brainstorms the optimal shape for these areas and tests its ideas in simulation. When the system finds the right design, you can save and then fabricate a working, real-world robot with a 3D printer, without requiring additional tweaks.

The researchers used this approach to create a robot that leaps up an average of roughly 2 feet, or 41 percent higher than a similar machine they created on their own. The machines are nearly identical in appearance: They’re both made of a type of plastic called polylactic acid, and while they initially appear flat, they spring up into a diamond shape when a motor pulls on the cord attached to them. So what exactly did AI do differently?

A closer look reveals that the AI-generated linkages are curved, and resemble thick drumsticks (the musical instrument drummers use), whereas the standard robot’s connecting parts are straight and rectangular.

Better and better blobs

The researchers began to refine their jumping robot by sampling 500 potential designs using an initial embedding vector — a numerical representation that captures high-level features to guide the designs generated by the AI model. From these, they selected the top 12 options based on performance in simulation and used them to optimize the embedding vector.

This process was repeated five times, progressively guiding the AI model to generate better designs. The resulting design resembled a blob, so the researchers prompted their system to scale the draft to fit their 3D model. They then fabricated the shape, finding that it indeed improved the robot’s jumping abilities.

The advantage of using diffusion models for this task, according to co-lead author and CSAIL postdoc Byungchul Kim, is that they can find unconventional solutions to refine robots.

“We wanted to make our machine jump higher, so we figured we could just make the links connecting its parts as thin as possible to make them light,” says Kim. “However, such a thin structure can easily break if we just use 3D printed material. Our diffusion model came up with a better idea by suggesting a unique shape that allowed the robot to store more energy before it jumped, without making the links too thin. This creativity helped us learn about the machine’s underlying physics.”

The team then tasked their system with drafting an optimized foot to ensure it landed safely. They repeated the optimization process, eventually choosing the best-performing design to attach to the bottom of their machine. Kim and his colleagues found that their AI-designed machine fell far less often than its baseline, to the tune of an 84 percent improvement.

The diffusion model’s ability to upgrade a robot’s jumping and landing skills suggests it could be useful in enhancing how other machines are designed. For example, a company working on manufacturing or household robots could use a similar approach to improve their prototypes, saving engineers time normally reserved for iterating on those changes.

The balance behind the bounce

To create a robot that could jump high and land stably, the researchers recognized that they needed to strike a balance between both goals. They represented both jumping height and landing success rate as numerical data, and then trained their system to find a sweet spot between both embedding vectors that could help build an optimal 3D structure.

The researchers note that while this AI-assisted robot outperformed its human-designed counterpart, it could soon reach even greater new heights. This iteration involved using materials that were compatible with a 3D printer, but future versions would jump even higher with lighter materials.

Co-lead author and MIT PhD student and CSAIL affiliate Tsun-Hsuan “Johnson” Wang says the project is a jumping-off point for new robotics designs that generative AI could help with.

“We want to branch out to more flexible goals,” says Wang. “Imagine using natural language to guide a diffusion model to draft a robot that can pick up a mug, or operate an electric drill.”

Kim says that a diffusion model could also help to generate articulation and ideate on how parts connect, potentially improving how high the robot would jump. The team is also exploring the possibility of adding more motors to control which direction the machine jumps and perhaps improve its landing stability.

The researchers’ work was supported, in part, by the National Science Foundation’s Emerging Frontiers in Research and Innovation program, the Singapore-MIT Alliance for Research and Technology’s Mens, Manus and Machina program, and the Gwangju Institute of Science and Technology (GIST)-CSAIL Collaboration. They presented their work at the 2025 International Conference on Robotics and Automation.

Four MIT rising seniors have been selected to receive a 2025 Barry Goldwater Scholarship, including Avani Ahuja and Jacqueline Prawira in the School of Engineering and Julianna Lian and Alex Tang from the School of Science. An estimated 5,000 college sophomores and juniors from across the United States were nominated for the scholarships, of whom only 441 were selected.

The Goldwater Scholarships have been conferred since 1989 by the Barry Goldwater Scholarship and Excellence in Education Foundation. These scholarships have supported undergraduates who go on to become leading scientists, engineers, and mathematicians in their respective fields.

Avani Ahuja, a mechanical engineering and electrical engineering major, conducts research in the Conformable Decoders group, where she is focused on developing a “wearable conformable breast ultrasound patch” that makes ultrasounds for breast cancer more accessible.

“Doing research in the Media Lab has had a huge impact on me, especially in the ways that we think about inclusivity in research,” Ahuja says.

In her research group, Ahuja works under Canan Dagdeviren, the LG Career Development Professor of Media Arts and Sciences. Ahuja plans to pursue a PhD in electrical engineering. She aspires to conduct research in electromechanical systems for women’s health applications and teach at the university level.

“I want to thank Professor Dagdeviren for all her support. It’s an honor to receive this scholarship, and it’s amazing to see that women’s health research is getting recognized in this way,” Ahuja says.

Julianna Lian studies mechanochemistry, organic, and polymer chemistry in the lab of Professor Jeremiah Johnson, the A. Thomas Guertin Professor of Chemistry. In addition to her studies, she serves the MIT community as an emergency medical technician (EMT) with MIT Emergency Medical Services, is a member of MIT THINK, and a ClubChem mentorship chair.

“Receiving this award has been a tremendous opportunity to not only reflect on how much I have learned, but also on the many, many people I have had the chance to learn from,” says Lian. “I am deeply grateful for the guidance, support, and encouragement of these teachers, mentors, and friends. And I am excited to carry forward the lasting curiosity and excitement for chemistry that they have helped inspire in me.”

Lian’s career goals post-graduation include pursuing a PhD in organic chemistry, to conduct research at the interface of synthetic chemistry and materials science, aided by computation, and to teach at the university level.

Jacqueline Prawira, a materials science and engineering major, joined the Center of Decarbonization and Electrification of Industry as a first-year Undergraduate Research Opportunities Program student and became a co-inventor on a patent and a research technician at spinout company Rock Zero. She has also worked in collaboration with Indigenous farmers and Diné College students on the Navajo Nation.

“I’ve become significantly more cognizant of how I listen to people and stories, the tangled messiness of real-world challenges, and the critical skills needed to tackle complex sustainability issues,” Prawira says.

Prawira is mentored by Yet-Ming Chiang, professor of materials science and engineering. Her career goals are to pursue a PhD in materials science and engineering and to research sustainable materials and processes to solve environmental challenges and build a sustainable society.

“Receiving the prestigious title of 2025 Goldwater Scholar validates my current trajectory in innovating sustainable materials and demonstrates my growth as a researcher,” Prawira says. “This award signifies my future impact in building a society where sustainability is the norm, instead of just another option.”

Alex Tang studies the effects of immunotherapy and targeted molecular therapy on the tumor microenvironment in metastatic colorectal cancer patients. He is supervised by professors Jonathan Chen at Northwestern University and Nir Hacohen at the Broad Institute of MIT and Harvard.

“My mentors and collaborators have been instrumental to my growth since I joined the lab as a freshman. I am incredibly grateful for the generous mentorship and support of Professor Hacohen and Professor Chen, who have taught me how to approach scientific investigation with curiosity and rigor,” says Tang. “I’d also like to thank my advisor Professor Adam Martin and first-year advisor Professor Angela Belcher for their guidance throughout my undergraduate career thus far. I am excited to carry forward this work as I progress in my career.” Tang intends to pursue physician-scientist training following graduation.

The Scholarship Program honoring Senator Barry Goldwater was designed to identify, encourage, and financially support outstanding undergraduates interested in pursuing research careers in the sciences, engineering, and mathematics. The Goldwater Scholarship is the preeminent undergraduate award of its type in these fields.

A large language model (LLM) deployed to make treatment recommendations can be tripped up by nonclinical information in patient messages, like typos, extra white space, missing gender markers, or the use of uncertain, dramatic, and informal language, according to a study by MIT researchers.

They found that making stylistic or grammatical changes to messages increases the likelihood an LLM will recommend that a patient self-manage their reported health condition rather than come in for an appointment, even when that patient should seek medical care.

Their analysis also revealed that these nonclinical variations in text, which mimic how people really communicate, are more likely to change a model’s treatment recommendations for female patients, resulting in a higher percentage of women who were erroneously advised not to seek medical care, according to human doctors.

This work “is strong evidence that models must be audited before use in health care — which is a setting where they are already in use,” says Marzyeh Ghassemi, an associate professor in the MIT Department of Electrical Engineering and Computer Science (EECS), a member of the Institute of Medical Engineering Sciences and the Laboratory for Information and Decision Systems, and senior author of the study.

These findings indicate that LLMs take nonclinical information into account for clinical decision-making in previously unknown ways. It brings to light the need for more rigorous studies of LLMs before they are deployed for high-stakes applications like making treatment recommendations, the researchers say.

“These models are often trained and tested on medical exam questions but then used in tasks that are pretty far from that, like evaluating the severity of a clinical case. There is still so much about LLMs that we don’t know,” adds Abinitha Gourabathina, an EECS graduate student and lead author of the study.

They are joined on the paper, which will be presented at the ACM Conference on Fairness, Accountability, and Transparency, by graduate student Eileen Pan and postdoc Walter Gerych.

Mixed messages

Launched in February of this year, the MIT Generative AI Impact Consortium (MGAIC), a presidential initiative led by MIT’s Office of Innovation and Strategy and administered by the MIT Stephen A. Schwarzman College of Computing, issued a call for proposals, inviting researchers from across MIT to submit ideas for innovative projects studying high-impact uses of generative AI models.

The call received 180 submissions from nearly 250 faculty members, spanning all of MIT’s five schools and the college. The overwhelming response across the Institute exemplifies the growing interest in AI and follows in the wake of MIT’s Generative AI Week and call for impact papers. Fifty-five proposals were selected for MGAIC’s inaugural seed grants, with several more selected to be funded by the consortium’s founding company members.

Over 30 funding recipients presented their proposals to the greater MIT community at a kickoff event on May 13. Anantha P. Chandrakasan, chief innovation and strategy officer and dean of the School of Engineering who is head of the consortium, welcomed the attendees and thanked the consortium’s founding industry members.

“The amazing response to our call for proposals is an incredible testament to the energy and creativity that MGAIC has sparked at MIT. We are especially grateful to our founding members, whose support and vision helped bring this endeavor to life,” adds Chandrakasan. “One of the things that has been most remarkable about MGAIC is that this is a truly cross-Institute initiative. Deans from all five schools and the college collaborated in shaping and implementing it.”

Vivek F. Farias, the Patrick J. McGovern (1959) Professor at the MIT Sloan School of Management and co-faculty director of the consortium with Tim Kraska, associate professor of electrical engineering and computer science in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), emceed the afternoon of five-minute lightning presentations.

Presentation highlights include:

“AI-Driven Tutors and Open Datasets for Early Literacy Education,” presented by Ola Ozernov-Palchik, a research scientist at the McGovern Institute for Brain Research, proposed a refinement for AI-tutors for pK-7 students to potentially decrease literacy disparities.

“Developing jam_bots: Real-Time Collaborative Agents for Live Human-AI Musical Improvisation,” presented by Anna Huang, assistant professor of music and assistant professor of electrical engineering and computer science, and Joe Paradiso, the Alexander W. Dreyfoos (1954) Professor in Media Arts and Sciences at the MIT Media Lab, aims to enhance human-AI musical collaboration in real-time for live concert improvisation.

“GENIUS: GENerative Intelligence for Urban Sustainability,” presented by Norhan Bayomi, a postdoc at the MIT Environmental Solutions Initiative and a research assistant in the Urban Metabolism Group, which aims to address the critical gap of a standardized approach in evaluating and benchmarking cities’ climate policies.

Georgia Perakis, the John C Head III Dean (Interim) of the MIT Sloan School of Management and professor of operations management, operations research, and statistics, who serves as co-chair of the GenAI Dean’s oversight group with Dan Huttenlocher, dean of the MIT Schwarzman College of Computing, ended the event with closing remarks that emphasized “the readiness and eagerness of our community to lead in this space.”

“This is only the beginning,” she continued. “We are at the front edge of a historic moment — one where MIT has the opportunity, and the responsibility, to shape the future of generative AI with purpose, with excellence, and with care.”

Volcanic islands, such as the islands of Hawaii and the Caribbean, are surrounded by coral reefs that encircle an island in a labyrinthine, living ring. A coral reef is punctured at points by reef passes — wide channels that cut through the coral and serve as conduits for ocean water and nutrients to filter in and out. These watery passageways provide circulation throughout a reef, helping to maintain the health of corals by flushing out freshwater and transporting key nutrients.

Now, MIT scientists have found that reef passes are shaped by island rivers. In a study appearing today in the journal Geophysical Research Letters, the team shows that the locations of reef passes along coral reefs line up with where rivers funnel out from an island’s coast.

Their findings provide the first quantitative evidence of rivers forming reef passes.  Scientists and explorers had speculated that this may be the case: Where a river on a volcanic island meets the coast, the freshwater and sediment it carries flows toward the reef, where a strong enough flow can tunnel into the surrounding coral. This idea has been proposed from time to time but never quantitatively tested, until now.

“The results of this study help us to understand how the health of coral reefs depends on the islands they surround,” says study author Taylor Perron, the Cecil and Ida Green Professor of Earth, Atmospheric and Planetary Sciences at MIT.

“A lot of discussion around rivers and their impact on reefs today has been negative because of human impact and the effects of agricultural practices,” adds lead author Megan Gillen, a graduate student in the MIT-WHOI Joint Program in Oceanography. “This study shows the potential long-term benefits rivers can have on reefs, which I hope reshapes the paradigm and highlights the natural state of rivers interacting with reefs.”

The study’s other co-author is Andrew Ashton of the Woods Hole Oceanographic Institution.

Drawing the lines

The new study is based on the team’s analysis of the Society Islands, a chain of islands in the South Pacific Ocean that includes Tahiti and Bora Bora. Gillen, who joined the MIT-WHOI program in 2020, was interested in exploring connections between coral reefs and the islands they surround. With limited options for on-site work during the Covid-19 pandemic, she and Perron looked to see what they could learn through satellite images and maps of island topography. They did a quick search using Google Earth and zeroed in on the Society Islands for their uniquely visible reef and island features.

“The islands in this chain have these iconic, beautiful reefs, and we kept noticing these reef passes that seemed to align with deeply embayed portions of the coastline,” Gillen says. “We started asking ourselves, is there a correlation here?”

Viewed from above, the coral reefs that circle some islands bear what look to be notches, like cracks that run straight through a ring. These breaks in the coral are reef passes — large channels that run tens of meters deep and can be wide enough for some boats to pass through. On first look, Gillen noticed that the most obvious reef passes seemed to line up with flooded river valleys — depressions in the coastline that have been eroded over time by island rivers that flow toward the ocean. She wondered whether and to what extent island rivers might shape reef passes.

“People have examined the flow through reef passes to understand how ocean waves and seawater circulate in and out of lagoons, but there have been no claims of how these passes are formed,” Gillen says. “Reef pass formation has been mentioned infrequently in the literature, and people haven’t explored it in depth.”

Reefs unraveled

To get a detailed view of the topography in and around the Society Islands, the team used data from the NASA Shuttle Radar Topography Mission — two radar antennae that flew aboard the space shuttle in 1999 and measured the topography across 80 percent of the Earth’s surface.

The researchers used the mission’s topographic data in the Society Islands to create a map of every drainage basin along the coast of each island, to get an idea of where major rivers flow or once flowed. They also marked the locations of every reef pass in the surrounding coral reefs. They then essentially “unraveled” each island’s coastline and reef into a straight line, and compared the locations of basins versus reef passes.

“Looking at the unwrapped shorelines, we find a significant correlation in the spatial relationship between these big river basins and where the passes line up,” Gillen says. “So we can say that statistically, the alignment of reef passes and large rivers does not seem random. The big rivers have a role in forming passes.”

As for how rivers shape the coral conduits, the team has two ideas, which they call, respectively, reef incision and reef encroachment. In reef incision, they propose that reef passes can form in times when the sea level is relatively low, such that the reef is exposed above the sea surface and a river can flow directly over the reef. The water and sediment carried by the river can then erode the coral, progressively carving a path through the reef.

When sea level is relatively higher, the team suspects a reef pass can still form, through reef encroachment. Coral reefs naturally live close to the water surface, where there is light and opportunity for photosynthesis. When sea levels rise, corals naturally grow upward and inward toward an island, to try to “catch up” to the water line.

“Reefs migrate toward the islands as sea levels rise, trying to keep pace with changing average sea level,” Gillen says.

However, part of the encroaching reef can end up in old river channels that were previously carved out by large rivers and that are lower than the rest of the island coastline. The corals in these river beds end up deeper than light can extend into the water column, and inevitably drown, leaving a gap in the form of a reef pass.

“We don’t think it’s an either/or situation,” Gillen says. “Reef incision occurs when sea levels fall, and reef encroachment happens when sea levels rise. Both mechanisms, occurring over dozens of cycles of sea-level rise and island evolution, are likely responsible for the formation and maintenance of reef passes over time.”

The team also looked to see whether there were differences in reef passes in older versus younger islands. They observed that younger islands were surrounded by more reef passes that were spaced closer together, versus older islands that had fewer reef passes that were farther apart.

As islands age, they subside, or sink, into the ocean, which reduces the amount of land that funnels rainwater into rivers. Eventually, rivers are too weak to keep the reef passes open, at which point, the ocean likely takes over, and incoming waves could act to close up some passes.

Gillen is exploring ideas for how rivers, or river-like flow, can be engineered to create paths through coral reefs in ways that would promote circulation and benefit reef health.

“Part of me wonders: If you had a more persistent flow, in places where you don’t naturally have rivers interacting with the reef, could that potentially be a way to increase health, by incorporating that river component back into the reef system?” Gillen says. “That’s something we’re thinking about.”

This research was supported, in part, by the WHOI Watson and Von Damm fellowships.

Water makes up around 60 percent of the human body. More than half of this water sloshes around inside the cells that make up organs and tissues. Much of the remaining water flows in the nooks and crannies between cells, much like seawater between grains of sand.

Now, MIT engineers have found that this “intercellular” fluid plays a major role in how tissues respond when squeezed, pressed, or physically deformed. Their findings could help scientists understand how cells, tissues, and organs physically adapt to conditions such as aging, cancer, diabetes, and certain neuromuscular diseases.

In a paper appearing today in Nature Physics, the researchers show that when a tissue is pressed or squeezed, it is more compliant and relaxes more quickly when the fluid between its cells flows easily. When the cells are packed together and there is less room for intercellular flow, the tissue as a whole is stiffer and resists being pressed or squeezed.

The findings challenge conventional wisdom, which has assumed that a tissue’s compliance depends mainly on what’s inside, rather than around, a cell. Now that the researchers have shown that intercellular flow determines how tissues will adapt to physical forces, the results can be applied to understand a wide range of physiological conditions, including how muscles withstand exercise and recover from injury, and how a tissue’s physical adaptability may affect the progression of aging, cancer, and other medical conditions.

The team envisions the results could also inform the design of artificial tissues and organs. For instance, in engineering artificial tissue, scientists might optimize intercellular flow within the tissue to improve its function or resilience. The researchers suspect that intercellular flow could also be a route for delivering nutrients or therapies, either to heal a tissue or eradicate a tumor.

“People know there is a lot of fluid between cells in tissues, but how important that is, in particular in tissue deformation, is completely ignored,” says Ming Guo, associate professor of mechanical engineering at MIT. “Now we really show we can observe this flow. And as the tissue deforms, flow between cells dominates the behavior. So, let’s pay attention to this when we study diseases and engineer tissues.”

Guo is a co-author of the new study, which includes lead author and MIT postdoc Fan Liu PhD ’24, along with Bo Gao and Hui Li of Beijing Normal University, and Liran Lei and Shuainan Liu of Peking Union Medical College.

Pressed and squeezed

The tissues and organs in our body are constantly undergoing physical deformations, from the large stretch and strain of muscles during motion to the small and steady contractions of the heart. In some cases, how easily tissues adapt to deformation can relate to how quickly a person can recover from, for instance, an allergic reaction, a sports injury, or a brain stroke. However, exactly what sets a tissue’s response to deformation is largely unknown.

Guo and his group at MIT looked into the mechanics of tissue deformation, and the role of intercellular flow in particular, following a study they published in 2020. In that study, they focused on tumors and observed the way in which fluid can flow from the center of a tumor out to its edges, through the cracks and crevices between individual tumor cells. They found that when a tumor was squeezed or pressed, the intercellular flow increased, acting as a conveyor belt to transport fluid from the center to the edges. Intercellular flow, they found, could fuel tumor invasion into surrounding regions.

In their new study, the team looked to see what role this intercellular flow might play in other, noncancerous tissues.

“Whether you allow the fluid to flow between cells or not seems to have a major impact,” Guo says. “So we decided to look beyond tumors to see how this flow influences how other tissues respond to deformation.”

A fluid pancake

Guo, Liu, and their colleagues studied the intercellular flow in a variety of biological tissues, including cells derived from pancreatic tissue. They carried out experiments in which they first cultured small clusters of tissue, each measuring less than a quarter of a millimeter wide and numbering tens of thousands of individual cells. They placed each tissue cluster in a custom-designed testing platform that the team built specifically for the study.

“These microtissue samples are in this sweet zone where they are too large to see with atomic force microscopy techniques and too small for bulkier devices,” Guo says. “So, we decided to build a device.”

The researchers adapted a high-precision microbalance that measures minute changes in weight. They combined this with a step motor that is designed to press down on a sample with nanometer precision. The team placed tissue clusters one at a time on the balance and recorded each cluster’s changing weight as it relaxed from a sphere into the shape of a pancake in response to the compression. The team also took videos of the clusters as they were squeezed.

For each type of tissue, the team made clusters of varying sizes. They reasoned that if the tissue’s response is ruled by the flow between cells, then the bigger a tissue, the longer it should take for water to seep through, and therefore, the longer it should take the tissue to relax. It should take the same amount of time, regardless of size, if a tissue’s response is determined by the structure of the tissue rather than fluid.

Over multiple experiments with a variety of tissue types and sizes, the team observed a similar trend: The bigger the cluster, the longer it took to relax, indicating that intercellular flow dominates a tissue’s response to deformation.

“We show that this intercellular flow is a crucial component to be considered in the fundamental understanding of tissue mechanics and also applications in engineering living systems,” Liu says.

Going forward, the team plans to look into how intercellular flow influences brain function, particularly in disorders such as Alzheimer’s disease.

“Intercellular or interstitial flow can help you remove waste and deliver nutrients to the brain,” Liu adds. “Enhancing this flow in some cases might be a good thing.”

“As this work shows, as we apply pressure to a tissue, fluid will flow,” Guo says. “In the future, we can think of designing ways to massage a tissue to allow fluid to transport nutrients between cells.”

More than half of the nation’s 623,218 bridges are experiencing significant deterioration. Through an in-field case study conducted in western Massachusetts, a team led by the University of Massachusetts at Amherst in collaboration with researchers from the MIT Department of Mechanical Engineering (MechE) has just successfully demonstrated that 3D printing may provide a cost-effective, minimally disruptive solution.

“Anytime you drive, you go under or over a corroded bridge,” says Simos Gerasimidis, associate professor of civil and environmental engineering at UMass Amherst and former visiting professor in the Department of Civil and Environmental Engineering at MIT, in a press release. “They are everywhere. It’s impossible to avoid, and their condition often shows significant deterioration. We know the numbers.”

The numbers, according to the American Society of Civil Engineers’ 2025 Report Card for America’s Infrastructure, are staggering: Across the United States, 49.1 percent of the nation’s 623,218 bridges are in “fair” condition and 6.8 percent are in “poor” condition. The projected cost to restore all of these failing bridges exceeds $191 billion.

A proof-of-concept repair took place last month on a small, corroded section of a bridge in Great Barrington, Massachusetts. The technique, called cold spray, can extend the life of beams, reinforcing them with newly deposited steel. The process accelerates particles of powdered steel in heated, compressed gas, and then a technician uses an applicator to spray the steel onto the beam. Repeated sprays create multiple layers, restoring thickness and other structural properties.

This method has proven to be an effective solution for other large structures like submarines, airplanes, and ships, but bridges present a problem on a greater scale. Unlike movable vessels, stationary bridges cannot be brought to the 3D printer — the printer must be brought on-site — and, to lessen systemic impacts, repairs must also be made with minimal disruptions to traffic, which the new approach allows.

“Now that we’ve completed this proof-of-concept repair, we see a clear path to a solution that is much faster, less costly, easier, and less invasive,” says Gerasimidis. “To our knowledge, this is a first. Of course, there is some R&D that needs to be developed, but this is a huge milestone to that.”

“This is a tremendous collaboration where cutting-edge technology is brought to address a critical need for infrastructure in the commonwealth and across the United States,” says John Hart, Class of 1922 Professor and head of the Department of MechE at MIT. Hart and Haden Quinlan, senior program manager in the Center for Advanced Production Technologies at MIT, are leading MIT’s efforts in in the project. Hart is also faculty co-lead of the recently announced MIT Initiative for New Manufacturing.

“Integrating digital systems with advanced physical processing is the future of infrastructure,” says Quinlan. “We’re excited to have moved this technology beyond the lab and into the field, and grateful to our collaborators in making this work possible.”

UMass says the Massachusetts Department of Transportation (MassDOT) has been a valued research partner, helping to identify the problem and providing essential support for the development and demonstration of the technology. Technical guidance and funding support were provided by the MassDOT Highway Division and the Research and Technology Transfer Program.

Equipment for this project was supported through the Massachusetts Manufacturing Innovation Initiative, a statewide program led by the Massachusetts Technology Collaborative (MassTech)’s Center for Advanced Manufacturing that helps bridge the gap between innovation and commercialization in hard tech manufacturing.

“It’s a very Massachusetts success story,” Gerasimidis says. “It involves MassDOT being open-minded to new ideas. It involves UMass and MIT putting [together] the brains to do it. It involves MassTech to bring manufacturing back to Massachusetts. So, I think it’s a win-win for everyone involved here.”

The bridge in Great Barrington is scheduled for demolition in a few years. After demolition occurs, the recently-sprayed beams will be taken back to UMass for testing and measurement to study how well the deposited steel powder adhered to the structure in the field compared to in a controlled lab setting, if it corroded further after it was sprayed, and determine its mechanical properties.

This demonstration builds on several years of research by the UMass and MIT teams, including development of a “digital thread” approach to scan corroded beam surfaces and determine material deposition profiles, alongside laboratory studies of cold spray and other additive manufacturing approaches that are suited to field deployment.

Altogether, this work is a collaborative effort among UMass Amherst, MIT MechE, MassDOT, the Massachusetts Technology Collaborative (MassTech), the U.S. Department of Transportation, and the Federal Highway Administration. Research reports are available on the MassDOT website.

When the Earth froze over, where did life shelter? MIT scientists say one refuge may have been pools of melted ice that dotted the planet’s icy surface.

In a study appearing today in Nature Communications, the researchers report that 635 million to 720 million years ago, during periods known as “Snowball Earth,” when much of the planet was covered in ice, some of our ancient cellular ancestors could have waited things out in meltwater ponds.

The scientists found that eukaryotes — complex cellular lifeforms that eventually evolved into the diverse multicellular life we see today — could have survived the global freeze by living in shallow pools of water. These small, watery oases may have persisted atop relatively shallow ice sheets present in equatorial regions. There, the ice surface could accumulate dark-colored dust and debris from below, which enhanced its ability to melt into pools. At temperatures hovering around 0 degrees Celsius, the resulting meltwater ponds could have served as habitable environments for certain forms of early complex life.

The team drew its conclusions based on an analysis of modern-day meltwater ponds. Today in Antarctica, small pools of melted ice can be found along the margins of ice sheets. The conditions along these polar ice sheets are similar to what likely existed along ice sheets near the equator during Snowball Earth.

The researchers analyzed samples from a variety of meltwater ponds located on the McMurdo Ice Shelf in an area that was first described by members of Robert Falcon Scott's 1903 expedition as “dirty ice.” The MIT researchers discovered clear signatures of eukaryotic life in every pond. The communities of eukaryotes varied from pond to pond, revealing a surprising diversity of life across the setting. The team also found that salinity plays a key role in the kind of life a pond can host: Ponds that were more brackish or salty had more similar eukaryotic communities, which differed from those in ponds with fresher waters.

“We’ve shown that meltwater ponds are valid candidates for where early eukaryotes could have sheltered during these planet-wide glaciation events,” says lead author Fatima Husain, a graduate student in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS). “This shows us that diversity is present and possible in these sorts of settings. It’s really a story of life’s resilience.”

The study’s MIT co-authors include Schlumberger Professor of Geobiology Roger Summons and former postdoc Thomas Evans, along with Jasmin Millar of Cardiff University, Anne Jungblut at the Natural History Museum in London, and Ian Hawes of the University of Waikato in New Zealand.

Polar plunge

“Snowball Earth” is the colloquial term for periods of time in Earth history during which the planet iced over. It is often used as a reference to the two consecutive, multi-million-year glaciation events which took place during the Cryogenian Period, which geologists refer to as the time between 635 and 720 million years ago. Whether the Earth was more of a hardened snowball or a softer “slushball” is still up for debate. But scientists are certain of one thing: Most of the planet was plunged into a deep freeze, with average global temperatures of minus 50 degrees Celsius. The question has been: How and where did life survive?

“We’re interested in understanding the foundations of complex life on Earth. We see evidence for eukaryotes before and after the Cryogenian in the fossil record, but we largely lack direct evidence of where they may have lived during,” Husain says. “The great part of this mystery is, we know life survived. We’re just trying to understand how and where.”

There are a number of ideas for where organisms could have sheltered during Snowball Earth, including in certain patches of the open ocean (if such environments existed), in and around deep-sea hydrothermal vents, and under ice sheets. In considering meltwater ponds, Husain and her colleagues pursued the hypothesis that surface ice meltwaters may also have been capable of supporting early eukaryotic life at the time.

“There are many hypotheses for where life could have survived and sheltered during the Cryogenian, but we don’t have excellent analogs for all of them,” Husain notes. “Above-ice meltwater ponds occur on Earth today and are accessible, giving us the opportunity to really focus in on the eukaryotes which live in these environments.”

Small pond, big life

For their new study, the researchers analyzed samples taken from meltwater ponds in Antarctica. In 2018, Summons and colleagues from New Zealand traveled to a region of the McMurdo Ice Shelf in East Antarctica, known to host small ponds of melted ice, each just a few feet deep and a few meters wide. There, water freezes all the way to the seafloor, in the process trapping dark-colored sediments and marine organisms. Wind-driven loss of ice from the surface creates a sort of conveyer belt that brings this trapped debris to the surface over time, where it absorbs the sun’s warmth, causing ice to melt, while surrounding debris-free ice reflects incoming sunlight, resulting in the formation of shallow meltwater ponds.

The bottom of each pond is lined with mats of microbes that have built up over years to form layers of sticky cellular communities.

“These mats can be a few centimeters thick, colorful, and they can be very clearly layered,” Husain says.

These microbial mats are made up of cyanobacteria, prokaryotic, single-celled photosynthetic organisms that lack a cell nucleus or other organelles. While these ancient microbes are known to survive within some of the the harshest environments on Earth including meltwater ponds, the researchers wanted to know whether eukaryotes — complex organisms that evolved a cell nucleus and other membrane bound organelles — could also weather similarly challenging circumstances. Answering this question would take more than a microscope, as the defining characteristics of the microscopic eukaryotes present among the microbial mats are too subtle to distinguish by eye.

To characterize the eukaryotes, the team analyzed the mats for specific lipids they make called sterols, as well as genetic components called ribosomal ribonucleic acid (rRNA), both of which can be used to identify organisms with varying degrees of specificity. These two independent sets of analyses provided complementary fingerprints for certain eukaryotic groups. As part of the team’s lipid research, they found many sterols and rRNA genes closely associated with specific types of algae, protists, and microscopic animals among the microbial mats. The researchers were able to assess the types and relative abundance of lipids and rRNA genes from pond to pond, and found the ponds hosted a surprising diversity of eukaryotic life.

“No two ponds were alike,” Husain says. “There are repeating casts of characters, but they’re present in different abundances. And we found diverse assemblages of eukaryotes from all the major groups in all the ponds studied. These eukaryotes are the descendants of the eukaryotes that survived the Snowball Earth. This really highlights that meltwater ponds during Snowball Earth could have served as above-ice oases that nurtured the eukaryotic life that enabled the diversification and proliferation of complex life — including us — later on.”

This research was supported, in part, by the NASA Exobiology Program, the Simons Collaboration on the Origins of Life, and a MISTI grant from MIT-New Zealand.

Researchers at MIT and the Scripps Research Institute have shown that they can generate a strong immune response to HIV with just one vaccine dose, by adding two powerful adjuvants — materials that help stimulate the immune system.

In a study of mice, the researchers showed that this approach produced a much wider diversity of antibodies against an HIV antigen, compared to the vaccine given on its own or with just one of the adjuvants. The dual-adjuvant vaccine accumulated in the lymph nodes and remained there for up to a month, allowing the immune system to build up a much greater number of antibodies against the HIV protein.

This strategy could lead to the development of vaccines that only need to be given once, for infectious diseases including HIV or SARS-CoV-2, the researchers say.

“This approach is compatible with many protein-based vaccines, so it offers the opportunity to engineer new formulations for these types of vaccines across a wide range of different diseases, such as influenza, SARS-CoV-2, or other pandemic outbreaks,” says J. Christopher Love, the Raymond A. and Helen E. St. Laurent Professor of Chemical Engineering at MIT, and a member of the Koch Institute for Integrative Cancer Research and the Ragon Institute of MGH, MIT, and Harvard.

Love and Darrell Irvine, a professor of immunology and microbiology at the Scripps Research Institute, are the senior authors of the study, which appears today in Science Translational Medicine. Kristen Rodrigues PhD ’23 and Yiming Zhang PhD ’25 are the lead authors of the paper.

More powerful vaccines

Most vaccines are delivered along with adjuvants, which help to stimulate a stronger immune response to the antigen. One adjuvant commonly used with protein-based vaccines, including those for hepatitis A and B, is aluminum hydroxide, also known as alum. This adjuvant works by activating the innate immune response, helping the body to form a stronger memory of the vaccine antigen.

Several years ago, Irvine developed another adjuvant based on saponin, an FDA-approved adjuvant derived from the bark of the Chilean soapbark tree. His work showed that nanoparticles containing both saponin and a molecule called MPLA, which promotes inflammation, worked better than saponin on its own. That nanoparticle, known as SMNP, is now being used as an adjuvant for an HIV vaccine that is currently in clinical trials.

Irvine and Love then tried combining alum and SMNP and showed that vaccines containing both of those adjuvants could generate even more powerful immune responses against either HIV or SARS-CoV-2.

In the new paper, the researchers wanted to explore why these two adjuvants work so well together to boost the immune response, specifically the B cell response. B cells produce antibodies that can circulate in the bloodstream and recognize a pathogen if the body is exposed to it again.

For this study, the researchers used an HIV protein called MD39 as their vaccine antigen, and anchored dozens of these proteins to each alum particle, along with SMNP.

After vaccinating mice with these particles, the researchers found that the vaccine accumulated in the lymph nodes — structures where B cells encounter antigens and undergo rapid mutations that generate antibodies with high affinity for a particular antigen. This process takes place within clusters of cells known as germinal centers.

The researchers showed that SMNP and alum helped the HIV antigen to penetrate through the protective layer of cells surrounding the lymph nodes without being broken down into fragments. The adjuvants also helped the antigens to remain intact in the lymph nodes for up to 28 days.

“As a result, the B cells that are cycling in the lymph nodes are constantly being exposed to the antigen over that time period, and they get the chance to refine their solution to the antigen,” Love says.

This approach may mimic what occurs during a natural infection, when antigens can remain in the lymph nodes for weeks, giving the body time to build up an immune response.

Antibody diversity

Single-cell RNA sequencing of B cells from the vaccinated mice revealed that the vaccine containing both adjuvants generated a much more diverse repertoire of B cells and antibodies. Mice that received the dual-adjuvant vaccine produced two to three times more unique B cells than mice that received just one of the adjuvants.

That increase in B cell number and diversity boosts the chances that the vaccine could generate broadly neutralizing antibodies — antibodies that can recognize a variety of strains of a given virus, such as HIV.

“When you think about the immune system sampling all of the possible solutions, the more chances we give it to identify an effective solution, the better,” Love says. “Generating broadly neutralizing antibodies is something that likely requires both the kind of approach that we showed here, to get that strong and diversified response, as well as antigen design to get the right part of the immunogen shown.”

Using these two adjuvants together could also contribute to the development of more potent vaccines against other infectious diseases, with just a single dose.

“What’s potentially powerful about this approach is that you can achieve long-term exposures based on a combination of adjuvants that are already reasonably well-understood, so it doesn’t require a different technology. It’s just combining features of these adjuvants to enable low-dose or potentially even single-dose treatments,” Love says.

The research was funded by the National Institutes of Health; the Koch Institute Support (core) Grant from the National Cancer Institute; the Ragon Institute of MGH, MIT, and Harvard; and the Howard Hughes Medical Institute.

The advanced semiconductor material gallium nitride will likely be key for the next generation of high-speed communication systems and the power electronics needed for state-of-the-art data centers.

Unfortunately, the high cost of gallium nitride (GaN) and the specialization required to incorporate this semiconductor material into conventional electronics have limited its use in commercial applications.

Now, researchers from MIT and elsewhere have developed a new fabrication process that integrates high-performance GaN transistors onto standard silicon CMOS chips in a way that is low-cost and scalable, and compatible with existing semiconductor foundries.

Their method involves building many tiny transistors on the surface of a GaN chip, cutting out each individual transistor, and then bonding just the necessary number of transistors onto a silicon chip using a low-temperature process that preserves the functionality of both materials.

The cost remains minimal since only a tiny amount of GaN material is added to the chip, but the resulting device can receive a significant performance boost from compact, high-speed transistors. In addition, by separating the GaN circuit into discrete transistors that can be spread over the silicon chip, the new technology is able to reduce the temperature of the overall system.

The researchers used this process to fabricate a power amplifier, an essential component in mobile phones, that achieves higher signal strength and efficiencies than devices with silicon transistors. In a smartphone, this could improve call quality, boost wireless bandwidth, enhance connectivity, and extend battery life.

Because their method fits into standard procedures, it could improve electronics that exist today as well as future technologies. Down the road, the new integration scheme could even enable quantum applications, as GaN performs better than silicon at the cryogenic temperatures essential for many types of quantum computing.

“If we can bring the cost down, improve the scalability, and, at the same time, enhance the performance of the electronic device, it is a no-brainer that we should adopt this technology. We’ve combined the best of what exists in silicon with the best possible gallium nitride electronics. These hybrid chips can revolutionize many commercial markets,” says Pradyot Yadav, an MIT graduate student and lead author of a paper on this method.

He is joined on the paper by fellow MIT graduate students Jinchen Wang and Patrick Darmawi-Iskandar; MIT postdoc John Niroula; senior authors Ulrich L. Rohde, a visiting scientist at the Microsystems Technology Laboratories (MTL), and Ruonan Han, an associate professor in the Department of Electrical Engineering and Computer Science (EECS) and member of MTL; and Tomás Palacios, the Clarence J. LeBel Professor of EECS, and director of MTL; as well as collaborators at Georgia Tech and the Air Force Research Laboratory. The research was recently presented at the IEEE Radio Frequency Integrated Circuits Symposium.

Swapping transistors

Gallium nitride is the second most widely used semiconductor in the world, just after silicon, and its unique properties make it ideal for applications such as lighting, radar systems and power electronics.

The material has been around for decades and, to get access to its maximum performance, it is important for chips made of GaN to be connected to digital chips made of silicon, also called CMOS chips. To enable this, some integration methods bond GaN transistors onto a CMOS chip by soldering the connections, but this limits how small the GaN transistors can be. The tinier the transistors, the higher the frequency at which they can work.

Other methods integrate an entire gallium nitride wafer on top of a silicon wafer, but using so much material is extremely costly, especially since the GaN is only needed in a few tiny transistors. The rest of the material in the GaN wafer is wasted.

“We wanted to combine the functionality of GaN with the power of digital chips made of silicon, but without having to compromise on either cost of bandwidth. We achieved that by adding super-tiny discrete gallium nitride transistors right on top of the silicon chip,” Yadav explains.

The new chips are the result of a multistep process.

First, a tightly packed collection of miniscule transistors is fabricated across the entire surface of a GaN wafer. Using very fine laser technology, they cut each one down to just the size of the transistor, which is 240 by 410 microns, forming what they call a dielet. (A micron is one millionth of a meter.)

Each transistor is fabricated with tiny copper pillars on top, which they use to bond directly to the copper pillars on the surface of a standard silicon CMOS chip. Copper to copper bonding can be done at temperatures below 400 degrees Celsius, which is low enough to avoid damaging either material.

Current GaN integration techniques require bonds that utilize gold, an expensive material that needs much higher temperatures and stronger bonding forces than copper. Since gold can contaminate the tools used in most semiconductor foundries, it typically requires specialized facilities.

“We wanted a process that was low-cost, low-temperature, and low-force, and copper wins on all of those related to gold. At the same time, it has better conductivity,” Yadav says.

A new tool

To enable the integration process, they created a specialized new tool that can carefully integrate the extremely tiny GaN transistor with the silicon chips. The tool uses a vacuum to hold the dielet as it moves on top of a silicon chip, zeroing in on the copper bonding interface with nanometer precision.

They used advanced microscopy to monitor the interface, and then when the dielet is in the right position, they apply heat and pressure to bond the GaN transistor to the chip.

“For each step in the process, I had to find a new collaborator who knew how to do the technique that I needed, learn from them, and then integrate that into my platform. It was two years of constant learning,” Yadav says.

Once the researchers had perfected the fabrication process, they demonstrated it by developing power amplifiers, which are radio frequency circuits that boost wireless signals.

Their devices achieved higher bandwidth and better gain than devices made with traditional silicon transistors. Each compact chip has an area of less than half a square millimeter.

In addition, because the silicon chip they used in their demonstration is based on Intel 16 22nm FinFET state-of-the-art metallization and passive options, they were able to incorporate components often used in silicon circuits, such as neutralization capacitors. This significantly improved the gain of the amplifier, bringing it one step closer to enabling the next generation of wireless technologies.

“To address the slowdown of Moore’s Law in transistor scaling, heterogeneous integration has emerged as a promising solution for continued system scaling, reduced form factor, improved power efficiency, and cost optimization. Particularly in wireless technology, the tight integration of compound semiconductors with silicon-based wafers is critical to realizing unified systems of front-end integrated circuits, baseband processors, accelerators, and memory for next-generation antennas-to-AI platforms. This work makes a significant advancement by demonstrating 3D integration of multiple GaN chips with silicon CMOS and pushes the boundaries of current technological capabilities,” says Atom Watanabe, a research scientist at IBM who was not involved with this paper.

This work is supported, in part, by the U.S. Department of Defense through the National Defense Science and Engineering Graduate (NDSEG) Fellowship Program and CHIMES, one of the seven centers in JUMP 2.0, a Semiconductor Research Corporation Program by the Department of Defense and the Defense Advanced Research Projects Agency (DARPA). Fabrication was carried out using facilities at MIT.Nano, the Air Force Research Laboratory, and Georgia Tech.

Research has shown that large language models (LLMs) tend to overemphasize information at the beginning and end of a document or conversation, while neglecting the middle.

This “position bias” means that, if a lawyer is using an LLM-powered virtual assistant to retrieve a certain phrase in a 30-page affidavit, the LLM is more likely to find the right text if it is on the initial or final pages.

MIT researchers have discovered the mechanism behind this phenomenon.

They created a theoretical framework to study how information flows through the machine-learning architecture that forms the backbone of LLMs. They found that certain design choices which control how the model processes input data can cause position bias.

Their experiments revealed that model architectures, particularly those affecting how information is spread across input words within the model, can give rise to or intensify position bias, and that training data also contribute to the problem.

In addition to pinpointing the origins of position bias, their framework can be used to diagnose and correct it in future model designs.

This could lead to more reliable chatbots that stay on topic during long conversations, medical AI systems that reason more fairly when handling a trove of patient data, and code assistants that pay closer attention to all parts of a program.

“These models are black boxes, so as an LLM user, you probably don’t know that position bias can cause your model to be inconsistent. You just feed it your documents in whatever order you want and expect it to work. But by understanding the underlying mechanism of these black-box models better, we can improve them by addressing these limitations,” says Xinyi Wu, a graduate student in the MIT Institute for Data, Systems, and Society (IDSS) and the Laboratory for Information and Decision Systems (LIDS), and first author of a paper on this research.

Her co-authors include Yifei Wang, an MIT postdoc; and senior authors Stefanie Jegelka, an associate professor of electrical engineering and computer science (EECS) and a member of IDSS and the Computer Science and Artificial Intelligence Laboratory (CSAIL); and Ali Jadbabaie, professor and head of the Department of Civil and Environmental Engineering, a core faculty member of IDSS, and a principal investigator in LIDS. The research will be presented at the International Conference on Machine Learning.

Analyzing attention

LLMs like Claude, Llama, and GPT-4 are powered by a type of neural network architecture known as a transformer. Transformers are designed to process sequential data, encoding a sentence into chunks called tokens and then learning the relationships between tokens to predict what words comes next.

These models have gotten very good at this because of the attention mechanism, which uses interconnected layers of data processing nodes to make sense of context by allowing tokens to selectively focus on, or attend to, related tokens.

But if every token can attend to every other token in a 30-page document, that quickly becomes computationally intractable. So, when engineers build transformer models, they often employ attention masking techniques which limit the words a token can attend to.

For instance, a causal mask only allows words to attend to those that came before it.

Engineers also use positional encodings to help the model understand the location of each word in a sentence, improving performance.

The MIT researchers built a graph-based theoretical framework to explore how these modeling choices, attention masks and positional encodings, could affect position bias.

“Everything is coupled and tangled within the attention mechanism, so it is very hard to study. Graphs are a flexible language to describe the dependent relationship among words within the attention mechanism and trace them across multiple layers,” Wu says.

Their theoretical analysis suggested that causal masking gives the model an inherent bias toward the beginning of an input, even when that bias doesn’t exist in the data.

If the earlier words are relatively unimportant for a sentence’s meaning, causal masking can cause the transformer to pay more attention to its beginning anyway.

“While it is often true that earlier words and later words in a sentence are more important, if an LLM is used on a task that is not natural language generation, like ranking or information retrieval, these biases can be extremely harmful,” Wu says.

As a model grows, with additional layers of attention mechanism, this bias is amplified because earlier parts of the input are used more frequently in the model’s reasoning process.

They also found that using positional encodings to link words more strongly to nearby words can mitigate position bias. The technique refocuses the model’s attention in the right place, but its effect can be diluted in models with more attention layers.

And these design choices are only one cause of position bias — some can come from training data the model uses to learn how to prioritize words in a sequence.

“If you know your data are biased in a certain way, then you should also finetune your model on top of adjusting your modeling choices,” Wu says.

Lost in the middle

After they’d established a theoretical framework, the researchers performed experiments in which they systematically varied the position of the correct answer in text sequences for an information retrieval task.

The experiments showed a “lost-in-the-middle” phenomenon, where retrieval accuracy followed a U-shaped pattern. Models performed best if the right answer was located at the beginning of the sequence. Performance declined the closer it got to the middle before rebounding a bit if the correct answer was near the end.

Ultimately, their work suggests that using a different masking technique, removing extra layers from the attention mechanism, or strategically employing positional encodings could reduce position bias and improve a model’s accuracy.

“By doing a combination of theory and experiments, we were able to look at the consequences of model design choices that weren’t clear at the time. If you want to use a model in high-stakes applications, you must know when it will work, when it won’t, and why,” Jadbabaie says.

In the future, the researchers want to further explore the effects of positional encodings and study how position bias could be strategically exploited in certain applications.

“These researchers offer a rare theoretical lens into the attention mechanism at the heart of the transformer model. They provide a compelling analysis that clarifies longstanding quirks in transformer behavior, showing that attention mechanisms, especially with causal masks, inherently bias models toward the beginning of sequences. The paper achieves the best of both worlds — mathematical clarity paired with insights that reach into the guts of real-world systems,” says Amin Saberi, professor and director of the Stanford University Center for Computational Market Design, who was not involved with this work.

This research is supported, in part, by the U.S. Office of Naval Research, the National Science Foundation, and an Alexander von Humboldt Professorship.

MIT researchers have designed a compact, low-power receiver for 5G-compatible smart devices that is about 30 times more resilient to a certain type of interference than some traditional wireless receivers.

The low-cost receiver would be ideal for battery-powered internet of things (IoT) devices like environmental sensors, smart thermostats, or other devices that need to run continuously for a long time, such as health wearables, smart cameras, or industrial monitoring sensors.

The researchers’ chip uses a passive filtering mechanism that consumes less than a milliwatt of static power while protecting both the input and output of the receiver’s amplifier from unwanted wireless signals that could jam the device.

Key to the new approach is a novel arrangement of precharged, stacked capacitors, which are connected by a network of tiny switches. These miniscule switches need much less power to be turned on and off than those typically used in IoT receivers.

The receiver’s capacitor network and amplifier are carefully arranged to leverage a phenomenon in amplification that allows the chip to use much smaller capacitors than would typically be necessary. 

“This receiver could help expand the capabilities of IoT gadgets. Smart devices like health monitors or industrial sensors could become smaller and have longer battery lives. They would also be more reliable in crowded radio environments, such as factory floors or smart city networks,” says Soroush Araei, an electrical engineering and computer science (EECS) graduate student at MIT and lead author of a paper on the receiver.

He is joined on the paper by Mohammad Barzgari, a postdoc in the MIT Research Laboratory of Electronics (RLE); Haibo Yang, an EECS graduate student; and senior author Negar Reiskarimian, the X-Window Consortium Career Development Assistant Professor in EECS at MIT and a member of the Microsystems Technology Laboratories and RLE. The research was recently presented at the IEEE Radio Frequency Integrated Circuits Symposium.

A new standard

A receiver acts as the intermediary between an IoT device and its environment. Its job is to detect and amplify a wireless signal, filter out any interference, and then convert it into digital data for processing.

Traditionally, IoT receivers operate on fixed frequencies and suppress interference using a single narrow-band filter, which is simple and inexpensive.

But the new technical specifications of the 5G mobile network enable reduced-capability devices that are more affordable and energy-efficient. This opens a range of IoT applications to the faster data speeds and increased network capability of 5G. These next-generation IoT devices need receivers that can tune across a wide range of frequencies while still being cost-effective and low-power.

“This is extremely challenging because now we need to not only think about the power and cost of the receiver, but also flexibility to address numerous interferers that exist in the environment,” Araei says.

To reduce the size, cost, and power consumption of an IoT device, engineers can’t rely on the bulky, off-chip filters that are typically used in devices that operate on a wide frequency range.

One solution is to use a network of on-chip capacitors that can filter out unwanted signals. But these capacitor networks are prone to special type of signal noise known as harmonic interference.

In prior work, the MIT researchers developed a novel switch-capacitor network that targets these harmonic signals as early as possible in the receiver chain, filtering out unwanted signals before they are amplified and converted into digital bits for processing.

Shrinking the circuit

Here, they extended that approach by using the novel switch-capacitor network as the feedback path in an amplifier with negative gain. This configuration leverages the Miller effect, a phenomenon that enables small capacitors to behave like much larger ones.

“This trick lets us meet the filtering requirement for narrow-band IoT without physically large components, which drastically shrinks the size of the circuit,” Araei says.

Their receiver has an active area of less than 0.05 square millimeters.

One challenge the researchers had to overcome was determining how to apply enough voltage to drive the switches while keeping the overall power supply of the chip at only 0.6 volts.

In the presence of interfering signals, such tiny switches can turn on and off in error, especially if the voltage required for switching is extremely low.

To address this, the researchers came up with a novel solution, using a special circuit technique called bootstrap clocking. This method boosts the control voltage just enough to ensure the switches operate reliably while using less power and fewer components than traditional clock boosting methods.

Taken together, these innovations enable the new receiver to consume less than a milliwatt of power while blocking about 30 times more harmonic interference than traditional IoT receivers.

“Our chip also is very quiet, in terms of not polluting the airwaves. This comes from the fact that our switches are very small, so the amount of signal that can leak out of the antenna is also very small,” Araei adds.

Because their receiver is smaller than traditional devices and relies on switches and precharged capacitors instead of more complex electronics, it could be more cost-effective to fabricate. In addition, since the receiver design can cover a wide range of signal frequencies, it could be implemented on a variety of current and future IoT devices.

Now that they have developed this prototype, the researchers want to enable the receiver to operate without a dedicated power supply, perhaps by harvesting Wi-Fi or Bluetooth signals from the environment to power the chip.

This research is supported, in part, by the National Science Foundation.

In 2023, about 4.4 percent (176 terawatt-hours) of total energy consumption in the United States was by data centers that are essential for processing large quantities of information. Of that 176 TWh, approximately 100 TWh (57 percent) was used by CPU and GPU equipment. Energy requirements have escalated substantially in the past decade and will only continue to grow, making the development of energy-efficient computing crucial. 

Superconducting electronics have arisen as a promising alternative for classical and quantum computing, although their full exploitation for high-end computing requires a dramatic reduction in the amount of wiring linking ambient temperature electronics and low-temperature superconducting circuits. To make systems that are both larger and more streamlined, replacing commonplace components such as semiconductors with superconducting versions could be of immense value. It’s a challenge that has captivated MIT Plasma Science and Fusion Center senior research scientist Jagadeesh Moodera and his colleagues, who described a significant breakthrough in a recent Nature Electronics paper, “Efficient superconducting diodes and rectifiers for quantum circuitry.”

Moodera was working on a stubborn problem. One of the critical long-standing requirements is the need for the efficient conversion of AC currents into DC currents on a chip while operating at the extremely cold cryogenic temperatures required for superconductors to work efficiently. For example, in superconducting “energy-efficient rapid single flux quantum” (ERSFQ) circuits, the AC-to-DC issue is limiting ERSFQ scalability and preventing their use in larger circuits with higher complexities. To respond to this need, Moodera and his team created superconducting diode (SD)-based superconducting rectifiers — devices that can convert AC to DC on the same chip. These rectifiers would allow for the efficient delivery of the DC current necessary to operate superconducting classical and quantum processors.

Quantum computer circuits can only operate at temperatures close to 0 kelvins (absolute zero), and the way power is supplied must be carefully controlled to limit the effects of interference introduced by too much heat or electromagnetic noise. Most unwanted noise and heat come from the wires connecting cold quantum chips to room-temperature electronics. Instead, using superconducting rectifiers to convert AC currents into DC within a cryogenic environment reduces the number of wires, cutting down on heat and noise and enabling larger, more stable quantum systems.

In a 2023 experiment, Moodera and his co-authors developed SDs that are made of very thin layers of superconducting material that display nonreciprocal (or unidirectional) flow of current and could be the superconducting counterpart to standard semiconductors. Even though SDs have garnered significant attention, especially since 2020, up until this point the research has focused only on individual SDs for proof of concept. The group’s 2023 paper outlined how they created and refined a method by which SDs could be scaled for broader application. 

Now, by building a diode bridge circuit, they demonstrated the successful integration of four SDs and realized AC-to-DC rectification at cryogenic temperatures. 

The new approach described in their recent Nature Electronics paper will significantly cut down on the thermal and electromagnetic noise traveling from ambient into cryogenic circuitry, enabling cleaner operation. The SDs could also potentially serve as isolators/circulators, assisting in insulating qubit signals from external influence. The successful assimilation of multiple SDs into the first integrated SD circuit represents a key step toward making superconducting computing a commercial reality. 

“Our work opens the door to the arrival of highly energy-efficient, practical superconductivity-based supercomputers in the next few years,” says Moodera. “Moreover, we expect our research to enhance the qubit stability while boosting the quantum computing program, bringing its realization closer." Given the multiple beneficial roles these components could play, Moodera and his team are already working toward the integration of such devices into actual superconducting logic circuits, including in dark matter detection circuits that are essential to the operation of experiments at CERN and LUX-ZEPLIN in at the Berkeley National Lab.

This work was partially funded by MIT Lincoln Laboratory’s Advanced Concepts Committee, the U.S. National Science Foundation, U.S. Army Research Office, and U.S. Air Force Office of Scientific Research. 

This work was carried out, in part, through the use of MIT.nano’s facilities.

In 1989, New York City opened a new jail. But not on dry land. The city leased a barge, then called the “Bibby Resolution,” which had been topped with five stories of containers made into housing, and anchored it in the East River. For five years, the vessel lodged inmates.

A floating detention center is a curiosity. But then, the entire history of this barge is curious. Built in 1979 in Sweden, it housed British troops during the Falkland Islands war with Argentina, became worker housing for Volkswagen employees in West Germany, got sent to New York, also became a detention center off the coast of England, then finally was deployed as oil worker housing off the coast of Nigeria. The barge has had nine names, several owners, and flown the flags of five countries.

In this one vessel, then, we can see many currents: globalization, the transience of economic activity, and the hazy world of transactions many analysts and observers call “the offshore,” the lightly regulated sphere of economic activity that encourages short-term actions.

“The offshore presents a quick and potentially cheap solution to a crisis,” says MIT lecturer Ian Kumekawa. “It is not a durable solution. The story of the barge is the story of it being used as a quick fix in all sorts of crises. Then these expediences become the norm, and people get used to them and have an expectation that this is the way the world works.”

Now Kumekawa, a historian who started teaching as a lecturer at MIT earlier this year, explores the ship’s entire history in “Empty Vessel: The Global Economy in One Barge,” just published by Knopf and John Murray. In it, he traces the barge’s trajectory and the many economic and geopolitical changes that helped create the ship’s distinctive deployments around the world.

“The book is about a barge, but it’s also about the developing, emerging offshore world, where you see these layers of globalization, financialization, privatization, and the dissolution of territoriality and orders,” Kumekawa says. “The barge is a vehicle through which I can tell the story of those layers together.”

“Never meant to be permanent”

Kumekawa first found out about the vessel several years ago; New York City obtained another floating detention center in the 1990s, which prompted Kumekawa to start looking into the past of the older jail ship, the former “Bibby Resolution,” from the 1990s. The more he found out about its distinctive past, the more curious he became.

“You start pulling on a thread, and you realize you can keep pulling,” Kumekawa says.

The barge Kumekawa follows in the book was built in Sweden in 1979 as the “Balder Scapa.” Even then, commerce was plenty globalized: The vessel was commissioned by a Norwegian shell company, with negotiations run by an expatriate Swedish shipping agent whose firm was registered in Panama and used a Miami bank.

The barge was built at an inflection point following the economic slowdown and oil shocks of the 1970s. Manufacturing was on the verge of declining in both Western Europe and the U.S.; about half as many people now work in manufacturing in those regions, compared to 1960. Companies were looking to find cheaper global locations for production, reinforcing the sense that economic activity was now less durable in any given place.

The barge became part of this transience. The five-story accommodation block was added in the early 1980s; in 1983 it was re-registered in the UK and sent to the Falkland Islands as a troop accommodation named the “COASTEL 3.” Then it was re-registered in the Bahamas and sent to Emden, West Germany, as housing for Volkswagen workers. The vessel then served its stints as inmate housing — first in New York, then off the coast of England from 1997 to 2005. By 2010, it had been re-re-re-registered, in St. Vincent and Grenadines, and was housing oil workers off the coast of Nigeria.

“Globalization is more about flow than about stocks, and the barge is a great example of that,” Kumekawa says. “It’s always on the move, and never meant to be a permanent container. It’s understood people are going to be passing through.”

As Kumekawa explores in the book, this sense of social dislocation overlapped with the shrinking of state capacity, as many states increasingly encouraged companies to pursue globalized production and lightly regulated financial activities in numerous jurisdictions, in the hope it would enhance growth. And it has, albeit with unresolved questions about who the benefits accrue to, the social dislocation of workers, and more.

“In a certain sense it’s not an erosion of state power at all,” Kumekawa says. “These states are making very active choices to use offshore tools, to circumvent certain roadblocks.” He adds: “What happens in the 1970s and certainly in the 1980s is that the offshore comes into its own as an entity, and didn’t exist in the same way even in the 1950s and 1960s. There’s a money interest in that, and there’s a political interest as well.”

Abstract forces, real materials and people

Kumekawa is a scholar with a strong interest in economic history; his previous book, “The First Serious Optimist: A.C. Pigou and the Birth of Welfare Economics,” was published in 2017. This coming fall, Kumekawa will be team-teaching a class on the relationship between economics and history, along with MIT economists Abhijit Banerjee and Jacob Moscona.

Working on “Empty Vessel” also necessitated that Kumekawa use a variety of research techniques, from archival work to journalistic interviews with people who knew the vessel well.

“I had a wonderful set of conversations with the man who was the last bargemaster,” Kumekawa says. “He was the person in effect steering the vessel for many years. He was so aware of all of the forces at play — the market for oil, the prices of accommodations, the regulations, the fact no one had reinforced the frame.”

“Empty Vessel” has already received critical acclaim. Reviewing it in The New York Times, Jennifer Szalai writes that this “elegant and enlightening book is an impressive feat.”

For his part, Kumekawa also took inspiration from a variety of writings about ships, voyages, commerce, and exploration, recognizing that these vessels contain stories and vignettes that illuminate the wider world.

“Ships work very well as devices connecting the global and the local,” he says. Using the barge as the organizing principle of his book, Kumekawa adds, “makes a whole bunch of abstract processes very concrete. The offshore itself is an abstraction, but it’s also entirely dependent on physical infrastructure and physical places. My hope for the book is it reinforces the material dimension of these abstract global forces.”

The long-term aspirational goal of the Paris Agreement on climate change is to cap global warming at 1.5 degrees Celsius above preindustrial levels, and thereby reduce the frequency and severity of floods, droughts, wildfires, and other extreme weather events. Achieving that goal will require a massive reduction in global carbon dioxide (CO₂) emissions across all economic sectors. A major roadblock, however, could be the industrial sector, which accounts for roughly 25 percent of global energy- and process-related CO₂ emissions — particularly within the iron and steel sector, industry’s largest emitter of CO₂.

Iron and steel production now relies heavily on fossil fuels (coal or natural gas) for heat, converting iron ore to iron, and making steel strong. Steelmaking could be decarbonized by a combination of several methods, including carbon capture technology, the use of low- or zero-carbon fuels, and increased use of recycled steel. Now a new study in the Journal of Cleaner Production systematically explores the viability of different iron-and-steel decarbonization strategies.

Today’s strategy menu includes improving energy efficiency, switching fuels and technologies, using more scrap steel, and reducing demand. Using the MIT Economic Projection and Policy Analysis model, a multi-sector, multi-region model of the world economy, researchers at MIT, the University of Illinois at Urbana-Champaign, and ExxonMobil Technology and Engineering Co. evaluate the decarbonization potential of replacing coal-based production processes with electric arc furnaces (EAF), along with either scrap steel or “direct reduced iron” (DRI), which is fueled by natural gas with carbon capture and storage (NG CCS DRI-EAF) or by hydrogen (H₂ DRI-EAF).

Under a global climate mitigation scenario aligned with the 1.5 C climate goal, these advanced steelmaking technologies could result in deep decarbonization of the iron and steel sector by 2050, as long as technology costs are low enough to enable large-scale deployment. Higher costs would favor the replacement of coal with electricity and natural gas, greater use of scrap steel, and reduced demand, resulting in a more-than-50-percent reduction in emissions relative to current levels. Lower technology costs would enable massive deployment of NG CCS DRI-EAF or H₂ DRI-EAF, reducing emissions by up to 75 percent.

Even without adoption of these advanced technologies, the iron-and-steel sector could significantly reduce its CO₂ emissions intensity (how much CO₂ is released per unit of production) with existing steelmaking technologies, primarily by replacing coal with gas and electricity (especially if it is generated by renewable energy sources), using more scrap steel, and implementing energy efficiency measures.

“The iron and steel industry needs to combine several strategies to substantially reduce its emissions by mid-century, including an increase in recycling, but investing in cost reductions in hydrogen pathways and carbon capture and sequestration will enable even deeper emissions mitigation in the sector,” says study supervising author Sergey Paltsev, deputy director of the MIT Center for Sustainability Science and Strategy (MIT CS3) and a senior research scientist at the MIT Energy Initiative (MITEI).

This study was supported by MIT CS3 and ExxonMobil through its membership in MITEI.

In 15 TED Talk-style presentations, MIT faculty recently discussed their pioneering research that incorporates social, ethical, and technical considerations and expertise, each supported by seed grants established by the Social and Ethical Responsibilities of Computing (SERC), a cross-cutting initiative of the MIT Schwarzman College of Computing. The call for proposals last summer was met with nearly 70 applications. A committee with representatives from every MIT school and the college convened to select the winning projects that received up to $100,000 in funding.

“SERC is committed to driving progress at the intersection of computing, ethics, and society. The seed grants are designed to ignite bold, creative thinking around the complex challenges and possibilities in this space,” said Nikos Trichakis, co-associate dean of SERC and the J.C. Penney Professor of Management. “With the MIT Ethics of Computing Research Symposium, we felt it important to not just showcase the breadth and depth of the research that’s shaping the future of ethical computing, but to invite the community to be part of the conversation as well.”

“What you’re seeing here is kind of a collective community judgment about the most exciting work when it comes to research, in the social and ethical responsibilities of computing being done at MIT,” said Caspar Hare, co-associate dean of SERC and professor of philosophy.

The full-day symposium on May 1 was organized around four key themes: responsible health-care technology, artificial intelligence governance and ethics, technology in society and civic engagement, and digital inclusion and social justice. Speakers delivered thought-provoking presentations on a broad range of topics, including algorithmic bias, data privacy, the social implications of artificial intelligence, and the evolving relationship between humans and machines. The event also featured a poster session, where student researchers showcased projects they worked on throughout the year as SERC Scholars.

Highlights from the MIT Ethics of Computing Research Symposium in each of the theme areas, many of which are available to watch on YouTube, included:

Making the kidney transplant system fairer

Policies regulating the organ transplant system in the United States are made by a national committee that often takes more than six months to create, and then years to implement, a timeline that many on the waiting list simply can’t survive.

Dimitris Bertsimas, vice provost for open learning, associate dean of business analytics, and Boeing Professor of Operations Research, shared his latest work in analytics for fair and efficient kidney transplant allocation. Bertsimas’ new algorithm examines criteria like geographic location, mortality, and age in just 14 seconds, a monumental change from the usual six hours.

Bertsimas and his team work closely with the United Network for Organ Sharing (UNOS), a nonprofit that manages most of the national donation and transplant system through a contract with the federal government. During his presentation, Bertsimas shared a video from James Alcorn, senior policy strategist at UNOS, who offered this poignant summary of the impact the new algorithm has:

“This optimization radically changes the turnaround time for evaluating these different simulations of policy scenarios. It used to take us a couple months to look at a handful of different policy scenarios, and now it takes a matter of minutes to look at thousands and thousands of scenarios. We are able to make these changes much more rapidly, which ultimately means that we can improve the system for transplant candidates much more rapidly.”

The ethics of AI-generated social media content

As AI-generated content becomes more prevalent across social media platforms, what are the implications of disclosing (or not disclosing) that any part of a post was created by AI? Adam Berinsky, Mitsui Professor of Political Science, and Gabrielle Péloquin-Skulski, PhD student in the Department of Political Science, explored this question in a session that examined recent studies on the impact of various labels on AI-generated content.

In a series of surveys and experiments affixing labels to AI-generated posts, the researchers looked at how specific words and descriptions impacted users’ perception of deception, their intent to engage with the post, and ultimately if the post was true or false.

“The big takeaway from our initial set of findings is that one size doesn’t fit all,” said Péloquin-Skulski. “We found that labeling AI-generated images with a process-oriented label reduces belief in both false and true posts. This is quite problematic, as labeling intends to reduce people’s belief in false information, not necessarily true information. This suggests that labels combining both process and veracity might be better at countering AI-generated misinformation.”

Using AI to increase civil discourse online

“Our research aims to address how people increasingly want to have a say in the organizations and communities they belong to,” Lily Tsai explained in a session on experiments in generative AI and the future of digital democracy. Tsai, Ford Professor of Political Science and director of the MIT Governance Lab, is conducting ongoing research with Alex Pentland, Toshiba Professor of Media Arts arts Sciences, and a larger team.

Online deliberative platforms have recently been rising in popularity across the United States in both public- and private-sector settings. Tsai explained that with technology, it’s now possible for everyone to have a say — but doing so can be overwhelming, or even feel unsafe. First, too much information is available, and secondly, online discourse has become increasingly “uncivil.”

The group focuses on “how we can build on existing technologies and improve them with rigorous, interdisciplinary research, and how we can innovate by integrating generative AI to enhance the benefits of online spaces for deliberation.” They have developed their own AI-integrated platform for deliberative democracy, DELiberation.io, and rolled out four initial modules. All studies have been in the lab so far, but they are also working on a set of forthcoming field studies, the first of which will be in partnership with the government of the District of Columbia.

Tsai told the audience, “If you take nothing else from this presentation, I hope that you’ll take away this — that we should all be demanding that technologies that are being developed are assessed to see if they have positive downstream outcomes, rather than just focusing on maximizing the number of users.”

A public think tank that considers all aspects of AI

When Catherine D’Ignazio, associate professor of urban science and planning, and Nikko Stevens, postdoc at the Data + Feminism Lab at MIT, initially submitted their funding proposal, they weren’t intending to develop a think tank, but a framework — one that articulated how artificial intelligence and machine learning work could integrate community methods and utilize participatory design.

In the end, they created Liberatory AI, which they describe as a “rolling public think tank about all aspects of AI.” D’Ignazio and Stevens gathered 25 researchers from a diverse array of institutions and disciplines who authored more than 20 position papers examining the most current academic literature on AI systems and engagement. They intentionally grouped the papers into three distinct themes: the corporate AI landscape, dead ends, and ways forward.

“Instead of waiting for Open AI or Google to invite us to participate in the development of their products, we’ve come together to contest the status quo, think bigger-picture, and reorganize resources in this system in hopes of a larger societal transformation,” said D’Ignazio.

As more connected devices demand an increasing amount of bandwidth for tasks like teleworking and cloud computing, it will become extremely challenging to manage the finite amount of wireless spectrum available for all users to share.

Engineers are employing artificial intelligence to dynamically manage the available wireless spectrum, with an eye toward reducing latency and boosting performance. But most AI methods for classifying and processing wireless signals are power-hungry and can’t operate in real-time.

Now, MIT researchers have developed a novel AI hardware accelerator that is specifically designed for wireless signal processing. Their optical processor performs machine-learning computations at the speed of light, classifying wireless signals in a matter of nanoseconds.

The photonic chip is about 100 times faster than the best digital alternative, while converging to about 95 percent accuracy in signal classification. The new hardware accelerator is also scalable and flexible, so it could be used for a variety of high-performance computing applications. At the same time, it is smaller, lighter, cheaper, and more energy-efficient than digital AI hardware accelerators.

The device could be especially useful in future 6G wireless applications, such as cognitive radios that optimize data rates by adapting wireless modulation formats to the changing wireless environment.

By enabling an edge device to perform deep-learning computations in real-time, this new hardware accelerator could provide dramatic speedups in many applications beyond signal processing. For instance, it could help autonomous vehicles make split-second reactions to environmental changes or enable smart pacemakers to continuously monitor the health of a patient’s heart.

“There are many applications that would be enabled by edge devices that are capable of analyzing wireless signals. What we’ve presented in our paper could open up many possibilities for real-time and reliable AI inference. This work is the beginning of something that could be quite impactful,” says Dirk Englund, a professor in the MIT Department of Electrical Engineering and Computer Science, principal investigator in the Quantum Photonics and Artificial Intelligence Group and the Research Laboratory of Electronics (RLE), and senior author of the paper.

He is joined on the paper by lead author Ronald Davis III PhD ’24; Zaijun Chen, a former MIT postdoc who is now an assistant professor at the University of Southern California; and Ryan Hamerly, a visiting scientist at RLE and senior scientist at NTT Research. The research appears today in Science Advances.

Light-speed processing  

State-of-the-art digital AI accelerators for wireless signal processing convert the signal into an image and run it through a deep-learning model to classify it. While this approach is highly accurate, the computationally intensive nature of deep neural networks makes it infeasible for many time-sensitive applications.

Optical systems can accelerate deep neural networks by encoding and processing data using light, which is also less energy intensive than digital computing. But researchers have struggled to maximize the performance of general-purpose optical neural networks when used for signal processing, while ensuring the optical device is scalable.

By developing an optical neural network architecture specifically for signal processing, which they call a multiplicative analog frequency transform optical neural network (MAFT-ONN), the researchers tackled that problem head-on.

The MAFT-ONN addresses the problem of scalability by encoding all signal data and performing all machine-learning operations within what is known as the frequency domain — before the wireless signals are digitized.

The researchers designed their optical neural network to perform all linear and nonlinear operations in-line. Both types of operations are required for deep learning.

Thanks to this innovative design, they only need one MAFT-ONN device per layer for the entire optical neural network, as opposed to other methods that require one device for each individual computational unit, or “neuron.”

“We can fit 10,000 neurons onto a single device and compute the necessary multiplications in a single shot,” Davis says.   

The researchers accomplish this using a technique called photoelectric multiplication, which dramatically boosts efficiency. It also allows them to create an optical neural network that can be readily scaled up with additional layers without requiring extra overhead.

Results in nanoseconds

MAFT-ONN takes a wireless signal as input, processes the signal data, and passes the information along for later operations the edge device performs. For instance, by classifying a signal’s modulation, MAFT-ONN would enable a device to automatically infer the type of signal to extract the data it carries.

One of the biggest challenges the researchers faced when designing MAFT-ONN was determining how to map the machine-learning computations to the optical hardware.

“We couldn’t just take a normal machine-learning framework off the shelf and use it. We had to customize it to fit the hardware and figure out how to exploit the physics so it would perform the computations we wanted it to,” Davis says.

When they tested their architecture on signal classification in simulations, the optical neural network achieved 85 percent accuracy in a single shot, which can quickly converge to more than 99 percent accuracy using multiple measurements.  MAFT-ONN only required about 120 nanoseconds to perform entire process.

“The longer you measure, the higher accuracy you will get. Because MAFT-ONN computes inferences in nanoseconds, you don’t lose much speed to gain more accuracy,” Davis adds.

While state-of-the-art digital radio frequency devices can perform machine-learning inference in a microseconds, optics can do it in nanoseconds or even picoseconds.

Moving forward, the researchers want to employ what are known as multiplexing schemes so they could perform more computations and scale up the MAFT-ONN. They also want to extend their work into more complex deep learning architectures that could run transformer models or LLMs.

This work was funded, in part, by the U.S. Army Research Laboratory, the U.S. Air Force, MIT Lincoln Laboratory, Nippon Telegraph and Telephone, and the National Science Foundation.

Art restoration takes steady hands and a discerning eye. For centuries, conservators have restored paintings by identifying areas needing repair, then mixing an exact shade to fill in one area at a time. Often, a painting can have thousands of tiny regions requiring individual attention. Restoring a single painting can take anywhere from a few weeks to over a decade.

In recent years, digital restoration tools have opened a route to creating virtual representations of original, restored works. These tools apply techniques of computer vision, image recognition, and color matching, to generate a “digitally restored” version of a painting relatively quickly.

Still, there has been no way to translate digital restorations directly onto an original work, until now. In a paper appearing today in the journal Nature, Alex Kachkine, a mechanical engineering graduate student at MIT, presents a new method he’s developed to physically apply a digital restoration directly onto an original painting.

The restoration is printed on a very thin polymer film, in the form of a mask that can be aligned and adhered to an original painting. It can also be easily removed. Kachkine says that a digital file of the mask can be stored and referred to by future conservators, to see exactly what changes were made to restore the original painting.

“Because there’s a digital record of what mask was used, in 100 years, the next time someone is working with this, they’ll have an extremely clear understanding of what was done to the painting,” Kachkine says. “And that’s never really been possible in conservation before.”

As a demonstration, he applied the method to a highly damaged 15th century oil painting. The method automatically identified 5,612 separate regions in need of repair, and filled in these regions using 57,314 different colors. The entire process, from start to finish, took 3.5 hours, which he estimates is about 66 times faster than traditional restoration methods.

Kachkine acknowledges that, as with any restoration project, there are ethical issues to consider, in terms of whether a restored version is an appropriate representation of an artist’s original style and intent. Any application of his new method, he says, should be done in consultation with conservators with knowledge of a painting’s history and origins.

“There is a lot of damaged art in storage that might never be seen,” Kachkine says. “Hopefully with this new method, there’s a chance we’ll see more art, which I would be delighted by.”

Digital connections

The new restoration process started as a side project. In 2021, as Kachkine made his way to MIT to start his PhD program in mechanical engineering, he drove up the East Coast and made a point to visit as many art galleries as he could along the way.

“I’ve been into art for a very long time now, since I was a kid,” says Kachkine, who restores paintings as a hobby, using traditional hand-painting techniques. As he toured galleries, he came to realize that the art on the walls is only a fraction of the works that galleries hold. Much of the art that galleries acquire is stored away because the works are aged or damaged, and take time to properly restore.

“Restoring a painting is fun, and it’s great to sit down and infill things and have a nice evening,” Kachkine says. “But that’s a very slow process.”

As he has learned, digital tools can significantly speed up the restoration process. Researchers have developed artificial intelligence algorithms that quickly comb through huge amounts of data. The algorithms learn connections within this visual data, which they apply to generate a digitally restored version of a particular painting, in a way that closely resembles the style of an artist or time period. However, such digital restorations are usually displayed virtually or printed as stand-alone works and cannot be directly applied to retouch original art.

“All this made me think: If we could just restore a painting digitally, and effect the results physically, that would resolve a lot of pain points and drawbacks of a conventional manual process,” Kachkine says.

“Align and restore”

For the new study, Kachkine developed a method to physically apply a digital restoration onto an original painting, using a 15th-century painting that he acquired when he first came to MIT. His new method involves first using traditional techniques to clean a painting and remove any past restoration efforts.

“This painting is almost 600 years old and has gone through conservation many times,” he says. “In this case there was a fair amount of overpainting, all of which has to be cleaned off to see what’s actually there to begin with.”

He scanned the cleaned painting, including the many regions where paint had faded or cracked. He then used existing artificial intelligence algorithms to analyze the scan and create a virtual version of what the painting likely looked like in its original state.

Then, Kachkine developed software that creates a map of regions on the original painting that require infilling, along with the exact colors needed to match the digitally restored version. This map is then translated into a physical, two-layer mask that is printed onto thin polymer-based films. The first layer is printed in color, while the second layer is printed in the exact same pattern, but in white.

“In order to fully reproduce color, you need both white and color ink to get the full spectrum,” Kachkine explains. “If those two layers are misaligned, that’s very easy to see. So I also developed a few computational tools, based on what we know of human color perception, to determine how small of a region we can practically align and restore.”

Kachkine used high-fidelity commercial inkjets to print the mask’s two layers, which he carefully aligned and overlaid by hand onto the original painting and adhered with a thin spray of conventional varnish. The printed films are made from materials that can be easily dissolved with conservation-grade solutions, in case conservators need to reveal the original, damaged work. The digital file of the mask can also be saved as a detailed record of what was restored.

For the painting that Kachkine used, the method was able to fill in thousands of losses in just a few hours. “A few years ago, I was restoring this baroque Italian painting with probably the same order magnitude of losses, and it took me nine months of part-time work,” he recalls. “The more losses there are, the better this method is.”

He estimates that the new method can be orders of magnitude faster than traditional, hand-painted approaches. If the method is adopted widely, he emphasizes that conservators should be involved at every step in the process, to ensure that the final work is in keeping with an artist’s style and intent.

“It will take a lot of deliberation about the ethical challenges involved at every stage in this process to see how can this be applied in a way that’s most consistent with conservation principles,” he says. “We’re setting up a framework for developing further methods. As others work on this, we’ll end up with methods that are more precise.”

This work was supported, in part, by the John O. and Katherine A. Lutz Memorial Fund. The research was carried out, in part, through the use of equipment and facilities at MIT.Nano, with additional support from the MIT Microsystems Technology Laboratories, the MIT Department of Mechanical Engineering, and the MIT Libraries.

Today, 2.2 billion people in the world lack access to safe drinking water. In the United States, more than 46 million people experience water insecurity, living with either no running water or water that is unsafe to drink. The increasing need for drinking water is stretching traditional resources such as rivers, lakes, and reservoirs.

To improve access to safe and affordable drinking water, MIT engineers are tapping into an unconventional source: the air. The Earth’s atmosphere contains millions of billions of gallons of water in the form of vapor. If this vapor can be efficiently captured and condensed, it could supply clean drinking water in places where traditional water resources are inaccessible.

With that goal in mind, the MIT team has developed and tested a new atmospheric water harvester and shown that it efficiently captures water vapor and produces safe drinking water across a range of relative humidities, including dry desert air.

The new device is a black, window-sized vertical panel, made from a water-absorbent hydrogel material, enclosed in a glass chamber coated with a cooling layer. The hydrogel resembles black bubble wrap, with small dome-shaped structures that swell when the hydrogel soaks up water vapor. When the captured vapor evaporates, the domes shrink back down in an origami-like transformation. The evaporated vapor then condenses on the the glass, where it can flow down and out through a tube, as clean and drinkable water.

The system runs entirely on its own, without a power source, unlike other designs that require batteries, solar panels, or electricity from the grid. The team ran the device for over a week in Death Valley, California — the driest region in North America. Even in very low-humidity conditions, the device squeezed drinking water from the air at rates of up to 160 milliliters (about two-thirds of a cup) per day.

The team estimates that multiple vertical panels, set up in a small array, could passively supply a household with drinking water, even in arid desert environments. What’s more, the system’s water production should increase with humidity, supplying drinking water in temperate and tropical climates.

“We have built a meter-scale device that we hope to deploy in resource-limited regions, where even a solar cell is not very accessible,” says Xuanhe Zhao, the Uncas and Helen Whitaker Professor of Mechanical Engineering and Civil and Environmental Engineering at MIT. “It’s a test of feasibility in scaling up this water harvesting technology. Now people can build it even larger, or make it into parallel panels, to supply drinking water to people and achieve real impact.”

Zhao and his colleagues present the details of the new water harvesting design in a paper appearing today in the journal Nature Water. The study’s lead author is former MIT postdoc “Will” Chang Liu, who is currently an assistant professor at the National University of Singapore (NUS). MIT co-authors include Xiao-Yun Yan, Shucong Li, and Bolei Deng, along with collaborators from multiple other institutions.

Carrying capacity

Hydrogels are soft, porous materials that are made mainly from water and a microscopic network of interconnecting polymer fibers. Zhao’s group at MIT has primarily explored the use of hydrogels in biomedical applications, including adhesive coatings for medical implants, soft and flexible electrodes, and noninvasive imaging stickers.

“Through our work with soft materials, one property we know very well is the way hydrogel is very good at absorbing water from air,” Zhao says.

Researchers are exploring a number of ways to harvest water vapor for drinking water. Among the most efficient so far are devices made from metal-organic frameworks, or MOFs — ultra-porous materials that have also been shown to capture water from dry desert air. But the MOFs do not swell or stretch when absorbing water, and are limited in vapor-carrying capacity.

Water from air

The group’s new hydrogel-based water harvester addresses another key problem in similar designs. Other groups have designed water harvesters out of micro- or nano-porous hydrogels. But the water produced from these designs can be salty, requiring additional filtering. Salt is a naturally absorbent material, and researchers embed salts — typically, lithium chloride — in hydrogel to increase the material’s water absorption. The drawback, however, is that this salt can leak out with the water when it is eventually collected.

The team’s new design significantly limits salt leakage. Within the hydrogel itself, they included an extra ingredient: glycerol, a liquid compound that naturally stabilizes salt, keeping it within the gel rather than letting it crystallize and leak out with the water. The hydrogel itself has a microstructure that lacks nanoscale pores, which further prevents salt from escaping the material. The salt levels in the water they collected were below the standard threshold for safe drinking water, and significantly below the levels produced by many other hydrogel-based designs.

In addition to tuning the hydrogel’s composition, the researchers made improvements to its form. Rather than keeping the gel as a flat sheet, they molded it into a pattern of small domes resembling bubble wrap, that act to increase the gel’s surface area, along with the amount of water vapor it can absorb.

The researchers fabricated a half-square-meter of hydrogel and encased the material in a window-like glass chamber. They coated the exterior of the chamber with a special polymer film, which helps to cool the glass and stimulates any water vapor in the hydrogel to evaporate and condense onto the glass. They installed a simple tubing system to collect the water as it flows down the glass.

In November 2023, the team traveled to Death Valley, California, and set up the device as a vertical panel. Over seven days, they took measurements as the hydrogel absorbed water vapor during the night (the time of day when water vapor in the desert is highest). In the daytime, with help from the sun, the harvested water evaporated out from the hydrogel and condensed onto the glass.

Over this period, the device worked across a range of humidities, from 21 to 88 percent, and produced between 57 and 161.5 milliliters of drinking water per day. Even in the driest conditions, the device harvested more water than other passive and some actively powered designs.

“This is just a proof-of-concept design, and there are a lot of things we can optimize,” Liu says. “For instance, we could have a multipanel design. And we’re working on a next generation of the material to further improve its intrinsic properties.”

“We imagine that you could one day deploy an array of these panels, and the footprint is very small because they are all vertical,” says Zhao, who has plans to further test the panels in many resource-limited regions. “Then you could have many panels together, collecting water all the time, at household scale.”

This work was supported, in part, by the MIT J-WAFS Water and Food Seed Grant, the MIT-Chinese University of Hong Kong collaborative research program, and the UM6P-MIT collaborative research program.

The human brain is very good at solving complicated problems. One reason for that is that humans can break problems apart into manageable subtasks that are easy to solve one at a time.

This allows us to complete a daily task like going out for coffee by breaking it into steps: getting out of our office building, navigating to the coffee shop, and once there, obtaining the coffee. This strategy helps us to handle obstacles easily. For example, if the elevator is broken, we can revise how we get out of the building without changing the other steps.

While there is a great deal of behavioral evidence demonstrating humans’ skill at these complicated tasks, it has been difficult to devise experimental scenarios that allow precise characterization of the computational strategies we use to solve problems.

In a new study, MIT researchers have successfully modeled how people deploy different decision-making strategies to solve a complicated task — in this case, predicting how a ball will travel through a maze when the ball is hidden from view. The human brain cannot perform this task perfectly because it is impossible to track all of the possible trajectories in parallel, but the researchers found that people can perform reasonably well by flexibly adopting two strategies known as hierarchical reasoning and counterfactual reasoning.

The researchers were also able to determine the circumstances under which people choose each of those strategies.

“What humans are capable of doing is to break down the maze into subsections, and then solve each step using relatively simple algorithms. Effectively, when we don’t have the means to solve a complex problem, we manage by using simpler heuristics that get the job done,” says Mehrdad Jazayeri, a professor of brain and cognitive sciences, a member of MIT’s McGovern Institute for Brain Research, an investigator at the Howard Hughes Medical Institute, and the senior author of the study.

Mahdi Ramadan PhD ’24 and graduate student Cheng Tang are the lead authors of the paper, which appears today in Nature Human Behavior. Nicholas Watters PhD ’25 is also a co-author.

Rational strategies

When humans perform simple tasks that have a clear correct answer, such as categorizing objects, they perform extremely well. When tasks become more complex, such as planning a trip to your favorite cafe, there may no longer be one clearly superior answer. And, at each step, there are many things that could go wrong. In these cases, humans are very good at working out a solution that will get the task done, even though it may not be the optimal solution.

Those solutions often involve problem-solving shortcuts, or heuristics. Two prominent heuristics humans commonly rely on are hierarchical and counterfactual reasoning. Hierarchical reasoning is the process of breaking down a problem into layers, starting from the general and proceeding toward specifics. Counterfactual reasoning involves imagining what would have happened if you had made a different choice. While these strategies are well-known, scientists don’t know much about how the brain decides which one to use in a given situation.

“This is really a big question in cognitive science: How do we problem-solve in a suboptimal way, by coming up with clever heuristics that we chain together in a way that ends up getting us closer and closer until we solve the problem?” Jazayeri says.

To overcome this, Jazayeri and his colleagues devised a task that is just complex enough to require these strategies, yet simple enough that the outcomes and the calculations that go into them can be measured.

The task requires participants to predict the path of a ball as it moves through four possible trajectories in a maze. Once the ball enters the maze, people cannot see which path it travels. At two junctions in the maze, they hear an auditory cue when the ball reaches that point. Predicting the ball’s path is a task that is impossible for humans to solve with perfect accuracy.

“It requires four parallel simulations in your mind, and no human can do that. It’s analogous to having four conversations at a time,” Jazayeri says. “The task allows us to tap into this set of algorithms that the humans use, because you just can’t solve it optimally.”

The researchers recruited about 150 human volunteers to participate in the study. Before each subject began the ball-tracking task, the researchers evaluated how accurately they could estimate timespans of several hundred milliseconds, about the length of time it takes the ball to travel along one arm of the maze.

For each participant, the researchers created computational models that could predict the patterns of errors that would be seen for that participant (based on their timing skill) if they were running parallel simulations, using hierarchical reasoning alone, counterfactual reasoning alone, or combinations of the two reasoning strategies.

The researchers compared the subjects’ performance with the models’ predictions and found that for every subject, their performance was most closely associated with a model that used hierarchical reasoning but sometimes switched to counterfactual reasoning.

That suggests that instead of tracking all the possible paths that the ball could take, people broke up the task. First, they picked the direction (left or right), in which they thought the ball turned at the first junction, and continued to track the ball as it headed for the next turn. If the timing of the next sound they heard wasn’t compatible with the path they had chosen, they would go back and revise their first prediction — but only some of the time.

Switching back to the other side, which represents a shift to counterfactual reasoning, requires people to review their memory of the tones that they heard. However, it turns out that these memories are not always reliable, and the researchers found that people decided whether to go back or not based on how good they believed their memory to be.

“People rely on counterfactuals to the degree that it’s helpful,” Jazayeri says. “People who take a big performance loss when they do counterfactuals avoid doing them. But if you are someone who’s really good at retrieving information from the recent past, you may go back to the other side.”

Human limitations

To further validate their results, the researchers created a machine-learning neural network and trained it to complete the task. A machine-learning model trained on this task will track the ball’s path accurately and make the correct prediction every time, unless the researchers impose limitations on its performance.

When the researchers added cognitive limitations similar to those faced by humans, they found that the model altered its strategies. When they eliminated the model’s ability to follow all possible trajectories, it began to employ hierarchical and counterfactual strategies like humans do. If the researchers reduced the model’s memory recall ability, it began to switch to counterfactual only if it thought its recall would be good enough to get the right answer — just as humans do.

“What we found is that networks mimic human behavior when we impose on them those computational constraints that we found in human behavior,” Jazayeri says. “This is really saying that humans are acting rationally under the constraints that they have to function under.”

By slightly varying the amount of memory impairment programmed into the models, the researchers also saw hints that the switching of strategies appears to happen gradually, rather than at a distinct cut-off point. They are now performing further studies to try to determine what is happening in the brain as these shifts in strategy occur.

The research was funded by a Lisa K. Yang ICoN Fellowship, a Friends of the McGovern Institute Student Fellowship, a National Science Foundation Graduate Research Fellowship, the Simons Foundation, the Howard Hughes Medical Institute, and the McGovern Institute.

For many patients with schizophrenia, other psychiatric illnesses, or diseases such as hypertension and asthma, it can be difficult to take their medicine every day. To help overcome that challenge, MIT researchers have developed a pill that can be taken just once a week and gradually releases medication from within the stomach.

In a phase 3 clinical trial conducted by MIT spinout Lyndra Therapeutics, the researchers used the once-a-week pill to deliver a widely used medication for managing the symptoms of schizophrenia. They found that this treatment regimen maintained consistent levels of the drug in patients’ bodies and controlled their symptoms just as well as daily doses of the drug. The results are published today in Lancet Psychiatry.

“We’ve converted something that has to be taken once a day to once a week, orally, using a technology that can be adapted for a variety of medications,” says Giovanni Traverso, an associate professor of mechanical engineering at MIT, a gastroenterologist at Brigham and Women’s Hospital, an associate member of the Broad Institute, and an author of the study. “The ability to provide a sustained level of drug for a prolonged period, in an easy-to-administer system, makes it easier to ensure patients are receiving their medication.”

Traverso’s lab began developing the ingestible capsule studied in this trial more than 10 years ago, as part of an ongoing effort to make medications easier for patients to take. The capsule is about the size of a multivitamin, and once swallowed, it expands into a star shape that helps it remain in the stomach until all of the drug is released.

Richard Scranton, chief medical officer of Lyndra Therapeutics, is the senior author of the paper, and Leslie Citrome, a clinical professor of psychiatry and behavioral sciences at New York Medical College School of Medicine, is the lead author. Nayana Nagaraj, medical director at Lyndra Therapeutics, and Todd Dumas, senior director of pharmacometrics at Certara, are also authors.

Sustained delivery

Over the past decade, Traverso’s lab has been working on a variety of capsules that can be swallowed and remain in the digestive tract for days or weeks, slowly releasing their drug payload. In 2016, his team reported the star-shaped device, which was then further developed by Lyndra for clinical trials in patients with schizophrenia.

The device contains six arms that can be folded in, allowing it to fit inside a capsule. The capsule dissolves when the device reaches the stomach, allowing the arms to spring out. Once the arms are extended, the device becomes too large to pass through the pylorus (the exit of the stomach), so it remains freely floating in the stomach as drugs are slowly released from the arms. After about a week, the arms break off on their own, and each segment exits the stomach and passes through the digestive tract.

For the clinical trials, the capsule was loaded with risperidone, a commonly prescribed medication used to treat schizophrenia. Most patients take the drug orally once a day. There are also injectable versions that can be given every two weeks, every month, or every two months, but they require administration by a health care provider and are not always acceptable to patients.

The MIT and Lyndra team chose to focus on schizophrenia in hopes that a drug regimen that could be administered less frequently, through oral delivery, could make treatment easier for patients and their caregivers.

“One of the areas of unmet need that was recognized early on is neuropsychiatric conditions, where the illness can limit or impair one’s ability to remember to take their medication,” Traverso says. “With that in mind, one of the conditions that has been a big focus has been schizophrenia.”

The phase 3 trial was coordinated by researchers at Lyndra and enrolled 83 patients at five different sites around the United States. Forty-five of those patients completed the full five weeks of the study, in which they took one risperidone-loaded capsule per week.

Throughout the study, the researchers measured the amount of drug in each patient’s bloodstream. Each week, they found a sharp increase on the day the pill was given, followed by a slow decline over the next week. The levels were all within the optimal range, and there was less variation over time than is seen when patients take a pill each day.

Effective treatment

Using an evaluation known as the Positive and Negative Syndrome Scale (PANSS), the researchers also found that the patients’ symptoms remained stable throughout the study.

“One of the biggest obstacles in the care of people with chronic illnesses in general is that medications are not taken consistently. This leads to worsening symptoms, and in the case of schizophrenia, potential relapse and hospitalization,” Citrome says. “Having the option to take medication by mouth once a week represents an important option that can assist with adherence for the many patients who would prefer oral medications versus injectable formulations.”

Side effects from the treatment were minimal, the researchers found. Some patients experienced mild acid reflux and constipation early in the study, but these did not last long. The results, showing effectiveness of the capsule and few side effects, represent a major milestone in this approach to drug delivery, Traverso says.

“This really demonstrates that what we had hypothesized a decade ago, which is that a single capsule providing a drug depot within the GI tract could be possible,” he says. “Here what you see is that the capsule can achieve the drug levels that were predicted, and also control symptoms in a sizeable cohort of patients with schizophrenia.”

The investigators now hope to complete larger phase 3 studies before applying for FDA approval of this delivery approach for risperidone. They are also preparing for phase 1 trials using this capsule to deliver other drugs, including contraceptives.

“We are delighted that this technology which started at MIT has reached the point of phase 3 clinical trials,” says Robert Langer, the David H. Koch Institute Professor at MIT, who was an author of the original study on the star capsule and is a co-founder of Lyndra Therapeutics.

The research was funded by Lyndra Therapeutics.

Travel agents help to provide end-to-end logistics — like transportation, accommodations, meals, and lodging — for businesspeople, vacationers, and everyone in between. For those looking to make their own arrangements, large language models (LLMs) seem like they would be a strong tool to employ for this task because of their ability to iteratively interact using natural language, provide some commonsense reasoning, collect information, and call other tools in to help with the task at hand. However, recent work has found that state-of-the-art LLMs struggle with complex logistical and mathematical reasoning, as well as problems with multiple constraints, like trip planning, where they’ve been found to provide viable solutions 4 percent or less of the time, even with additional tools and application programming interfaces (APIs).

Subsequently, a research team from MIT and the MIT-IBM Watson AI Lab reframed the issue to see if they could increase the success rate of LLM solutions for complex problems. “We believe a lot of these planning problems are naturally a combinatorial optimization problem,” where you need to satisfy several constraints in a certifiable way, says Chuchu Fan, associate professor in the MIT Department of Aeronautics and Astronautics (AeroAstro) and the Laboratory for Information and Decision Systems (LIDS). She is also a researcher in the MIT-IBM Watson AI Lab. Her team applies machine learning, control theory, and formal methods to develop safe and verifiable control systems for robotics, autonomous systems, controllers, and human-machine interactions.

Noting the transferable nature of their work for travel planning, the group sought to create a user-friendly framework that can act as an AI travel broker to help develop realistic, logical, and complete travel plans. To achieve this, the researchers combined common LLMs with algorithms and a complete satisfiability solver. Solvers are mathematical tools that rigorously check if criteria can be met and how, but they require complex computer programming for use. This makes them natural companions to LLMs for problems like these, where users want help planning in a timely manner, without the need for programming knowledge or research into travel options. Further, if a user’s constraint cannot be met, the new technique can identify and articulate where the issue lies and propose alternative measures to the user, who can then choose to accept, reject, or modify them until a valid plan is formulated, if one exists.

“Different complexities of travel planning are something everyone will have to deal with at some point. There are different needs, requirements, constraints, and real-world information that you can collect,” says Fan. “Our idea is not to ask LLMs to propose a travel plan. Instead, an LLM here is acting as a translator to translate this natural language description of the problem into a problem that a solver can handle [and then provide that to the user],” says Fan.

Co-authoring a paper on the work with Fan are Yang Zhang of MIT-IBM Watson AI Lab, AeroAstro graduate student Yilun Hao, and graduate student Yongchao Chen of MIT LIDS and Harvard University. This work was recently presented at the Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics.

Breaking down the solver

Math tends to be domain-specific. For example, in natural language processing, LLMs perform regressions to predict the next token, a.k.a. “word,” in a series to analyze or create a document. This works well for generalizing diverse human inputs. LLMs alone, however, wouldn’t work for formal verification applications, like in aerospace or cybersecurity, where circuit connections and constraint tasks need to be complete and proven, otherwise loopholes and vulnerabilities can sneak by and cause critical safety issues. Here, solvers excel, but they need fixed formatting inputs and struggle with unsatisfiable queries.  A hybrid technique, however, provides an opportunity to develop solutions for complex problems, like trip planning, in a way that’s intuitive for everyday people.

“The solver is really the key here, because when we develop these algorithms, we know exactly how the problem is being solved as an optimization problem,” says Fan. Specifically, the research group used a solver called satisfiability modulo theories (SMT), which determines whether a formula can be satisfied. “With this particular solver, it’s not just doing optimization. It’s doing reasoning over a lot of different algorithms there to understand whether the planning problem is possible or not to solve. That’s a pretty significant thing in travel planning. It’s not a very traditional mathematical optimization problem because people come up with all these limitations, constraints, restrictions,” notes Fan.

Translation in action

The “travel agent” works in four steps that can be repeated, as needed. The researchers used GPT-4, Claude-3, or Mistral-Large as the method’s LLM. First, the LLM parses a user’s requested travel plan prompt into planning steps, noting preferences for budget, hotels, transportation, destinations, attractions, restaurants, and trip duration in days, as well as any other user prescriptions. Those steps are then converted into executable Python code (with a natural language annotation for each of the constraints), which calls APIs like CitySearch, FlightSearch, etc. to collect data, and the SMT solver to begin executing the steps laid out in the constraint satisfaction problem. If a sound and complete solution can be found, the solver outputs the result to the LLM, which then provides a coherent itinerary to the user.

If one or more constraints cannot be met, the framework begins looking for an alternative. The solver outputs code identifying the conflicting constraints (with its corresponding annotation) that the LLM then provides to the user with a potential remedy. The user can then decide how to proceed, until a solution (or the maximum number of iterations) is reached.

Generalizable and robust planning

The researchers tested their method using the aforementioned LLMs against other baselines: GPT-4 by itself, OpenAI o1-preview by itself, GPT-4 with a tool to collect information, and a search algorithm that optimizes for total cost. Using the TravelPlanner dataset, which includes data for viable plans, the team looked at multiple performance metrics: how frequently a method could deliver a solution, if the solution satisfied commonsense criteria like not visiting two cities in one day, the method’s ability to meet one or more constraints, and a final pass rate indicating that it could meet all constraints. The new technique generally achieved over a 90 percent pass rate, compared to 10 percent or lower for the baselines. The team also explored the addition of a JSON representation within the query step, which further made it easier for the method to provide solutions with 84.4-98.9 percent pass rates.

The MIT-IBM team posed additional challenges for their method. They looked at how important each component of their solution was — such as removing human feedback or the solver — and how that affected plan adjustments to unsatisfiable queries within 10 or 20 iterations using a new dataset they created called UnsatChristmas, which includes unseen constraints, and a modified version of TravelPlanner. On average, the MIT-IBM group’s framework achieved 78.6  and 85 percent success, which rises to 81.6 and 91.7 percent with additional plan modification rounds. The researchers analyzed how well it handled new, unseen constraints and paraphrased query-step and step-code prompts. In both cases, it performed very well, especially with an 86.7 percent pass rate for the paraphrasing trial.

Lastly, the MIT-IBM researchers applied their framework to other domains with tasks like block picking, task allocation, the traveling salesman problem, and warehouse. Here, the method must select numbered, colored blocks and maximize its score; optimize robot task assignment for different scenarios; plan trips minimizing distance traveled; and robot task completion and optimization.

“I think this is a very strong and innovative framework that can save a lot of time for humans, and also, it’s a very novel combination of the LLM and the solver,” says Hao.

This work was funded, in part, by the Office of Naval Research and the MIT-IBM Watson AI Lab.

Suppose you were shown that an artificial intelligence tool offers accurate predictions about some stocks you own. How would you feel about using it? Now, suppose you are applying for a job at a company where the HR department uses an AI system to screen resumes. Would you be comfortable with that?

A new study finds that people are neither entirely enthusiastic nor totally averse to AI. Rather than falling into camps of techno-optimists and Luddites, people are discerning about the practical upshot of using AI, case by case.

“We propose that AI appreciation occurs when AI is perceived as being more capable than humans and personalization is perceived as being unnecessary in a given decision context,” says MIT Professor Jackson Lu, co-author of a newly published paper detailing the study’s results. “AI aversion occurs when either of these conditions is not met, and AI appreciation occurs only when both conditions are satisfied.”

The paper, “AI Aversion or Appreciation? A Capability–Personalization Framework and a Meta-Analytic Review,” appears in Psychological Bulletin. The paper has eight co-authors, including Lu, who is the Career Development Associate Professor of Work and Organization Studies at the MIT Sloan School of Management.

New framework adds insight

People’s reactions to AI have long been subject to extensive debate, often producing seemingly disparate findings. An influential 2015 paper on “algorithm aversion” found that people are less forgiving of AI-generated errors than of human errors, whereas a widely noted 2019 paper on “algorithm appreciation” found that people preferred advice from AI, compared to advice from humans.

To reconcile these mixed findings, Lu and his co-authors conducted a meta-analysis of 163 prior studies that compared people’s preferences for AI versus humans. The researchers tested whether the data supported their proposed “Capability–Personalization Framework” — the idea that in a given context, both the perceived capability of AI and the perceived necessity for personalization shape our preferences for either AI or humans.

Across the 163 studies, the research team analyzed over 82,000 reactions to 93 distinct “decision contexts” — for instance, whether or not participants would feel comfortable with AI being used in cancer diagnoses. The analysis confirmed that the Capability–Personalization Framework indeed helps account for people’s preferences.

“The meta-analysis supported our theoretical framework,” Lu says. “Both dimensions are important: Individuals evaluate whether or not AI is more capable than people at a given task, and whether the task calls for personalization. People will prefer AI only if they think the AI is more capable than humans and the task is nonpersonal.”

He adds: “The key idea here is that high perceived capability alone does not guarantee AI appreciation. Personalization matters too.”

For example, people tend to favor AI when it comes to detecting fraud or sorting large datasets — areas where AI’s abilities exceed those of humans in speed and scale, and personalization is not required. But they are more resistant to AI in contexts like therapy, job interviews, or medical diagnoses, where they feel a human is better able to recognize their unique circumstances.

“People have a fundamental desire to see themselves as unique and distinct from other people,” Lu says. “AI is often viewed as impersonal and operating in a rote manner. Even if the AI is trained on a wealth of data, people feel AI can’t grasp their personal situations. They want a human recruiter, a human doctor who can see them as distinct from other people.”

Context also matters: From tangibility to unemployment

The study also uncovered other factors that influence individuals’ preferences for AI. For instance, AI appreciation is more pronounced for tangible robots than for intangible algorithms.

Economic context also matters. In countries with lower unemployment, AI appreciation is more pronounced.

“It makes intuitive sense,” Lu says. “If you worry about being replaced by AI, you’re less likely to embrace it.”  

Lu is continuing to examine people’s complex and evolving attitudes toward AI. While he does not view the current meta-analysis as the last word on the matter, he hopes the Capability–Personalization Framework offers a valuable lens for understanding how people evaluate AI across different contexts.

“We’re not claiming perceived capability and personalization are the only two dimensions that matter, but according to our meta-analysis, these two dimensions capture much of what shapes people’s preferences for AI versus humans across a wide range of studies,” Lu concludes.

In addition to Lu, the paper’s co-authors are Xin Qin, Chen Chen, Hansen Zhou, Xiaowei Dong, and Limei Cao of Sun Yat-sen University; Xiang Zhou of Shenzhen University; and Dongyuan Wu of Fudan University.

The research was supported, in part, by grants to Qin and Wu from the National Natural Science Foundation of China. 

MIT has an unparalleled history of bringing together interdisciplinary teams to solve pressing problems — think of the development of radar during World War II, or leading the international coalition that cracked the code of the human genome — but the challenge of climate change could demand a scale of collaboration unlike any that’s come before at MIT.

“Solving climate change is not just about new technologies or better models. It’s about forging new partnerships across campus and beyond — between scientists and economists, between architects and data scientists, between policymakers and physicists, between anthropologists and engineers, and more,” MIT Vice President for Energy and Climate Evelyn Wang told an energetic crowd of faculty, students, and staff on May 6. “Each of us holds a piece of the solution — but only together can we see the whole.”

Undeterred by heavy rain, approximately 300 campus community members filled the atrium in the Tina and Hamid Moghadam Building (Building 55) for a spring gathering hosted by Wang and the Climate Project at MIT. The initiative seeks to direct the full strength of MIT to address climate change, which Wang described as one of the defining challenges of this moment in history — and one of its greatest opportunities.

“It calls on us to rethink how we power our world, how we build, how we live — and how we work together,” Wang said. “And there is no better place than MIT to lead this kind of bold, integrated effort. Our culture of curiosity, rigor, and relentless experimentation makes us uniquely suited to cross boundaries — to break down silos and build something new.”

The Climate Project is organized around six missions, thematic areas in which MIT aims to make significant impact, ranging from decarbonizing industry to new policy approaches to designing resilient cities. The faculty leaders of these missions posed challenges to the crowd before circulating among the crowd to share their perspectives and to discuss community questions and ideas.

Wang and the Climate Project team were joined by a number of research groups, startups, and MIT offices conducting relevant work today on issues related to energy and climate. For example, the MIT Office of Sustainability showcased efforts to use the MIT campus as a living laboratory; MIT spinouts such as Forma Systems, which is developing high-performance, low-carbon building systems, and Addis Energy, which envisions using the earth as a reactor to produce clean ammonia, presented their technologies; and visitors learned about current projects in MIT labs, including DebunkBot, an artificial intelligence-powered chatbot that can persuade people to shift their attitudes about conspiracies, developed by David Rand, the Erwin H. Schell Professor at the MIT Sloan School of Management.

Benedetto Marelli, an associate professor in the Department of Civil and Environmental Engineering who leads the Wild Cards Mission, said the energy and enthusiasm that filled the room was inspiring — but that the individual conversations were equally valuable.

“I was especially pleased to see so many students come out. I also spoke with other faculty, talked to staff from across the Institute, and met representatives of external companies interested in collaborating with MIT,” Marelli said. “You could see connections being made all around the room, which is exactly what we need as we build momentum for the Climate Project.”

Researchers from the Disruptive and Sustainable Technologies for Agricultural Precision (DiSTAP) interdisciplinary research group within the Singapore-MIT Alliance for Research and Technology have developed the world’s first near-infrared fluorescent nanosensor capable of real-time, nondestructive, and species-agnostic detection of indole-3-acetic acid (IAA) — the primary bioactive auxin hormone that controls the way plants develop, grow, and respond to stress.

Auxins, particularly IAA, play a central role in regulating key plant processes such as cell division, elongation, root and shoot development, and response to environmental cues like light, heat, and drought. External factors like light affect how auxin moves within the plant, temperature influences how much is produced, and a lack of water can disrupt hormone balance. When plants cannot effectively regulate auxins, they may not grow well, adapt to changing conditions, or produce as much food. 

Existing IAA detection methods, such as liquid chromatography, require taking plant samples from the plant — which harms or removes part of it. Conventional methods also measure the effects of IAA rather than detecting it directly, and cannot be used universally across different plant types. In addition, since IAA are small molecules that cannot be easily tracked in real time, biosensors that contain fluorescent proteins need to be inserted into the plant’s genome to measure auxin, making it emit a fluorescent signal for live imaging.

SMART’s newly developed nanosensor enables direct, real-time tracking of auxin levels in living plants with high precision. The sensor uses near infrared imaging to monitor IAA fluctuations non-invasively across tissues like leaves, roots, and cotyledons, and it is capable of bypassing chlorophyll interference to ensure highly reliable readings even in densely pigmented tissues. The technology does not require genetic modification and can be integrated with existing agricultural systems — offering a scalable precision tool to advance both crop optimization and fundamental plant physiology research. 

By providing real-time, precise measurements of auxin, the sensor empowers farmers with earlier and more accurate insights into plant health. With these insights and comprehensive data, farmers can make smarter, data-driven decisions on irrigation, nutrient delivery, and pruning, tailored to the plant’s actual needs — ultimately improving crop growth, boosting stress resilience, and increasing yields.

“We need new technologies to address the problems of food insecurity and climate change worldwide. Auxin is a central growth signal within living plants, and this work gives us a way to tap it to give new information to farmers and researchers,” says Michael Strano, co-lead principal investigator at DiSTAP, Carbon P. Dubbs Professor of Chemical Engineering at MIT, and co-corresponding author of the paper. “The applications are many, including early detection of plant stress, allowing for timely interventions to safeguard crops. For urban and indoor farms, where light, water, and nutrients are already tightly controlled, this sensor can be a valuable tool in fine-tuning growth conditions with even greater precision to optimize yield and sustainability.”

The research team documented the nanosensor’s development in a paper titled, “A Near-Infrared Fluorescent Nanosensor for Direct and Real-Time Measurement of Indole-3-Acetic Acid in Plants,” published in the journal ACS Nano. The sensor comprises single-walled carbon nanotubes wrapped in a specially designed polymer, which enables it to detect IAA through changes in near infrared fluorescence intensity. Successfully tested across multiple species, including Arabidopsis, Nicotiana benthamiana, choy sum, and spinach, the nanosensor can map IAA responses under various environmental conditions such as shade, low light, and heat stress. 

“This sensor builds on DiSTAP’s ongoing work in nanotechnology and the CoPhMoRe technique, which has already been used to develop other sensors that can detect important plant compounds such as gibberellins and hydrogen peroxide. By adapting this approach for IAA, we’re adding to our inventory of novel, precise, and nondestructive tools for monitoring plant health. Eventually, these sensors can be multiplexed, or combined, to monitor a spectrum of plant growth markers for more complete insights into plant physiology,” says Duc Thinh Khong, research scientist at DiSTAP and co-first author of the paper.

“This small but mighty nanosensor tackles a long-standing challenge in agriculture: the need for a universal, real-time, and noninvasive tool to monitor plant health across various species. Our collaborative achievement not only empowers researchers and farmers to optimize growth conditions and improve crop yield and resilience, but also advances our scientific understanding of hormone pathways and plant-environment interactions,” says In-Cheol Jang, senior principal investigator at TLL, principal investigator at DiSTAP, and co-corresponding author of the paper.

Looking ahead, the research team is looking to combine multiple sensing platforms to simultaneously detect IAA and its related metabolites to create a comprehensive hormone signaling profile, offering deeper insights into plant stress responses and enhancing precision agriculture. They are also working on using microneedles for highly localized, tissue-specific sensing, and collaborating with industrial urban farming partners to translate the technology into practical, field-ready solutions. 

The research was carried out by SMART, and supported by the National Research Foundation of Singapore under its Campus for Research Excellence And Technological Enterprise program. The universal nanosensor was developed in collaboration with Temasek Life Sciences Laboratory (TLL) and MIT.

An autonomous drone carrying water to help extinguish a wildfire in the Sierra Nevada might encounter swirling Santa Ana winds that threaten to push it off course. Rapidly adapting to these unknown disturbances inflight presents an enormous challenge for the drone’s flight control system.

To help such a drone stay on target, MIT researchers developed a new, machine learning-based adaptive control algorithm that could minimize its deviation from its intended trajectory in the face of unpredictable forces like gusty winds.

Unlike standard approaches, the new technique does not require the person programming the autonomous drone to know anything in advance about the structure of these uncertain disturbances. Instead, the control system’s artificial intelligence model learns all it needs to know from a small amount of observational data collected from 15 minutes of flight time.

Importantly, the technique automatically determines which optimization algorithm it should use to adapt to the disturbances, which improves tracking performance. It chooses the algorithm that best suits the geometry of specific disturbances this drone is facing.

The researchers train their control system to do both things simultaneously using a technique called meta-learning, which teaches the system how to adapt to different types of disturbances.

Taken together, these ingredients enable their adaptive control system to achieve 50 percent less trajectory tracking error than baseline methods in simulations and perform better with new wind speeds it didn’t see during training.

In the future, this adaptive control system could help autonomous drones more efficiently deliver heavy parcels despite strong winds or monitor fire-prone areas of a national park.

“The concurrent learning of these components is what gives our method its strength. By leveraging meta-learning, our controller can automatically make choices that will be best for quick adaptation,” says Navid Azizan, who is the Esther and Harold E. Edgerton Assistant Professor in the MIT Department of Mechanical Engineering and the Institute for Data, Systems, and Society (IDSS), a principal investigator of the Laboratory for Information and Decision Systems (LIDS), and the senior author of a paper on this control system.

Azizan is joined on the paper by lead author Sunbochen Tang, a graduate student in the Department of Aeronautics and Astronautics, and Haoyuan Sun, a graduate student in the Department of Electrical Engineering and Computer Science. The research was recently presented at the Learning for Dynamics and Control Conference.

Finding the right algorithm

Typically, a control system incorporates a function that models the drone and its environment, and includes some existing information on the structure of potential disturbances. But in a real world filled with uncertain conditions, it is often impossible to hand-design this structure in advance.

Many control systems use an adaptation method based on a popular optimization algorithm, known as gradient descent, to estimate the unknown parts of the problem and determine how to keep the drone as close as possible to its target trajectory during flight. However, gradient descent is only one algorithm in a larger family of algorithms available to choose, known as mirror descent.

“Mirror descent is a general family of algorithms, and for any given problem, one of these algorithms can be more suitable than others. The name of the game is how to choose the particular algorithm that is right for your problem. In our method, we automate this choice,” Azizan says.

In their control system, the researchers replaced the function that contains some structure of potential disturbances with a neural network model that learns to approximate them from data. In this way, they don’t need to have an a priori structure of the wind speeds this drone could encounter in advance.

Their method also uses an algorithm to automatically select the right mirror-descent function while learning the neural network model from data, rather than assuming a user has the ideal function picked out already. The researchers give this algorithm a range of functions to pick from, and it finds the one that best fits the problem at hand.

“Choosing a good distance-generating function to construct the right mirror-descent adaptation matters a lot in getting the right algorithm to reduce the tracking error,” Tang adds.

Learning to adapt

While the wind speeds the drone may encounter could change every time it takes flight, the controller’s neural network and mirror function should stay the same so they don’t need to be recomputed each time.

To make their controller more flexible, the researchers use meta-learning, teaching it to adapt by showing it a range of wind speed families during training.

“Our method can cope with different objectives because, using meta-learning, we can learn a shared representation through different scenarios efficiently from data,” Tang explains.

In the end, the user feeds the control system a target trajectory and it continuously recalculates, in real-time, how the drone should produce thrust to keep it as close as possible to that trajectory while accommodating the uncertain disturbance it encounters.

In both simulations and real-world experiments, the researchers showed that their method led to significantly less trajectory tracking error than baseline approaches with every wind speed they tested.

“Even if the wind disturbances are much stronger than we had seen during training, our technique shows that it can still handle them successfully,” Azizan adds.

In addition, the margin by which their method outperformed the baselines grew as the wind speeds intensified, showing that it can adapt to challenging environments.

The team is now performing hardware experiments to test their control system on real drones with varying wind conditions and other disturbances.

They also want to extend their method so it can handle disturbances from multiple sources at once. For instance, changing wind speeds could cause the weight of a parcel the drone is carrying to shift in flight, especially when the drone is carrying sloshing payloads.

They also want to explore continual learning, so the drone could adapt to new disturbances without the need to also be retrained on the data it has seen so far.

“Navid and his collaborators have developed breakthrough work that combines meta-learning with conventional adaptive control to learn nonlinear features and the suitable adaptation law from data. Key to their approach is the use of mirror descent techniques that exploit the underlying geometry of the problem and do so automatically. Their work can contribute significantly to the design of autonomous systems that need to operate in complex and uncertain environments,” says Babak Hassibi, the Mose and Lillian S. Bohn Professor of Electrical Engineering and Computing and Mathematical Sciences at Caltech, who was not involved with this work.

This research was supported, in part, by MathWorks, the MIT-IBM Watson AI Lab, the MIT-Amazon Science Hub, and the MIT-Google Program for Computing Innovation.

Fusion energy has the potential to enable the energy transition from fossil fuels, enhance domestic energy security, and power artificial intelligence. Private companies have already invested more than $8 billion to develop commercial fusion and seize the opportunities it offers. An urgent challenge, however, is the discovery and evaluation of cost-effective materials that can withstand extreme conditions for extended periods, including 150-million-degree plasmas and intense particle bombardment.

To meet this challenge, MIT’s Plasma Science and Fusion Center (PSFC) has launched the Schmidt Laboratory for Materials in Nuclear Technologies, or LMNT (pronounced “element”). Backed by a philanthropic consortium led by Eric and Wendy Schmidt, LMNT is designed to speed up the discovery and selection of materials for a variety of fusion power plant components. 

By drawing on MIT's expertise in fusion and materials science, repurposing existing research infrastructure, and tapping into its close collaborations with leading private fusion companies, the PSFC aims to drive rapid progress in the materials that are necessary for commercializing fusion energy on rapid timescales. LMNT will also help develop and assess materials for nuclear power plants, next-generation particle physics experiments, and other science and industry applications.

Zachary Hartwig, head of LMNT and an associate professor in the Department of Nuclear Science and Engineering (NSE), says, “We need technologies today that will rapidly develop and test materials to support the commercialization of fusion energy. LMNT’s mission includes discovery science but seeks to go further, ultimately helping select the materials that will be used to build fusion power plants in the coming years.”

A different approach to fusion materials

For decades, researchers have worked to understand how materials behave under fusion conditions using methods like exposing test specimens to low-energy particle beams, or placing them in the core of nuclear fission reactors. These approaches, however, have significant limitations. Low-energy particle beams only irradiate the thinnest surface layer of materials, while fission reactor irradiation doesn’t accurately replicate the mechanism by which fusion damages materials. Fission irradiation is also an expensive, multiyear process that requires specialized facilities.

To overcome these obstacles, researchers at MIT and peer institutions are exploring the use of energetic beams of protons to simulate the damage materials undergo in fusion environments. Proton beams can be tuned to match the damage expected in fusion power plants, and protons penetrate deep enough into test samples to provide insights into how exposure can affect structural integrity. They also offer the advantage of speed: first, intense proton beams can rapidly damage dozens of material samples at once, allowing researchers to test them in days, rather than years. Second, high-energy proton beams can be generated with a type of particle accelerator known as a cyclotron commonly used in the health-care industry. As a result, LMNT will be built around a cost-effective, off-the-shelf cyclotron that is easy to obtain and highly reliable.

LMNT will surround its cyclotron with four experimental areas dedicated to materials science research. The lab is taking shape inside the large shielded concrete vault at PSFC that once housed the Alcator C-Mod tokamak, a record-setting fusion experiment that ran at the PSFC from 1992 to 2016. By repurposing C-Mod’s former space, the center is skipping the need for extensive, costly new construction and accelerating the research timeline significantly. The PSFC’s veteran team — who have led major projects like the Alcator tokamaks and advanced high-temperature superconducting magnet development — are overseeing the facilities design, construction, and operation, ensuring LMNT moves quickly from concept to reality. The PSFC expects to receive the cyclotron by the end of 2025, with experimental operations starting in early 2026.

“LMNT is the start of a new era of fusion research at MIT, one where we seek to tackle the most complex fusion technology challenges on timescales commensurate with the urgency of the problem we face: the energy transition,” says Nuno Loureiro, director of the PSFC, a professor of nuclear science and engineering, and the Herman Feshbach Professor of Physics. “It’s ambitious, bold, and critical — and that’s exactly why we do it.”

“What’s exciting about this project is that it aligns the resources we have today — substantial research infrastructure, off-the-shelf technologies, and MIT expertise — to address the key resource we lack in tackling climate change: time. Using the Schmidt Laboratory for Materials in Nuclear Technologies, MIT researchers advancing fusion energy, nuclear power, and other technologies critical to the future of energy will be able to act now and move fast,” says Elsa Olivetti, the Jerry McAfee Professor in Engineering and a mission director of MIT’s Climate Project.

In addition to advancing research, LMNT will provide a platform for educating and training students in the increasingly important areas of fusion technology. LMNT’s location on MIT’s main campus gives students the opportunity to lead research projects and help manage facility operations. It also continues the hands-on approach to education that has defined the PSFC, reinforcing that direct experience in large-scale research is the best approach to create fusion scientists and engineers for the expanding fusion industry workforce.

Benoit Forget, head of NSE and the Korea Electric Power Professor of Nuclear Engineering, notes, “This new laboratory will give nuclear science and engineering students access to a unique research capability that will help shape the future of both fusion and fission energy.”

Accelerating progress on big challenges

Philanthropic support has helped LMNT leverage existing infrastructure and expertise to move from concept to facility in just one-and-a-half years — a fast timeline for establishing a major research project.

“I’m just as excited about this research model as I am about the materials science. It shows how focused philanthropy and MIT’s strengths can come together to build something that’s transformational — a major new facility that helps researchers from the public and private sectors move fast on fusion materials,” emphasizes Hartwig.

By utilizing this approach, the PSFC is executing a major public-private partnership in fusion energy, realizing a research model that the U.S. fusion community has only recently started to explore, and demonstrating the crucial role that universities can play in the acceleration of the materials and technology required for fusion energy.

“Universities have long been at the forefront of tackling society’s biggest challenges, and the race to identify new forms of energy and address climate change demands bold, high-risk, high-reward approaches,” says Ian Waitz, MIT’s vice president for research. “LMNT is helping turn fusion energy from a long-term ambition into a near-term reality.”

When navigating a place that we’re only somewhat familiar with, we often rely on unique landmarks to help make our way. However, if we’re looking for an office in a brick building, and there are many brick buildings along our route, we might use a rule like looking for the second building on a street, rather than relying on distinguishing the building itself.

Until that ambiguity is resolved, we must hold in mind that there are multiple possibilities (or hypotheses) for where we are in relation to our destination. In a study of mice, MIT neuroscientists have now discovered that these hypotheses are explicitly represented in the brain by distinct neural activity patterns.

This is the first time that neural activity patterns that encode simultaneous hypotheses have been seen in the brain. The researchers found that these representations, which were observed in the brain’s retrosplenial cortex (RSC), not only encode hypotheses but also could be used by the animals to choose the correct way to go.

“As far as we know, no one has shown in a complex reasoning task that there’s an area in association cortex that holds two hypotheses in mind and then uses one of those hypotheses, once it gets more information, to actually complete the task,” says Mark Harnett, an associate professor of brain and cognitive sciences, a member of MIT’s McGovern Institute for Brain Research, and the senior author of the study.

Jakob Voigts PhD ’17, a former postdoc in Harnett’s lab and now a group leader at the Howard Hughes Medical Institute Janelia Research Campus, is the lead author of the paper, which appears today in Nature Neuroscience.

Ambiguous landmarks

The RSC receives input from the visual cortex, the hippocampal formation, and the anterior thalamus, which it integrates to help guide navigation.

In a 2020 paper, Harnett’s lab found that the RSC uses both visual and spatial information to encode landmarks used for navigation. In that study, the researchers showed that neurons in the RSC of mice integrate visual information about the surrounding environment with spatial feedback of the mice’s own position along a track, allowing them to learn where to find a reward based on landmarks that they saw.

In their new study, the researchers wanted to delve further into how the RSC uses spatial information and situational context to guide navigational decision-making. To do that, the researchers devised a much more complicated navigational task than typically used in mouse studies. They set up a large, round arena, with 16 small openings, or ports, along the side walls. One of these openings would give the mice a reward when they stuck their nose through it. In the first set of experiments, the researchers trained the mice to go to different reward ports indicated by dots of light on the floor that were only visible when the mice get close to them.

Once the mice learned to perform this relatively simple task, the researchers added a second dot. The two dots were always the same distance from each other and from the center of the arena. But now the mice had to go to the port by the counterclockwise dot to get the reward. Because the dots were identical and only became visible at close distances, the mice could never see both dots at once and could not immediately determine which dot was which.

To solve this task, mice therefore had to remember where they expected a dot to show up, integrating their own body position, the direction they were heading, and path they took to figure out which landmark is which. By measuring RSC activity as the mice approached the ambiguous landmarks, the researchers could determine whether the RSC encodes hypotheses about spatial location. The task was carefully designed to require the mice to use the visual landmarks to obtain rewards, instead of other strategies like odor cues or dead reckoning.

“What is important about the behavior in this case is that mice need to remember something and then use that to interpret future input,” says Voigts, who worked on this study while a postdoc in Harnett’s lab. “It’s not just remembering something, but remembering it in such a way that you can act on it.”

The researchers found that as the mice accumulated information about which dot might be which, populations of RSC neurons displayed distinct activity patterns for incomplete information. Each of these patterns appears to correspond to a hypothesis about where the mouse thought it was with respect to the reward.

When the mice get close enough to figure out which dot was indicating the reward port, these patterns collapsed into the one that represents the correct hypothesis. The findings suggest that these patterns not only passively store hypotheses, they can also be used to compute how to get to the correct location, the researchers say.

“We show that RSC has the required information for using this short-term memory to distinguish the ambiguous landmarks. And we show that this type of hypothesis is encoded and processed in a way that allows the RSC to use it to solve the computation,” Voigts says.

Interconnected neurons

When analyzing their initial results, Harnett and Voigts consulted with MIT Professor Ila Fiete, who had run a study about 10 years ago using an artificial neural network to perform a similar navigation task.

That study, previously published on bioRxiv, showed that the neural network displayed activity patterns that were conceptually similar to those seen in the animal studies run by Harnett’s lab. The neurons of the artificial neural network ended up forming highly interconnected low-dimensional networks, like the neurons of the RSC.

“That interconnectivity seems, in ways that we still don’t understand, to be key to how these dynamics emerge and how they’re controlled. And it’s a key feature of how the RSC holds these two hypotheses in mind at the same time,” Harnett says.

In his lab at Janelia, Voigts now plans to investigate how other brain areas involved in navigation, such as the prefrontal cortex, are engaged as mice explore and forage in a more naturalistic way, without being trained on a specific task.

“We’re looking into whether there are general principles by which tasks are learned,” Voigts says. “We have a lot of knowledge in neuroscience about how brains operate once the animal has learned a task, but in comparison we know extremely little about how mice learn tasks or what they choose to learn when given freedom to behave naturally.”

The research was funded, in part, by the National Institutes of Health, a Simons Center for the Social Brain at MIT postdoctoral fellowship, the National Institute of General Medical Sciences, and the Center for Brains, Minds, and Machines at MIT, funded by the National Science Foundation.

Animators could create more realistic bouncy, stretchy, and squishy characters for movies and video games thanks to a new simulation method developed by researchers at MIT.

Their approach allows animators to simulate rubbery and elastic materials in a way that preserves the physical properties of the material and avoids pitfalls like instability.

The technique simulates elastic objects for animation and other applications, with improved reliability compared to other methods. In comparison, many existing simulation techniques can produce elastic animations that become erratic or sluggish or can even break down entirely.

To achieve this improvement, the MIT researchers uncovered a hidden mathematical structure in equations that capture how elastic materials deform on a computer. By leveraging this property, known as convexity, they designed a method that consistently produces accurate, physically faithful simulations.

“The way animations look often depends on how accurately we simulate the physics of the problem,” says Leticia Mattos Da Silva, an MIT graduate student and lead author of a paper on this research. “Our method aims to stay true to physical laws while giving more control and stability to animation artists.”

Beyond 3D animation, the researchers also see potential future uses in the design of real elastic objects, such as flexible shoes, garments, or toys. The method could be extended to help engineers explore how stretchy objects will perform before they are built.

She is joined on the paper by Silvia Sellán, an assistant professor of computer science at Columbia University; Natalia Pacheco-Tallaj, an MIT graduate student; and senior author Justin Solomon, an associate professor in the MIT Department of Electrical Engineering and Computer Science and leader of the Geometric Data Processing Group in the Computer Science and Artificial Intelligence Laboratory (CSAIL). The research will be presented at the SIGGRAPH conference.

Truthful to physics

If you drop a rubber ball on a wooden floor, it bounces back up. Viewers expect to see the same behavior in an animated world, but recreating such dynamics convincingly can be difficult. Many existing techniques simulate elastic objects using fast solvers that trade physical realism for speed, which can result in excessive energy loss or even simulation failure.

More accurate approaches, including a class of techniques called variational integrators, preserve the physical properties of the object, such as its total energy or momentum, and, in this way, mimic real-world behavior more closely. But these methods are often unreliable because they depend on complex equations that are hard to solve efficiently.

The MIT researchers tackled this problem by rewriting the equations of variational integrators to reveal a hidden convex structure. They broke the deformation of elastic materials into a stretch component and a rotation component, and found that the stretch portion forms a convex problem that is well-suited for stable optimization algorithms.

“If you just look at the original formulation, it seems fully non-convex. But because we can rewrite it so that is convex in at least some of its variables, we can inherit some advantages of convex optimization algorithms,” she says.

These convex optimization algorithms, when applied under the right conditions, come with guarantees of convergence, meaning they are more likely to find the correct answer to the problem. This generates more stable simulations over time, avoiding issues like a bouncing rubber ball losing too much energy or exploding mid-animation.

One of the biggest challenges the researchers faced was reinterpreting the formulation so they could extract that hidden convexity. Some other works explored hidden convexity in static problems, but it was not clear whether the structures remained solid for dynamic problems like simulating elastic objects in motion, Mattos Da Silva says.

Stability and efficiency

In experiments, their solver was able to simulate a wide range of elastic behavior, from bouncing shapes to squishy characters, with preservation of important physical properties and stability over long periods of time. Other simulation methods quickly ran into trouble: Some became unstable, causing erratic behavior, while others showed visible damping.

“Because our method demonstrates more stability, it can give animators more reliability and confidence when simulating anything elastic, whether it’s something from the real world or even something completely imaginary,” she says.

While the solver is not as fast as some simulation tools that prioritize speed over accuracy, it avoids many of the trade-offs those methods make. Compared to other physics-based approaches, it also avoids the need for complex, nonlinear solvers that can be sensitive and prone to failure.

In the future, the researchers want to explore techniques to further reduce computational cost. In addition, they want to explore applications of this technique in fabrication and engineering, where reliable simulations of elastic materials could support the design of real-world objects, like garments and toys.

“We were able to revive an old class of integrators in our work. My guess is there are other examples where researchers can revisit a problem to find a hidden convexity structure that could offer a lot of advantages,” she says.

This research is funded, in part, by a MathWorks Engineering Fellowship, the Army Research Office, the National Science Foundation, the CSAIL Future of Data Program, the MIT-IBM Watson AI Laboratory, the Wistron Corporation, and the Toyota-CSAIL Joint Research Center.

Academic research groups and startups are essential drivers of scientific progress. But some projects, like the Hubble Space Telescope or the Human Genome Project, are too big for any one academic lab or loose consortium. They’re also not immediately profitable enough for industry to take on.

That’s the gap researchers at MIT were trying to fill when they created the concept of focused research organizations, or FROs. They describe a FRO as a new type of entity, often philanthropically funded, that undertakes large research efforts using tightly coordinated teams to create a public good that accelerates scientific progress.

The original idea for focused research organizations came out of talks among researchers, most of whom were working to map the brain in MIT Professor Ed Boyden’s lab. After they began publishing their ideas, however, the researchers realized FROs could be a powerful tool to unlock scientific advances across many other applications.

“We were quite pleasantly surprised by the range of fields where we see FRO-shaped problems,” says Adam Marblestone, a former MIT research scientist who co-founded the nonprofit Convergent Research to help launch FROs in 2021. “Convergent has FRO proposals from climate, materials science, chemistry, biology — we even have launched a FRO on software for math. You wouldn’t expect math to be something with a large-scale technological research bottleneck, but it turns out even there, we found a software engineering bottleneck that needed to be solved.”

Marblestone helped formulate the idea for focused research organizations at MIT with a group including Andrew Payne SM ’17, PhD ’21 and Sam Rodriques PhD ’19, who were PhD students in Boyden’s lab at the time. Since then, the FRO concept has caught on. Convergent has helped attract philanthropic funding for FROs working to decode the immune system, identify the unintended targets of approved drugs, and understand the impacts of carbon dioxide removal in our oceans.

In total, Convergent has supported the creation of 10 FROs since its founding in 2021. Many of those groups have already released important tools for better understanding our world — and their leaders believe the best is yet to come.

“We’re starting to see these first open-source tools released in important areas,” Marblestone says. “We’re seeing the first concrete evidence that FROs are effective, because no other entity could have released these tools, and I think 2025 is going to be a significant year in terms of our newer FROs putting out new datasets and tools.”

A new model

Marblestone joined Boyden’s lab in 2014 as a research scientist after completing his PhD at Harvard University. He also worked in a new position called director of scientific architecting at the MIT Media Lab, which Boyden helped create, through which he tried to organize individual research efforts into larger projects. His own research focused on overcoming the challenges of measuring brain activity across large scales.

Marblestone discussed this and other large-scale neuroscience problems with Payne and Rodriques, and the researchers began thinking about gaps in scientific funding more broadly.

“The combination of myself, Sam, Andrew, Ed, and others’ experiences trying to start various large brain-mapping projects convinced us of the gap in support for medium-sized science and engineering teams with startup-inspired structures, built for the nonprofit purpose of building scientific infrastructure,” Marblestone says.

Through MIT, the researchers also connected with Tom Kalil, who was at the time chief innovation officer at Schmidt Futures, a philanthropic initiative of Eric and Wendy Schmidt. Rodriques wrote about the concept of a focused research organization as the last chapter of his PhD thesis in 2019.

“Ed always encouraged us to dream very, very big,” Rodriques says. “We were always trying to think about the hardest problems in biology and how to tackle them. My thesis basically ended with me explaining why we needed a new structure that is like a company, but nonprofit and dedicated to science.”

As part of a fellowship with the Federation of American Scientists in 2020, and working with Kalil, Marblestone interviewed scientists in dozens of fields outside of neuroscience and learned that the funding gap existed across disciplines.

When Rodriques and Marblestone published an essay about their findings, it helped attract philanthropic funding, which Marblestone, Kalil, and co-founder Anastasia Gamick used to launch Convergent Research, a nonprofit science studio for launching FROs.

“I see Ed’s lab as a melting pot where myself, Ed, Sam, and others worked on articulating a need and identifying specific projects that might make sense as FROs,” Marblestone says. “All those ideas later got crystallized when we created Convergent Research.”

In 2021, Convergent helped launch the first FROs: E11 Bio, which is led by Payne and committed to developing tools to understand how the brain is wired, and Cultivarium, a FRO making microorganisms more accessible for work in synthetic biology.

“From our brain mapping work we started asking the question, ‘Are there other projects that look like this that aren’t getting funded?’” Payne says. “We realized there was a gap in the research ecosystem, where some of these interdisciplinary, team science projects were being systematically overlooked. We knew a lot of amazing things would come out of getting those projects funded.”

Tools to advance science

Early progress from the first focused research organizations has strengthened Marblestone’s conviction that they’re filling a gap.

[C]Worthy is the FRO building tools to ensure safe, ocean-based carbon dioxide removal. It recently released an interactive map of alkaline activity to improve our understanding of one method for sequestering carbon known as ocean alkalinity enhancement. Last year, a math FRO, Lean, released a programming language and proof assistant that was used by Google’s DeepMind AI lab to solve problems in the International Mathematical Olympiad, achieving the same level as a silver medalist in the competition for the first time. The synthetic biology FRO Cultivarium, in turn, has already released software that can predict growth conditions for microbes based on their genome.

Last year, E11 Bio previewed a new method for mapping the brain called PRISM, which it has used to map out a portion of the mouse hippocampus. It will be making the data and mapping tool available to all researchers in coming months.

“A lot of this early work has proven you can put a really talented team together and move fast to go from zero to one,” Payne says. “The next phase is proving FROs can continue to build on that momentum and develop even more datasets and tools, establish even bigger collaborations, and scale their impact.”

Payne credits Boyden for fostering an ecosystem where researchers could think about problems beyond their narrow area of study.

“Ed’s lab was a really intellectually stimulating, collaborative environment,” Payne says. “He trains his students to think about impact first and work backward. It was a bunch of people thinking about how they were going to change the world, and that made it a particularly good place to develop the FRO idea.”

Marblestone says supporting FROs has been the highest-impact thing he’s been able to do in his career. Still, he believes the success of FROs should be judged over closer to 10-year periods and will depend on not just the tools they produce but also whether they spin out companies, partner with other institutes, and create larger, long-lasting initiatives to deploy what they built.

“We were initially worried people wouldn’t be willing to join these organizations because it doesn’t offer tenure and it doesn’t offer equity in a startup,” Marblestone says. “But we’ve been able to recruit excellent leaders, scientists, engineers, and others to create highly motivated teams. That’s good evidence this is working. As we get strong projects and good results, I hope it will create this flywheel where it becomes easier to fund these ideas, more scientists will come up with them, and I think we’re starting to get there.”

At the level of molecules and cells, ketamine and dexmedetomidine work very differently, but in the operating room they do the same exact thing: anesthetize the patient. By demonstrating how these distinct drugs achieve the same result, a new study in animals by neuroscientists at The Picower Institute for Learning and Memory at MIT identifies a potential signature of unconsciousness that is readily measurable to improve anesthesiology care.

What the two drugs have in common, the researchers discovered, is the way they push around brain waves, which are produced by the collective electrical activity of neurons. When brain waves are in phase, meaning the peaks and valleys of the waves are aligned, local groups of neurons in the brain’s cortex can share information to produce conscious cognitive functions such as attention, perception, and reasoning, says Picower Professor Earl K. Miller, senior author of the new study in Cell Reports. When brain waves fall out of phase, local communications, and therefore functions, fall apart, producing unconsciousness.

The finding, led by graduate student Alexandra Bardon, not only adds to scientists’ understanding of the dividing line between consciousness and unconsciousness, Miller says, but also could provide a common new measure for anesthesiologists who use a variety of different anesthetics to maintain patients on the proper side of that line during surgery.

“If you look at the way phase is shifted in our recordings, you can barely tell which drug it was,” says Miller, a faculty member in the Picower Institute and MIT’s Department of Brain and Cognitive Sciences. “That’s valuable for medical practice. Plus if unconsciousness has a universal signature, it could also reveal the mechanisms that generate consciousness.”

If more anesthetic drugs are also shown to affect phase in the same way, then anesthesiologists might be able to use brain wave phase alignment as a reliable marker of unconsciousness as they titrate doses of anesthetic drugs, Miller says, regardless of which particular mix of drugs they are using. That insight could aid efforts to build closed-loop systems that can aid anesthesiologists by constantly adjusting drug dose based on brain wave measurements of the patient’s unconsciousness.

Miller has been collaborating with study co-author Emery N. Brown, an anesthesiologist and Edward Hood Taplin Professor of Computational Neuroscience and Medical Engineering in the Picower Institute, on building such a system. In a recent clinical trial with colleagues in Japan, Brown demonstrated that monitoring brain wave power signals using EEG enabled an anesthesiologist to use much less sevoflurane during surgery with young children. The reduced doses proved safe and were associated with many improved clinical outcomes, including a reduced incidence of post-operative delirium.

Phase findings

Neuroscientists studying anesthesia have rarely paid attention to phase, but in the new study, Bardon, Brown, and Miller’s team made a point of it as they anesthetized two animals.

After the animals lost consciousness, the measurements indicated a substantial increase in “phase locking,” especially at low frequencies. Phase locking means that the relative differences in phase remained more stable. But what caught the researchers’ attention were the differences that became locked in: within each hemisphere, regardless of which anesthetic they used, brain wave phase became misaligned between the dorsolateral and ventrolateral regions of the prefrontal cortex.

Surprisingly, brain wave phase across hemispheres became more aligned, not less. But Miller notes that case is still a big shift from the conscious state, in which brain hemispheres are typically not aligned well, so the finding is a further indication that major changes of phase alignment, albeit in different ways at different distances, are a correlate of unconsciousness compared to wakefulness.

“The increase in interhemispheric alignment of activity by anesthetics seems to reverse the pattern observed in the awake, cognitively engaged brain,” the Bardon and Miller team wrote in Cell Reports.

Determined by distance

Distance proved to be a major factor in determining the change in phase alignment. Even across the 2.5 millimeters of a single electrode array, low-frequency waves moved 20-30 degrees out of alignment. Across the 20 or so millimeters between arrays in the upper (dorsolateral) and lower (ventrolateral) regions within a hemisphere, that would mean a roughly 180-degree shift in phase alignment, which is a complete offset of the waves.

The dependence on distance is consistent with the idea of waves traveling across the cortex, Miller says. Indeed, in a 2022 study, Miller and Brown’s labs showed that the anesthetic propofol induced a powerful low-frequency traveling wave that swept straight across the cortex, overwhelming higher-frequency straight and rotating waves.

The new results raise many opportunities for follow-up studies, Miller says. Does propofol also produce this signature of changed phase alignment? What role do traveling waves play in the phenomenon? And given that sleep is also characterized by increased power in slow wave frequencies, but is definitely not the same state as anesthesia-induced unconsciousness, could phase alignment explain the difference?

In addition to Bardon, Brown, and Miller, the paper’s other authors are Jesus Ballesteros, Scott Brincat, Jefferson Roy, Meredith Mahnke, and Yumiko Ishizawa.

The U.S. Department of Energy, the National Institutes of Health, the Simons Center for the Social Brain, the Freedom Together Foundation, and the Picower Institute provided support for the research.

Ready for that long-awaited summer vacation? First, you’ll need to pack all items required for your trip into a suitcase, making sure everything fits securely without crushing anything fragile.

Because humans possess strong visual and geometric reasoning skills, this is usually a straightforward problem, even if it may take a bit of finagling to squeeze everything in.

To a robot, though, it is an extremely complex planning challenge that requires thinking simultaneously about many actions, constraints, and mechanical capabilities. Finding an effective solution could take the robot a very long time — if it can even come up with one.

Researchers from MIT and NVIDIA Research have developed a novel algorithm that dramatically speeds up the robot’s planning process. Their approach enables a robot to “think ahead” by evaluating thousands of possible solutions in parallel and then refining the best ones to meet the constraints of the robot and its environment.

Instead of testing each potential action one at a time, like many existing approaches, this new method considers thousands of actions simultaneously, solving multistep manipulation problems in a matter of seconds.

The researchers harness the massive computational power of specialized processors called graphics processing units (GPUs) to enable this speedup.

In a factory or warehouse, their technique could enable robots to rapidly determine how to manipulate and tightly pack items that have different shapes and sizes without damaging them, knocking anything over, or colliding with obstacles, even in a narrow space.

“This would be very helpful in industrial settings where time really does matter and you need to find an effective solution as fast as possible. If your algorithm takes minutes to find a plan, as opposed to seconds, that costs the business money,” says MIT graduate student William Shen SM ’23, lead author of the paper on this technique.

He is joined on the paper by Caelan Garrett ’15, MEng ’15, PhD ’21, a senior research scientist at NVIDIA Research; Nishanth Kumar, an MIT graduate student; Ankit Goyal, a NVIDIA research scientist; Tucker Hermans, a NVIDIA research scientist and associate professor at the University of Utah; Leslie Pack Kaelbling, the Panasonic Professor of Computer Science and Engineering at MIT and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL); Tomás Lozano-Pérez, an MIT professor of computer science and engineering and a member of CSAIL; and Fabio Ramos, principal research scientist at NVIDIA and a professor at the University of Sydney. The research will be presented at the Robotics: Science and Systems Conference.

Planning in parallel

The researchers’ algorithm is designed for what is called task and motion planning (TAMP). The goal of a TAMP algorithm is to come up with a task plan for a robot, which is a high-level sequence of actions, along with a motion plan, which includes low-level action parameters, like joint positions and gripper orientation, that complete that high-level plan.

To create a plan for packing items in a box, a robot needs to reason about many variables, such as the final orientation of packed objects so they fit together, as well as how it is going to pick them up and manipulate them using its arm and gripper.

It must do this while determining how to avoid collisions and achieve any user-specified constraints, such as a certain order in which to pack items.

With so many potential sequences of actions, sampling possible solutions at random and trying one at a time could take an extremely long time.

“It is a very large search space, and a lot of actions the robot does in that space don’t actually achieve anything productive,” Garrett adds.

Instead, the researchers’ algorithm, called cuTAMP, which is accelerated using a parallel computing platform called CUDA, simulates and refines thousands of solutions in parallel. It does this by combining two techniques, sampling and optimization.

Sampling involves choosing a solution to try. But rather than sampling solutions randomly, cuTAMP limits the range of potential solutions to those most likely to satisfy the problem’s constraints. This modified sampling procedure allows cuTAMP to broadly explore potential solutions while narrowing down the sampling space.

“Once we combine the outputs of these samples, we get a much better starting point than if we sampled randomly. This ensures we can find solutions more quickly during optimization,” Shen says.

Once cuTAMP has generated that set of samples, it performs a parallelized optimization procedure that computes a cost, which corresponds to how well each sample avoids collisions and satisfies the motion constraints of the robot, as well as any user-defined objectives.

It updates the samples in parallel, chooses the best candidates, and repeats the process until it narrows them down to a successful solution.

Harnessing accelerated computing

The researchers leverage GPUs, specialized processors that are far more powerful for parallel computation and workloads than general-purpose CPUs, to scale up the number of solutions they can sample and optimize simultaneously. This maximized the performance of their algorithm.

“Using GPUs, the computational cost of optimizing one solution is the same as optimizing hundreds or thousands of solutions,” Shen explains.

When they tested their approach on Tetris-like packing challenges in simulation, cuTAMP took only a few seconds to find successful, collision-free plans that might take sequential planning approaches much longer to solve.

And when deployed on a real robotic arm, the algorithm always found a solution in under 30 seconds.

The system works across robots and has been tested on a robotic arm at MIT and a humanoid robot at NVIDIA. Since cuTAMP is not a machine-learning algorithm, it requires no training data, which could enable it to be readily deployed in many situations.

“You can give it a brand-new problem and it will provably solve it,” Garrett says.

The algorithm is generalizable to situations beyond packing, like a robot using tools. A user could incorporate different skill types into the system to expand a robot’s capabilities automatically.

In the future, the researchers want to leverage large language models and vision language models within cuTAMP, enabling a robot to formulate and execute a plan that achieves specific objectives based on voice commands from a user.

This work is supported, in part, by the National Science Foundation (NSF), Air Force Office for Scientific Research, Office of Naval Research, MIT Quest for Intelligence, NVIDIA, and the Robotics and Artificial Intelligence Institute.

MIT physicists have demonstrated a new form of magnetism that could one day be harnessed to build faster, denser, and less power-hungry “spintronic” memory chips.

The new magnetic state is a mash-up of two main forms of magnetism: the ferromagnetism of everyday fridge magnets and compass needles, and antiferromagnetism, in which materials have magnetic properties at the microscale yet are not macroscopically magnetized.

Now, the MIT team has demonstrated a new form of magnetism, termed “p-wave magnetism.”

Physicists have long observed that electrons of atoms in regular ferromagnets share the same orientation of “spin,” like so many tiny compasses pointing in the same direction. This spin alignment generates a magnetic field, which gives a ferromagnet its inherent magnetism. Electrons belonging to magnetic atoms in an antiferromagnet also have spin, although these spins alternate, with electrons orbiting neighboring atoms aligning their spins antiparallel to each other. Taken together, the equal and opposite spins cancel out, and the antiferromagnet does not exhibit macroscopic magnetization.

The team discovered the new p-wave magnetism in nickel iodide (NiI₂), a two-dimensional crystalline material that they synthesized in the lab. Like a ferromagnet, the electrons exhibit a preferred spin orientation, and, like an antiferromagnet, equal populations of opposite spins result in a net cancellation. However, the spins on the nickel atoms exhibit a unique pattern, forming spiral-like configurations within the material that are mirror-images of each other, much like the left hand is the right hand’s mirror image.

What’s more, the researchers found this spiral spin configuration enabled them to carry out “spin switching”: Depending on the direction of spiraling spins in the material, they could apply a small electric field in a related direction to easily flip a left-handed spiral of spins into a right-handed spiral of spins, and vice-versa.

The ability to switch electron spins is at the heart of “spintronics,” which is a proposed alternative to conventional electronics. With this approach, data can be written in the form of an electron’s spin, rather than its electronic charge, potentially allowing orders of magnitude more data to be packed onto a device while using far less power to write and read that data.   

“We showed that this new form of magnetism can be manipulated electrically,” says Qian Song, a research scientist in MIT’s Materials Research Laboratory. “This breakthrough paves the way for a new class of ultrafast, compact, energy-efficient, and nonvolatile magnetic memory devices.”

Song and his colleagues published their results May 28 in the journal Nature. MIT co-authors include Connor Occhialini, Batyr Ilyas, Emre Ergeçen, Nuh Gedik, and Riccardo Comin, along with Rafael Fernandes at the University of Illinois Urbana-Champaign, and collaborators from multiple other institutions.

Connecting the dots

The discovery expands on work by Comin’s group in 2022. At that time, the team probed the magnetic properties of the same material, nickel iodide. At the microscopic level, nickel iodide resembles a triangular lattice of nickel and iodine atoms. Nickel is the material’s main magnetic ingredient, as the electrons on the nickel atoms exhibit spin, while those on iodine atoms do not.

In those experiments, the team observed that the spins of those nickel atoms were arranged in a spiral pattern throughout the material’s lattice, and that this pattern could spiral in two different orientations.

At the time, Comin had no idea that this unique pattern of atomic spins could enable precise switching of spins in surrounding electrons. This possibility was later raised by collaborator Rafael Fernandes, who along with other theorists was intrigued by a recently proposed idea for a new, unconventional, “p-wave” magnet, in which electrons moving along opposite directions in the material would have their spins aligned in opposite directions.

Fernandes and his colleagues recognized that if the spins of atoms in a material form the geometric spiral arrangement that Comin observed in nickel iodide, that would be a realization of a “p-wave” magnet. Then, when an electric field is applied to switch the “handedness” of the spiral, it should also switch the spin alignment of the electrons traveling along the same direction.

In other words, such a p-wave magnet could enable simple and controllable switching of electron spins, in a way that could be harnessed for spintronic applications.

“It was a completely new idea at the time, and we decided to test it experimentally because we realized nickel iodide was a good candidate to show this kind of p-wave magnet effect,” Comin says.

Spin current

For their new study, the team synthesized single-crystal flakes of nickel iodide by first depositing powders of the respective elements on a crystalline substrate, which they placed in a high-temperature furnace. The process causes the elements to settle into layers, each arranged microscopically in a triangular lattice of nickel and iodine atoms.

“What comes out of the oven are samples that are several millimeters wide and thin, like cracker bread,” Comin says. “We then exfoliate the material, peeling off even smaller flakes, each several microns wide, and a few tens of nanometers thin.”

The researchers wanted to know if, indeed, the spiral geometry of the nickel atoms’s spins would force electrons traveling in opposite directions to have opposite spins, like what Fernandes expected a p-wave magnet should exhibit. To observe this, the group applied to each flake a beam of circularly polarized light — light that produces an electric field that rotates in a particular direction, for instance, either clockwise or counterclockwise.

They reasoned that if travelling electrons interacting with the spin spirals have a spin that is aligned in the same direction, then incoming light, polarized in that same direction, should resonate and produce a characteristic signal. Such a signal would confirm that the traveling electrons’ spins align because of the spiral configuration, and furthermore, that the material does in fact exhibit p-wave magnetism.

And indeed, that’s what the group found. In experiments with multiple nickel iodide flakes, the researchers directly observed that the direction of the electron’s spin was correlated to the handedness of the light used to excite those electrons. Such is a telltale signature of p-wave magnetism, here observed for the first time.

Going a step further, they looked to see whether they could switch the spins of the electrons by applying an electric field, or a small amount of voltage, along different directions through the material. They found that when the direction of the electric field was in line with the direction of the spin spiral, the effect switched electrons along the route to spin in the same direction, producing a current of like-spinning electrons.

“With such a current of spin, you can do interesting things at the device level, for instance, you could flip magnetic domains that can be used for control of a magnetic bit,” Comin explains. “These spintronic effects are more efficient than conventional electronics because you’re just moving spins around, rather than moving charges. That means you’re not subject to any dissipation effects that generate heat, which is essentially the reason computers heat up.”

“We just need a small electric field to control this magnetic switching,” Song adds. “P-wave magnets could save five orders of magnitude of energy. Which is huge.”

“We are excited to see these cutting-edge experiments confirm our prediction of p-wave spin polarized states,” says Libor Šmejkal, head of the Max Planck Research Group in Dresden, Germany, who is one of the authors of the theoretical work that proposed the concept of p-wave magnetism but was not involved in the new paper. “The demonstration of electrically switchable p-wave spin polarization also highlights the promising applications of unconventional magnetic states.”

The team observed p-wave magnetism in nickel iodide flakes, only at ultracold temperatures of about 60 kelvins.

“That’s below liquid nitrogen, which is not necessarily practical for applications,” Comin says. “But now that we’ve realized this new state of magnetism, the next frontier is finding a material with these properties, at room temperature. Then we can apply this to a spintronic device.”

This research was supported, in part, by the National Science Foundation, the Department of Energy, and the Air Force Office of Scientific Research.

The accumulation of microplastics in the environment, and within our bodies, is an increasingly worrisome issue. But predicting where these ubiquitous particles will accumulate, and therefore where remediation efforts should be focused, has been difficult because of the many factors that contribute to their dispersal and deposition.

New research from MIT shows that one key factor in determining where microparticles are likely to build up has to do with the presence of biofilms. These thin, sticky biopolymer layers are shed by microorganisms and can accumulate on surfaces, including along sandy riverbeds or seashores. The study found that, all other conditions being equal, microparticles are less likely to accumulate in sediment infused with biofilms, because if they land there, they are more likely to be resuspended by flowing water and carried away.

The open-access findings appear in the journal Geophysical Research Letters, in a paper by MIT postdoc Hyoungchul Park and professor of civil and environmental engineering Heidi Nepf. “Microplastics are definitely in the news a lot,” Nepf says, “and we don’t fully understand where the hotspots of accumulation are likely to be. This work gives a little bit of guidance” on some of the factors that can cause these particles, and small particles in general, to accumulate in certain locations.

Most experiments looking at the ways microparticles are transported and deposited have been conducted over bare sand, Park says. “But in nature, there are a lot of microorganisms, such as bacteria, fungi, and algae, and when they adhere to the stream bed they generate some sticky things.” These substances are known as extracellular polymeric substances, or EPS, and they “can significantly affect the channel bed characteristics,” he says. The new research focused on determining exactly how these substances affected the transport of microparticles, including microplastics.

The research involved a flow tank with a bottom lined with fine sand, and sometimes with vertical plastic tubes simulating the presence of mangrove roots. In some experiments the bed consisted of pure sand, and in others the sand was mixed with a biological material to simulate the natural biofilms found in many riverbed and seashore environments.

Water mixed with tiny plastic particles was pumped through the tank for three hours, and then the bed surface was photographed under ultraviolet light that caused the plastic particles to fluoresce, allowing a quantitative measurement of their concentration.

The results revealed two different phenomena that affected how much of the plastic accumulated on the different surfaces. Immediately around the rods that stood in for above-ground roots, turbulence prevented particle deposition. In addition, as the amount of simulated biofilms in the sediment bed increased, the accumulation of particles also decreased.

Nepf and Park concluded that the biofilms filled up the spaces between the sand grains, leaving less room for the microparticles to fit in. The particles were more exposed because they penetrated less deeply in between the sand grains, and as a result they were much more easily resuspended and carried away by the flowing water.

“These biological films fill the pore spaces between the sediment grains,” Park explains, “and that makes the deposited particles — the particles that land on the bed — more exposed to the forces generated by the flow, which makes it easier for them to be resuspended. What we found was that in a channel with the same flow conditions and the same vegetation and the same sand bed, if one is without EPS and one is with EPS, then the one without EPS has a much higher deposition rate than the one with EPS.”

Nepf adds: “The biofilm is blocking the plastics from accumulating in the bed because they can’t go deep into the bed. They just stay right on the surface, and then they get picked up and moved elsewhere. So, if I spilled a large amount of microplastic in two rivers, and one had a sandy or gravel bottom, and one was muddier with more biofilm, I would expect more of the microplastics to be retained in the sandy or gravelly river.”

All of this is complicated by other factors, such as the turbulence of the water or the roughness of the bottom surface, she says. But it provides a “nice lens” to provide some suggestions for people who are trying to study the impacts of microplastics in the field. “They’re trying to determine what kinds of habitats these plastics are in, and this gives a framework for how you might categorize those habitats,” she says. “It gives guidance to where you should go to find more plastics versus less.”

As an example, Park suggests, in mangrove ecosystems, microplastics may preferentially accumulate in the outer edges, which tend to be sandy, while the interior zones have sediment with more biofilm. Thus, this work suggests “the sandy outer regions may be potential hotspots for microplastic accumulation,” he says, and can make this a priority zone for monitoring and protection.

“This is a highly relevant finding,” says Isabella Schalko, a research scientist at ETH Zurich, who was not associated with this research. “It suggests that restoration measures such as re-vegetation or promoting biofilm growth could help mitigate microplastic accumulation in aquatic systems. It highlights the powerful role of biological and physical features in shaping particle transport processes.”

The work was supported by Shell International Exploration and Production through the MIT Energy Initiative.

Hydrogen has the potential to be a climate-friendly fuel since it doesn’t release carbon dioxide when used as an energy source. Currently, however, most methods for producing hydrogen involve fossil fuels, making hydrogen less of a “green” fuel over its entire life cycle.

A new process developed by MIT engineers could significantly shrink the carbon footprint associated with making hydrogen.

Last year, the team reported that they could produce hydrogen gas by combining seawater, recycled soda cans, and caffeine. The question then was whether the benchtop process could be applied at an industrial scale, and at what environmental cost.

Now, the researchers have carried out a “cradle-to-grave” life cycle assessment, taking into account every step in the process at an industrial scale. For instance, the team calculated the carbon emissions associated with acquiring and processing aluminum, reacting it with seawater to produce hydrogen, and transporting the fuel to gas stations, where drivers could tap into hydrogen tanks to power engines or fuel cell cars. They found that, from end to end, the new process could generate a fraction of the carbon emissions that is associated with conventional hydrogen production.

In a study appearing today in Cell Reports Sustainability, the team reports that for every kilogram of hydrogen produced, the process would generate 1.45 kilograms of carbon dioxide over its entire life cycle. In comparison, fossil-fuel-based processes emit 11 kilograms of carbon dioxide per kilogram of hydrogen generated.

The low-carbon footprint is on par with other proposed “green hydrogen” technologies, such as those powered by solar and wind energy.

“We’re in the ballpark of green hydrogen,” says lead author Aly Kombargi PhD ’25, who graduated this spring from MIT with a doctorate in mechanical engineering. “This work highlights aluminum’s potential as a clean energy source and offers a scalable pathway for low-emission hydrogen deployment in transportation and remote energy systems.”

The study’s MIT co-authors are Brooke Bao, Enoch Ellis, and professor of mechanical engineering Douglas Hart.

Gas bubble

Dropping an aluminum can in water won’t normally cause much of a chemical reaction. That’s because when aluminum is exposed to oxygen, it instantly forms a shield-like layer. Without this layer, aluminum exists in its pure form and can readily react when mixed with water. The reaction that occurs involves aluminum atoms that efficiently break up molecules of water, producing aluminum oxide and pure hydrogen. And it doesn’t take much of the metal to bubble up a significant amount of the gas.

“One of the main benefits of using aluminum is the energy density per unit volume,” Kombargi says. “With a very small amount of aluminum fuel, you can conceivably supply much of the power for a hydrogen-fueled vehicle.”

Last year, he and Hart developed a recipe for aluminum-based hydrogen production. They found they could puncture aluminum’s natural shield by treating it with a small amount of gallium-indium, which is a rare-metal alloy that effectively scrubs aluminum into its pure form. The researchers then mixed pellets of pure aluminum with seawater and observed that the reaction produced pure hydrogen. What’s more, the salt in the water helped to precipitate gallium-indium, which the team could subsequently recover and reuse to generate more hydrogen, in a cost-saving, sustainable cycle.

“We were explaining the science of this process in conferences, and the questions we would get were, ‘How much does this cost?’ and, ‘What’s its carbon footprint?’” Kombargi says. “So we wanted to look at the process in a comprehensive way.”

A sustainable cycle

For their new study, Kombargi and his colleagues carried out a life cycle assessment to estimate the environmental impact of aluminum-based hydrogen production, at every step of the process, from sourcing the aluminum to transporting the hydrogen after production. They set out to calculate the amount of carbon associated with generating 1 kilogram of hydrogen — an amount that they chose as a practical, consumer-level illustration.

“With a hydrogen fuel cell car using 1 kilogram of hydrogen, you can go between 60 to 100 kilometers, depending on the efficiency of the fuel cell,” Kombargi notes.

They performed the analysis using Earthster — an online life cycle assessment tool that draws data from a large repository of products and processes and their associated carbon emissions. The team considered a number of scenarios to produce hydrogen using aluminum, from starting with “primary” aluminum mined from the Earth, versus “secondary” aluminum that is recycled from soda cans and other products, and using various methods to transport the aluminum and hydrogen.

After running life cycle assessments for about a dozen scenarios, the team identified one scenario with the lowest carbon footprint. This scenario centers on recycled aluminum — a source that saves a significant amount of emissions compared with mining aluminum — and seawater — a natural resource that also saves money by recovering gallium-indium. They found that this scenario, from start to finish, would generate about 1.45 kilograms of carbon dioxide for every kilogram of hydrogen produced. The cost of the fuel produced, they calculated, would be about $9 per kilogram, which is comparable to the price of hydrogen that would be generated with other green technologies such as wind and solar energy.

The researchers envision that if the low-carbon process were ramped up to a commercial scale, it would look something like this: The production chain would start with scrap aluminum sourced from a recycling center. The aluminum would be shredded into pellets and treated with gallium-indium. Then, drivers could transport the pretreated pellets as aluminum “fuel,” rather than directly transporting hydrogen, which is potentially volatile. The pellets would be transported to a fuel station that ideally would be situated near a source of seawater, which could then be mixed with the aluminum, on demand, to produce hydrogen. A consumer could then directly pump the gas into a car with either an internal combustion engine or a fuel cell.

The entire process does produce an aluminum-based byproduct, boehmite, which is a mineral that is commonly used in fabricating semiconductors, electronic elements, and a number of industrial products. Kombargi says that if this byproduct were recovered after hydrogen production, it could be sold to manufacturers, further bringing down the cost of the process as a whole.

“There are a lot of things to consider,” Kombargi says. “But the process works, which is the most exciting part. And we show that it can be environmentally sustainable.”

The group is continuing to develop the process. They recently designed a small reactor, about the size of a water bottle, that takes in aluminum pellets and seawater to generate hydrogen, enough to power an electric bike for several hours. They previously demonstrated that the process can produce enough hydrogen to fuel a small car. The team is also exploring underwater applications, and are designing a hydrogen reactor that would take in surrounding seawater to power a small boat or underwater vehicle.

This research was supported, in part, by the MIT Portugal Program.

Hearing aids, mouth guards, dental implants, and other highly tailored structures are often products of 3D printing. These structures are typically made via vat photopolymerization — a form of 3D printing that uses patterns of light to shape and solidify a resin, one layer at a time.

The process also involves printing structural supports from the same material to hold the product in place as it’s printed. Once a product is fully formed, the supports are removed manually and typically thrown out as unusable waste.

MIT engineers have found a way to bypass this last finishing step, in a way that could significantly speed up the 3D-printing process. They developed a resin that turns into two different kinds of solids, depending on the type of light that shines on it: Ultraviolet light cures the resin into an highly resilient solid, while visible light turns the same resin into a solid that is easily dissolvable in certain solvents.

The team exposed the new resin simultaneously to patterns of UV light to form a sturdy structure, as well as patterns of visible light to form the structure’s supports. Instead of having to carefully break away the supports, they simply dipped the printed material into solution that dissolved the supports away, revealing the sturdy, UV-printed part.

The supports can dissolve in a variety of food-safe solutions, including baby oil. Interestingly, the supports could even dissolve in the main liquid ingredient of the original resin, like a cube of ice in water. This means that the material used to print structural supports could be continuously recycled: Once a printed structure’s supporting material dissolves, that mixture can be blended directly back into fresh resin and used to print the next set of parts — along with their dissolvable supports.

The researchers applied the new method to print complex structures, including functional gear trains and intricate lattices.

“You can now print — in a single print — multipart, functional assemblies with moving or interlocking parts, and you can basically wash away the supports,” says graduate student Nicholas Diaco. “Instead of throwing out this material, you can recycle it on site and generate a lot less waste. That’s the ultimate hope.”

He and his colleagues report the details of the new method in a paper appearing today in Advanced Materials Technologies. The MIT study’s co-authors include Carl Thrasher, Max Hughes, Kevin Zhou, Michael Durso, Saechow Yap, Professor Robert Macfarlane, and Professor A. John Hart, head of MIT’s Department of Mechanical Engineering.

Waste removal

Conventional vat photopolymerization (VP) begins with a 3D computer model of a structure to be printed — for instance, of two interlocking gears. Along with the gears themselves, the model includes small support structures around, under, and between the gears to keep every feature in place as the part is printed. This computer model is then sliced into many digital layers that are sent to a VP printer for printing.

A standard VP printer includes a small vat of liquid resin that sits over a light source. Each slice of the model is translated into a matching pattern of light that is projected onto the liquid resin, which solidifies into the same pattern. Layer by layer, a solid, light-printed version of the model’s gears and supports forms on the build platform. When printing is finished, the platform lifts the completed part above the resin bath. Once excess resin is washed away, a person can go in by hand to remove the intermediary supports, usually by clipping and filing, and the support material is ultimately thrown away.

“For the most part, these supports end up generating a lot of waste,” Diaco says.

Print and dip

Diaco and the team looked for a way to simplify and speed up the removal of printed supports and, ideally, recycle them in the process. They came up with a general concept for a resin that, depending on the type of light that it is exposed to, can take on one of two phases: a resilient phase that would form the desired 3D structure and a secondary phase that would function as a supporting material but also be easily dissolved away.

After working out some chemistry, the team found they could make such a two-phase resin by mixing two commercially available monomers, the chemical building blocks that are found in many types of plastic. When ultraviolet light shines on the mixture, the monomers link together into a tightly interconnected network, forming a tough solid that resists dissolution. When the same mixture is exposed to visible light, the same monomers still cure, but at the molecular scale the resulting monomer strands remain separate from one another. This solid can quickly dissolve when placed in certain solutions.

In benchtop tests with small vials of the new resin, the researchers found the material did transform into both the insoluble and soluble forms in response to ultraviolet and visible light, respectively. But when they moved to a 3D printer with LEDs dimmer than the benchtop setup, the UV-cured material fell apart in solution. The weaker light only partially linked the monomer strands, leaving them too loosely tangled to hold the structure together.

Diaco and his colleagues found that adding a small amount of a third “bridging” monomer could link the two original monomers together under UV light, knitting them into a much sturdier framework. This fix enabled the researchers to simultaneously print resilient 3D structures and dissolvable supports using timed pulses of UV and visible light in one run.

The team applied the new method to print a variety of intricate structures, including interlocking gears, intricate lattices, a ball within a square frame, and, for fun, a small dinosaur encased in an egg-shaped support that dissolved away when dipped in solution.

“With all these structures, you need a lattice of supports inside and out while printing,” Diaco says. “Removing those supports normally requires careful, manual removal. This shows we can print multipart assemblies with a lot of moving parts, and detailed, personalized products like hearing aids and dental implants, in a way that’s fast and sustainable.”

“We’ll continue studying the limits of this process, and we want to develop additional resins with this wavelength-selective behavior and mechanical properties necessary for durable products,” says professor of mechanical engineering John Hart. “Along with automated part handling and closed-loop reuse of the dissolved resin, this is an exciting path to resource-efficient and cost-effective polymer 3D printing at scale.”

This research was supported, in part, by the Center for Perceptual and Interactive Intelligence (InnoHK) in Hong Kong, the U.S. National Science Foundation, the U.S. Office of Naval Research, and the U.S. Army Research Office.

For weeks, the whiteboard in the lab was crowded with scribbles, diagrams, and chemical formulas. A research team across the Olivetti Group and the MIT Concrete Sustainability Hub (CSHub) was working intensely on a key problem: How can we reduce the amount of cement in concrete to save on costs and emissions? 

The question was certainly not new; materials like fly ash, a byproduct of coal production, and slag, a byproduct of steelmaking, have long been used to replace some of the cement in concrete mixes. However, the demand for these products is outpacing supply as industry looks to reduce its climate impacts by expanding their use, making the search for alternatives urgent. The challenge that the team discovered wasn’t a lack of candidates; the problem was that there were too many to sort through.

On May 17, the team, led by postdoc Soroush Mahjoubi, published an open-access paper in Nature’s Communications Materials outlining their solution. “We realized that AI was the key to moving forward,” notes Mahjoubi. “There is so much data out there on potential materials — hundreds of thousands of pages of scientific literature. Sorting through them would have taken many lifetimes of work, by which time more materials would have been discovered!”

With large language models, like the chatbots many of us use daily, the team built a machine-learning framework that evaluates and sorts candidate materials based on their physical and chemical properties. 

“First, there is hydraulic reactivity. The reason that concrete is strong is that cement — the ‘glue’ that holds it together — hardens when exposed to water. So, if we replace this glue, we need to make sure the substitute reacts similarly,” explains Mahjoubi. “Second, there is pozzolanicity. This is when a material reacts with calcium hydroxide, a byproduct created when cement meets water, to make the concrete harder and stronger over time.  We need to balance the hydraulic and pozzolanic materials in the mix so the concrete performs at its best.”

Analyzing scientific literature and over 1 million rock samples, the team used the framework to sort candidate materials into 19 types, ranging from biomass to mining byproducts to demolished construction materials. Mahjoubi and his team found that suitable materials were available globally — and, more impressively, many could be incorporated into concrete mixes just by grinding them. This means it’s possible to extract emissions and cost savings without much additional processing. 

“Some of the most interesting materials that could replace a portion of cement are ceramics,” notes Mahjoubi. “Old tiles, bricks, pottery — all these materials may have high reactivity. That’s something we’ve observed in ancient Roman concrete, where ceramics were added to help waterproof structures. I’ve had many interesting conversations on this with Professor Admir Masic, who leads a lot of the ancient concrete studies here at MIT.”

The potential of everyday materials like ceramics and industrial materials like mine tailings is an example of how materials like concrete can help enable a circular economy. By identifying and repurposing materials that would otherwise end up in landfills, researchers and industry can help to give these materials a second life as part of our buildings and infrastructure.

Looking ahead, the research team is planning to upgrade the framework to be capable of assessing even more materials, while experimentally validating some of the best candidates. “AI tools have gotten this research far in a short time, and we are excited to see how the latest developments in large language models enable the next steps,” says Professor Elsa Olivetti, senior author on the work and member of the MIT Department of Materials Science and Engineering. She serves as an MIT Climate Project mission director, a CSHub principal investigator, and the leader of the Olivetti Group.

“Concrete is the backbone of the built environment,” says Randolph Kirchain, co-author and CSHub director. “By applying data science and AI tools to material design, we hope to support industry efforts to build more sustainably, without compromising on strength, safety, or durability.

In addition to Mahjoubi, Olivetti, and Kirchain, co-authors on the work include MIT postdoc Vineeth Venugopal, Ipek Bensu Manav SM ’21, PhD ’24; and CSHub Deputy Director Hessam AzariJafari.

This research was conducted through the MIT Concrete Sustainability Hub, which is supported by the Concrete Advancement Foundation. This work also received funding from the MIT-IBM Watson AI Lab.

When you’re trying to communicate or understand ideas, words don’t always do the trick. Sometimes the more efficient approach is to do a simple sketch of that concept — for example, diagramming a circuit might help make sense of how the system works.

But what if artificial intelligence could help us explore these visualizations? While these systems are typically proficient at creating realistic paintings and cartoonish drawings, many models fail to capture the essence of sketching: its stroke-by-stroke, iterative process, which helps humans brainstorm and edit how they want to represent their ideas.

A new drawing system from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Stanford University can sketch more like we do. Their method, called “SketchAgent,” uses a multimodal language model — AI systems that train on text and images, like Anthropic’s Claude 3.5 Sonnet — to turn natural language prompts into sketches in a few seconds. For example, it can doodle a house either on its own or through collaboration, drawing with a human or incorporating text-based input to sketch each part separately.

The researchers showed that SketchAgent can create abstract drawings of diverse concepts, like a robot, butterfly, DNA helix, flowchart, and even the Sydney Opera House. One day, the tool could be expanded into an interactive art game that helps teachers and researchers diagram complex concepts or give users a quick drawing lesson.

CSAIL postdoc Yael Vinker, who is the lead author of a paper introducing SketchAgent, notes that the system introduces a more natural way for humans to communicate with AI.

“Not everyone is aware of how much they draw in their daily life. We may draw our thoughts or workshop ideas with sketches,” she says. “Our tool aims to emulate that process, making multimodal language models more useful in helping us visually express ideas.”

SketchAgent teaches these models to draw stroke-by-stroke without training on any data — instead, the researchers developed a “sketching language” in which a sketch is translated into a numbered sequence of strokes on a grid. The system was given an example of how things like a house would be drawn, with each stroke labeled according to what it represented — such as the seventh stroke being a rectangle labeled as a “front door” — to help the model generalize to new concepts.

Vinker wrote the paper alongside three CSAIL affiliates — postdoc Tamar Rott Shaham, undergraduate researcher Alex Zhao, and MIT Professor Antonio Torralba — as well as Stanford University Research Fellow Kristine Zheng and Assistant Professor Judith Ellen Fan. They’ll present their work at the 2025 Conference on Computer Vision and Pattern Recognition (CVPR) this month.

Assessing AI’s sketching abilities

While text-to-image models such as DALL-E 3 can create intriguing drawings, they lack a crucial component of sketching: the spontaneous, creative process where each stroke can impact the overall design. On the other hand, SketchAgent’s drawings are modeled as a sequence of strokes, appearing more natural and fluid, like human sketches.

Prior works have mimicked this process, too, but they trained their models on human-drawn datasets, which are often limited in scale and diversity. SketchAgent uses pre-trained language models instead, which are knowledgeable about many concepts, but don’t know how to sketch. When the researchers taught language models this process, SketchAgent began to sketch diverse concepts it hadn’t explicitly trained on.

Still, Vinker and her colleagues wanted to see if SketchAgent was actively working with humans on the sketching process, or if it was working independently of its drawing partner. The team tested their system in collaboration mode, where a human and a language model work toward drawing a particular concept in tandem. Removing SketchAgent’s contributions revealed that their tool’s strokes were essential to the final drawing. In a drawing of a sailboat, for instance, removing the artificial strokes representing a mast made the overall sketch unrecognizable.

In another experiment, CSAIL and Stanford researchers plugged different multimodal language models into SketchAgent to see which could create the most recognizable sketches. Their default backbone model, Claude 3.5 Sonnet, generated the most human-like vector graphics (essentially text-based files that can be converted into high-resolution images). It outperformed models like GPT-4o and Claude 3 Opus.

“The fact that Claude 3.5 Sonnet outperformed other models like GPT-4o and Claude 3 Opus suggests that this model processes and generates visual-related information differently,” says co-author Tamar Rott Shaham.

She adds that SketchAgent could become a helpful interface for collaborating with AI models beyond standard, text-based communication. “As models advance in understanding and generating other modalities, like sketches, they open up new ways for users to express ideas and receive responses that feel more intuitive and human-like,” says Rott Shaham. “This could significantly enrich interactions, making AI more accessible and versatile.”

While SketchAgent’s drawing prowess is promising, it can’t make professional sketches yet. It renders simple representations of concepts using stick figures and doodles, but struggles to doodle things like logos, sentences, complex creatures like unicorns and cows, and specific human figures.

At times, their model also misunderstood users’ intentions in collaborative drawings, like when SketchAgent drew a bunny with two heads. According to Vinker, this may be because the model breaks down each task into smaller steps (also called “Chain of Thought” reasoning). When working with humans, the model creates a drawing plan, potentially misinterpreting which part of that outline a human is contributing to. The researchers could possibly refine these drawing skills by training on synthetic data from diffusion models.

Additionally, SketchAgent often requires a few rounds of prompting to generate human-like doodles. In the future, the team aims to make it easier to interact and sketch with multimodal language models, including refining their interface. 

Still, the tool suggests AI could draw diverse concepts the way humans do, with step-by-step human-AI collaboration that results in more aligned final designs.

This work was supported, in part, by the U.S. National Science Foundation, a Hoffman-Yee Grant from the Stanford Institute for Human-Centered AI, the Hyundai Motor Co., the U.S. Army Research Laboratory, the Zuckerman STEM Leadership Program, and a Viterbi Fellowship.

Every year, thousands of students take courses that teach them how to deploy artificial intelligence models that can help doctors diagnose disease and determine appropriate treatments. However, many of these courses omit a key element: training students to detect flaws in the training data used to develop the models.

Leo Anthony Celi, a senior research scientist at MIT’s Institute for Medical Engineering and Science, a physician at Beth Israel Deaconess Medical Center, and an associate professor at Harvard Medical School, has documented these shortcomings in a new paper and hopes to persuade course developers to teach students to more thoroughly evaluate their data before incorporating it into their models. Many previous studies have found that models trained mostly on clinical data from white males don’t work well when applied to people from other groups. Here, Celi describes the impact of such bias and how educators might address it in their teachings about AI models.

Q: How does bias get into these datasets, and how can these shortcomings be addressed?

A: Any problems in the data will be baked into any modeling of the data. In the past we have described instruments and devices that don’t work well across individuals. As one example, we found that pulse oximeters overestimate oxygen levels for people of color, because there weren’t enough people of color enrolled in the clinical trials of the devices. We remind our students that medical devices and equipment are optimized on healthy young males. They were never optimized for an 80-year-old woman with heart failure, and yet we use them for those purposes. And the FDA does not require that a device work well on this diverse of a population that we will be using it on. All they need is proof that it works on healthy subjects.

Additionally, the electronic health record system is in no shape to be used as the building blocks of AI. Those records were not designed to be a learning system, and for that reason, you have to be really careful about using electronic health records. The electronic health record system is to be replaced, but that’s not going to happen anytime soon, so we need to be smarter. We need to be more creative about using the data that we have now, no matter how bad they are, in building algorithms.

One promising avenue that we are exploring is the development of a transformer model of numeric electronic health record data, including but not limited to laboratory test results. Modeling the underlying relationship between the laboratory tests, the vital signs and the treatments can mitigate the effect of missing data as a result of social determinants of health and provider implicit biases.

Q: Why is it important for courses in AI to cover the sources of potential bias? What did you find when you analyzed such courses’ content?

A: Our course at MIT started in 2016, and at some point we realized that we were encouraging people to race to build models that are overfitted to some statistical measure of model performance, when in fact the data that we’re using is rife with problems that people are not aware of. At that time, we were wondering: How common is this problem?

Our suspicion was that if you looked at the courses where the syllabus is available online, or the online courses, that none of them even bothers to tell the students that they should be paranoid about the data. And true enough, when we looked at the different online courses, it’s all about building the model. How do you build the model? How do you visualize the data? We found that of 11 courses we reviewed, only five included sections on bias in datasets, and only two contained any significant discussion of bias.

That said, we cannot discount the value of these courses. I’ve heard lots of stories where people self-study based on these online courses, but at the same time, given how influential they are, how impactful they are, we need to really double down on requiring them to teach the right skillsets, as more and more people are drawn to this AI multiverse. It’s important for people to really equip themselves with the agency to be able to work with AI. We’re hoping that this paper will shine a spotlight on this huge gap in the way we teach AI now to our students.

Q: What kind of content should course developers be incorporating?

A: One, giving them a checklist of questions in the beginning. Where did this data came from? Who were the observers? Who were the doctors and nurses who collected the data? And then learn a little bit about the landscape of those institutions. If it’s an ICU database, they need to ask who makes it to the ICU, and who doesn’t make it to the ICU, because that already introduces a sampling selection bias. If all the minority patients don’t even get admitted to the ICU because they cannot reach the ICU in time, then the models are not going to work for them. Truly, to me, 50 percent of the course content should really be understanding the data, if not more, because the modeling itself is easy once you understand the data.

Since 2014, the MIT Critical Data consortium has been organizing datathons (data “hackathons”) around the world. At these gatherings, doctors, nurses, other health care workers, and data scientists get together to comb through databases and try to examine health and disease in the local context. Textbooks and journal papers present diseases based on observations and trials involving a narrow demographic typically from countries with resources for research. 

Our main objective now, what we want to teach them, is critical thinking skills. And the main ingredient for critical thinking is bringing together people with different backgrounds.

You cannot teach critical thinking in a room full of CEOs or in a room full of doctors. The environment is just not there. When we have datathons, we don’t even have to teach them how do you do critical thinking. As soon as you bring the right mix of people — and it’s not just coming from different backgrounds but from different generations — you don’t even have to tell them how to think critically. It just happens. The environment is right for that kind of thinking. So, we now tell our participants and our students, please, please do not start building any model unless you truly understand how the data came about, which patients made it into the database, what devices were used to measure, and are those devices consistently accurate across individuals?

When we have events around the world, we encourage them to look for data sets that are local, so that they are relevant. There’s resistance because they know that they will discover how bad their data sets are. We say that that’s fine. This is how you fix that. If you don’t know how bad they are, you’re going to continue collecting them in a very bad manner and they’re useless. You have to acknowledge that you’re not going to get it right the first time, and that’s perfectly fine. MIMIC (the Medical Information Marked for Intensive Care database built at Beth Israel Deaconess Medical Center) took a decade before we had a decent schema, and we only have a decent schema because people were telling us how bad MIMIC was.

We may not have the answers to all of these questions, but we can evoke something in people that helps them realize that there are so many problems in the data. I’m always thrilled to look at the blog posts from people who attended a datathon, who say that their world has changed. Now they’re more excited about the field because they realize the immense potential, but also the immense risk of harm if they don’t do this correctly.

Scientists at the McGovern Institute for Brain Research at MIT and the Broad Institute of MIT and Harvard have re-engineered a compact RNA-guided enzyme they found in bacteria into an efficient, programmable editor of human DNA. 

The protein they created, called NovaIscB, can be adapted to make precise changes to the genetic code, modulate the activity of specific genes, or carry out other editing tasks. Because its small size simplifies delivery to cells, NovaIscB’s developers say it is a promising candidate for developing gene therapies to treat or prevent disease.

The study was led by Feng Zhang, the James and Patricia Poitras Professor of Neuroscience at MIT who is also an investigator at the McGovern Institute and the Howard Hughes Medical Institute, and a core member of the Broad Institute. Zhang and his team reported their open-access work this month in the journal Nature Biotechnology.

NovaIscB is derived from a bacterial DNA cutter that belongs to a family of proteins called IscBs, which Zhang’s lab discovered in 2021. IscBs are a type of OMEGA system, the evolutionary ancestors to Cas9, which is part of the bacterial CRISPR system that Zhang and others have developed into powerful genome-editing tools. Like Cas9, IscB enzymes cut DNA at sites specified by an RNA guide. By reprogramming that guide, researchers can redirect the enzymes to target sequences of their choosing.

IscBs had caught the team’s attention not only because they share key features of CRISPR’s DNA-cutting Cas9, but also because they are a third of its size. That would be an advantage for potential gene therapies: compact tools are easier to deliver to cells, and with a small enzyme, researchers would have more flexibility to tinker, potentially adding new functionalities without creating tools that were too bulky for clinical use.

From their initial studies of IscBs, researchers in Zhang’s lab knew that some members of the family could cut DNA targets in human cells. None of the bacterial proteins worked well enough to be deployed therapeutically, however: the team would have to modify an IscB to ensure it could edit targets in human cells efficiently without disturbing the rest of the genome.

To begin that engineering process, Soumya Kannan, a graduate student in Zhang’s lab who is now a junior fellow at the Harvard Society of Fellows, and postdoc Shiyou Zhu first searched for an IscB that would make good starting point. They tested nearly 400 different IscB enzymes that can be found in bacteria. Ten were capable of editing DNA in human cells.

Even the most active of those would need to be enhanced to make it a useful genome editing tool. The challenge would be increasing the enzyme’s activity, but only at the sequences specified by its RNA guide. If the enzyme became more active, but indiscriminately so, it would cut DNA in unintended places. “The key is to balance the improvement of both activity and specificity at the same time,” explains Zhu.

Zhu notes that bacterial IscBs are directed to their target sequences by relatively short RNA guides, which makes it difficult to restrict the enzyme’s activity to a specific part of the genome. If an IscB could be engineered to accommodate a longer guide, it would be less likely to act on sequences beyond its intended target.

To optimize IscB for human genome editing, the team leveraged information that graduate student Han Altae-Tran, who is now a postdoc at the University of Washington, had learned about the diversity of bacterial IscBs and how they evolved. For instance, the researchers noted that IscBs that worked in human cells included a segment they called REC, which was absent in other IscBs. They suspected the enzyme might need that segment to interact with the DNA in human cells. When they took a closer look at the region, structural modeling suggested that by slightly expanding part of the protein, REC might also enable IscBs to recognize longer RNA guides.

Based on these observations, the team experimented with swapping in parts of REC domains from different IscBs and Cas9s, evaluating how each change impacted the protein’s function. Guided by their understanding of how IscBs and Cas9s interact with both DNA and their RNA guides, the researchers made additional changes, aiming to optimize both efficiency and specificity.

In the end, they generated a protein they called NovaIscB, which was over 100 times more active in human cells than the IscB they had started with, and that had demonstrated good specificity for its targets.

Kannan and Zhu constructed and screened hundreds of new IscBs before arriving at NovaIscB — and every change they made to the original protein was strategic. Their efforts were guided by their team’s knowledge of IscBs’s natural evolution, as well as predictions of how each alteration would impact the protein’s structure, made using an artificial intelligence tool called AlphaFold2. Compared to traditional methods of introducing random changes into a protein and screening for their effects, this rational engineering approach greatly accelerated the team’s ability to identify a protein with the features they were looking for.

The team demonstrated that NovaIscB is a good scaffold for a variety of genome editing tools. “It biochemically functions very similarly to Cas9, and that makes it easy to port over tools that were already optimized with the Cas9 scaffold,” Kannan says. With different modifications, the researchers used NovaIscB to replace specific letters of the DNA code in human cells and to change the activity of targeted genes.

Importantly, the NovaIscB-based tools are compact enough to be easily packaged inside a single adeno-associated virus (AAV) — the vector most commonly used to safely deliver gene therapy to patients. Because they are bulkier, tools developed using Cas9 can require a more complicated delivery strategy.

Demonstrating NovaIscB’s potential for therapeutic use, Zhang’s team created a tool called OMEGAoff that adds chemical markers to DNA to dial down the activity of specific genes. They programmed OMEGAoff to repress a gene involved in cholesterol regulation, then used AAV to deliver the system to the livers of mice, leading to lasting reductions in cholesterol levels in the animals’ blood.

The team expects that NovaIscB can be used to target genome editing tools to most human genes, and look forward to seeing how other labs deploy the new technology. They also hope others will adopt their evolution-guided approach to rational protein engineering. “Nature has such diversity, and its systems have different advantages and disadvantages,” Zhu says. “By learning about that natural diversity, we can make the systems we are trying to engineer better and better.”

This study was funded, in part, by the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics at MIT, Broad Institute Programmable Therapeutics Gift Donors, Pershing Square Foundation, William Ackman, Neri Oxman, the Phillips family, and J. and P. Poitras.

Consuming a high-fat diet can lead to a variety of health problems — not only weight gain but also an increased risk of diabetes and other chronic diseases.

At the cellular level, hundreds of changes take place in response to a high-fat diet. MIT researchers have now mapped out some of those changes, with a focus on metabolic enzyme dysregulation that is associated with weight gain.

Their study, conducted in mice, revealed that hundreds of enzymes involved in sugar, lipid, and protein metabolism are affected by a high-fat diet, and that these disruptions lead to an increase in insulin resistance and an accumulation of damaging molecules called reactive oxygen species. These effects were more pronounced in males than females.

The researchers also showed that most of the damage could be reversed by giving the mice an antioxidant along with their high-fat diet.

“Under metabolic stress conditions, enzymes can be affected to produce a more harmful state than what was initially there,” says Tigist Tamir, a former MIT postdoc. “Then what we’ve shown with the antioxidant study is that you can bring them to a different state that is less dysfunctional.”

Tamir, who is now an assistant professor of biochemistry and biophysics at the University of North Carolina at Chapel Hill School of Medicine, is the lead author of the new study, which appears today in Molecular Cell. Forest White, the Ned C. and Janet C. Rice Professor of Biological Engineering and a member of the Koch Institute for Integrative Cancer Research at MIT, is the senior author of the paper.

Metabolic networks

In previous work, White’s lab has found that a high-fat diet stimulates cells to turn on many of the same signaling pathways that are linked to chronic stress. In the new study, the researchers wanted to explore the role of enzyme phosphorylation in those responses.

Phosphorylation, or the addition of a phosphate group, can turn enzyme activity on or off. This process, which is controlled by enzymes called kinases, gives cells a way to quickly respond to environmental conditions by fine-tuning the activity of existing enzymes within the cell.

Many enzymes involved in metabolism — the conversion of food into the building blocks of key molecules such as proteins, lipids, and nucleic acids — are known to undergo phosphorylation.

The researchers began by analyzing databases of human enzymes that can be phosphorylated, focusing on enzymes involved in metabolism. They found that many of the metabolic enzymes that undergo phosphorylation belong to a class called oxidoreductases, which transfer electrons from one molecule to another. Such enzymes are key to metabolic reactions such as glycolysis — the breakdown of glucose into a smaller molecule known as pyruvate.

Among the hundreds of enzymes the researchers identified are IDH1, which is involved in breaking down sugar to generate energy, and AKR1C1, which is required for metabolizing fatty acids. The researchers also found that many phosphorylated enzymes are important for the management of reactive oxygen species, which are necessary for many cell functions but can be harmful if too many of them accumulate in a cell.

Phosphorylation of these enzymes can lead them to become either more or less active, as they work together to respond to the intake of food. Most of the metabolic enzymes identified in this study are phosphorylated on sites found in regions of the enzyme that are important for binding to the molecules that they act upon or for forming dimers — pairs of proteins that join together to form a functional enzyme.

“Tigist’s work has really shown categorically the importance of phosphorylation in controlling the flux through metabolic networks. It’s fundamental knowledge that emerges from this systemic study that she’s done, and it’s something that is not classically captured in the biochemistry textbooks,” White says.

Out of balance

To explore these effects in an animal model, the researchers compared two groups of mice, one that received a high-fat diet and one that consumed a normal diet. They found that overall, phosphorylation of metabolic enzymes led to a dysfunctional state in which cells were in redox imbalance, meaning that their cells were producing more reactive oxygen species than they could neutralize. These mice also became overweight and developed insulin resistance.

“In the context of continued high fat diet, what we see is a gradual drift away from redox homeostasis towards a more disease-like setting,” White says.

These effects were much more pronounced in male mice than female mice. Female mice were better able to compensate for the high fat diet by activating pathways involved in processing fat and metabolizing it for other uses, the researchers found.

“One of the things we learned is that the overall systemic effect of these phosphorylation events led to, especially in males, an increased imbalance in redox homeostasis. They were expressing a lot more stress and a lot more of the metabolic dysfunction phenotype compared to females,” Tamir says.

The researchers also found that if they gave mice who were on a high-fat diet an antioxidant called BHA, many of these effects were reversed. These mice showed a significant decrease in weight gain and did not become prediabetic, unlike the other mice fed a high-fat diet.

It appears that the antioxidant treatment leads cells back into a more balanced state, with fewer reactive oxygen species, the researchers say. Additionally, metabolic enzymes showed a systemic rewiring and changed state of phosphorylation in those mice.

“They’re experiencing a lot of metabolic dysfunction, but if you co-administer something that counters that, then they have enough reserve to maintain some sort of normalcy,” Tamir says. “The study suggests that there is something biochemically happening in cells to bring them to a different state — not a normal state, just a different state in which now, at the tissue and organism levels, the mice are healthier.”

In her new lab at the University of North Carolina, Tamir now plans to further explore whether antioxidant treatment could be an effective way to prevent or treat obesity-associated metabolic dysfunction, and what the optimal timing of such a treatment would be.

The research was funded in part by the Burroughs Wellcome Fund, the National Cancer Institute, the National Institutes of Health, the Ludwig Center at MIT, and the MIT Center for Precision Cancer Medicine.

A $20 million gift from the Leinweber Foundation, in addition to a $5 million commitment from the MIT School of Science, will support theoretical physics research and education at MIT.

Leinweber Foundation gifts to five institutions, totaling $90 million, will establish the newly renamed MIT Center for Theoretical Physics – A Leinweber Institute within the Department of Physics, affiliated with the Laboratory for Nuclear Science at the School of Science, as well as Leinweber Institutes for Theoretical Physics at three other top research universities: the University of Michigan, the University of California at Berkeley, and the University of Chicago, as well as a Leinweber Forum for Theoretical and Quantum Physics at the Institute for Advanced Study.

“MIT has one of the strongest and broadest theory groups in the world,” says Professor Washington Taylor, the director of the newly funded center and a leading researcher in string theory and its connection to observable particle physics and cosmology.

“This landmark endowment from the Leinweber Foundation will enable us to support the best graduate students and postdoctoral researchers to develop their own independent research programs and to connect with other researchers in the Leinweber Institute network. By pledging to support this network and fundamental curiosity-driven science, Larry Leinweber and his family foundation have made a huge contribution to maintaining a thriving scientific enterprise in the United States in perpetuity.”

The Leinweber Foundation’s investment across five institutions — constituting the largest philanthropic commitment ever for theoretical physics research, according to the Science Philanthropy Alliance, a nonprofit organization that supports philanthropic support for science — will strengthen existing programs at each institution and foster collaboration across the universities. Recipient institutions will work both independently and collaboratively to explore foundational questions in theoretical physics. Each institute will continue to shape its own research focus and programs, while also committing to big-picture cross-institutional convenings around topics of shared interest. Moreover, each institute will have significantly more funding for graduate students and postdocs, including fellowship support for three to eight fully endowed Leinweber Physics Fellows at each institute.

“This gift is a commitment to America’s scientific future,” says Larry Leinweber, founder and president of the Leinweber Foundation. “Theoretical physics may seem abstract to many, but it is the tip of the spear for innovation. It fuels our understanding of how the world works and opens the door to new technologies that can shape society for generations. As someone who has had a lifelong fascination with theoretical physics, I hope this investment not only strengthens U.S. leadership in basic science, but also inspires curiosity, creativity, and groundbreaking discoveries for generations to come.”

The gift to MIT will create a postdoc program that, once fully funded, will initially provide support for up to six postdocs, with two selected per year for a three-year program. In addition, the gift will provide student financial support, including fellowship support, for up to six graduate students per year studying theoretical physics. The goal is to attract the top talent to the MIT Center for Theoretical Physics – A Leinweber Institute and support the ongoing research programs in a more robust way.

A portion of the funding will also provide support for visitors, seminars, and other scholarly activities of current postdocs, faculty, and students in theoretical physics, as well as helping with administrative support.

“Graduate students are the heart of our country’s scientific research programs. Support for their education to become the future leaders of the field is essential for the advancement of the discipline,” says Nergis Mavalvala, dean of the MIT School of Science and the Curtis (1963) and Kathleen Marble Professor of Astrophysics.

The Leinweber Foundation gift is the second significant gift for the center. “We are always grateful to Virgil Elings, whose generous gift helped make possible the space that houses the center,” says Deepto Chakrabarty, head of the Department of Physics. Elings PhD ’66, co-founder of Digital Instruments, which designed and sold scanning probe microscopes, made his gift more than 20 years ago to support a space for theoretical physicists to collaborate.

“Gifts like those from Larry Leinweber and Virgil Elings are critical, especially now in this time of uncertain funding from the federal government for support of fundamental scientific research carried out by our nation’s leading postdocs, research scientists, faculty and students,” adds Mavalvala.

Professor Tracy Slatyer, whose work is motivated by questions of fundamental particle physics — particularly the nature and interactions of dark matter — will be the subsequent director of the MIT Center for Theoretical Physics – A Leinweber Institute beginning this fall. Slatyer will join Mavalvala, Taylor, Chakrabarty, and the entirety of the theoretical physics community for a dedication ceremony planned for the near future.

The Leinweber Foundation was founded in 2015 by software entrepreneur Larry Leinweber, and has worked with the Science Philanthropy Alliance since 2021 to shape its philanthropic strategy. “It’s been a true pleasure to work with Larry and the Leinweber family over the past four years and to see their vision take shape,” says France Córdova, president of the Science Philanthropy Alliance. “Throughout his life, Larry has exemplified curiosity, intellectual openness, and a deep commitment to learning. This gift reflects those values, ensuring that generations of scientists will have the freedom to explore, to question, and to pursue ideas that could change how we understand the universe.”

Batteries are nearing their limits in terms of how much power they can store for a given weight. That’s a serious obstacle for energy innovation and the search for new ways to power airplanes, trains, and ships. Now, researchers at MIT and elsewhere have come up with a solution that could help electrify these transportation systems.

Instead of a battery, the new concept is a kind of fuel cell — which is similar to a battery but can be quickly refueled rather than recharged. In this case, the fuel is liquid sodium metal, an inexpensive and widely available commodity. The other side of the cell is just ordinary air, which serves as a source of oxygen atoms. In between, a layer of solid ceramic material serves as the electrolyte, allowing sodium ions to pass freely through, and a porous air-facing electrode helps the sodium to chemically react with oxygen and produce electricity.

In a series of experiments with a prototype device, the researchers demonstrated that this cell could carry more than three times as much energy per unit of weight as the lithium-ion batteries used in virtually all electric vehicles today. Their findings are being published today in the journal Joule, in a paper by MIT doctoral students Karen Sugano, Sunil Mair, and Saahir Ganti-Agrawal; professor of materials science and engineering Yet-Ming Chiang; and five others.

“We expect people to think that this is a totally crazy idea,” says Chiang, who is the Kyocera Professor of Ceramics. “If they didn’t, I’d be a bit disappointed because if people don’t think something is totally crazy at first, it probably isn’t going to be that revolutionary.”

And this technology does appear to have the potential to be quite revolutionary, he suggests. In particular, for aviation, where weight is especially crucial, such an improvement in energy density could be the breakthrough that finally makes electrically powered flight practical at significant scale.

“The threshold that you really need for realistic electric aviation is about 1,000 watt-hours per kilogram,” Chiang says. Today’s electric vehicle lithium-ion batteries top out at about 300 watt-hours per kilogram — nowhere near what’s needed. Even at 1,000 watt-hours per kilogram, he says, that wouldn’t be enough to enable transcontinental or trans-Atlantic flights.

That’s still beyond reach for any known battery chemistry, but Chiang says that getting to 1,000 watts per kilogram would be an enabling technology for regional electric aviation, which accounts for about 80 percent of domestic flights and 30 percent of the emissions from aviation.

The technology could be an enabler for other sectors as well, including marine and rail transportation. “They all require very high energy density, and they all require low cost,” he says. “And that’s what attracted us to sodium metal.”

A great deal of research has gone into developing lithium-air or sodium-air batteries over the last three decades, but it has been hard to make them fully rechargeable. “People have been aware of the energy density you could get with metal-air batteries for a very long time, and it’s been hugely attractive, but it’s just never been realized in practice,” Chiang says.

By using the same basic electrochemical concept, only making it a fuel cell instead of a battery, the researchers were able to get the advantages of the high energy density in a practical form. Unlike a battery, whose materials are assembled once and sealed in a container, with a fuel cell the energy-carrying materials go in and out.

The team produced two different versions of a lab-scale prototype of the system. In one, called an H cell, two vertical glass tubes are connected by a tube across the middle, which contains a solid ceramic electrolyte material and a porous air electrode. Liquid sodium metal fills the tube on one side, and air flows through the other, providing the oxygen for the electrochemical reaction at the center, which ends up gradually consuming the sodium fuel. The other prototype uses a horizontal design, with a tray of the electrolyte material holding the liquid sodium fuel. The porous air electrode, which facilitates the reaction, is affixed to the bottom of the tray. 

Tests using an air stream with a carefully controlled humidity level produced a level of more than 1,500 watt-hours per kilogram at the level of an individual “stack,” which would translate to over 1,000 watt-hours at the full system level, Chiang says.

The researchers envision that to use this system in an aircraft, fuel packs containing stacks of cells, like racks of food trays in a cafeteria, would be inserted into the fuel cells; the sodium metal inside these packs gets chemically transformed as it provides the power. A stream of its chemical byproduct is given off, and in the case of aircraft this would be emitted out the back, not unlike the exhaust from a jet engine.

But there’s a very big difference: There would be no carbon dioxide emissions. Instead the emissions, consisting of sodium oxide, would actually soak up carbon dioxide from the atmosphere. This compound would quickly combine with moisture in the air to make sodium hydroxide — a material commonly used as a drain cleaner — which readily combines with carbon dioxide to form a solid material, sodium carbonate, which in turn forms sodium bicarbonate, otherwise known as baking soda.

“There’s this natural cascade of reactions that happens when you start with sodium metal,” Chiang says. “It’s all spontaneous. We don’t have to do anything to make it happen, we just have to fly the airplane.”

As an added benefit, if the final product, the sodium bicarbonate, ends up in the ocean, it could help to de-acidify the water, countering another of the damaging effects of greenhouse gases.

Using sodium hydroxide to capture carbon dioxide has been proposed as a way of mitigating carbon emissions, but on its own, it’s not an economic solution because the compound is too expensive. “But here, it’s a byproduct,” Chiang explains, so it’s essentially free, producing environmental benefits at no cost.

Importantly, the new fuel cell is inherently safer than many other batteries, he says. Sodium metal is extremely reactive and must be well-protected. As with lithium batteries, sodium can spontaneously ignite if exposed to moisture. “Whenever you have a very high energy density battery, safety is always a concern, because if there’s a rupture of the membrane that separates the two reactants, you can have a runaway reaction,” Chiang says. But in this fuel cell, one side is just air, “which is dilute and limited. So you don’t have two concentrated reactants right next to each other. If you’re pushing for really, really high energy density, you’d rather have a fuel cell than a battery for safety reasons.”

While the device so far exists only as a small, single-cell prototype, Chiang says the system should be quite straightforward to scale up to practical sizes for commercialization. Members of the research team have already formed a company, Propel Aero, to develop the technology. The company is currently housed in MIT’s startup incubator, The Engine.

Producing enough sodium metal to enable widespread, full-scale global implementation of this technology should be practical, since the material has been produced at large scale before. When leaded gasoline was the norm, before it was phased out, sodium metal was used to make the tetraethyl lead used as an additive, and it was being produced in the U.S. at a capacity of 200,000 tons a year. “It reminds us that sodium metal was once produced at large scale and safely handled and distributed around the U.S.,” Chiang says.

What’s more, sodium primarily originates from sodium chloride, or salt, so it is abundant, widely distributed around the world, and easily extracted, unlike lithium and other materials used in today’s EV batteries.

The system they envisage would use a refillable cartridge, which would be filled with liquid sodium metal and sealed. When it’s depleted, it would be returned to a refilling station and loaded with fresh sodium. Sodium melts at 98 degrees Celsius, just below the boiling point of water, so it is easy to heat to the melting point to refuel the cartridges.

Initially, the plan is to produce a brick-sized fuel cell that can deliver about 1,000 watt-hours of energy, enough to power a large drone, in order to prove the concept in a practical form that could be used for agriculture, for example. The team hopes to have such a demonstration ready within the next year.

Sugano, who conducted much of the experimental work as part of her doctoral thesis and will now work at the startup, says that a key insight was the importance of moisture in the process. As she tested the device with pure oxygen, and then with air, she found that the amount of humidity in the air was crucial to making the electrochemical reaction efficient. The humid air resulted in the sodium producing its discharge products in liquid rather than solid form, making it much easier for these to be removed by the flow of air through the system. “The key was that we can form this liquid discharge product and remove it easily, as opposed to the solid discharge that would form in dry conditions,” she says.

Ganti-Agrawal notes that the team drew from a variety of different engineering subfields. For example, there has been much research on high-temperature sodium, but none with a system with controlled humidity. “We’re pulling from fuel cell research in terms of designing our electrode, we’re pulling from older high-temperature battery research as well as some nascent sodium-air battery research, and kind of mushing it together,” which led to the “the big bump in performance” the team has achieved, he says.

The research team also included Alden Friesen, an MIT summer intern who attends Desert Mountain High School in Scottsdale, Arizona; Kailash Raman and William Woodford of Form Energy in Somerville, Massachusetts; Shashank Sripad of And Battery Aero in California, and Venkatasubramanian Viswanathan of the University of Michigan. The work was supported by ARPA-E, Breakthrough Energy Ventures, and the National Science Foundation, and used facilities at MIT.nano.

The human brain contains about 86 billion neurons. These cells fire electrical signals that help the brain store memories and send information and commands throughout the brain and the nervous system.

The brain also contains billions of astrocytes — star-shaped cells with many long extensions that allow them to interact with millions of neurons. Although they have long been thought to be mainly supportive cells, recent studies have suggested that astrocytes may play a role in memory storage and other cognitive functions.

MIT researchers have now put forth a new hypothesis for how astrocytes might contribute to memory storage. The architecture suggested by their model would help to explain the brain’s massive storage capacity, which is much greater than would be expected using neurons alone.

“Originally, astrocytes were believed to just clean up around neurons, but there’s no particular reason that evolution did not realize that, because each astrocyte can contact hundreds of thousands of synapses, they could also be used for computation,” says Jean-Jacques Slotine, an MIT professor of mechanical engineering and of brain and cognitive sciences, and an author of the new study.

Dmitry Krotov, a research staff member at the MIT-IBM Watson AI Lab and IBM Research, is the senior author of the open-access paper, which appeared May 23 in the Proceedings of the National Academy of Sciences. Leo Kozachkov PhD ’22 is the paper’s lead author.

Memory capacity

Astrocytes have a variety of support functions in the brain: They clean up debris, provide nutrients to neurons, and help to ensure an adequate blood supply.

Astrocytes also send out many thin tentacles, known as processes, which can each wrap around a single synapse — the junctions where two neurons interact with each other — to create a tripartite (three-part) synapse.

Within the past couple of years, neuroscientists have shown that if the connections between astrocytes and neurons in the hippocampus are disrupted, memory storage and retrieval are impaired.

Unlike neurons, astrocytes can’t fire action potentials, the electrical impulses that carry information throughout the brain. However, they can use calcium signaling to communicate with other astrocytes. Over the past few decades, as the resolution of calcium imaging has improved, researchers have found that calcium signaling also allows astrocytes to coordinate their activity with neurons in the synapses that they associate with.

These studies suggest that astrocytes can detect neural activity, which leads them to alter their own calcium levels. Those changes may trigger astrocytes to release gliotransmitters — signaling molecules similar to neurotransmitters — into the synapse.

“There’s a closed circle between neuron signaling and astrocyte-to-neuron signaling,” Kozachkov says. “The thing that is unknown is precisely what kind of computations the astrocytes can do with the information that they’re sensing from neurons.”

The MIT team set out to model what those connections might be doing and how they might contribute to memory storage. Their model is based on Hopfield networks — a type of neural network that can store and recall patterns.

Hopfield networks, originally developed by John Hopfield and Shun-Ichi Amari in the 1970s and 1980s, are often used to model the brain, but it has been shown that these networks can’t store enough information to account for the vast memory capacity of the human brain. A newer, modified version of a Hopfield network, known as dense associative memory, can store much more information through a higher order of couplings between more than two neurons.

However, it is unclear how the brain could implement these many-neuron couplings at a hypothetical synapse, since conventional synapses only connect two neurons: a presynaptic cell and a postsynaptic cell. This is where astrocytes come into play.

“If you have a network of neurons, which couple in pairs, there’s only a very small amount of information that you can encode in those networks,” Krotov says. “In order to build dense associative memories, you need to couple more than two neurons. Because a single astrocyte can connect to many neurons, and many synapses, it is tempting to hypothesize that there might exist an information transfer between synapses mediated by this biological cell. That was the biggest inspiration for us to look into astrocytes and led us to start thinking about how to build dense associative memories in biology.”

The neuron-astrocyte associative memory model that the researchers developed in their new paper can store significantly more information than a traditional Hopfield network — more than enough to account for the brain’s memory capacity.

Intricate connections

The extensive biological connections between neurons and astrocytes offer support for the idea that this type of model might explain how the brain’s memory storage systems work, the researchers say. They hypothesize that within astrocytes, memories are encoded by gradual changes in the patterns of calcium flow. This information is conveyed to neurons by gliotransmitters released at synapses that astrocyte processes connect to.

“By careful coordination of these two things — the spatial temporal pattern of calcium in the cell and then the signaling back to the neurons — you can get exactly the dynamics you need for this massively increased memory capacity,” Kozachkov says.

One of the key features of the new model is that it treats astrocytes as collections of processes, rather than a single entity. Each of those processes can be considered one computational unit. Because of the high information storage capabilities of dense associative memories, the ratio of the amount of information stored to the number of computational units is very high and grows with the size of the network. This makes the system not only high capacity, but also energy efficient.

“By conceptualizing tripartite synaptic domains — where astrocytes interact dynamically with pre- and postsynaptic neurons — as the brain’s fundamental computational units, the authors argue that each unit can store as many memory patterns as there are neurons in the network. This leads to the striking implication that, in principle, a neuron-astrocyte network could store an arbitrarily large number of patterns, limited only by its size,” says Maurizio De Pitta, an assistant professor of physiology at the Krembil Research Institute at the University of Toronto, who was not involved in the study.

To test whether this model might accurately represent how the brain stores memory, researchers could try to develop ways to precisely manipulate the connections between astrocytes’ processes, then observe how those manipulations affect memory function.

“We hope that one of the consequences of this work could be that experimentalists would consider this idea seriously and perform some experiments testing this hypothesis,” Krotov says.

In addition to offering insight into how the brain may store memory, this model could also provide guidance for researchers working on artificial intelligence. By varying the connectivity of the process-to-process network, researchers could generate a huge range of models that could be explored for different purposes, for instance, creating a continuum between dense associative memories and attention mechanisms in large language models.

“While neuroscience initially inspired key ideas in AI, the last 50 years of neuroscience research have had little influence on the field, and many modern AI algorithms have drifted away from neural analogies,” Slotine says. “In this sense, this work may be one of the first contributions to AI informed by recent neuroscience research.” 

MIT today launched its Initiative for New Manufacturing (INM), an Institute-wide effort to reinfuse U.S. industrial production with leading-edge technologies, bolster crucial U.S. economic sectors, and ignite job creation.

The initiative will encompass advanced research, innovative education programs, and partnership with companies across many sectors, in a bid to help transform manufacturing and elevate its impact.

“We want to work with firms big and small, in cities, small towns and everywhere in between, to help them adopt new approaches for increased productivity,” MIT President Sally A. Kornbluth wrote in a letter to the Institute community this morning. “We want to deliberately design high-quality, human-centered manufacturing jobs that bring new life to communities across the country.”

Kornbluth added: “Helping America build a future of new manufacturing is a perfect job for MIT — and I’m convinced that there is no more important work we can do to meet the moment and serve the nation now.”

The Initiative for New Manufacturing also announced its first six founding industry consortium members: Amgen, Flex, GE Vernova, PTC, Sanofi, and Siemens. Participants in the INM Industry Consortium will support seed projects proposed by MIT researchers, initially in the area of artificial intelligence for manufacturing.

INM joins the ranks of MIT’s other presidential initiatives — including The Climate Project at MIT; MITHIC, which supports the human-centered disciplines; MIT HEALS, centered on the life sciences and health; and MGAIC, the MIT Generative AI Impact Consortium.

“There is tremendous opportunity to bring together a vibrant community working across every scale — from nanotechnology to large-scale manufacturing — and across a wide-range of applications including semiconductors, medical devices, automotive, energy systems, and biotechnology,” says Anantha Chandrakasan, MIT’s chief innovation and strategy officer and dean of engineering, who is part of the initiative’s leadership team. “MIT is uniquely positioned to harness the transformative power of digital tools and AI to shape future of manufacturing. I’m truly excited about what we can build together and the synergies this creates with other cross-cutting initiatives across the Institute.”

The initiative is just the latest MIT-centered effort in recent decades aiming to expand American manufacturing. A faculty research group wrote the 1989 bestseller “Made in America: Regaining the Productive Edge,” advocating for a renewal of manufacturing; another MIT project, called Production in the Innovation Economy, called for expanded manufacturing in the early 2010s. In 2016, MIT also founded The Engine, a venture fund investing in hardware-based “tough tech” start-ups including many with potential to became substantial manufacturing firms.

As developed, the MIT Initiative for New Manufacturing is based around four major themes:

• Reimagining manufacturing technologies and systems: realizing breakthrough technologies and system-level approaches to advance energy production, health care, computing, transportation, consumer products, and more;
• Elevating the productivity and experience of manufacturing: developing and deploying new digitally driven methods and tools to amplify productivity and improve the human experience of manufacturing;
• Scaling new manufacturing: accelerating the scaling of manufacturing companies and transforming supply chains to maximize efficiency and resilience, fostering product innovation and business growth; and
• Transforming the manufacturing base: driving the deployment of a sustainable global manufacturing ecosystem that provides compelling opportunities to workers, with major efforts focused on the U.S.

The initiative has mapped out many concrete activities and programs, which will include an Institute-wide research program on emerging technologies and other major topics; workforce and education programs; and industry engagement and participation. INM also aims to establish new labs for developing manufacturing tools and techniques; a “factory observatory” program which immerses students in manufacturing through visits to production sites; and key “pillars” focusing on areas from semiconductors and biomanufacturing to defense and aviation.

The workforce and education element of INM will include TechAMP, an MIT-created program that works with community colleges to bridge the gap between technicians and engineers; AI-driven teaching tools; professional education; and an effort to expand manufacturing education on campus in collaboration with MIT departments and degree programs.

INM’s leadership team has three faculty co-directors: John Hart, the Class of 1922 Professor and head of the Department of Mechanical Engineering; Suzanne Berger, Institute Professor at MIT and a political scientist who has conducted influential empirical studies of manufacturing; and Chris Love, the Raymond A. and Helen E. St. Laurent Professor of Chemical Engineering. The initiative’s executive director is Julie Diop.

The initiative is in the process of forming a faculty steering committee with representation from across the Institute, as well as an external advisory board. INM stems partly from the work of the Manufacturing@MIT working group, formed in 2022 to assess many of these issues.

The launch of the new initiative was previewed at a daylong MIT symposium on May 7, titled “A Vision for New Manufacturing.” The event, held before a capacity audience in MIT’s Wong Auditorium, featured over 30 speakers from a wide range of manufacturing sectors.

“The rationale for growing and transforming U.S. manufacturing has never been more urgent than it is today,” Berger said at the event. “What we are trying to build at MIT now is not just another research project. … Together, with people in this room and outside this room, we’re trying to change what’s happening in our country.”

“We need to think about the importance of manufacturing again, because it is what brings product ideas to people,” Love told MIT News. “For instance, in biotechnology, new life-saving medicines can’t reach patients without manufacturing. There is a real urgency about this issue for both economic prosperity and creating jobs. We have seen the impact for our country when we have lost our lead in manufacturing in some sectors. Biotechnology, where the U.S. has been the global leader for more than 40 years, offers the potential to promote new robust economies here, but we need to advance our capabilities in biomanufacturing to maintain our advantage in this area.”

Hart adds: “While manufacturing feels very timely today, it is of enduring importance. Manufactured products enable our daily lives and manufacturing is critical to advancing the frontiers of technology and society. Our efforts leading up to launch of the initiative revealed great excitement about manufacturing across MIT, especially from students. Working with industry — from small to large companies, and from young startups to industrial giants — will be instrumental to creating impact and realizing the vision for new manufacturing.”

In her letter to the MIT community today, Kornbluth stressed that the initiative’s goal is to drive transformation by making manufacturing more productive, resilient, and sustainable.

“We want to reimagine manufacturing technologies and systems to advance fields like energy production, health care, computing, transportation, consumer products, and more,” she wrote. “And we want to reach well beyond the shop floor to tackle challenges like how to make supply chains more resilient, and how to inform public policy to foster a broad, healthy manufacturing ecosystem that can drive decades of innovation and growth.”

Editor’s note: A seventh founding member, Autodesk, was announced on May 30.

Where did the moon’s magnetism go? Scientists have puzzled over this question for decades, ever since orbiting spacecraft picked up signs of a high magnetic field in lunar surface rocks. The moon itself has no inherent magnetism today. 

Now, MIT scientists may have solved the mystery. They propose that a combination of an ancient, weak magnetic field and a large, plasma-generating impact may have temporarily created a strong magnetic field, concentrated on the far side of the moon.

In a study appearing today in the journal Science Advances, the researchers show through detailed simulations that an impact, such as from a large asteroid, could have generated a cloud of ionized particles that briefly enveloped the moon. This plasma would have streamed around the moon and concentrated at the opposite location from the initial impact. There, the plasma would have interacted with and momentarily amplified the moon’s weak magnetic field. Any rocks in the region could have recorded signs of the heightened magnetism before the field quickly died away.

This combination of events could explain the presence of highly magnetic rocks detected in a region near the south pole, on the moon’s far side. As it happens, one of the largest impact basins — the Imbrium basin — is located in the exact opposite spot on the near side of the moon. The researchers suspect that whatever made that impact likely released the cloud of plasma that kicked off the scenario in their simulations.

“There are large parts of lunar magnetism that are still unexplained,” says lead author Isaac Narrett, a graduate student in the MIT Department of Earth, Atmospheric and Planetary Sciences (EAPS). “But the majority of the strong magnetic fields that are measured by orbiting spacecraft can be explained by this process — especially on the far side of the moon.”

Narrett’s co-authors include Rona Oran and Benjamin Weiss at MIT, along with Katarina Miljkovic at Curtin University, Yuxi Chen and Gábor Tóth at the University of Michigan at Ann Arbor, and Elias Mansbach PhD ’24 at Cambridge University. Nuno Loureiro, professor of nuclear science and engineering at MIT, also contributed insights and advice.

Beyond the sun

Scientists have known for decades that the moon holds remnants of a strong magnetic field. Samples from the surface of the moon, returned by astronauts on NASA’s Apollo missions of the 1960s and 70s, as well as global measurements of the moon taken remotely by orbiting spacecraft, show signs of remnant magnetism in surface rocks, especially on the far side of the moon.

The typical explanation for surface magnetism is a global magnetic field, generated by an internal “dynamo,” or a core of molten, churning material. The Earth today generates a magnetic field through a dynamo process, and it’s thought that the moon once may have done the same, though its much smaller core would have produced a much weaker magnetic field that may not explain the highly magnetized rocks observed, particularly on the moon’s far side.

An alternative hypothesis that scientists have tested from time to time involves a giant impact that generated plasma, which in turn amplified any weak magnetic field. In 2020, Oran and Weiss tested this hypothesis with simulations of a giant impact on the moon, in combination with the solar-generated magnetic field, which is weak as it stretches out to the Earth and moon.

In simulations, they tested whether an impact to the moon could amplify such a solar field, enough to explain the highly magnetic measurements of surface rocks. It turned out that it wasn’t, and their results seemed to rule out plasma-induced impacts as playing a role in the moon’s missing magnetism.

A spike and a jitter

But in their new study, the researchers took a different tack. Instead of accounting for the sun’s magnetic field, they assumed that the moon once hosted a dynamo that produced a magnetic field of its own, albeit a weak one. Given the size of its core, they estimated that such a field would have been about 1 microtesla, or 50 times weaker than the Earth’s field today.

From this starting point, the researchers simulated a large impact to the moon’s surface, similar to what would have created the Imbrium basin, on the moon’s near side. Using impact simulations from Katarina Miljkovic, the team then simulated the cloud of plasma that such an impact would have generated as the force of the impact vaporized the surface material. They adapted a second code, developed by collaborators at the University of Michigan, to simulate how the resulting plasma would flow and interact with the moon’s weak magnetic field.

These simulations showed that as a plasma cloud arose from the impact, some of it would have expanded into space, while the rest would stream around the moon and concentrate on the opposite side. There, the plasma would have compressed and briefly amplified the moon’s weak magnetic field. This entire process, from the moment the magnetic field was amplified to the time that it decays back to baseline, would have been incredibly fast — somewhere around 40 minutes, Narrett says.

Would this brief window have been enough for surrounding rocks to record the momentary magnetic spike? The researchers say, yes, with some help from another, impact-related effect.

They found that an Imbrium-scale impact would have sent a pressure wave through the moon, similar to a seismic shock. These waves would have converged to the other side, where the shock would have “jittered” the surrounding rocks, briefly unsettling the rocks’ electrons — the subatomic particles that naturally orient their spins to any external magnetic field. The researchers suspect the rocks were shocked just as the impact’s plasma amplified the moon’s magnetic field. As the rocks’ electrons settled back, they assumed a new orientation, in line with the momentary high magnetic field.

“It’s as if you throw a 52-card deck in the air, in a magnetic field, and each card has a compass needle,” Weiss says. “When the cards settle back to the ground, they do so in a new orientation. That’s essentially the magnetization process.”

The researchers say this combination of a dynamo plus a large impact, coupled with the impact’s shockwave, is enough to explain the moon’s highly magnetized surface rocks — particularly on the far side. One way to know for sure is to directly sample the rocks for signs of shock, and high magnetism. This could be a possibility, as the rocks lie on the far side, near the lunar south pole, where missions such as NASA’s Artemis program plan to explore.

“For several decades, there’s been sort of a conundrum over the moon’s magnetism — is it from impacts or is it from a dynamo?” Oran says. “And here we’re saying, it’s a little bit of both. And it’s a testable hypothesis, which is nice.”

The team’s simulations were carried out using the MIT SuperCloud. This research was supported, in part, by NASA. 

A team of international astronomers led by Richard Teague, the Kerr-McGee Career Development Professor in the Department of Earth, Atmospheric and Planetary Sciences (EAPS) has gathered the most sensitive and detailed observations of 15 protoplanetary disks to date, giving the astronomy community a new look at the mechanisms of early planetary formation.

“The new approaches we’ve developed to gather this data and images are like switching from reading glasses to high-powered binoculars — they reveal a whole new level of detail in these planet-forming systems,” says Teague.

Their open-access findings were published in a special collection of 17 papers in the Astrophysical Journal of Letters, with several more coming out this summer. The report sheds light on a breadth of questions, including ways to calculate the mass of a disk by measuring its gravitational influence and extracting rotational velocity profiles to a precision of meters per second.

Protoplanetary disks are a collection of dust and gas around young stars, from which planets form. Observing the dust in these disks is easier because it is brighter, but the information that can be gleaned from dust alone is only a snapshot of what is going on. Teague’s research focus has shifted attention to the gas in these systems, as they can tell us more about the dynamics in a disk, including properties such as gravity, velocity, and mass.

To achieve the resolution necessary to study gas, the exoALMA program spent five years coordinating longer observation windows on the Atacama Large Millimeter/submillimeter Array (ALMA) in Chile. As a result, the international team of astronomers, many of whom are early-career scientists, were able to collect some of the most detailed images ever taken of protoplanetary disks.

“The impressive thing about the data is that it’s so good, the community is developing new tools to extract signatures from planets,” says Marcelo Barraza-Alfaro, a postdoc in the Planet Formation Lab and a member of the exoALMA project. Several new techniques to improve and calibrate the images taken were developed to maximize the higher resolution and sensitivity that was used.

As a result, “we are seeing new things that require us to modify our understanding of what’s going on in protoplanetary disks,” he says.

One of the papers with the largest EAPS influence explores planetary formation through vortices. It has been known for some time that the simple model of formation often proposed, where dust grains clump together and “snowball” into a planetary core, is not enough. One possible way to help is through vortices, or localized perturbations in the gas that pull dust into the center. Here, they are more likely to clump, the way soap bubbles collect in a draining tub.

“We can see the concentration of dust in different regions, but we cannot see how it is moving,” says Lisa Wölfer, another postdoc in the Planet Formation Lab at MIT and first author on the paper. While astronomers can see that the dust has gathered, there isn’t enough information to rule out how it got to that point.

“Only through the dynamics in the gas can we actually confirm that it’s a vortex, and not something else, creating the structure,” she says.

During the data collection period, Teague, Wölfer, and Barraza-Alfaro developed simple models of protoplanetary disks to compare to their observations. When they got the data back, however, the models couldn’t explain what they were seeing.

“We saw the data and nothing worked anymore. It was way too complicated,” says Teague. “Before, everyone thought they were not dynamic. That’s completely not the case.”

The team was forced to reevaluate their models and work with more complex ones incorporating more motion in the gas, which take more time and resources to run. But early results look promising.

“We see that the patterns look very similar; we think this is the best test case to study further with more observations,” says Wölfer.

The new data, which have been made public, come at a fortuitous time: ALMA will be going dark for a period in the next few years while it undergoes upgrades. During this time, astronomers can continue the monumental process of sifting through all the data.

“It’s going to just keep on producing results for years and years to come,” says Teague.

Separating crude oil into products such as gasoline, diesel, and heating oil is an energy-intensive process that accounts for about 6 percent of the world’s CO₂ emissions. Most of that energy goes into the heat needed to separate the components by their boiling point.

In an advance that could dramatically reduce the amount of energy needed for crude oil fractionation, MIT engineers have developed a membrane that filters the components of crude oil by their molecular size.

“This is a whole new way of envisioning a separation process. Instead of boiling mixtures to purify them, why not separate components based on shape and size? The key innovation is that the filters we developed can separate very small molecules at an atomistic length scale,” says Zachary P. Smith, an associate professor of chemical engineering at MIT and the senior author of the new study.

The new filtration membrane can efficiently separate heavy and light components from oil, and it is resistant to the swelling that tends to occur with other types of oil separation membranes. The membrane is a thin film that can be manufactured using a technique that is already widely used in industrial processes, potentially allowing it to be scaled up for widespread use.

Taehoon Lee, a former MIT postdoc who is now an assistant professor at Sungkyunkwan University in South Korea, is the lead author of the paper, which appears today in Science.

Oil fractionation

Conventional heat-driven processes for fractionating crude oil make up about 1 percent of global energy use, and it has been estimated that using membranes for crude oil separation could reduce the amount of energy needed by about 90 percent. For this to succeed, a separation membrane needs to allow hydrocarbons to pass through quickly, and to selectively filter compounds of different sizes.

Until now, most efforts to develop a filtration membrane for hydrocarbons have focused on polymers of intrinsic microporosity (PIMs), including one known as PIM-1. Although this porous material allows the fast transport of hydrocarbons, it tends to excessively absorb some of the organic compounds as they pass through the membrane, leading the film to swell, which impairs its size-sieving ability.

To come up with a better alternative, the MIT team decided to try modifying polymers that are used for reverse osmosis water desalination. Since their adoption in the 1970s, reverse osmosis membranes have reduced the energy consumption of desalination by about 90 percent — a remarkable industrial success story.

The most commonly used membrane for water desalination is a polyamide that is manufactured using a method known as interfacial polymerization. During this process, a thin polymer film forms at the interface between water and an organic solvent such as hexane. Water and hexane do not normally mix, but at the interface between them, a small amount of the compounds dissolved in them can react with each other.

In this case, a hydrophilic monomer called MPD, which is dissolved in water, reacts with a hydrophobic monomer called TMC, which is dissolved in hexane. The two monomers are joined together by a connection known as an amide bond, forming a polyamide thin film (named MPD-TMC) at the water-hexane interface.

While highly effective for water desalination, MPD-TMC doesn’t have the right pore sizes and swelling resistance that would allow it to separate hydrocarbons.

To adapt the material to separate the hydrocarbons found in crude oil, the researchers first modified the film by changing the bond that connects the monomers from an amide bond to an imine bond. This bond is more rigid and hydrophobic, which allows hydrocarbons to quickly move through the membrane without causing noticeable swelling of the film compared to the polyamide counterpart.

“The polyimine material has porosity that forms at the interface, and because of the cross-linking chemistry that we have added in, you now have something that doesn’t swell,” Smith says. “You make it in the oil phase, react it at the water interface, and with the crosslinks, it’s now immobilized. And so those pores, even when they’re exposed to hydrocarbons, no longer swell like other materials.”

The researchers also introduced a monomer called triptycene. This shape-persistent, molecularly selective molecule further helps the resultant polyimines to form pores that are the right size for hydrocarbons to fit through.

This approach represents “an important step toward reducing industrial energy consumption,” says Andrew Livingston, a professor of chemical engineering at Queen Mary University of London, who was not involved in the study.

“This work takes the workhorse technology of the membrane desalination industry, interfacial polymerization, and creates a new way to apply it to organic systems such as hydrocarbon feedstocks, which currently consume large chunks of global energy,” Livingston says. “The imaginative approach using an interfacial catalyst coupled to hydrophobic monomers leads to membranes with high permeance and excellent selectivity, and the work shows how these can be used in relevant separations.”

Efficient separation

When the researchers used the new membrane to filter a mixture of toluene and triisopropylbenzene (TIPB) as a benchmark for evaluating separation performance, it was able to achieve a concentration of toluene 20 times greater than its concentration in the original mixture. They also tested the membrane with an industrially relevant mixture consisting of naphtha, kerosene, and diesel, and found that it could efficiently separate the heavier and lighter compounds by their molecular size.

If adapted for industrial use, a series of these filters could be used to generate a higher concentration of the desired products at each step, the researchers say.

“You can imagine that with a membrane like this, you could have an initial stage that replaces a crude oil fractionation column. You could partition heavy and light molecules and then you could use different membranes in a cascade to purify complex mixtures to isolate the chemicals that you need,” Smith says.

Interfacial polymerization is already widely used to create membranes for water desalination, and the researchers believe it should be possible to adapt those processes to mass produce the films they designed in this study.

“The main advantage of interfacial polymerization is it’s already a well-established method to prepare membranes for water purification, so you can imagine just adopting these chemistries into existing scale of manufacturing lines,” Lee says.

The research was funded, in part, by ExxonMobil through the MIT Energy Initiative. 

Magnets and superconductors go together like oil and water — or so scientists have thought. But a new finding by MIT physicists is challenging this century-old assumption.

In a paper appearing today in the journal Nature, the physicists report that they have discovered a “chiral superconductor” — a material that conducts electricity without resistance, and also, paradoxically, is intrinsically magnetic. What’s more, they observed this exotic superconductivity in a surprisingly ordinary material: graphite, the primary material in pencil lead.

Graphite is made from many layers of graphene — atomically thin, lattice-like sheets of carbon atoms — that are stacked together and can easily flake off when pressure is applied, as when pressing down to write on a piece of paper. A single flake of graphite can contain several million sheets of graphene, which are normally stacked such that every other layer aligns. But every so often, graphite contains tiny pockets where graphene is stacked in a different pattern, resembling a staircase of offset layers.

The MIT team has found that when four or five sheets of graphene are stacked in this “rhombohedral” configuration, the resulting structure can exhibit exceptional electronic properties that are not seen in graphite as a whole.

In their new study, the physicists isolated microscopic flakes of rhombohedral graphene from graphite, and subjected the flakes to a battery of electrical tests. They found that when the flakes are cooled to 300 millikelvins (about -273 degrees Celsius), the material turns into a superconductor, meaning that any electrical current passing through the material can flow through without resistance.

They also found that when they swept an external magnetic field up and down, the flakes could be switched between two different superconducting states, just like a magnet. This suggests that the superconductor has some internal, intrinsic magnetism. Such switching behavior is absent in other superconductors.

“The general lore is that superconductors do not like magnetic fields,” says Long Ju, assistant professor of physics at MIT. “But we believe this is the first observation of a superconductor that behaves as a magnet with such direct and simple evidence. And that’s quite a bizarre thing because it is against people’s general impression on superconductivity and magnetism.”

Ju is senior author of the study, which includes MIT co-authors Tonghang Han, Zhengguang Lu, Zach Hadjri, Lihan Shi, Zhenghan Wu, Wei Xu, Yuxuan Yao, Jixiang Yang, Junseok Seo, Shenyong Ye, Muyang Zhou, and Liang Fu, along with collaborators from Florida State University, the University of Basel in Switzerland, and the National Institute for Materials Science in Japan.

Graphene twist

In everyday conductive materials, electrons flow through in a chaotic scramble, whizzing by each other, and pinging off the material’s atomic latticework. Each time an electron scatters off an atom, it has, in essence, met some resistance, and loses some energy as a result, normally in the form of heat. In contrast, when certain materials are cooled to ultracold temperatures, they can become superconducting, meaning that the material can allow electrons to pair up, in what physicists term “Cooper pairs.” Rather than scattering away, these electron pairs glide through a material without resistance. With a superconductor, then, no energy is lost in translation.

Since superconductivity was first observed in 1911, physicists have shown many times over that zero electrical resistance is a hallmark of a superconductor. Another defining property was first observed in 1933, when the physicist Walther Meissner discovered that a superconductor will expel an external magnetic field. This “Meissner effect” is due in part to a superconductor’s electron pairs, which collectively act to push away any magnetic field.

Physicists have assumed that all superconducting materials should exhibit both zero electrical resistance, and a natural magnetic repulsion. Indeed, these two properties are what could enable Maglev, or “magnetic levitation” trains, whereby a superconducting rail repels and therefore levitates a magnetized car.

Ju and his colleagues had no reason to question this assumption as they carried out their experiments at MIT. In the last few years, the team has been exploring the electrical properties of pentalayer rhombohedral graphene. The researchers have observed surprising properties in the five-layer, staircase-like graphene structure, most recently that it enables electrons to split into fractions of themselves. This phenomenon occurs when the pentalayer structure is placed atop a sheet of hexagonal boron nitride (a material similar to graphene), and slightly offset by a specific angle, or twist. 

Curious as to how electron fractions might change with changing conditions, the researchers followed up their initial discovery with similar tests, this time by misaligning the graphene and hexagonal boron nitride structures. To their surprise, they found that when they misaligned the two materials and sent an electrical current through, at temperatures less than 300 millikelvins, they measured zero resistance. It seemed that the phenomenon of electron fractions disappeared, and what emerged instead was superconductivity.

The researchers went a step further to see how this new superconducting state would respond to an external magnetic field. They applied a magnet to the material, along with a voltage, and measured the electrical current coming out of the material. As they dialed the magnetic field from negative to positive (similar to a north and south polarity) and back again, they observed that the material maintained its superconducting, zero-resistance state, except in two instances, once at either magnetic polarity. In these instances, the resistance briefly spiked, before switching back to zero, and returning to a superconducting state.

“If this were a conventional superconductor, it would just remain at zero resistance, until the magnetic field reaches a critical point, where superconductivity would be killed,” Zach Hadjri, a first-year student in the group, says. “Instead, this material seems to switch between two superconducting states, like a magnet that starts out pointing upward, and can flip downwards when you apply a magnetic field. So it looks like this is a superconductor that also acts like a magnet. Which doesn’t make any sense!”

“One of a kind”

As counterintuitive as the discovery may seem, the team observed the same phenomenon in six similar samples. They suspect that the unique configuration of rhombohedral graphene is the key. The material has a very simple arrangement of carbon atoms. When cooled to ultracold temperatures, the thermal fluctuation is minimized, allowing any electrons flowing through the material to slow down, sense each other, and interact.

Such quantum interactions can lead electrons to pair up and superconduct. These interactions can also encourage electrons to coordinate. Namely, electrons can collectively occupy one of two opposite momentum states, or “valleys.” When all electrons are in one valley, they effectively spin in one direction, versus the opposite direction. In conventional superconductors, electrons can occupy either valley, and any pair of electrons is typically made from electrons of opposite valleys that cancel each other out. The pair overall then, has zero momentum, and does not spin.

In the team’s material structure, however, they suspect that all electrons interact such that they share the same valley, or momentum state. When electrons then pair up, the superconducting pair overall has a “non-zero” momentum, and spinning, that, along with many other pairs, can amount to an internal, superconducting magnetism.

“You can think of the two electrons in a pair spinning clockwise, or counterclockwise, which corresponds to a magnet pointing up, or down,” Tonghang Han, a fifth-year student in the group, explains. “So we think this is the first observation of a superconductor that behaves as a magnet due to the electrons’ orbital motion, which is known as a chiral superconductor. It’s one of a kind. It is also a candidate for a topological superconductor which could enable robust quantum computation.”

“Everything we’ve discovered in this material has been completely out of the blue,” says Zhengguang Lu, a former postdoc in the group and now an assistant professor at Florida State University. “But because this is a simple system, we think we have a good chance of understanding what is going on, and could demonstrate some very profound and deep physics principles.”

“It is truly remarkable that such an exotic chiral superconductor emerges from such simple ingredients,” adds Liang Fu, professor of physics at MIT. “Superconductivity in rhombodedral graphene will surely have a lot to offer.”     

The part of the research carried out at MIT was supported by the U.S. Department of Energy and a MathWorks Fellowship. This research was carried out, in part, using facilities at MIT.nano.

On Dec. 21, 2022, just as peak holiday season travel was getting underway, Southwest Airlines went through a cascading series of failures in their scheduling, initially triggered by severe winter weather in the Denver area. But the problems spread through their network, and over the course of the next 10 days the crisis ended up stranding over 2 million passengers and causing losses of $750 million for the airline.

How did a localized weather system end up triggering such a widespread failure? Researchers at MIT have examined this widely reported failure as an example of cases where systems that work smoothly most of the time suddenly break down and cause a domino effect of failures. They have now developed a computational system for using the combination of sparse data about a rare failure event, in combination with much more extensive data on normal operations, to work backwards and try to pinpoint the root causes of the failure, and hopefully be able to find ways to adjust the systems to prevent such failures in the future.

The findings were presented at the International Conference on Learning Representations (ICLR), which was held in Singapore from April 24-28 by MIT doctoral student Charles Dawson, professor of aeronautics and astronautics Chuchu Fan, and colleagues from Harvard University and the University of Michigan.

“The motivation behind this work is that it’s really frustrating when we have to interact with these complicated systems, where it’s really hard to understand what’s going on behind the scenes that’s creating these issues or failures that we’re observing,” says Dawson.

The new work builds on previous research from Fan’s lab, where they looked at problems involving hypothetical failure prediction problems, she says, such as with groups of robots working together on a task, or complex systems such as the power grid, looking for ways to predict how such systems may fail. “The goal of this project,” Fan says, “was really to turn that into a diagnostic tool that we could use on real-world systems.”

The idea was to provide a way that someone could “give us data from a time when this real-world system had an issue or a failure,” Dawson says, “and we can try to diagnose the root causes, and provide a little bit of a look behind the curtain at this complexity.”

The intent is for the methods they developed “to work for a pretty general class of cyber-physical problems,” he says. These are problems in which “you have an automated decision-making component interacting with the messiness of the real world,” he explains. There are available tools for testing software systems that operate on their own, but the complexity arises when that software has to interact with physical entities going about their activities in a real physical setting, whether it be the scheduling of aircraft, the movements of autonomous vehicles, the interactions of a team of robots, or the control of the inputs and outputs on an electric grid. In such systems, what often happens, he says, is that “the software might make a decision that looks OK at first, but then it has all these domino, knock-on effects that make things messier and much more uncertain.”

One key difference, though, is that in systems like teams of robots, unlike the scheduling of airplanes, “we have access to a model in the robotics world,” says Fan, who is a principal investigator in MIT’s Laboratory for Information and Decision Systems (LIDS). “We do have some good understanding of the physics behind the robotics, and we do have ways of creating a model” that represents their activities with reasonable accuracy. But airline scheduling involves processes and systems that are proprietary business information, and so the researchers had to find ways to infer what was behind the decisions, using only the relatively sparse publicly available information, which essentially consisted of just the actual arrival and departure times of each plane.

“We have grabbed all this flight data, but there is this entire system of the scheduling system behind it, and we don’t know how the system is working,” Fan says. And the amount of data relating to the actual failure is just several day’s worth, compared to years of data on normal flight operations.

The impact of the weather events in Denver during the week of Southwest’s scheduling crisis clearly showed up in the flight data, just from the longer-than-normal turnaround times between landing and takeoff at the Denver airport. But the way that impact cascaded though the system was less obvious, and required more analysis. The key turned out to have to do with the concept of reserve aircraft.

Airlines typically keep some planes in reserve at various airports, so that if problems are found with one plane that is scheduled for a flight, another plane can be quickly substituted. Southwest uses only a single type of plane, so they are all interchangeable, making such substitutions easier. But most airlines operate on a hub-and-spoke system, with a few designated hub airports where most of those reserve aircraft may be kept, whereas Southwest does not use hubs, so their reserve planes are more scattered throughout their network. And the way those planes were deployed turned out to play a major role in the unfolding crisis.

“The challenge is that there’s no public data available in terms of where the aircraft are stationed throughout the Southwest network,” Dawson says. “What we’re able to find using our method is, by looking at the public data on arrivals, departures, and delays, we can use our method to back out what the hidden parameters of those aircraft reserves could have been, to explain the observations that we were seeing.”

What they found was that the way the reserves were deployed was a “leading indicator” of the problems that cascaded in a nationwide crisis. Some parts of the network that were affected directly by the weather were able to recover quickly and get back on schedule. “But when we looked at other areas in the network, we saw that these reserves were just not available, and things just kept getting worse.”

For example, the data showed that Denver’s reserves were rapidly dwindling because of the weather delays, but then “it also allowed us to trace this failure from Denver to Las Vegas,” he says. While there was no severe weather there, “our method was still showing us a steady decline in the number of aircraft that were able to serve flights out of Las Vegas.”

He says that “what we found was that there were these circulations of aircraft within the Southwest network, where an aircraft might start the day in California and then fly to Denver, and then end the day in Las Vegas.” What happened in the case of this storm was that the cycle got interrupted. As a result, “this one storm in Denver breaks the cycle, and suddenly the reserves in Las Vegas, which is not affected by the weather, start to deteriorate.”

In the end, Southwest was forced to take a drastic measure to resolve the problem: They had to do a “hard reset” of their entire system, canceling all flights and flying empty aircraft around the country to rebalance their reserves.

Working with experts in air transportation systems, the researchers developed a model of how the scheduling system is supposed to work. Then, “what our method does is, we’re essentially trying to run the model backwards.” Looking at the observed outcomes, the model allows them to work back to see what kinds of initial conditions could have produced those outcomes.

While the data on the actual failures were sparse, the extensive data on typical operations helped in teaching the computational model “what is feasible, what is possible, what’s the realm of physical possibility here,” Dawson says. “That gives us the domain knowledge to then say, in this extreme event, given the space of what’s possible, what’s the most likely explanation” for the failure.

This could lead to a real-time monitoring system, he says, where data on normal operations are constantly compared to the current data, and determining what the trend looks like. “Are we trending toward normal, or are we trending toward extreme events?” Seeing signs of impending issues could allow for preemptive measures, such as redeploying reserve aircraft in advance to areas of anticipated problems.

Work on developing such systems is ongoing in her lab, Fan says. In the meantime, they have produced an open-source tool for analyzing failure systems, called CalNF, which is available for anyone to use. Meanwhile Dawson, who earned his doctorate last year, is working as a postdoc to apply the methods developed in this work to understanding failures in power networks.

The research team also included Max Li from the University of Michigan and Van Tran from Harvard University. The work was supported by NASA, the Air Force Office of Scientific Research, and the MIT-DSTA program.

Global warming will likely hinder our future ability to control ground-level ozone, a harmful air pollutant that is a primary component of smog, according to a new MIT study.

The results could help scientists and policymakers develop more effective strategies for improving both air quality and human health. Ground-level ozone causes a host of detrimental health impacts, from asthma to heart disease, and contributes to thousands of premature deaths each year.

The researchers’ modeling approach reveals that, as the Earth warms due to climate change, ground-level ozone will become less sensitive to reductions in nitrogen oxide emissions in eastern North America and Western Europe. In other words, it will take greater nitrogen oxide emission reductions to get the same air quality benefits.

However, the study also shows that the opposite would be true in northeast Asia, where cutting emissions would have a greater impact on reducing ground-level ozone in the future. 

The researchers combined a climate model that simulates meteorological factors, such as temperature and wind speeds, with a chemical transport model that estimates the movement and composition of chemicals in the atmosphere.

By generating a range of possible future outcomes, the researchers’ ensemble approach better captures inherent climate variability, allowing them to paint a fuller picture than many previous studies.

“Future air quality planning should consider how climate change affects the chemistry of air pollution. We may need steeper cuts in nitrogen oxide emissions to achieve the same air quality goals,” says Emmie Le Roy, a graduate student in the MIT Department of Earth, Atmospheric and Planetary Sciences (EAPS) and lead author of a paper on this study.

Her co-authors include Anthony Y.H. Wong, a postdoc in the MIT Center for Sustainability Science and Strategy; Sebastian D. Eastham, principal research scientist in the MIT Center for Sustainability Science and Strategy; Arlene Fiore, the Peter H. Stone and Paola Malanotte Stone Professor of EAPS; and senior author Noelle Selin, a professor in the Institute for Data, Systems, and Society (IDSS) and EAPS. The research appears today in Environmental Science and Technology.

Controlling ozone

Ground-level ozone differs from the stratospheric ozone layer that protects the Earth from harmful UV radiation. It is a respiratory irritant that is harmful to the health of humans, animals, and plants.

Controlling ground-level ozone is particularly challenging because it is a secondary pollutant, formed in the atmosphere by complex reactions involving nitrogen oxides and volatile organic compounds in the presence of sunlight.

“That is why you tend to have higher ozone days when it is warm and sunny,” Le Roy explains.

Regulators typically try to reduce ground-level ozone by cutting nitrogen oxide emissions from industrial processes. But it is difficult to predict the effects of those policies because ground-level ozone interacts with nitrogen oxide and volatile organic compounds in nonlinear ways.

Depending on the chemical environment, reducing nitrogen oxide emissions could cause ground-level ozone to increase instead.

“Past research has focused on the role of emissions in forming ozone, but the influence of meteorology is a really important part of Emmie’s work,” Selin says.

To conduct their study, the researchers combined a global atmospheric chemistry model with a climate model that simulate future meteorology.

They used the climate model to generate meteorological inputs for each future year in their study, simulating factors such as likely temperature and wind speeds, in a way that captures the inherent variability of a region’s climate.

Then they fed those inputs to the atmospheric chemistry model, which calculates how the chemical composition of the atmosphere would change because of meteorology and emissions.

The researchers focused on Eastern North America, Western Europe, and Northeast China, since those regions have historically high levels of the precursor chemicals that form ozone and well-established monitoring networks to provide data.

They chose to model two future scenarios, one with high warming and one with low warming, over a 16-year period between 2080 and 2095. They compared them to a historical scenario capturing 2000 to 2015 to see the effects of a 10 percent reduction in nitrogen oxide emissions.

Capturing climate variability

“The biggest challenge is that the climate naturally varies from year to year. So, if you want to isolate the effects of climate change, you need to simulate enough years to see past that natural variability,” Le Roy says.

They could overcome that challenge due to recent advances in atmospheric chemistry modeling and by taking advantage of parallel computing to simulate multiple years at the same time. They simulated five 16-year realizations, resulting in 80 model years for each scenario.

The researchers found that eastern North America and Western Europe are especially sensitive to increases in nitrogen oxide emissions from the soil, which are natural emissions driven by increases in temperature.

Due to that sensitivity, as the Earth warms and more nitrogen oxide from soil enters the atmosphere, reducing nitrogen oxide emissions from human activities will have less of an impact on ground-level ozone.

“This shows how important it is to improve our representation of the biosphere in these models to better understand how climate change may impact air quality,” Le Roy says.

On the other hand, since industrial processes in northeast Asia cause more ozone per unit of nitrogen oxide emitted, cutting emissions there would cause greater reductions in ground-level ozone in future warming scenarios.

“But I wouldn’t say that is a good thing because it means that, overall, there are higher levels of ozone,” Le Roy adds.

Running detailed meteorology simulations, rather than relying on annual average weather data, gave the researchers a more complete picture of the potential effects on human health.

“Average climate isn’t the only thing that matters. One high ozone day, which might be a statistical anomaly, could mean we don’t meet our air quality target and have negative human health impacts that we should care about,” Le Roy says.

In the future, the researchers want to continue exploring the intersection of meteorology and air quality. They also want to expand their modeling approach to consider other climate change factors with high variability, like wildfires or biomass burning.

“We’ve shown that it is important for air quality scientists to consider the full range of climate variability, even if it is hard to do in your models, because it really does affect the answer that you get,” says Selin.

This work is funded, in part, by the MIT Praecis Presidential Fellowship, the J.H. and E.V. Wade Fellowship, and the MIT Martin Family Society of Fellows for Sustainability.

Humans naturally learn by making connections between sight and sound. For instance, we can watch someone playing the cello and recognize that the cellist’s movements are generating the music we hear.

A new approach developed by researchers from MIT and elsewhere improves an AI model’s ability to learn in this same fashion. This could be useful in applications such as journalism and film production, where the model could help with curating multimodal content through automatic video and audio retrieval.

In the longer term, this work could be used to improve a robot’s ability to understand real-world environments, where auditory and visual information are often closely connected.

Improving upon prior work from their group, the researchers created a method that helps machine-learning models align corresponding audio and visual data from video clips without the need for human labels.

They adjusted how their original model is trained so it learns a finer-grained correspondence between a particular video frame and the audio that occurs in that moment. The researchers also made some architectural tweaks that help the system balance two distinct learning objectives, which improves performance.

Taken together, these relatively simple improvements boost the accuracy of their approach in video retrieval tasks and in classifying the action in audiovisual scenes. For instance, the new method could automatically and precisely match the sound of a door slamming with the visual of it closing in a video clip.

“We are building AI systems that can process the world like humans do, in terms of having both audio and visual information coming in at once and being able to seamlessly process both modalities. Looking forward, if we can integrate this audio-visual technology into some of the tools we use on a daily basis, like large language models, it could open up a lot of new applications,” says Andrew Rouditchenko, an MIT graduate student and co-author of a paper on this research.

He is joined on the paper by lead author Edson Araujo, a graduate student at Goethe University in Germany; Yuan Gong, a former MIT postdoc; Saurabhchand Bhati, a current MIT postdoc; Samuel Thomas, Brian Kingsbury, and Leonid Karlinsky of IBM Research; Rogerio Feris, principal scientist and manager at the MIT-IBM Watson AI Lab; James Glass, senior research scientist and head of the Spoken Language Systems Group in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL); and senior author Hilde Kuehne, professor of computer science at Goethe University and an affiliated professor at the MIT-IBM Watson AI Lab. The work will be presented at the Conference on Computer Vision and Pattern Recognition.

Syncing up

This work builds upon a machine-learning method the researchers developed a few years ago, which provided an efficient way to train a multimodal model to simultaneously process audio and visual data without the need for human labels.

The researchers feed this model, called CAV-MAE, unlabeled video clips and it encodes the visual and audio data separately into representations called tokens. Using the natural audio from the recording, the model automatically learns to map corresponding pairs of audio and visual tokens close together within its internal representation space.

They found that using two learning objectives balances the model’s learning process, which enables CAV-MAE to understand the corresponding audio and visual data while improving its ability to recover video clips that match user queries.

But CAV-MAE treats audio and visual samples as one unit, so a 10-second video clip and the sound of a door slamming are mapped together, even if that audio event happens in just one second of the video.

In their improved model, called CAV-MAE Sync, the researchers split the audio into smaller windows before the model computes its representations of the data, so it generates separate representations that correspond to each smaller window of audio.

During training, the model learns to associate one video frame with the audio that occurs during just that frame.

“By doing that, the model learns a finer-grained correspondence, which helps with performance later when we aggregate this information,” Araujo says.

They also incorporated architectural improvements that help the model balance its two learning objectives.

Adding “wiggle room”

The model incorporates a contrastive objective, where it learns to associate similar audio and visual data, and a reconstruction objective which aims to recover specific audio and visual data based on user queries.

In CAV-MAE Sync, the researchers introduced two new types of data representations, or tokens, to improve the model’s learning ability.

They include dedicated “global tokens” that help with the contrastive learning objective and dedicated “register tokens” that help the model focus on important details for the reconstruction objective.

“Essentially, we add a bit more wiggle room to the model so it can perform each of these two tasks, contrastive and reconstructive, a bit more independently. That benefitted overall performance,” Araujo adds.

While the researchers had some intuition these enhancements would improve the performance of CAV-MAE Sync, it took a careful combination of strategies to shift the model in the direction they wanted it to go.

“Because we have multiple modalities, we need a good model for both modalities by themselves, but we also need to get them to fuse together and collaborate,” Rouditchenko says.

In the end, their enhancements improved the model’s ability to retrieve videos based on an audio query and predict the class of an audio-visual scene, like a dog barking or an instrument playing.

Its results were more accurate than their prior work, and it also performed better than more complex, state-of-the-art methods that require larger amounts of training data.

“Sometimes, very simple ideas or little patterns you see in the data have big value when applied on top of a model you are working on,” Araujo says.

In the future, the researchers want to incorporate new models that generate better data representations into CAV-MAE Sync, which could improve performance. They also want to enable their system to handle text data, which would be an important step toward generating an audiovisual large language model.

This work is funded, in part, by the German Federal Ministry of Education and Research and the MIT-IBM Watson AI Lab.

We’ve all felt the sting of guilt when fruit and vegetables go bad before we could eat them. Now, researchers from MIT and the Singapore-MIT Alliance for Research and Technology (SMART) have shown they can extend the shelf life of harvested plants by injecting them with melatonin using biodegradable microneedles.

That’s a big deal because the problem of food waste goes way beyond our salads. More than 30 percent of the world’s food is lost after it’s harvested — enough to feed more than 1 billion people. Refrigeration is the most common way to preserve foods, but it requires energy and infrastructure that many regions of the world can’t afford or lack access to.

The researchers believe their system could offer an alternative or complement to refrigeration. Central to their approach are patches of silk microneedles. The microneedles can get through the tough, waxy skin of plants without causing a stress response, and deliver precise amounts of melatonin into plants’ inner tissues.

“This is the first time that we’ve been able to apply these microneedles to extend the shelf life of a fresh-cut crop,” says Benedetto Marelli, the study’s senior author, associate professor of civil and environmental engineering at MIT, and the director of the Wild Cards mission of the MIT Climate Project. “We thought we could use this technology to deliver something that could regulate or control the plant’s post-harvest physiology. Eventually, we looked at hormones, and melatonin is already used by plants to regulate such functions. The food we waste could feed about 1.6 billion people. Even in the U.S., this approach could one day expand access to healthy foods.”

For the study, which appears today in Nano Letters, Marelli and researchers from SMART applied small patches of the microneedles containing melatonin to the base of the leafy vegetable pak choy. After application, the researchers found the melatonin was able to extend the vegetables’ shelf life by four days at room temperature and 10 days when refrigerated, which could allow more crops to reach consumers before they’re wasted.

“Post-harvest waste is a huge issue. This problem is extremely important in emerging markets around Africa and Southeast Asia, where many crops are produced but can't be maintained in the journey from farms to markets,” says Sarojam Rajani, co-senior author of the study and a senior principal investigator at the Temasek Life Sciences Laboratory in Singapore.

Plant destressors

For years, Marelli’s lab has been exploring the use of silk microneedles for things like delivering nutrients to crops and monitoring plant health. Microneedles made from silk fibroin protein are nontoxic and biodegradable, and Marelli’s previous work has described ways of manufacturing them at scale.

To test microneedle’s ability to extend the shelf life of food, the researchers wanted to study their ability to deliver a hormone known to affect the senescence process. Aside from helping humans sleep, melatonin is also a natural hormone in many plants that helps them regulate growth and aging.

“The dose of melatonin we’re delivering is so low that it’s fully metabolized by the crops, so it would not significantly increase the amount of melatonin normally present in the food; we would not ingest more melatonin than usual,” Marelli says. “We chose pak choy because it's a very important crop in Asia, and also because pak choy is very perishable.”

Pak choy is typically harvested by cutting the leafy plant from the root system, exposing the shoot base that provides easy access to vascular bundles which distribute water and nutrients to the rest of the plant. To begin their study, the researchers first used their microneedles to inject a fluorescent dye into the base to confirm that vasculature could spread the dye throughout the plant.

The researchers then compared the shelf life of regular pak choy plants and plants that had been sprayed with or dipped into melatonin, finding no difference.

With their baseline shelf life established, the researchers applied small patches of the melatonin-filled microneedles to the bottom of pak choy plants by hand. They then stored the treated plants, along with controls, in plastic boxes both at room temperature and under refrigeration.

The team evaluated the plants by monitoring their weight, visual appearance, and concentration of chlorophyll, a green pigment that decreases as plants age.

At room temperature, the leaves of the untreated control group began yellowing within two or three days. By the fourth day, the yellowing accelerated to the point that the plants likely could not be sold. Plants treated with the melatonin-loaded silk microneedles, in contrast, remained green on day five, and the yellowing process was significantly delayed. The weight loss and chlorophyll reduction of treated plants also slowed significantly at room temperature. Overall, the researchers estimated the microneedle-treated plants retained their saleable value until the eighth day.

“We clearly saw we could enhance the shelf life of pak choy without the cold chain,” Marelli says.

In refrigerated conditions of about 40 degrees Fahrenheit, plant yellowing was delayed by about five days on average, with treated plants remaining relatively green until day 25.

“Spectrophotometric analysis of the plants indicated the treated plants had higher antioxidant activity, while gene analysis showed the melatonin set off a protective chain reaction inside the plants, preserving chlorophyll and adjusting hormones to slow senescence,” says Monika Jangir, co-first author and former postdoc at the Temasek Life Sciences Laboratory.

“We studied melatonin’s effects and saw it improves the stress response of the plant after it’s been cut, so it’s basically decreasing the stress that plant’s experience, and that extends its shelf life,” says Yangyang Han, co-first author and research scientist at the Disruptive and Sustainable Technologies for Agricultural Precision (DiSTAP) interdisciplinary research group at SMART.

Toward postharvest preservation

While the microneedles could make it possible to minimize waste when compared to other application methods like spraying or dipping crops, the researchers say more work is needed to deploy microneedles at scale. For instance, although the researchers applied the microneedle patches by hand in this experiment, the patches could be applied using tractors, autonomous drones, and other farming equipment in the future.

“For this to be widely adopted, we’d need to reach a performance versus cost threshold to justify its use,” Marelli explains. “This method would need to become cheap enough to be used by farmers regularly.”

Moving forward, the research team plans to study the effects of a variety of hormones on different crops using its microneedle delivery technology. The team believes the technique should work with all kinds of produce.

“We’re going to continue to analyze how we can increase the impact this can have on the value and quality of crops,” Marelli says. “For example, could this let us modulate the nutritional values of the crop, how it’s shaped, its texture, etc.? We're also going to continue looking into scaling up the technology so this can be used in the field.”

The work was supported by the Singapore-MIT Alliance for Research and Technology (SMART) and the National Research Foundation of Singapore.

One of the most profound open questions in modern physics is: “Is gravity quantum?” 

The other fundamental forces — electromagnetic, weak, and strong — have all been successfully described, but no complete and consistent quantum theory of gravity yet exists.  

“Theoretical physicists have proposed many possible scenarios, from gravity being inherently classical to fully quantum, but the debate remains unresolved because we’ve never had a clear way to test gravity’s quantum nature in the lab,” says Dongchel Shin, a PhD candidate in the MIT Department of Mechanical Engineering (MechE). “The key to answering this lies in preparing mechanical systems that are massive enough to feel gravity, yet quiet enough — quantum enough — to reveal how gravity interacts with them.”

Shin, who is also a MathWorks Fellow, researches quantum and precision metrology platforms that probe fundamental physics and are designed to pave the way for future industrial technology. He is the lead author of a new paper that demonstrates laser cooling of a centimeter-long torsional oscillator. The open-access paper, “Active laser cooling of a centimeter-scale torsional oscillator,” was recently published in the journal Optica. 

Lasers have been routinely employed to cool down atomic gases since the 1980s, and have been used in the linear motion of nanoscale mechanical oscillators since around 2010. The new paper presents the first time this technique has been extended to torsional oscillators, which are key to a worldwide effort to study gravity using these systems.

“Torsion pendulums have been classical tools for gravity research since [Henry] Cavendish’s famous experiment in 1798. They’ve been used to measure Newton’s gravitational constant, G, test the inverse-square law, and search for new gravitational phenomena,” explains Shin.

By using lasers to remove nearly all thermal motion from atoms, in recent decades scientists have created ultracold atomic gases at micro- and nanokelvin temperatures. These systems now power the world’s most precise clocks — optical lattice clocks — with timekeeping precision so high that they would gain or lose less than a second over the age of the universe.

“Historically, these two technologies developed separately — one in gravitational physics, the other in atomic and optical physics,” says Shin. “In our work, we bring them together. By applying laser cooling techniques originally developed for atoms to a centimeter-scale torsional oscillator, we try to bridge the classical and quantum worlds. This hybrid platform enables a new class of experiments — ones that could finally let us test whether gravity needs to be described by quantum theory.”

The new paper demonstrates laser cooling of a centimeter-scale torsional oscillator from room temperature to a temperature of 10 millikelvins (1/1,000th of a kelvin) using a mirrored optical lever.

“An optical lever is a simple but powerful measurement technique: You shine a laser onto a mirror, and even a tiny tilt of the mirror causes the reflected beam to shift noticeably on a detector. This magnifies small angular motions into easily measurable signals,” explains Shin, noting that while the premise is simple, the team faced challenges in practice. “The laser beam itself can jitter slightly due to air currents, vibrations, or imperfections in the optics. These jitters can falsely appear as motion of the mirror, limiting our ability to measure true physical signals.”

To overcome this, the team used the mirrored optical lever approach, which employs a second, mirrored version of the laser beam to cancel out the unwanted jitter.

“One beam interacts with the torsional oscillator, while the other reflects off a corner-cube mirror, reversing any jitter without picking up the oscillator’s motion,” Shin says. “When the two beams are combined at the detector, the real signal from the oscillator is preserved, and the false motion from [the] laser jitter is canceled.”

This approach reduced noise by a factor of a thousand, which allowed the researchers to detect motion with extreme precision, nearly 10 times better than the oscillator’s own quantum zero-point fluctuations. “That level of sensitivity made it possible for us to cool the system down to just 10 milli-kelvins using laser light,” Shin says.

Shin says this work is just the beginning. “While we’ve achieved quantum-limited precision below the zero-point motion of the oscillator, reaching the actual quantum ground state remains our next goal,” he says. “To do that, we’ll need to further strengthen the optical interaction — using an optical cavity that amplifies angular signals, or optical trapping strategies. These improvements could open the door to experiments where two such oscillators interact only through gravity, allowing us to directly test whether gravity is quantum or not.”

The paper’s other authors from the Department of Mechanical Engineering include Vivishek Sudhir, assistant professor of mechanical engineering and the Class of 1957 Career Development Professor, and PhD candidate Dylan Fife. Additional authors are Tina Heyward and Rajesh Menon of the Department of Electrical and Computer Engineering at the University of Utah. Shin and Fife are both members of Sudhir’s lab, the Quantum and Precision Measurements Group.

Shin says one thing he’s come to appreciate through this work is the breadth of the challenge the team is tackling. “Studying quantum aspects of gravity experimentally doesn’t just require deep understanding of physics — relativity, quantum mechanics — but also demands hands-on expertise in system design, nanofabrication, optics, control, and electronics,” he says.

“Having a background in mechanical engineering, which spans both the theoretical and practical aspects of physical systems, gave me the right perspective to navigate and contribute meaningfully across these diverse domains,” says Shin. “It’s been incredibly rewarding to see how this broad training can help tackle one of the most fundamental questions in science.”

Removing carbon dioxide from the atmosphere efficiently is often seen as a crucial need for combatting climate change, but systems for removing carbon dioxide suffer from a tradeoff. Chemical compounds that efficiently remove CO₂ from the air do not easily release it once captured, and compounds that release CO₂ efficiently are not very efficient at capturing it. Optimizing one part of the cycle tends to make the other part worse.

Now, using nanoscale filtering membranes, researchers at MIT have added a simple intermediate step that facilitates both parts of the cycle. The new approach could improve the efficiency of electrochemical carbon dioxide capture and release by six times and cut costs by at least 20 percent, they say.

The new findings are reported today in the journal ACS Energy Letters, in a paper by MIT doctoral students Simon Rufer, Tal Joseph, and Zara Aamer, and professor of mechanical engineering Kripa Varanasi.

“We need to think about scale from the get-go when it comes to carbon capture, as making a meaningful impact requires processing gigatons of CO₂,” says Varanasi. “Having this mindset helps us pinpoint critical bottlenecks and design innovative solutions with real potential for impact. That’s the driving force behind our work.”

Many carbon-capture systems work using chemicals called hydroxides, which readily combine with carbon dioxide to form carbonate. That carbonate is fed into an electrochemical cell, where the carbonate reacts with an acid to form water and release carbon dioxide. The process can take ordinary air with only about 400 parts per million of carbon dioxide and generate a stream of 100 percent pure carbon dioxide, which can then be used to make fuels or other products.

Both the capture and release steps operate in the same water-based solution, but the first step needs a solution with a high concentration of hydroxide ions, and the second step needs one high in carbonate ions. “You can see how these two steps are at odds,” says Varanasi. “These two systems are circulating the same sorbent back and forth. They’re operating on the exact same liquid. But because they need two different types of liquids to operate optimally, it’s impossible to operate both systems at their most efficient points.”

The team’s solution was to decouple the two parts of the system and introduce a third part in between. Essentially, after the hydroxide in the first step has been mostly chemically converted to carbonate, special nanofiltration membranes then separate ions in the solution based on their charge. Carbonate ions have a charge of 2, while hydroxide ions have a charge of 1. “The nanofiltration is able to separate these two pretty well,” Rufer says.

Once separated, the hydroxide ions are fed back to the absorption side of the system, while the carbonates are sent ahead to the electrochemical release stage. That way, both ends of the system can operate at their more efficient ranges. Varanasi explains that in the electrochemical release step, protons are being added to the carbonate to cause the conversion to carbon dioxide and water, but if hydroxide ions are also present, the protons will react with those ions instead, producing just water.

“If you don’t separate these hydroxides and carbonates,” Rufer says, “the way the system fails is you’ll add protons to hydroxide instead of carbonate, and so you’ll just be making water rather than extracting carbon dioxide. That’s where the efficiency is lost. Using nanofiltration to prevent this was something that we aren’t aware of anyone proposing before.”

Testing showed that the nanofiltration could separate the carbonate from the hydroxide solution with about 95 percent efficiency, validating the concept under realistic conditions, Rufer says. The next step was to assess how much of an effect this would have on the overall efficiency and economics of the process. They created a techno-economic model, incorporating electrochemical efficiency, voltage, absorption rate, capital costs, nanofiltration efficiency, and other factors.

The analysis showed that present systems cost at least $600 per ton of carbon dioxide captured, while with the nanofiltration component added, that drops to about $450 a ton. What’s more, the new system is much more stable, continuing to operate at high efficiency even under variations in the ion concentrations in the solution. “In the old system without nanofiltration, you’re sort of operating on a knife’s edge,” Rufer says; if the concentration varies even slightly in one direction or the other, efficiency drops off drastically. “But with our nanofiltration system, it kind of acts as a buffer where it becomes a lot more forgiving. You have a much broader operational regime, and you can achieve significantly lower costs.”

He adds that this approach could apply not only to the direct air capture systems they studied specifically, but also to point-source systems — which are attached directly to the emissions sources such as power plant emissions — or to the next stage of the process, converting captured carbon dioxide into useful products such as fuel or chemical feedstocks.  Those conversion processes, he says, “are also bottlenecked in this carbonate and hydroxide tradeoff.”

In addition, this technology could lead to safer alternative chemistries for carbon capture, Varanasi says. “A lot of these absorbents can at times be toxic, or damaging to the environment. By using a system like ours, you can improve the reaction rate, so you can choose chemistries that might not have the best absorption rate initially but can be improved to enable safety.”

Varanasi adds that “the really nice thing about this is we’ve been able to do this with what’s commercially available,” and with a system that can easily be retrofitted to existing carbon-capture installations. If the costs can be further brought down to about $200 a ton, it could be viable for widespread adoption. With ongoing work, he says, “we’re confident that we’ll have something that can become economically viable” and that will ultimately produce valuable, saleable products.

Rufer notes that even today, “people are buying carbon credits at a cost of over $500 per ton. So, at this cost we’re projecting, it is already commercially viable in that there are some buyers who are willing to pay that price.” But by bringing the price down further, that should increase the number of buyers who would consider buying the credit, he says. “It’s just a question of how widespread we can make it.” Recognizing this growing market demand, Varanasi says, “Our goal is to provide industry scalable, cost-effective, and reliable technologies and systems that enable them to directly meet their decarbonization targets.”

The research was supported by Shell International Exploration and Production Inc. through the MIT Energy Initiative, and the U.S. National Science Foundation, and made use of the facilities at MIT.nano.

Measuring the density of a cell can reveal a great deal about the cell’s state. As cells proliferate, differentiate, or undergo cell death, they may gain or lose water and other molecules, which is revealed by changes in density.

Tracking these tiny changes in cells’ physical state is difficult to do at a large scale, especially with single-cell resolution, but a team of MIT researchers has now found a way to measure cell density quickly and accurately — measuring up to 30,000 cells in a single hour.

The researchers also showed that density changes could be used to make valuable predictions, including whether immune cells such as T cells have become activated to kill tumors, or whether tumor cells are susceptible to a specific drug.

“These predictions are all based on looking at very small changes in the physical properties of cells, which can tell you how they’re going to respond,” says Scott Manalis, the David H. Koch Professor of Engineering in the departments of Biological Engineering and Mechanical Engineering, and a member of the Koch Institute for Integrative Cancer Research.

Manalis is the senior author of the new study, which appears today in Nature Biomedical Engineering. The paper’s lead author is MIT Research Scientist Weida (Richard) Wu.

Measuring density

As cells enter new states, their molecular contents, including lipids, proteins, and nucleic acids, can become more or less crowded. Measuring the density of a cell offers an indirect view of this crowding.

The new density measurement technique reported in this study builds on work that Manalis’ lab has done over the past two decades on technologies for making measurements of cells and tiny particles. In 2007, his lab developed a microfluidic device known as a suspended microchannel resonator (SMR), which consists of a microchannel across a tiny silicon cantilever that vibrates at a specific frequency. As a cell passes through the channel, the frequency of the vibration changes slightly, and the magnitude of that change can be used to calculate the cell’s mass.

In 2011, the researchers adapted the technique to measure the density of cells. To achieve that, cells are sent through the device twice, suspended in two liquids of different densities. A cell’s buoyant mass (its mass as it floats in fluid) depends on its absolute mass and volume, so by measuring two different buoyant masses for a cell, its mass, volume, and density can be calculated.

That technique works well, but swapping fluids and flowing cells through each one is time-consuming, so it can only be used to measure a few hundred cells at a time.

To create a faster, more streamlined system, the researchers combined their SMR device with a fluorescent microscope, which enables measurements of cell volume. The microscope is positioned at the entrance to the resonator, and cells flow through the device while floating in a fluorescent dye that can’t be absorbed by cells. When cells pass by the microscope, the dip in the fluorescent signal can be used to determine the volume of the cell.

After that volume measurement is taken, the cells flow into the resonator, which measures their mass. This process, which allows for rapid calculation of density, can be used to measure up to 30,000 cells in an hour.

“Instead of trying to flow the cells back and forth at least twice through the cantilever to get cell density, we wanted to try to create a method to do a streamlined measurement, so the cells only need to pass through the cantilever once,” Wu says. “From a cell’s mass and volume, we can then derive its density, without compromising the throughput or the precision.”

Evaluating T cells

The researchers used their new technique to track what happens to the density of T cells after they are activated by signaling molecules.

As T cells transition from a quiescent state to an active state, they gain new molecules, as well as water, the researchers found. From their pre-activation state to the first day of activation, the densities of the cells dropped from an average of 1.08 grams per milliliter to 1.06 grams per milliliter. This means that the cells are becoming less crowded, as they gain water faster than they gain other molecules.

“This is suggesting that cell density is very likely reflecting an increase in cellular water content as the cells transit from a quiescent, non-proliferative state to a high-growth state,” Wu says. “These data are pointing to the notion that cell density is an interesting biomarker that is changing during T-cell activation and may have functional relevance to how well the T cells could proliferate.”

Travera, a clinical-stage company co-founded by Manalis, is working on using the SMR mass measurements to predict whether individual cancer patients’ T cells will respond to drugs meant to stimulate a strong anti-tumor immune response. The company has also begun using the density measurement technique, and preliminary studies have found that using mass and density measurements together gives a much more accurate prediction that using either one alone.

“Both mass and density are revealing something about the overall fitness of the immune cells,” Manalis says.

Using physical measurements of cells to monitor their immune activation “is very exciting and may offer a new way of evaluating and measuring changes in immune cells in circulation,” says Genevieve Boland, an associate professor of surgery at Harvard Medical School and vice chair of research for the Integrated Department of Surgery at Mass General Brigham, who was not involved in the study.

“This is a complementary, but very different, method than those currently used for immune assessments in cancer and other diseases, potentially offering a novel tool to assist in clinical decision-making regarding the need for and the choice of a specific cancer therapy, allow monitoring of response to therapy, and/or in early detection of side effects of immune-based therapies,” she says.

Making predictions

Another potential application for this approach is predicting how tumor cells will respond to different types of cancer drugs. In previous work, Manalis has shown that tracking changes in cell mass after treatment can predict whether a tumor cell is undergoing drug-induced apoptosis. In the new study, he found that density could also reveal these responses.

In those experiments, the researchers treated pancreatic cancer cells with one of two different drugs — one that the cells are susceptible to, and one they are resistant to. They found that density changes after treatment accurately reflected the cells’ known responses to treatment.

“We capture something about the cells that is highly predictive within the first couple of days after they get taken out from the tumor,” Wu says. “Cell density is a rapid biomarker to predict in vivo drug response in a very timely manner.”

Manalis’ lab is now working on using measurements of cell mass and density as a way to evaluate the fitness of cells used to synthesize complex proteins such as therapeutic antibodies.

“As cells are producing these proteins, we can learn from these markers of cell fitness and metabolic state to try to make predictions about how well these cells can produce these proteins, and hopefully in the future also guide design and control strategies to even further improve the yield of these complex proteins,” Wu says.

The research was funded by the Paul G. Allen Frontiers Group, the Virginia and Daniel K. Ludwig Fund for Cancer Research, the MIT Center for Precision Cancer Medicine, the Stand up to Cancer Convergence Program, Bristol Myers Squibb, and the Koch Institute Support (core) Grant from the National Cancer Institute.

By combining information from many large datasets, MIT researchers have identified several new potential targets for treating or preventing Alzheimer’s disease.

The study revealed genes and cellular pathways that haven’t been linked to Alzheimer’s before, including one involved in DNA repair. Identifying new drug targets is critical because many of the Alzheimer’s drugs that have been developed to this point haven’t been as successful as hoped.

Working with researchers at Harvard Medical School, the team used data from humans and fruit flies to identify cellular pathways linked to neurodegeneration. This allowed them to identify additional pathways that may be contributing to the development of Alzheimer’s.

“All the evidence that we have indicates that there are many different pathways involved in the progression of Alzheimer’s. It is multifactorial, and that may be why it’s been so hard to develop effective drugs,” says Ernest Fraenkel, the Grover M. Hermann Professor in Health Sciences and Technology in MIT’s Department of Biological Engineering and the senior author of the study. “We will need some kind of combination of treatments that hit different parts of this disease.”

Matthew Leventhal PhD ’25 is the lead author of the paper, which appears today in Nature Communications.

Alternative pathways

Over the past few decades, many studies have suggested that Alzheimer’s disease is caused by the buildup of amyloid plaques in the brain, which triggers a cascade of events that leads to neurodegeneration.

A handful of drugs have been developed to block or break down these plaques, but these drugs usually do not have a dramatic effect on disease progression. In hopes of identifying new drug targets, many scientists are now working on uncovering other mechanisms that might contribute to the development of Alzheimer’s.

“One possibility is that maybe there’s more than one cause of Alzheimer’s, and that even in a single person, there could be multiple contributing factors,” Fraenkel says. “So, even if the amyloid hypothesis is correct — and there are some people who don’t think it is — you need to know what those other factors are. And then if you can hit all the causes of the disease, you have a better chance of blocking and maybe even reversing some losses.”

To try to identify some of those other factors, Fraenkel’s lab teamed up with Mel Feany, a professor of pathology at Harvard Medical School and a geneticist specializing in fruit fly genetics.

Using fruit flies as a model, Feany and others in her lab did a screen in which they knocked out nearly every conserved gene expressed in fly neurons. Then, they measured whether each of these gene knockdowns had any effect on the age at which the flies develop neurodegeneration. This allowed them to identify about 200 genes that accelerate neurodegeneration.

Some of these were already linked to neurodegeneration, including genes for the amyloid precursor protein and for proteins called presenillins, which play a role in the formation of amyloid proteins.

The researchers then analyzed this data using network algorithms that Fraenkel’s lab has been developing over the past several years. These are algorithms that can identify connections between genes that may be involved in the same cellular pathways and functions.

In this case, the aim was to try to link the genes identified in the fruit fly screen with specific processes and cellular pathways that might contribute to neurodegeneration. To do that, the researchers combined the fruit fly data with several other datasets, including genomic data from postmortem tissue of Alzheimer’s patients.

The first stage of their analysis revealed that many of the genes identified in the fruit fly study also decline as humans age, suggesting that they may be involved in neurodegeneration in humans.

Network analysis

In the next phase of their study, the researchers incorporated additional data relevant to Alzheimer’s disease, including eQTL (expression quantitative trait locus) data — ­a measure of how different gene variants affect the expression levels of certain proteins.

Using their network optimization algorithms on this data, the researchers identified pathways that link genes to their potential role in Alzheimer’s development. The team chose two of those pathways to focus on in the new study.

The first is a pathway, not previously linked to Alzheimer’s disease, related to RNA modification. The network suggested that when one of two of the genes in this pathway — MEPCE and HNRNPA2B1 — are missing, neurons become more vulnerable to the Tau tangles that form in the brains of Alzheimer’s patients. The researchers confirmed this effect by knocking down those genes in studies of fruit flies and in human neurons derived from induced pluripotent stem cells (IPSCs).

The second pathway reported in this study is involved in DNA damage repair. This network includes two genes called NOTCH1 and CSNK2A1, which have been linked to Alzheimer’s before, but not in the context of DNA repair. Both genes are most well-known for their roles in regulating cell growth.

In this study, the researchers found evidence that when these genes are missing, DNA damage builds up in cells, through two different DNA-damaging pathways. Buildup of unrepaired DNA has previously been shown to lead to neurodegeneration.

Now that these targets have been identified, the researchers hope to collaborate with other labs to help explore whether drugs that target them could improve neuron health. Fraenkel and other researchers are working on using IPSCs from Alzheimer’s patients to generate neurons that could be used to evaluate such drugs.

“The search for Alzheimer’s drugs will get dramatically accelerated when there are very good, robust experimental systems,” he says. “We’re coming to a point where a couple of really innovative systems are coming together. One is better experimental models based on IPSCs, and the other one is computational models that allow us to integrate huge amounts of data. When those two mature at the same time, which is what we’re about to see, then I think we’ll have some breakthroughs.”

The research was funded by the National Institutes of Health.

The ocean is teeming with life. But unless you get up close, much of the marine world can easily remain unseen. That’s because water itself can act as an effective cloak: Light that shines through the ocean can bend, scatter, and quickly fade as it travels through the dense medium of water and reflects off the persistent haze of ocean particles. This makes it extremely challenging to capture the true color of objects in the ocean without imaging them at close range.

Now a team from MIT and the Woods Hole Oceanographic Institution (WHOI) has developed an image-analysis tool that cuts through the ocean’s optical effects and generates images of underwater environments that look as if the water had been drained away, revealing an ocean scene’s true colors. The team paired the color-correcting tool with a computational model that converts images of a scene into a three-dimensional underwater “world,” that can then be explored virtually.

The researchers have dubbed the new tool “SeaSplat,” in reference to both its underwater application and a method known as 3D gaussian splatting (3DGS), which takes images of a scene and stitches them together to generate a complete, three-dimensional representation that can be viewed in detail, from any perspective.

“With SeaSplat, it can model explicitly what the water is doing, and as a result it can in some ways remove the water, and produces better 3D models of an underwater scene,” says MIT graduate student Daniel Yang.

The researchers applied SeaSplat to images of the sea floor taken by divers and underwater vehicles, in various locations including the U.S. Virgin Islands. The method generated 3D “worlds” from the images that were truer and more vivid and varied in color, compared to previous methods.

The team says SeaSplat could help marine biologists monitor the health of certain ocean communities. For instance, as an underwater robot explores and takes pictures of a coral reef, SeaSplat would simultaneously process the images and render a true-color, 3D representation, that scientists could then virtually “fly” through, at their own pace and path, to inspect the underwater scene, for instance for signs of coral bleaching.

“Bleaching looks white from close up, but could appear blue and hazy from far away, and you might not be able to detect it,” says Yogesh Girdhar, an associate scientist at WHOI. “Coral bleaching, and different coral species, could be easier to detect with SeaSplat imagery, to get the true colors in the ocean.”

Girdhar and Yang will present a paper detailing SeaSplat at the IEEE International Conference on Robotics and Automation (ICRA). Their study co-author is John Leonard, professor of mechanical engineering at MIT.

Aquatic optics

In the ocean, the color and clarity of objects is distorted by the effects of light traveling through water. In recent years, researchers have developed color-correcting tools that aim to reproduce the true colors in the ocean. These efforts involved adapting tools that were developed originally for environments out of water, for instance to reveal the true color of features in foggy conditions. One recent work accurately reproduces true colors in the ocean, with an algorithm named “Sea-Thru,” though this method requires a huge amount of computational power, which makes its use in producing 3D scene models challenging.

In parallel, others have made advances in 3D gaussian splatting, with tools that seamlessly stitch images of a scene together, and intelligently fill in any gaps to create a whole, 3D version of the scene. These 3D worlds enable “novel view synthesis,” meaning that someone can view the generated 3D scene, not just from the perspective of the original images, but from any angle and distance.

But 3DGS has only successfully been applied to environments out of water. Efforts to adapt 3D reconstruction to underwater imagery have been hampered, mainly by two optical underwater effects: backscatter and attenuation. Backscatter occurs when light reflects off of tiny particles in the ocean, creating a veil-like haze. Attenuation is the phenomenon by which light of certain wavelengths attenuates, or fades with distance. In the ocean, for instance, red objects appear to fade more than blue objects when viewed from farther away.

Out of water, the color of objects appears more or less the same regardless of the angle or distance from which they are viewed. In water, however, color can quickly change and fade depending on one’s perspective. When 3DGS methods attempt to stitch underwater images into a cohesive 3D whole, they are unable to resolve objects due to aquatic backscatter and attenuation effects that distort the color of objects at different angles.

“One dream of underwater robotic vision that we have is: Imagine if you could remove all the water in the ocean. What would you see?” Leonard says.

A model swim

In their new work, Yang and his colleagues developed a color-correcting algorithm that accounts for the optical effects of backscatter and attenuation. The algorithm determines the degree to which every pixel in an image must have been distorted by backscatter and attenuation effects, and then essentially takes away those aquatic effects, and computes what the pixel’s true color must be.

Yang then worked the color-correcting algorithm into a 3D gaussian splatting model to create SeaSplat, which can quickly analyze underwater images of a scene and generate a true-color, 3D virtual version of the same scene that can be explored in detail from any angle and distance.

The team applied SeaSplat to multiple underwater scenes, including images taken in the Red Sea, in the Carribean off the coast of Curaçao, and the Pacific Ocean, near Panama. These images, which the team took from a pre-existing dataset, represent a range of ocean locations and water conditions. They also tested SeaSplat on images taken by a remote-controlled underwater robot in the U.S. Virgin Islands.

From the images of each ocean scene, SeaSplat generated a true-color 3D world that the researchers were able to virtually explore, for instance zooming in and out of a scene and viewing certain features from different perspectives. Even when viewing from different angles and distances, they found objects in every scene retained their true color, rather than fading as they would if viewed through the actual ocean.

“Once it generates a 3D model, a scientist can just ‘swim’ through the model as though they are scuba-diving, and look at things in high detail, with real color,” Yang says.

For now, the method requires hefty computing resources in the form of a desktop computer that would be too bulky to carry aboard an underwater robot. Still, SeaSplat could work for tethered operations, where a vehicle, tied to a ship, can explore and take images that can be sent up to a ship’s computer.

“This is the first approach that can very quickly build high-quality 3D models with accurate colors, underwater, and it can create them and render them fast,” Girdhar says. “That will help to quantify biodiversity, and assess the health of coral reef and other marine communities.”

This work was supported, in part, by the Investment in Science Fund at WHOI, and by the U.S. National Science Foundation.

A protein located in the wrong part of a cell can contribute to several diseases, such as Alzheimer’s, cystic fibrosis, and cancer. But there are about 70,000 different proteins and protein variants in a single human cell, and since scientists can typically only test for a handful in one experiment, it is extremely costly and time-consuming to identify proteins’ locations manually.

A new generation of computational techniques seeks to streamline the process using machine-learning models that often leverage datasets containing thousands of proteins and their locations, measured across multiple cell lines. One of the largest such datasets is the Human Protein Atlas, which catalogs the subcellular behavior of over 13,000 proteins in more than 40 cell lines. But as enormous as it is, the Human Protein Atlas has only explored about 0.25 percent of all possible pairings of all proteins and cell lines within the database.

Now, researchers from MIT, Harvard University, and the Broad Institute of MIT and Harvard have developed a new computational approach that can efficiently explore the remaining uncharted space. Their method can predict the location of any protein in any human cell line, even when both protein and cell have never been tested before.

Their technique goes one step further than many AI-based methods by localizing a protein at the single-cell level, rather than as an averaged estimate across all the cells of a specific type. This single-cell localization could pinpoint a protein’s location in a specific cancer cell after treatment, for instance.

The researchers combined a protein language model with a special type of computer vision model to capture rich details about a protein and cell. In the end, the user receives an image of a cell with a highlighted portion indicating the model’s prediction of where the protein is located. Since a protein’s localization is indicative of its functional status, this technique could help researchers and clinicians more efficiently diagnose diseases or identify drug targets, while also enabling biologists to better understand how complex biological processes are related to protein localization.

“You could do these protein-localization experiments on a computer without having to touch any lab bench, hopefully saving yourself months of effort. While you would still need to verify the prediction, this technique could act like an initial screening of what to test for experimentally,” says Yitong Tseo, a graduate student in MIT’s Computational and Systems Biology program and co-lead author of a paper on this research.

Tseo is joined on the paper by co-lead author Xinyi Zhang, a graduate student in the Department of Electrical Engineering and Computer Science (EECS) and the Eric and Wendy Schmidt Center at the Broad Institute; Yunhao Bai of the Broad Institute; and senior authors Fei Chen, an assistant professor at Harvard and a member of the Broad Institute, and Caroline Uhler, the Andrew and Erna Viterbi Professor of Engineering in EECS and the MIT Institute for Data, Systems, and Society (IDSS), who is also director of the Eric and Wendy Schmidt Center and a researcher at MIT’s Laboratory for Information and Decision Systems (LIDS). The research appears today in Nature Methods.

Collaborating models

Many existing protein prediction models can only make predictions based on the protein and cell data on which they were trained or are unable to pinpoint a protein’s location within a single cell.

To overcome these limitations, the researchers created a two-part method for prediction of unseen proteins’ subcellular location, called PUPS.

The first part utilizes a protein sequence model to capture the localization-determining properties of a protein and its 3D structure based on the chain of  amino acids that forms it.

The second part incorporates an image inpainting model, which is designed to fill in missing parts of an image. This computer vision model looks at three stained images of a cell to gather information about the state of that cell, such as its type, individual features, and whether it is under stress.

PUPS joins the representations created by each model to predict where the protein is located within a single cell, using an image decoder to output a highlighted image that shows the predicted location.

“Different cells within a cell line exhibit different characteristics, and our model is able to understand that nuance,” Tseo says.

A user inputs the sequence of amino acids that form the protein and three cell stain images — one for the nucleus, one for the microtubules, and one for the endoplasmic reticulum. Then PUPS does the rest.

A deeper understanding

The researchers employed a few tricks during the training process to teach PUPS how to combine information from each model in such a way that it can make an educated guess on the protein’s location, even if it hasn’t seen that protein before.

For instance, they assign the model a secondary task during training: to explicitly name the compartment of localization, like the cell nucleus. This is done alongside the primary inpainting task to help the model learn more effectively.

A good analogy might be a teacher who asks their students to draw all the parts of a flower in addition to writing their names. This extra step was found to help the model improve its general understanding of the possible cell compartments.

In addition, the fact that PUPS is trained on proteins and cell lines at the same time helps it develop a deeper understanding of where in a cell image proteins tend to localize.

PUPS can even understand, on its own, how different parts of a protein’s sequence contribute separately to its overall localization.

“Most other methods usually require you to have a stain of the protein first, so you’ve already seen it in your training data. Our approach is unique in that it can generalize across proteins and cell lines at the same time,” Zhang says.

Because PUPS can generalize to unseen proteins, it can capture changes in localization driven by unique protein mutations that aren’t included in the Human Protein Atlas.

The researchers verified that PUPS could predict the subcellular location of new proteins in unseen cell lines by conducting lab experiments and comparing the results. In addition, when compared to a baseline AI method, PUPS exhibited on average less prediction error across the proteins they tested.

In the future, the researchers want to enhance PUPS so the model can understand protein-protein interactions and make localization predictions for multiple proteins within a cell. In the longer term, they want to enable PUPS to make predictions in terms of living human tissue, rather than cultured cells.

This research is funded by the Eric and Wendy Schmidt Center at the Broad Institute, the National Institutes of Health, the National Science Foundation, the Burroughs Welcome Fund, the Searle Scholars Foundation, the Harvard Stem Cell Institute, the Merkin Institute, the Office of Naval Research, and the Department of Energy.

Around the world, 20 percent of children are not fully immunized, leading to 1.5 million child deaths each year from diseases that are preventable by vaccination. About half of those underimmunized children received at least one vaccine dose but did not complete the vaccination series, while the rest received no vaccines at all.

To make it easier for children to receive all of their vaccines, MIT researchers are working to develop microparticles that can release their payload weeks or months after being injected. This could lead to vaccines that can be given just once, with several doses that would be released at different time points.

In a study appearing today in the journal Advanced Materials, the researchers showed that they could use these particles to deliver two doses of diphtheria vaccine — one released immediately, and the second two weeks later. Mice that received this vaccine generated as many antibodies as mice that received two separate doses two weeks apart.

The researchers now hope to extend those intervals, which could make the particles useful for delivering childhood vaccines that are given as several doses over a few months, such as the polio vaccine.

“The long-term goal of this work is to develop vaccines that make immunization more accessible — especially for children living in areas where it’s difficult to reach health care facilities. This includes rural regions of the United States as well as parts of the developing world where infrastructure and medical clinics are limited,” says Ana Jaklenec, a principal investigator at MIT’s Koch Institute for Integrative Cancer Research.

Jaklenec and Robert Langer, the David H. Koch Institute Professor at MIT, are the senior authors of the study. Linzixuan (Rhoda) Zhang, an MIT graduate student who recently completed her PhD in chemical engineering, is the paper’s lead author.

Self-boosting vaccines

In recent years, Jaklenec, Langer, and their colleagues have been working on vaccine delivery particles made from a polymer called PLGA. In 2018, they showed they could use these types of particles to deliver two doses of the polio vaccine, which were released about 25 days apart.

One drawback to PLGA is that as the particles slowly break down in the body, the immediate environment can become acidic, which may damage the vaccine contained within the particles.

The MIT team is now working on ways to overcome that issue in PLGA particles and is also exploring alternative materials that would create a less acidic environment. In the new study, led by Zhang, the researchers decided to focus on another type of polymer, known as polyanhydride.

“The goal of this work was to advance the field by exploring new strategies to address key challenges, particularly those related to pH sensitivity and antigen degradation,” Jaklenec says.

Polyanhydrides, biodegradable polymers that Langer developed for drug delivery more than 40 years ago, are very hydrophobic. This means that as the polymers gradually erode inside the body, the breakdown products hardly dissolve in water and generate a much less acidic environment.

Polyanhydrides usually consist of chains of two different monomers that can be assembled in a huge number of possible combinations. For this study, the researchers created a library of 23 polymers, which differed from each other based on the chemical structures of the monomer building blocks and the ratio of the two monomers that went into the final product.

The researchers evaluated these polymers based on their ability to withstand temperatures of at least 104 degrees Fahrenheit (40 degrees Celsius, or slightly above body temperature) and whether they could remain stable throughout the process required to form them into microparticles.

To make the particles, the researchers developed a process called stamped assembly of polymer layers, or SEAL. First, they use silicon molds to form cup-shaped particles that can be filled with the vaccine antigen. Then, a cap made from the same polymer is applied and sealed using heat. Polymers that proved too brittle or didn’t seal completely were eliminated from the pool, leaving six top candidates.

The researchers used those polymers to design particles that would deliver diphtheria vaccine two weeks after injection, and gave them to mice along with vaccine that was released immediately. Four weeks after the initial injection, those mice showed comparable levels of antibodies to mice that received two doses two weeks apart.

Extended release

As part of their study, the researchers also developed a machine-learning model to help them explore the factors that determine how long it takes the particles to degrade once in the body. These factors include the type of monomers that go into the material, the ratio of the monomers, the molecular weight of the polymer, and the loading capacity or how much vaccine can go into the particle.

Using this model, the researchers were able to rapidly evaluate nearly 500 possible particles and predict their release time. They tested several of these particles in controlled buffers and showed that the model’s predictions were accurate.

In future work, this model could also help researchers to develop materials that would release their payload after longer intervals — months or even years. This could make them useful for delivering many childhood vaccines, which require multiple doses over several years.

“If we want to extend this to longer time points, let’s say over a month or even further, we definitely have some ways to do this, such as increasing the molecular weight or the hydrophobicity of the polymer. We can also potentially do some cross-linking. Those are further changes to the chemistry of the polymer to slow down the release kinetics or to extend the retention time of the particle,” Zhang says.

The researchers now hope to explore using these delivery particles for other types of vaccines. The particles could also prove useful for delivering other types of drugs that are sensitive to acidity and need to be given in multiple doses, they say.

“This technology has broad potential for single-injection vaccines, but it could also be adapted to deliver small molecules or other biologics that require durability or multiple doses. Additionally, it can accommodate drugs with pH sensitivities,” Jaklenec says.

The research was funded, in part, by the Koch Institute Support (core) Grant from the National Cancer Institute.

At this moment, there are approximately 35,000 tracked human-generated objects in orbit around Earth. Of these, only about one-third are active payloads: science and communications satellites, research experiments, and other beneficial technology deployments. The rest are categorized as debris — defunct satellites, spent rocket bodies, and the detritus of hundreds of collisions, explosions, planned launch vehicle separations, and other “fragmentation events” that have occurred throughout humanity’s 67 years of space launches. 

The problem of space debris is well documented, and only set to grow in the near term as launch rates increase and fragmentation events escalate accordingly. The clutter of debris — which includes an estimated 1 million objects over 1 centimeter, in addition to the tracked objects — regularly causes damage to satellites, requires the repositioning of the International Space Station, and has the potential to cause catastrophic collisions with increasing frequency. 

To address this issue, in 2019 the World Economic Forum selected a team co-led by MIT Associate Professor Danielle Wood’s Space Enabled Research Group at the MIT Media Lab to create a system for scoring space mission operators on their launch and de-orbit plans, collision-avoidance measures, debris generation, and data sharing, among other factors that would allow for better coordination and maintenance of space objects. The team has developed a system called the Space Sustainability Rating (SSR), and launched it in 2021 as an independent nonprofit. 

“Satellites provide valuable services that impact everyone in the world by helping us understand the environment, communicate globally, navigate, and operate our modern infrastructure. As innovative new missions are proposed that operate thousands of satellites, a new approach is needed to provide space traffic management. National governments and space operators need to design coordination approaches to reduce the risk of losing access to valuable satellite missions,” says Wood, who is jointly appointed in the Program in Media Arts and Sciences and the Department of Aeronautics and Astronautics (AeroAstro). “The Space Sustainability Rating plays a role by compiling internationally recognized responsible on-orbit behaviors, and celebrating space actors that implement them.” 

France-based Eutelsat Group, a geostationary Earth orbit and low Earth orbit satellite operator, signed on as the first constellation operator with a large deployment of satellites to undergo a rating. Eutelsat submitted a mission to SSR for assessment, and was rated on a tiered scoring system based on six performance modules. Eutelsat earned a platinum rating with a score exceeding 80 percent, indicating that the mission demonstrated exceptional sustainability in design, operations, and disposal practices.

As of December 2024, SSR has also provided ratings to operators such as OHB Sweden AB, Stellar, and TU Delft. 

In a new open-access paper published in Acta Astronautica, lead author Minoo Rathnasabapathy, Wood, and the SSR team provide the detailed history, motivation, and design of the Space Sustainability Rating as an incentive system that provides a score for space operators based on their effort to reduce space debris and collision risk. The researchers include AeroAstro alumnus Miles Lifson SM '20, PhD '24; University of Texas at Austin professor and former MIT MLK Scholar Moriba Jah; and collaborators from the European Space Agency, BryceTech, and the Swiss Institute of Technology of Lausanne Space Center (eSpace).

The paper provides transparency about the inception of SSR as a cross-organizational collaboration and its development as a composite indicator that evaluates missions across multiple quantifiable factors. The aim of SSR is to provide actionable feedback and a score recognizing operators’ contributions to the space sustainability effort. The paper also addresses the challenges SSR faces in adoption and implementation, and its alignment with various international space debris mitigation guidelines. 

SSR draws heavily on proven rating methodologies from other industries, particularly Leadership in Energy and Environmental Design (LEED) in the building and manufacturing industries, Sustainability Assessment of Food and Agriculture systems (SAFA) in the agriculture industry, and Sustainability Tracking, Assessment and Rating System (STARS) in the education industry. 

“By grounding SSR in quantifiable metrics and testing it across diverse mission profiles, we created a rating system that recognizes sustainable decisions and operations by satellite operators, aligned with international guidelines and industry best practices,” says Rathnasabapathy. 

The Space Sustainability Rating is a nongovernmental approach to encourage space mission operators to take responsible actions to reduce space debris and collision risk. The paper highlights the roles for private sector space operators and public sector space regulators to put steps in place to ensure such responsible actions are pursued. 

The Space Enabled Research Group continues to perform academic research that illustrates the benefits of space missions and government oversight bodies enforcing sustainable and safe space practices. Future work will highlight the need for a sustainability focus as practices such as satellite service and in-space manufacturing start to become more common. 

Pavements form the backbone of our built environment. In the United States, almost 2.8 million lane-miles, or about 4.6 million lane-kilometers, are paved. They take us to work or school, take goods to their destinations, and much more.

To secure a more sustainable future, we must take a careful look at the long-term performance and environmental impacts of our pavements. Haoran Li, a postdoc at the MIT Concrete Sustainability Hub and the Department of Civil and Environmental Engineering, is deeply invested in studying how to give stakeholders the information and tools they need to make informed pavement decisions with the future in mind. Here, he discusses life-cycle assessments for pavements as well as research from MIT in addressing pavement sustainability.

Q: What is life-cycle assessment, and why does it matter for pavements?

A: Life-cycle assessment (LCA) is a method that helps us holistically assess the environmental impacts of products and systems throughout their life cycle — everything from the impacts of raw materials to construction, use, maintenance, and repair, and finally decommissioning. For pavements, up to 78 percent of the life-cycle impact comes from the use phase, with the majority stemming from vehicle fuel use impacted by pavement characteristics, such as stiffness and smoothness. This phase also includes the sunlight reflected by pavements: Lighter, more reflective pavement bounces heat back into the atmosphere instead of absorbing it, which can help keep nearby buildings and streets cooler. At the same time, there are positive use phase impacts like carbon uptake — the natural process by which cement-based products like concrete roads and infrastructure sequester CO₂ [carbon dioxide] from the atmosphere. Due to the sheer area of our pavements, they offer a great potential for the sustainability solution. Unlike many decarbonization solutions, pavements are managed by government agencies and influence the emissions from vehicles and surrounding buildings, allowing for a coordinated push toward sustainability through better materials, designs, and maintenance.

Q: What are the gaps in current pavement life-cycle assessment methods and tools and what has the MIT Concrete Sustainability Hub done to address them so far?

A: A key gap is the complexity of performing pavement LCA. Practitioners should assess both the long-term structural performance and environmental impacts of paving materials, considering the pavements’ interactions with the built environment. Another key gap is the great uncertainty associated with pavement LCA. Since pavements are designed to last for decades, it is necessary to handle the inherent uncertainty through their long-term performance evaluations.

To tackle these challenges, the MIT Concrete Sustainability Hub (CSHub) developed an innovative method and practical tools that address data intensity and uncertainty while offering context-specific and probabilistic LCA strategies. For instance, we demonstrated that it is possible to achieve meaningful results on the environmentally preferred pavement alternatives while reducing data collection efforts by focusing on the most influential and least variable parameters. By targeting key variables that significantly impact the pavement’s life cycle, we can streamline the process and still obtain robust conclusions. Overall, the efforts of the CSHub aim to enhance the accuracy and efficiency of pavement LCAs, making them better aligned with real-world conditions and more manageable in terms of data requirements.

Q: How does the MIT Concrete Sustainability Hub’s new streamlined pavement life-cycle assessment method improve on previous designs?

A: The CSHub recently developed a new framework to streamline both probabilistic and comparative LCAs for pavements. Probabilistic LCA accounts for randomness and variability in data, while comparative LCA allows the analysis of different options simultaneously to determine the most sustainable choice.

One key innovation is the use of a structured data underspecification approach, which prioritizes the data collection efforts. In pavement LCA, underspecifying can reduce the overall data collection burden by up to 85 percent, allowing for a reliable decision-making process with minimal data. By focusing on the most critical elements, we can still reach robust conclusions without the need for extensive data collection.

To make this framework practical and accessible, it is being integrated into an online LCA software tool. This tool facilitates use by practitioners, such as departments of transportation and metropolitan planning organizations. It helps them identify choices that lead to the highest-performing, longest-lasting, and most environmentally friendly pavements. Some of these solutions could include incorporating low-carbon concrete mixtures, prioritizing long-lasting treatment actions, and optimizing the design of pavement geometry to reduce life-cycle greenhouse gas emissions.

Overall, the CSHub’s new streamlined pavement LCA method significantly improves the efficiency and accessibility of conducting pavement LCAs, making it easier for stakeholders to make informed decisions that enhance pavement performance and sustainability.

Imagine a radiologist examining a chest X-ray from a new patient. She notices the patient has swelling in the tissue but does not have an enlarged heart. Looking to speed up diagnosis, she might use a vision-language machine-learning model to search for reports from similar patients.

But if the model mistakenly identifies reports with both conditions, the most likely diagnosis could be quite different: If a patient has tissue swelling and an enlarged heart, the condition is very likely to be cardiac related, but with no enlarged heart there could be several underlying causes.

In a new study, MIT researchers have found that vision-language models are extremely likely to make such a mistake in real-world situations because they don’t understand negation — words like “no” and “doesn’t” that specify what is false or absent. 

“Those negation words can have a very significant impact, and if we are just using these models blindly, we may run into catastrophic consequences,” says Kumail Alhamoud, an MIT graduate student and lead author of this study.

The researchers tested the ability of vision-language models to identify negation in image captions. The models often performed as well as a random guess. Building on those findings, the team created a dataset of images with corresponding captions that include negation words describing missing objects.

They show that retraining a vision-language model with this dataset leads to performance improvements when a model is asked to retrieve images that do not contain certain objects. It also boosts accuracy on multiple choice question answering with negated captions.

But the researchers caution that more work is needed to address the root causes of this problem. They hope their research alerts potential users to a previously unnoticed shortcoming that could have serious implications in high-stakes settings where these models are currently being used, from determining which patients receive certain treatments to identifying product defects in manufacturing plants.

“This is a technical paper, but there are bigger issues to consider. If something as fundamental as negation is broken, we shouldn’t be using large vision/language models in many of the ways we are using them now — without intensive evaluation,” says senior author Marzyeh Ghassemi, an associate professor in the Department of Electrical Engineering and Computer Science (EECS) and a member of the Institute of Medical Engineering Sciences and the Laboratory for Information and Decision Systems.

Ghassemi and Alhamoud are joined on the paper by Shaden Alshammari, an MIT graduate student; Yonglong Tian of OpenAI; Guohao Li, a former postdoc at Oxford University; Philip H.S. Torr, a professor at Oxford; and Yoon Kim, an assistant professor of EECS and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. The research will be presented at Conference on Computer Vision and Pattern Recognition.

Neglecting negation

Vision-language models (VLM) are trained using huge collections of images and corresponding captions, which they learn to encode as sets of numbers, called vector representations. The models use these vectors to distinguish between different images.

A VLM utilizes two separate encoders, one for text and one for images, and the encoders learn to output similar vectors for an image and its corresponding text caption.

“The captions express what is in the images — they are a positive label. And that is actually the whole problem. No one looks at an image of a dog jumping over a fence and captions it by saying ‘a dog jumping over a fence, with no helicopters,’” Ghassemi says.

Because the image-caption datasets don’t contain examples of negation, VLMs never learn to identify it.

To dig deeper into this problem, the researchers designed two benchmark tasks that test the ability of VLMs to understand negation.

For the first, they used a large language model (LLM) to re-caption images in an existing dataset by asking the LLM to think about related objects not in an image and write them into the caption. Then they tested models by prompting them with negation words to retrieve images that contain certain objects, but not others.

For the second task, they designed multiple choice questions that ask a VLM to select the most appropriate caption from a list of closely related options. These captions differ only by adding a reference to an object that doesn’t appear in the image or negating an object that does appear in the image.

The models often failed at both tasks, with image retrieval performance dropping by nearly 25 percent with negated captions. When it came to answering multiple choice questions, the best models only achieved about 39 percent accuracy, with several models performing at or even below random chance.

One reason for this failure is a shortcut the researchers call affirmation bias — VLMs ignore negation words and focus on objects in the images instead.

“This does not just happen for words like ‘no’ and ‘not.’ Regardless of how you express negation or exclusion, the models will simply ignore it,” Alhamoud says.

This was consistent across every VLM they tested.

“A solvable problem”

Since VLMs aren’t typically trained on image captions with negation, the researchers developed datasets with negation words as a first step toward solving the problem.

Using a dataset with 10 million image-text caption pairs, they prompted an LLM to propose related captions that specify what is excluded from the images, yielding new captions with negation words.

They had to be especially careful that these synthetic captions still read naturally, or it could cause a VLM to fail in the real world when faced with more complex captions written by humans.

They found that finetuning VLMs with their dataset led to performance gains across the board. It improved models’ image retrieval abilities by about 10 percent, while also boosting performance in the multiple-choice question answering task by about 30 percent.

“But our solution is not perfect. We are just recaptioning datasets, a form of data augmentation. We haven’t even touched how these models work, but we hope this is a signal that this is a solvable problem and others can take our solution and improve it,” Alhamoud says.

At the same time, he hopes their work encourages more users to think about the problem they want to use a VLM to solve and design some examples to test it before deployment.

In the future, the researchers could expand upon this work by teaching VLMs to process text and images separately, which may improve their ability to understand negation. In addition, they could develop additional datasets that include image-caption pairs for specific applications, such as health care.

Starting in July, MIT’s Shaping the Future of Work Initiative in the Department of Economics will usher in a significant new era of research, policy, and education of the next generation of scholars, made possible by a gift from the James M. and Cathleen D. Stone Foundation. In recognition of the gift and the expansion of priorities it supports, on July 1 the initiative will become part of the new James M. and Cathleen D. Stone Center on Inequality and Shaping the Future of Work. This center will be officially launched at a public event in fall 2025.

The Stone Center will be led by Daron Acemoglu, Institute Professor, and co-directors David Autor, the Daniel (1972) and Gail Rubinfeld Professor in Economics, and Simon Johnson, the Ronald A. Kurtz (1954) Professor of Entrepreneurship. It will join a global network of 11 other wealth inequality centers funded by the Stone Foundation as part of an effort to advance research on the causes and consequences of the growing accumulation at the top of the wealth distribution.

“This generous gift from the Stone Foundation advances our pioneering economics research on inequality, technology, and the future of the workforce. This work will create a pipeline of scholars in this critical area of study, and it will help to inform the public and policymakers,” says Provost Cynthia Barnhart.

Originally established as part of MIT Blueprint Labs with a foundational gift from the William and Flora Hewlett Foundation, the Shaping the Future of Work Initiative is a nonpartisan research organization that applies economics research to identify innovative ways to move the labor market onto a more equitable trajectory, with a central focus on revitalizing labor market opportunities for workers without a college education. Building on frontier micro- and macro-economics, economic sociology, political economy, and other disciplines, the initiative seeks to answer key questions about the decline in labor market opportunities for non-college workers in recent decades. These labor market changes have been a major driver of growing wealth inequality, a phenomenon that has, in turn, broadly reshaped our economy, democracy, and society.

Support from the Stone Foundation will allow the new Stone Center to build on the Shaping the Future of Work Initiative’s ongoing research agenda and extend its focus to include a growing emphasis on the interplay between technologies and inequality, as well as the technology sector’s role in defining future inequality.

Core objectives of the James M. and Cathleen D. Stone Center on Inequality and Shaping the Future of Work will include fostering connections between scholars doing pathbreaking research on automation, AI, the intersection of work and technology, and wealth inequality across disciplines, including within the Department of Economics, the MIT Sloan School of Management, and the MIT Stephen A. Schwarzman College of Computing; strengthening the pipeline of emerging scholars focused on these issues; and using research to inform and engage a wider audience including the public, undergraduate and graduate students, and policymakers.     

The Stone Foundation’s support will allow the center to strengthen and expand its commitments to produce new research, convene additional events to share research findings, promote connection and collaboration between scholars working on related topics, provide new resources for the center’s research affiliates, and expand public outreach to raise awareness of this important emerging challenge. “Cathy and I are thrilled to welcome MIT to the growing family of Stone Centers dedicated to studying the urgent challenges of accelerating wealth inequality,” James M. Stone says.

Agustín Rayo, dean of the School of Humanities, Arts, and Social Sciences, says, “I am thrilled to celebrate the creation of the James M. and Cathleen D. Stone Center in the MIT economics department. Not only will it enhance the cutting-edge work of MIT’s social scientists, but it will support cross-disciplinary interactions that will enable new insights and solutions to complex social challenges.”

Jonathan Gruber, chair of the Department of Economics, adds, “I couldn’t be more excited about the Stone Foundation’s support for the Shaping the Future of Work Initiative. The initiative’s leaders have been far ahead of the curve in anticipating the rapid changes that technological forces are bringing to the labor market, and their influential studies have helped us understand the potential effects of AI and other technologies on U.S. workers. The generosity of the Stone Foundation will allow them to continue this incredible work, while expanding their priorities to include other critical issues around inequality. This is a great moment for the paradigm-shifting research that Acemoglu, Autor, and Johnson are leading here at MIT.”

“We are grateful to the James M. and Cathleen D. Stone Foundation for their generous support enabling us to study two defining challenges of our age: inequality and the future of work,” says Acemoglu, who was awarded the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel in 2024 (with co-laureates Simon Johnson and James A. Robinson). “We hope to go beyond exploring the causes of inequality and the determinants of the availability of good jobs in the present and in the future, but also develop ideas about how society can shape both the work of the future and inequality by its choices of institutions and technological trajectories.”

“We are incredibly fortunate to be joining the family of Stone Centers around the world. Jim and Cathleen Stone are far-sighted and generous donors, and we are delighted that they are willing to back us and MIT in this way,” says Johnson. “We look forward to working with all our colleagues, at MIT and around the world, to advance understanding and practical approaches to inequality and the future of work.”

Autor adds, “This support will enable us — and many others — to focus our scholarship, teaching and public outreach towards shaping a labor market that offers opportunity, mobility, and economic security to a far broader set of people.” 

Just 10 to 15 minutes of mindfulness practice a day led to reduced stress and anxiety for autistic adults who participated in a study led by scientists at MIT’s McGovern Institute for Brain Research. Participants in the study used a free smartphone app to guide their practice, giving them the flexibility to practice when and where they chose.

Mindfulness is a state in which the mind is focused only on the present moment. It is a way of thinking that can be cultivated with practice, often through meditation or breathing exercises — and evidence is accumulating that practicing mindfulness has positive effects on mental health. The new open-access study, reported April 8 in the journal Mindfulness, adds to that evidence, demonstrating clear benefits for autistic adults.

“Everything you want from this on behalf of somebody you care about happened: reduced reports of anxiety, reduced reports of stress, reduced reports of negative emotions, and increased reports of positive emotions,” says McGovern investigator and MIT Professor John Gabrieli, who led the research with Liron Rozenkrantz, an investigator at the Azrieli Faculty of Medicine at Bar-Ilan University in Israel and a research affiliate in Gabrieli’s lab. “Every measure that we had of well-being moved in significantly in a positive direction,” adds Gabrieli, who is also the Grover Hermann Professor of Health Sciences and Technology and a professor of brain and cognitive sciences at MIT.

One of the reported benefits of practicing mindfulness is that it can reduce the symptoms of anxiety disorders. This prompted Gabrieli and his colleagues to wonder whether it might benefit adults with autism, who tend to report above average levels of anxiety and stress, which can interfere with daily living and quality of life. As many as 65 percent of autistic adults may also have an anxiety disorder.

Gabrieli adds that the opportunity for autistic adults to practice mindfulness with an app, rather than needing to meet with a teacher or class, seemed particularly promising. “The capacity to do it at your own pace in your own home, or any environment you like, might be good for anybody,” he says. “But maybe especially for people for whom social interactions can sometimes be challenging.”

The research team, including Cindy Li, the autism recruitment and outreach coordinator in Gabrieli’s lab, recruited 89 autistic adults to participate in their study. Those individuals were split into two groups: one would try the mindfulness practice for six weeks, while the others would wait and try the intervention later.

Participants were asked to practice daily using an app called Healthy Minds, which guides participants through seated or active meditations, each lasting 10 to 15 minutes. Participants reported that they found the app easy to use and had little trouble making time for the daily practice.

After six weeks, participants reported significant reductions in anxiety and perceived stress. These changes were not experienced by the wait-list group, which served as a control. However, after their own six weeks of practice, people in the wait-list group reported similar benefits. “We replicated the result almost perfectly. Every positive finding we found with the first sample we found with the second sample,” Gabrieli says.

The researchers followed up with study participants after another six weeks. Almost everyone had discontinued their mindfulness practice — but remarkably, their gains in well-being had persisted. Based on this finding, the team is eager to further explore the long-term effects of mindfulness practice in future studies. “There’s a hypothesis that a benefit of gaining mindfulness skills or habits is they stick with you over time — that they become incorporated in your daily life,” Gabrieli says. “If people are using the approach to being in the present and not dwelling on the past or worrying about the future, that’s what you want most of all. It’s a habit of thought that’s powerful and helpful.”

Even as they plan future studies, the researchers say they are already convinced that mindfulness practice can have clear benefits for autistic adults. “It’s possible mindfulness would be helpful at all kinds of ages,” Gabrieli says. But he points out the need is particularly great for autistic adults, who usually have fewer resources and support than autistic children have access to through their schools. Gabrieli is eager for more people with autism to try the Healthy Minds app. “Having scientifically proven resources for adults who are no longer in school systems might be a valuable thing,” he says.

This research was funded, in part, by The Hock E. Tan and K. Lisa Yang Center for Autism Research at MIT and the Yang Tan Collective.

How should personal data be protected? What are the best uses of it? In our networked world, questions about data privacy are ubiquitous and matter for companies, policymakers, and the public.

A new study by MIT researchers adds depth to the subject by suggesting that people’s views about privacy are not firmly fixed and can shift significantly, based on different circumstances and different uses of data.

“There is no absolute value in privacy,” says Fabio Duarte, principal research scientist in MIT’s Senseable City Lab and co-author of a new paper outlining the results. “Depending on the application, people might feel use of their data is more or less invasive.”

The study is based on an experiment the researchers conducted in multiple countries using a newly developed game that elicits public valuations of data privacy relating to different topics and domains of life.

“We show that values attributed to data are combinatorial, situational, transactional, and contextual,” the researchers write.

The open-access paper, “Data Slots: tradeoffs between privacy concerns and benefits of data-driven solutions,” is published today in Nature: Humanities and Social Sciences Communications. The authors are Martina Mazzarello, a postdoc in the Senseable City Lab; Duarte; Simone Mora, a research scientist at Senseable City Lab; Cate Heine PhD ’24 of University College London; and Carlo Ratti, director of the Senseable City Lab.

The study is based around a card game with poker-type chips the researchers created to study the issue, called Data Slots. In it, players hold hands of cards with 12 types of data — such as a personal profile, health data, vehicle location information, and more — that relate to three types of domains where data are collected: home life, work, and public spaces. After exchanging cards, the players generate ideas for data uses, then assess and invest in some of those concepts. The game has been played in-person in 18 different countries, with people from another 74 countries playing it online; over 2,000 individual player-rounds were included in the study.

The point behind the game is to examine the valuations that members of the public themselves generate about data privacy. Some research on the subject involves surveys with pre-set options that respondents choose from. But in Data Slots, the players themselves generate valuations for a wide range of data-use scenarios, allowing the researchers to estimate the relative weight people place on privacy in different situations. 

The idea is “to let people themselves come up with their own ideas and assess the benefits and privacy concerns of their peers’ ideas, in a participatory way,” Ratti explains.

The game strongly suggests that people’s ideas about data privacy are malleable, although the results do indicate some tendencies. The data privacy card whose use players most highly valued was for personal mobility; given the opportunity in the game to keep it or exchange it, players retained it in their hands 43 percent of the time, an indicator of its value. That was followed in order by personal health data, and utility use. (With apologies to pet owners, the type of data privacy card players held on to the least, about 10 percent of the time, involved animal health.)

However, the game distinctly suggests that the value of privacy is highly contingent on specific use-cases. The game shows that people care about health data to a substantial extent but also value the use of environmental data in the workplace, for instance. And the players of Data Slots also seem less concerned about data privacy when use of data is combined with clear benefits. In combination, that suggests a deal to be cut: Using health data can help people understand the effects of the workplace on wellness.

“Even in terms of health data in work spaces, if they are used in an aggregated way to improve the workspace, for some people it’s worth combining personal health data with environmental data,” Mora says.

Mazzarello adds: “Now perhaps the company can make some interventions to improve overall health. It might be invasive, but you might get some benefits back.”

In the bigger picture, the researchers suggest, taking a more flexible, user-driven approach to understanding what people think about data privacy can help inform better data policy. Cities — the core focus on the Senseable City Lab — often face such scenarios. City governments can collect a lot of aggregate traffic data, for instance, but public input can help determine how anonymized such data should be. Understanding public opinion along with the benefits of data use can produce viable policies for local officials to pursue.

“The bottom line is that if cities disclose what they plan to do with data, and if they involve resident stakeholders to come up with their own ideas about what they could do, that would be beneficial to us,” Duarte says. “And in those scenarios, people’s privacy concerns start to decrease a lot.” 

The United States population is older than it has ever been. Today, the country’s median age is 38.9, which is nearly a decade older than it was in 1980. And the number of adults older than 65 is expected to balloon from 58 million to 82 million by 2050. The challenge of caring for the elderly, amid shortages in care workers, rising health care costs, and evolving family structures, is an increasingly urgent societal issue.

To help address the eldercare challenge, a team of MIT engineers is looking to robotics. They have built and tested the Elderly Bodily Assistance Robot, or E-BAR, a mobile robot designed to physically support the elderly and prevent them from falling as they move around their homes.

E-BAR acts as a set of robotic handlebars that follows a person from behind. A user can walk independently or lean on the robot’s arms for support. The robot can support the person’s full weight, lifting them from sitting to standing and vice versa along a natural trajectory. And the arms of the robot can catch them by rapidly inflating side airbags if they begin to fall.

With their design, the researchers hope to prevent falls, which today are the leading cause of injury in adults who are 65 and older. 

“Many older adults underestimate the risk of fall and refuse to use physical aids, which are cumbersome, while others overestimate the risk and may not exercise, leading to declining mobility,” says Harry Asada, the Ford Professor of Engineering at MIT. “Our design concept is to provide older adults having balance impairment with robotic handlebars for stabilizing their body. The handlebars go anywhere and provide support anytime, whenever they need.”

In its current version, the robot is operated via remote control. In future iterations, the team plans to automate much of the bot’s functionality, enabling it to autonomously follow and physically assist a user. The researchers are also working on streamlining the device to make it slimmer and more maneuverable in small spaces.

“I think eldercare is the next great challenge,” says E-BAR designer Roberto Bolli, a graduate student in the MIT Department of Mechanical Engineering. “All the demographic trends point to a shortage of caregivers, a surplus of elderly persons, and a strong desire for elderly persons to age in place. We see it as an unexplored frontier in America, but also an intrinsically interesting challenge for robotics.”

Bolli and Asada will present a paper detailing the design of E-BAR at the IEEE Conference on Robotics and Automation (ICRA) later this month.

Asada’s group at MIT develops a variety of technologies and robotic aides to assist the elderly. In recent years, others have developed fall prediction algorithms, designed robots and automated devices including robotic walkers, wearable, self-inflating airbags, and robotic frames that secure a person with a harness and move with them as they walk.

In designing E-BAR, Asada and Bolli aimed for a robot that essentially does three tasks: providing physical support, preventing falls, and safely and unobtrusively moving with a person. What’s more, they looked to do away with any harness, to give a user more independence and mobility.

“Elderly people overwhelmingly do not like to wear harnesses or assistive devices,” Bolli says. “The idea behind the E-BAR structure is, it provides body weight support, active assistance with gait, and fall catching while also being completely unobstructed in the front. You can just get out anytime.”

The team looked to design a robot specifically for aging in place at home or helping in care facilities. Based on their interviews with older adults and their caregivers, they came up with several design requirements, including that the robot must fit through home doors, allow the user to take a full stride, and support their full weight to help with balance, posture, and transitions from sitting to standing.

The robot consists of a heavy, 220-pound base whose dimensions and structure were optimized to support the weight of an average human without tipping or slipping. Underneath the base is a set of omnidirectional wheels that allows the robot to move in any direction without pivoting, if needed. (Imagine a car’s wheels shifting to slide into a space between two other cars, without parallel parking.)

Extending out from the robot’s base is an articulated body made from 18 interconnected bars, or linkages, that can reconfigure like a foldable crane to lift a person from a sitting to standing position, and vice versa. Two arms with handlebars stretch out from the robot in a U-shape, which a person can stand between and lean against if they need additional support. Finally, each arm of the robot is embedded with airbags made from a soft yet grippable material that can inflate instantly to catch a person if they fall, without causing bruising on impact. The researchers believe that E-BAR is the first robot able to catch a falling person without wearable devices or use of a harness.

They tested the robot in the lab with an older adult who volunteered to use the robot in various household scenarios. The team found that E-BAR could actively support the person as they bent down to pick something up from the ground and stretched up to reach an object off a shelf — tasks that can be challenging to do while maintaining balance. The robot also was able to lift the person up and over the lip of a tub, simulating the task of getting out of a bathtub.

Bolli envisions a design like E-BAR would be ideal for use in the home by elderly people who still have a moderate degree of muscle strength but require assistive devices for activities of daily living.

“Seeing the technology used in real-life scenarios is really exciting,” says Bolli.

In their current paper, the researchers did not incorporate any fall-prediction capabilities in E-BAR’s airbag system. But another project in Asada’s lab, led by graduate student Emily Kamienski, has focused on developing algorithms with machine learning to control a new robot in response to the user’s real-time fall risk level.

Alongside E-BAR, Asada sees different technologies in his lab as providing different levels of assistance for people at certain phases of life or mobility.

“Eldercare conditions can change every few weeks or months,” Asada says. “We’d like to provide continuous and seamless support as a person’s disability or mobility changes with age.”

This work was supported, in part, by the National Robotics Initiative and the National Science Foundation.

Studies by a growing number of labs have identified neurological health benefits from exposing human volunteers or animal models to light, sound, and/or tactile stimulation at the brain’s “gamma” frequency rhythm of 40Hz. In the latest such research at The Picower Institute for Learning and Memory and Alana Down Syndrome Center at MIT, scientists found that 40Hz sensory stimulation improved cognition and circuit connectivity and encouraged the growth of new neurons in mice genetically engineered to model Down syndrome.

Li-Huei Tsai, Picower Professor at MIT and senior author of the new study in PLOS ONE, says that the results are encouraging, but also cautions that much more work is needed to test whether the method, called GENUS (for gamma entrainment using sensory stimulation), could provide clinical benefits for people with Down syndrome. Her lab has begun a small study with human volunteers at MIT.

“While this work, for the first time, shows beneficial effects of GENUS on Down syndrome using an imperfect mouse model, we need to be cautious, as there is not yet data showing whether this also works in humans,” says Tsai, who directs The Picower Institute and The Alana Center, and is a member of MIT’s Department of Brain and Cognitive Sciences faculty.

Still, she says, the newly published article adds evidence that GENUS can promote a broad-based, restorative, “homeostatic” health response in the brain amid a wide variety of pathologies. Most GENUS studies have addressed Alzheimer’s disease in humans or mice, but others have found benefits from the stimulation for conditions such as “chemo brain” and stroke.

Down syndrome benefits

In the study, the research team led by postdoc Md Rezaul Islam and Brennan Jackson PhD ’23 worked with the commonly used “Ts65Dn” Down syndrome mouse model. The model recapitulates key aspects of the disorder, although it does not exactly mirror the human condition, which is caused by carrying an extra copy of chromosome 21.

In the first set of experiments in the paper, the team shows that an hour a day of 40Hz light and sound exposure for three weeks was associated with significant improvements on three standard short-term memory tests — two involving distinguishing novelty from familiarity and one involving spatial navigation. Because these kinds of memory tasks involve a brain region called the hippocampus, the researchers looked at neural activity there and measured a significant increase in activity indicators among mice that received the GENUS stimulation versus those that did not.

To better understand how stimulated mice could show improved cognition, the researchers examined whether cells in the hippocampus changed how they express their genes. To do this, the team used a technique called single cell RNA sequencing, which provided a readout of how nearly 16,000 individual neurons and other cells transcribed their DNA into RNA, a key step in gene expression. Many of the genes whose expression varied most prominently in neurons between the mice that received stimulation and those that did not were directly related to forming and organizing neural circuit connections called synapses.

To confirm the significance of that finding, the researchers directly examined the hippocampus in stimulated and control mice. They found that in a critical subregion, the dentate gyrus, stimulated mice had significantly more synapses.

Diving deeper

The team not only examined gene expression across individual cells, but also analyzed those data to assess whether there were patterns of coordination across multiple genes. Indeed, they found several such “modules” of co-expression. Some of this evidence further substantiated the idea that 40Hz-stimulated mice made important improvements in synaptic connectivity, but another key finding highlighted a role for TCF4, a key regulator of gene transcription needed for generating new neurons, or “neurogenesis.”  

The team’s analysis of genetic data suggested that TCF4 is underexpressed in Down syndrome mice, but the researchers saw improved TCF4 expression in GENUS-stimulated mice. When the researchers went to the lab bench to determine whether the mice also exhibited a difference in neurogenesis, they found direct evidence that stimulated mice exhibited more than unstimulated mice in the dentate gyrus. These increases in TCF4 expression and neurogenesis are only correlational, the researchers noted, but they hypothesize that the increase in new neurons likely helps explain at least some of the increase in new synapses and improved short-term memory function.

“The increased putative functional synapses in the dentate gyrus is likely related to the increased adult neurogenesis observed in the Down syndrome mice following GENUS treatment,” Islam says.

This study is the first to document that GENUS is associated with increased neurogenesis.

The analysis of gene expression modules also yielded other key insights. One is that a cluster of genes whose expression typically declines with normal aging, and in Alzheimer’s disease, remained at higher expression levels among mice who received 40Hz sensory stimulation.

And the researchers also found evidence that mice that received stimulation retained more cells in the hippocampus that express Reelin. Reelin-expressing neurons are especially vulnerable in Alzheimer’s disease, but expression of the protein is associated with cognitive resilience amid Alzheimer’s disease pathology, which Ts65Dn mice develop. About 90 percent of people with Down syndrome develop Alzheimer’s disease, typically after the age of 40.

“In this study, we found that GENUS enhances the percentage of Reln+ neurons in hippocampus of a mouse model of Down syndrome, suggesting that GENUS may promote cognitive resilience,” Islam says.

Taken together with other studies, Tsai and Islam say, the new results add evidence that GENUS helps to stimulate the brain at the cellular and molecular level to mount a homeostatic response to aberrations caused by disease pathology, be it neurodegeneration in Alzheimer’s, demyelination in chemo brain, or deficits of neurogenesis in Down syndrome.

But the authors also cautioned that the study had limits. Not only is the Ts65Dn model an imperfect reflection of human Down syndrome, but also the mice used were all male. Moreover, the cognitive tests in the study only measured short-term memory. And finally, while the study was novel for extensively examining gene expression in the hippocampus amid GENUS stimulation, it did not look at changes in other cognitively critical brain regions, such as the prefrontal cortex.

In addition to Jackson, Islam, and Tsai, the paper’s other authors are Maeesha Tasnim Naomi, Brooke Schatz, Noah Tan, Mitchell Murdock, Dong Shin Park, Daniela Rodrigues Amorim, Fred Jiang, S. Sebastian Pineda, Chinnakkaruppan Adaikkan, Vanesa Fernandez, Ute Geigenmuller, Rosalind Mott Firenze, Manolis Kellis, and Ed Boyden.

Funding for the study came from the Alana Down Syndrome Center at MIT and the Alana USA Foundation, the U.S. National Science Foundation, the La Caixa Banking Foundation, a European Molecular Biology Organization long-term postdoctoral fellowship, Barbara J. Weedon, Henry E. Singleton, and the Hubolow family.

Researchers from MIT and Dana-Farber Cancer Institute have discovered that a class of peptides expressed in pancreatic cancer cells could be a promising target for T-cell therapies and other approaches that attack pancreatic tumors.

Known as cryptic peptides, these molecules are produced from sequences in the genome that were not thought to encode proteins. Such peptides can also be found in some healthy cells, but in this study, the researchers identified about 500 that appear to be found only in pancreatic tumors.

The researchers also showed they could generate T cells targeting those peptides. Those T cells were able to attack pancreatic tumor organoids derived from patient cells, and they significantly slowed down tumor growth in a study of mice.

“Pancreas cancer is one of the most challenging cancers to treat. This study identifies an unexpected vulnerability in pancreas cancer cells that we may be able to exploit therapeutically,” says Tyler Jacks, the David H. Koch Professor of Biology at MIT and a member of the Koch Institute for Integrative Cancer Research.

Jacks and William Freed-Pastor, a physician-scientist in the Hale Family Center for Pancreatic Cancer Research at Dana-Farber Cancer Institute and an assistant professor at Harvard Medical School, are the senior authors of the study, which appears today in Science. Zackery Ely PhD ’22 and Zachary Kulstad, a former research technician at Dana-Farber Cancer Institute and the Koch Institute, are the lead authors of the paper.

Cryptic peptides

Pancreatic cancer has one of the lowest survival rates of any cancer — about 10 percent of patients survive for five years after their diagnosis.

Most pancreatic cancer patients receive a combination of surgery, radiation treatment, and chemotherapy. Immunotherapy treatments such as checkpoint blockade inhibitors, which are designed to help stimulate the body’s own T cells to attack tumor cells, are usually not effective against pancreatic tumors. However, therapies that deploy T cells engineered to attack tumors have shown promise in clinical trials.

These therapies involve programming the T-cell receptor (TCR) of T cells to recognize a specific peptide, or antigen, found on tumor cells. There are many efforts underway to identify the most effective targets, and researchers have found some promising antigens that consist of mutated proteins that often show up when pancreatic cancer genomes are sequenced.

In the new study, the MIT and Dana-Farber team wanted to extend that search into tissue samples from patients with pancreatic cancer, using immunopeptidomics — a strategy that involves extracting the peptides presented on a cell surface and then identifying the peptides using mass spectrometry.

Using tumor samples from about a dozen patients, the researchers created organoids — three-dimensional growths that partially replicate the structure of the pancreas. The immunopeptidomics analysis, which was led by Jennifer Abelin and Steven Carr at the Broad Institute, found that the majority of novel antigens found in the tumor organoids were cryptic antigens. Cryptic peptides have been seen in other types of tumors, but this is the first time they have been found in pancreatic tumors.

Each tumor expressed an average of about 250 cryptic peptides, and in total, the researchers identified about 1,700 cryptic peptides.

“Once we started getting the data back, it just became clear that this was by far the most abundant novel class of antigens, and so that’s what we wound up focusing on,” Ely says.

The researchers then performed an analysis of healthy tissues to see if any of these cryptic peptides were found in normal cells. They found that about two-thirds of them were also found in at least one type of healthy tissue, leaving about 500 that appeared to be restricted to pancreatic cancer cells.

“Those are the ones that we think could be very good targets for future immunotherapies,” Freed-Pastor says.

Programmed T cells

To test whether these antigens might hold potential as targets for T-cell-based treatments, the researchers exposed about 30 of the cancer-specific antigens to immature T cells and found that 12 of them could generate large populations of T cells targeting those antigens.

The researchers then engineered a new population of T cells to express those T-cell receptors. These engineered T cells were able to destroy organoids grown from patient-derived pancreatic tumor cells. Additionally, when the researchers implanted the organoids into mice and then treated them with the engineered T cells, tumor growth was significantly slowed.

This is the first time that anyone has demonstrated the use of T cells targeting cryptic peptides to kill pancreatic tumor cells. Even though the tumors were not completely eradicated, the results are promising, and it is possible that the T-cells’ killing power could be strengthened in future work, the researchers say.

Freed-Pastor’s lab is also beginning to work on a vaccine targeting some of the cryptic antigens, which could help stimulate patients’ T cells to attack tumors expressing those antigens. Such a vaccine could include a collection of the antigens identified in this study, including those frequently found in multiple patients.

This study could also help researchers in designing other types of therapy, such as T cell engagers — antibodies that bind an antigen on one side and T cells on the other, which allows them to redirect any T cell to kill tumor cells.

Any potential vaccine or T cell therapy is likely a few years away from being tested in patients, the researchers say.

The research was funded in part by the Hale Family Center for Pancreatic Cancer Research, the Lustgarten Foundation, Stand Up To Cancer, the Pancreatic Cancer Action Network, the Burroughs Wellcome Fund, a Conquer Cancer Young Investigator Award, the National Institutes of Health, and the National Cancer Institute.

It’s been a scientific truth so universally acknowledged that it’s taught in classrooms and repeated in pop-science videos: An egg is strongest when dropped vertically, on its ends. But when MIT engineers actually put this assumption to the test, they cracked open a surprising revelation. 

Their experiments revealed that eggs dropped on their sides — not their tips — are far more resilient, thanks to a clever physics trick: Sideways eggs bend like shock absorbers, trading stiffness for superior energy absorption. Their open-access findings, published today in Communications Physics, don’t just rewrite the rules of the classic egg drop challenge — they’re a lesson in intellectual humility and curiosity. Even “settled” science can yield surprises when approached with rigor and an open mind.

At first glance, an eggshell may seem fragile, but its strength is a marvel of physics. Crack an egg on its side for your morning omelet and it breaks easily. Intuitively, we believe eggs are harder to break when positioned vertically. This notion has long been a cornerstone of the classic “egg drop challenge,” a popular science activity in STEM classrooms across the country that introduces students to physics concepts of impact, force, kinetic energy, and engineering design.

The annual egg drop competition is a highlight of first-year orientation in the MIT Department of Civil and Environmental Engineering. “Every year we follow the scientific literature and talk to the students about how to position the egg to avoid breakage on impact,” says Tal Cohen, associate professor of civil and environmental engineering and mechanical engineering. “But about three years ago, we started to question whether vertical really is stronger.” 

That curiosity sparked an initial experiment by Cohen’s research group, which leads the department’s egg drop event. They decided to put their remaining box of eggs to the test in the lab. “We expected to confirm the vertical side was tougher based on what we had read online,” says Cohen. “But when we looked at the data — it was really unclear.”

What began as casual inquiry evolved into a research project. To rigorously investigate the strength of both egg orientations, the researchers conducted two types of experiments: static compression tests, which applied gradually increasing force to measure stiffness and toughness; and dynamic drop tests, to quantify the likelihood of breaking on impact.

“In the static testing, we wanted to keep an egg at a standstill and push on it until it cracked,” explains Avishai Jeselsohn, an undergraduate researcher and an author in the study. “We used thin paper supports to precisely orient the eggs vertically and horizontally.”

What the researchers found was it required the same amount of force to initiate a crack in both orientations. “However, we noticed a key difference in how much the egg compressed before it broke, says Joseph Bonavia, PhD candidate who contributed to the work. “The horizontal egg compressed more under the same amount of force, meaning it was more compliant.”

Using mechanical modeling and numerical simulations to validate results of their experiments, the researchers concluded that even though the force to crack the egg was consistent, the horizontal eggs absorbed more energy due to their compliance. “This suggested that in situations where energy absorption is important, like in a drop, the horizontal orientation might be more resilient. We then performed the dynamic drop tests to see if this held true in practice,” says Jeselsohn.

The researchers designed a drop setup using solenoids and 3D-printed supports, ensuring simultaneous release and consistent egg orientation. Eggs were dropped from various heights to observe breakage patterns. The result: Horizontal eggs cracked less frequently when dropped from the same height.

“This confirmed what we saw in the static tests,” says Jeselsohn. “Even though both orientations experienced similar peak forces, the horizontal eggs absorbed energy better and were more resistant to breaking.”

Challenging common notions

The study reveals a misconception in popular science regarding the strength of an egg when subjected to impact. Even seasoned researchers in fracture mechanics initially assumed that vertical oriented eggs would be stronger. “It’s a widespread, accepted belief, referenced in many online sources,” notes Jeselsohn.

Everyday experience may reinforce that misconception. After all, we often crack eggs on their sides when cooking. “But that’s not the same as resisting impact,” explains Brendan Unikewicz, a PhD candidate and author on the paper. “Cracking an egg for cooking involves applying locally focused force for a clean break to retrieve the yolk, while its resistance to breaking from a drop involves distributing and absorbing energy across the shell.”

The difference is subtle but significant. A vertically oriented egg, while stiffer, is more brittle under sudden force. A horizontal egg, being more compliant, bends and absorbs energy over a greater distance — similar to how bending your knees during a fall softens the blow.

“In a way, our legs are ‘weaker’ when bent, but they’re actually tougher in absorbing impact,” Bonavia adds. “It’s the same with the egg. Toughness isn’t just about resisting force — it’s about how that force is dissipated.”

The research findings offer more than insight into egg behavior — they underscore a broader scientific principle: that widely accepted “truths” are worth re-examining.

Which came first?

“It’s great to see an example of ‘received wisdom’ being tested scientifically and shown to be incorrect. There are many such examples in the scientific literature, and it’s a real problem in some fields because it can be difficult to secure funding to challenge an existing, ‘well-known’ theory,” says David Taylor, emeritus professor in the Department of Mechanical, Manufacturing and Biomedical Engineering at Trinity College Dublin, who was not affiliated with the study.

The authors hope their findings encourage young people to remain curious and recognize just how much remains to be discovered in the physical world.

“Our paper is a reminder of the value in challenging common notions and relying on empirical evidence, rather than intuition,” says Cohen. “We hope our work inspires students to stay curious, question even the most familiar assumptions, and continue thinking critically about the physical world around them. That’s what we strive to do in our group — constantly challenge what we’re taught through thoughtful inquiry.”

In addition to Cohen, who serves as senior author on the paper, co-authors include lead authors Antony Sutanto MEng ’24 and Suhib Abu-Qbeitah, a postdoc at Tel Aviv University, as well as the following MIT affiliates: Avishai Jeselsohn, an undergraduate in mechanical engineering; Brendan Unikewicz, a PhD candidate in mechanical engineering; Joseph Bonavia, a PhD candidate in mechanical engineering; Stephen Rudolph, a lab instructor in civil and environmental engineering; Hudson Borja da Rocha, an MIT postdoc in civil and environmental engineering; and Kiana Naghibzadeh, Engineering Excellence Postdoctoral Fellow in civil and environmental engineering. The research was funded by U.S. Office of Naval Research with support from the U.S. National Science Foundation. 

MIT engineers are getting in on the robotic ping pong game with a powerful, lightweight design that returns shots with high-speed precision.

The new table tennis bot comprises a multijointed robotic arm that is fixed to one end of a ping pong table and wields a standard ping pong paddle. Aided by several high-speed cameras and a high-bandwidth predictive control system, the robot quickly estimates the speed and trajectory of an incoming ball and executes one of several swing types — loop, drive, or chop — to precisely hit the ball to a desired location on the table with various types of spin.

In tests, the engineers threw 150 balls at the robot, one after the other, from across the ping pong table. The bot successfully returned the balls with a hit rate of about 88 percent across all three swing types. The robot’s strike speed approaches the top return speeds of human players and is faster than that of other robotic table tennis designs.

Now, the team is looking to increase the robot’s playing radius so that it can return a wider variety of shots. Then, they envision the setup could be a viable competitor in the growing field of smart robotic training systems.

Beyond the game, the team says the table tennis tech could be adapted to improve the speed and responsiveness of humanoid robots, particularly for search-and-rescue scenarios, and situations in a which a robot would need to quickly react or anticipate.

“The problems that we’re solving, specifically related to intercepting objects really quickly and precisely, could potentially be useful in scenarios where a robot has to carry out dynamic maneuvers and plan where its end effector will meet an object, in real-time,” says MIT graduate student David Nguyen.

Nguyen is a co-author of the new study, along with MIT graduate student Kendrick Cancio and Sangbae Kim, associate professor of mechanical engineering and head of the MIT Biomimetics Robotics Lab. The researchers will present the results of those experiments in a paper at the IEEE International Conference on Robotics and Automation (ICRA) this month.

Precise play

Building robots to play ping pong is a challenge that researchers have taken up since the 1980s. The problem requires a unique combination of technologies, including high-speed machine vision, fast and nimble motors and actuators, precise manipulator control, and accurate, real-time prediction, as well as higher-level planning of game strategy.

“If you think of the spectrum of control problems in robotics, we have on one end manipulation, which is usually slow and very precise, such as picking up an object and making sure you’re grasping it well. On the other end, you have locomotion, which is about being dynamic and adapting to perturbations in your system,” Nguyen explains. “Ping pong sits in between those. You’re still doing manipulation, in that you have to be precise in hitting the ball, but you have to hit it within 300 milliseconds. So, it balances similar problems of dynamic locomotion and precise manipulation.”

Ping pong robots have come a long way since the 1980s, most recently with designs by Omron and Google DeepMind that employ artificial intelligence techniques to “learn” from previous ping pong data, to improve a robot’s performance against an increasing variety of strokes and shots. These designs have been shown to be fast and precise enough to rally with intermediate human players.

“These are really specialized robots designed to play ping pong,” Cancio says. “With our robot, we are exploring how the techniques used in playing ping pong could translate to a more generalized system, like a humanoid or anthropomorphic robot that can do many different, useful things.”

Game control

For their new design, the researchers modified a lightweight, high-power robotic arm that Kim’s lab developed as part of the MIT Humanoid — a bipedal, two-armed robot that is about the size of a small child. The group is using the robot to test various dynamic maneuvers, including navigating uneven and varying terrain as well as jumping, running, and doing backflips, with the aim of one day deploying such robots for search-and-rescue operations.

Each of the humanoid’s arms has four joints, or degrees of freedom, which are each controlled by an electrical motor. Cancio, Nguyen, and Kim built a similar robotic arm, which they adapted for ping pong by adding an additional degree of freedom in the wrist to allow for control of a paddle.

The team fixed the robotic arm to a table at one end of a standard ping pong table and set up high-speed motion capture cameras around the table to track balls that are bounced at the robot. They also developed optimal control algorithms that predict, based on the principles of math and physics, what speed and paddle orientation the arm should execute to hit an incoming ball with a particular type of swing: loop (or topspin), drive (straight-on), or chop (backspin).

They implemented the algorithms using three computers that simultaneously processed camera images, estimated a ball’s real-time state, and translated these estimations to commands for the robot’s motors to quickly react and take a swing.

After consecutively bouncing 150 balls at the arm, they found the robot’s hit rate, or accuracy of returning the ball, was about the same for all three types of swings: 88.4 percent for loop strikes, 89.2 percent for chops, and 87.5 percent for drives. They have since tuned the robot’s reaction time and found the arm hits balls faster than existing systems, at velocities of 20 meters per second.

In their paper, the team reports that the robot’s strike speed, or the speed at which the paddle hits the ball, is on average 11 meters per second. Advanced human players have been known to return balls at speeds of between 21 to 25 meters second. Since writing up the results of their initial experiments, the researchers have further tweaked the system, and have recorded strike speeds of up to 19 meters per second (about 42 miles per hour).

“Some of the goal of this project is to say we can reach the same level of athleticism that people have,” Nguyen says. “And in terms of strike speed, we’re getting really, really close.”

Their follow-up work has also enabled the robot to aim. The team incorporated control algorithms into the system that predict not only how but where to hit an incoming ball. With its latest iteration, the researchers can set a target location on the table, and the robot will hit a ball to that same location.

Because it is fixed to the table, the robot has limited mobility and reach, and can mostly return balls that arrive within a crescent-shaped area around the midline of the table. In the future, the engineers plan to rig the bot on a gantry or wheeled platform, enabling it to cover more of the table and return a wider variety of shots.

“A big thing about table tennis is predicting the spin and trajectory of the ball, given how your opponent hit it, which is information that an automatic ball launcher won’t give you,” Cancio says. “A robot like this could mimic the maneuvers that an opponent would do in a game environment, in a way that helps humans play and improve.”

This research is supported, in part, by the Robotics and AI Institute.

A human clearing junk out of an attic can often guess the contents of a box simply by picking it up and giving it a shake, without the need to see what’s inside. Researchers from MIT, Amazon Robotics, and the University of British Columbia have taught robots to do something similar.

They developed a technique that enables robots to use only internal sensors to learn about an object’s weight, softness, or contents by picking it up and gently shaking it. With their method, which does not require external measurement tools or cameras, the robot can accurately guess parameters like an object’s mass in a matter of seconds.

This low-cost technique could be especially useful in applications where cameras might be less effective, such as sorting objects in a dark basement or clearing rubble inside a building that partially collapsed after an earthquake.

Key to their approach is a simulation process that incorporates models of the robot and the object to rapidly identify characteristics of that object as the robot interacts with it. 

The researchers’ technique is as good at guessing an object’s mass as some more complex and expensive methods that incorporate computer vision. In addition, their data-efficient approach is robust enough to handle many types of unseen scenarios.

“This idea is general, and I believe we are just scratching the surface of what a robot can learn in this way. My dream would be to have robots go out into the world, touch things and move things in their environments, and figure out the properties of everything they interact with on their own,” says Peter Yichen Chen, an MIT postdoc and lead author of a paper on this technique.

His coauthors include fellow MIT postdoc Chao Liu; Pingchuan Ma PhD ’25; Jack Eastman MEng ’24; Dylan Randle and Yuri Ivanov of Amazon Robotics; MIT professors of electrical engineering and computer science Daniela Rus, who leads MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL); and Wojciech Matusik, who leads the Computational Design and Fabrication Group within CSAIL. The research will be presented at the International Conference on Robotics and Automation.

Sensing signals

The researchers’ method leverages proprioception, which is a human or robot’s ability to sense its movement or position in space.

For instance, a human who lifts a dumbbell at the gym can sense the weight of that dumbbell in their wrist and bicep, even though they are holding the dumbbell in their hand. In the same way, a robot can “feel” the heaviness of an object through the multiple joints in its arm.

“A human doesn’t have super-accurate measurements of the joint angles in our fingers or the precise amount of torque we are applying to an object, but a robot does. We take advantage of these abilities,” Liu says.

As the robot lifts an object, the researchers’ system gathers signals from the robot’s joint encoders, which are sensors that detect the rotational position and speed of its joints during movement. 

Most robots have joint encoders within the motors that drive their moveable parts, Liu adds. This makes their technique more cost-effective than some approaches because it doesn’t need extra components like tactile sensors or vision-tracking systems.

To estimate an object’s properties during robot-object interactions, their system relies on two models: one that simulates the robot and its motion and one that simulates the dynamics of the object.

“Having an accurate digital twin of the real-world is really important for the success of our method,” Chen adds.

Their algorithm “watches” the robot and object move during a physical interaction and uses joint encoder data to work backward and identify the properties of the object.

For instance, a heavier object will move slower than a light one if the robot applies the same amount of force.

Differentiable simulations

They utilize a technique called differentiable simulation, which allows the algorithm to predict how small changes in an object’s properties, like mass or softness, impact the robot’s ending joint position. The researchers built their simulations using NVIDIA’s Warp library, an open-source developer tool that supports differentiable simulations.

Once the differentiable simulation matches up with the robot’s real movements, the system has identified the correct property. The algorithm can do this in a matter of seconds and only needs to see one real-world trajectory of the robot in motion to perform the calculations.

“Technically, as long as you know the model of the object and how the robot can apply force to that object, you should be able to figure out the parameter you want to identify,” Liu says.

The researchers used their method to learn the mass and softness of an object, but their technique could also determine properties like moment of inertia or the viscosity of a fluid inside a container.

Plus, because their algorithm does not need an extensive dataset for training like some methods that rely on computer vision or external sensors, it would not be as susceptible to failure when faced with unseen environments or new objects.

In the future, the researchers want to try combining their method with computer vision to create a multimodal sensing technique that is even more powerful.

“This work is not trying to replace computer vision. Both methods have their pros and cons. But here we have shown that without a camera we can already figure out some of these properties,” Chen says.

They also want to explore applications with more complicated robotic systems, like soft robots, and more complex objects, including sloshing liquids or granular media like sand.

In the long run, they hope to apply this technique to improve robot learning, enabling future robots to quickly develop new manipulation skills and adapt to changes in their environments.

“Determining the physical properties of objects from data has long been a challenge in robotics, particularly when only limited or noisy measurements are available. This work is significant because it shows that robots can accurately infer properties like mass and softness using only their internal joint sensors, without relying on external cameras or specialized measurement tools,” says Miles Macklin, senior director of simulation technology at NVIDIA, who was not involved with this research.

This work is funded, in part, by Amazon and the GIST-CSAIL Research Program.

Dangers come but dangers also go, and when they do, the brain has an “all-clear” signal that teaches it to extinguish its fear. A new study in mice by MIT neuroscientists shows that the signal is the release of dopamine along a specific interregional brain circuit. The research therefore pinpoints a potentially critical mechanism of mental health, restoring calm when it works, but prolonging anxiety or even post-traumatic stress disorder when it doesn’t.

“Dopamine is essential to initiate fear extinction,” says Michele Pignatelli di Spinazzola, co-author of the new study from the lab of senior author Susumu Tonegawa, Picower Professor of biology and neuroscience at the RIKEN-MIT Laboratory for Neural Circuit Genetics within The Picower Institute for Learning and Memory at MIT, and a Howard Hughes Medical Institute (HHMI) investigator.

In 2020, Tonegawa’s lab showed that learning to be afraid, and then learning when that’s no longer necessary, result from a competition between populations of cells in the brain’s amygdala region. When a mouse learns that a place is “dangerous” (because it gets a little foot shock there), the fear memory is encoded by neurons in the anterior of the basolateral amygdala (aBLA) that express the gene Rspo2. When the mouse then learns that a place is no longer associated with danger (because they wait there and the zap doesn’t recur), neurons in the posterior basolateral amygdala (pBLA) that express the gene Ppp1r1b encode a new fear extinction memory that overcomes the original dread. Notably, those same neurons encode feelings of reward, helping to explain why it feels so good when we realize that an expected danger has dwindled.

In the new study, the lab, led by former members Xiangyu Zhang and Katelyn Flick, sought to determine what prompts these amygdala neurons to encode these memories. The rigorous set of experiments the team reports in the Proceedings of the National Academy of Sciences show that it’s dopamine sent to the different amygdala populations from distinct groups of neurons in the ventral tegmental area (VTA).

“Our study uncovers a precise mechanism by which dopamine helps the brain unlearn fear,” says Zhang, who also led the 2020 study and is now a senior associate at Orbimed, a health care investment firm. “We found that dopamine activates specific amygdala neurons tied to reward, which in turn drive fear extinction. We now see that unlearning fear isn’t just about suppressing it — it’s a positive learning process powered by the brain’s reward machinery. This opens up new avenues for understanding and potentially treating fear-related disorders, like PTSD.”

Forgetting fear

The VTA was the lab’s prime suspect to be the source of the signal because the region is well known for encoding surprising experiences and instructing the brain, with dopamine, to learn from them. The first set of experiments in the paper used multiple methods for tracing neural circuits to see whether and how cells in the VTA and the amygdala connect. They found a clear pattern: Rspo2 neurons were targeted by dopaminergic neurons in the anterior and left and right sides of the VTA. Ppp1r1b neurons received dopaminergic input from neurons in the center and posterior sections of the VTA. The density of connections was greater on the Ppp1r1b neurons than for the Rspo2 ones.

The circuit tracing showed that dopamine is available to amygdala neurons that encode fear and its extinction, but do those neurons care about dopamine? The team showed that indeed they express “D1” receptors for the neuromodulator. Commensurate with the degree of dopamine connectivity, Ppp1r1b cells had more receptors than Rspo2 neurons.

Dopamine does a lot of things, so the next question was whether its activity in the amygdala actually correlated with fear encoding and extinction. Using a method to track and visualize it in the brain, the team watched dopamine in the amygdala as mice underwent a three-day experiment. On Day One, they went to an enclosure where they experienced three mild shocks on the feet. On Day Two, they went back to the enclosure for 45 minutes, where they didn’t experience any new shocks — at first, the mice froze in anticipation of a shock, but then relaxed after about 15 minutes. On Day Three they returned again to test whether they had indeed extinguished the fear they showed at the beginning of Day Two.

The dopamine activity tracking revealed that during the shocks on Day One, Rspo2 neurons had the larger response to dopamine, but in the early moments of Day Two, when the anticipated shocks didn’t come and the mice eased up on freezing, the Ppp1r1b neurons showed the stronger dopamine activity. More strikingly, the mice that learned to extinguish their fear most strongly also showed the greatest dopamine signal at those neurons.

Causal connections

The final sets of experiments sought to show that dopamine is not just available and associated with fear encoding and extinction, but also actually causes them. In one set, they turned to optogenetics, a technology that enables scientists to activate or quiet neurons with different colors of light. Sure enough, when they quieted VTA dopaminergic inputs in the pBLA, doing so impaired fear extinction. When they activated those inputs, it accelerated fear extinction. The researchers were surprised that when they activated VTA dopaminergic inputs into the aBLA they could reinstate fear even without any new foot shocks, impairing fear extinction.

The other way they confirmed a causal role for dopamine in fear encoding and extinction was to manipulate the amygdala neurons’ dopamine receptors. In Ppp1r1b neurons, over-expressing dopamine receptors impaired fear recall and promoted extinction, whereas knocking the receptors down impaired fear extinction. Meanwhile in the Rspo2 cells, knocking down receptors reduced the freezing behavior.

“We showed that fear extinction requires VTA dopaminergic activity in the pBLA Ppp1r1b neurons by using optogenetic inhibition of VTA terminals and cell-type-specific knockdown of D1 receptors in these neurons,” the authors wrote.

The scientists are careful in the study to note that while they’ve identified the “teaching signal” for fear extinction learning, the broader phenomenon of fear extinction occurs brainwide, rather than in just this single circuit.

But the circuit seems to be a key node to consider as drug developers and psychiatrists work to combat anxiety and PTSD, Pignatelli di Spinazzola says.

“Fear learning and fear extinction provide a strong framework to study generalized anxiety and PTSD,” he says. “Our study investigates the underlying mechanisms suggesting multiple targets for a translational approach, such as pBLA and use of dopaminergic modulation.”

Marianna Rizzo is also a co-author of the study. Support for the research came from the RIKEN Center for Brain Science, the HHMI, the Freedom Together Foundation, and The Picower Institute.

As the world struggles to reduce climate-warming carbon emissions, India has pledged to do its part, and its success is critical: In 2023, India was the third-largest carbon emitter worldwide. The Indian government has committed to having net-zero carbon emissions by 2070.

To fulfill that promise, India will need to decarbonize its electric power system, and that will be a challenge: Fully 60 percent of India’s electricity comes from coal-burning power plants that are extremely inefficient. To make matters worse, the demand for electricity in India is projected to more than double in the coming decade due to population growth and increased use of air conditioning, electric cars, and so on.

Despite having set an ambitious target, the Indian government has not proposed a plan for getting there. Indeed, as in other countries, in India the government continues to permit new coal-fired power plants to be built, and aging plants to be renovated and their retirement postponed.

To help India define an effective — and realistic — plan for decarbonizing its power system, key questions must be addressed. For example, India is already rapidly developing carbon-free solar and wind power generators. What opportunities remain for further deployment of renewable generation? Are there ways to retrofit or repurpose India’s existing coal plants that can substantially and affordably reduce their greenhouse gas emissions? And do the responses to those questions differ by region?

With funding from IHI Corp. through the MIT Energy Initiative (MITEI), Yifu Ding, a postdoc at MITEI, and her colleagues set out to answer those questions by first using machine learning to determine the efficiency of each of India’s current 806 coal plants, and then investigating the impacts that different decarbonization approaches would have on the mix of power plants and the price of electricity in 2035 under increasingly stringent caps on emissions.

First step: Develop the needed dataset

An important challenge in developing a decarbonization plan for India has been the lack of a complete dataset describing the current power plants in India. While other studies have generated plans, they haven’t taken into account the wide variation in the coal-fired power plants in different regions of the country. “So, we first needed to create a dataset covering and characterizing all of the operating coal plants in India. Such a dataset was not available in the existing literature,” says Ding.

Making a cost-effective plan for expanding the capacity of a power system requires knowing the efficiencies of all the power plants operating in the system. For this study, the researchers used as their metric the “station heat rate,” a standard measurement of the overall fuel efficiency of a given power plant. The station heat rate of each plant is needed in order to calculate the fuel consumption and power output of that plant as plans for capacity expansion are being developed.

Some of the Indian coal plants’ efficiencies were recorded before 2022, so Ding and her team used machine-learning models to predict the efficiencies of all the Indian coal plants operating now. In 2024, they created and posted online the first comprehensive, open-sourced dataset for all 806 power plants in 30 regions of India. The work won the 2024 MIT Open Data Prize. This dataset includes each plant’s power capacity, efficiency, age, load factor (a measure indicating how much of the time it operates), water stress, and more.

In addition, they categorized each plant according to its boiler design. A “supercritical” plant operates at a relatively high temperature and pressure, which makes it thermodynamically efficient, so it produces a lot of electricity for each unit of heat in the fuel. A “subcritical” plant runs at a lower temperature and pressure, so it’s less thermodynamically efficient. Most of the Indian coal plants are still subcritical plants running at low efficiency.

Next step: Investigate decarbonization options

Equipped with their detailed dataset covering all the coal power plants in India, the researchers were ready to investigate options for responding to tightening limits on carbon emissions. For that analysis, they turned to GenX, a modeling platform that was developed at MITEI to help guide decision-makers as they make investments and other plans for the future of their power systems.

Ding built a GenX model based on India’s power system in 2020, including details about each power plant and transmission network across 30 regions of the country. She also entered the coal price, potential resources for wind and solar power installations, and other attributes of each region. Based on the parameters given, the GenX model would calculate the lowest-cost combination of equipment and operating conditions that can fulfill a defined future level of demand while also meeting specified policy constraints, including limits on carbon emissions. The model and all data sources were also released as open-source tools for all viewers to use.

Ding and her colleagues — Dharik Mallapragada, a former principal research scientist at MITEI who is now an assistant professor of chemical and biomolecular energy at NYU Tandon School of Engineering and a MITEI visiting scientist; and Robert J. Stoner, the founding director of the MIT Tata Center for Technology and Design and former deputy director of MITEI for science and technology — then used the model to explore options for meeting demands in 2035 under progressively tighter carbon emissions caps, taking into account region-to-region variations in the efficiencies of the coal plants, the price of coal, and other factors. They describe their methods and their findings in a paper published in the journal Energy for Sustainable Development.

In separate runs, they explored plans involving various combinations of current coal plants, possible new renewable plants, and more, to see their outcome in 2035. Specifically, they assumed the following four “grid-evolution scenarios:”

Baseline: The baseline scenario assumes limited onshore wind and solar photovoltaics development and excludes retrofitting options, representing a business-as-usual pathway.

High renewable capacity: This scenario calls for the development of onshore wind and solar power without any supply chain constraints.

Biomass co-firing: This scenario assumes the baseline limits on renewables, but here all coal plants — both subcritical and supercritical — can be retrofitted for “co-firing” with biomass, an approach in which clean-burning biomass replaces some of the coal fuel. Certain coal power plants in India already co-fire coal and biomass, so the technology is known.

Carbon capture and sequestration plus biomass co-firing: This scenario is based on the same assumptions as the biomass co-firing scenario with one addition: All of the high-efficiency supercritical plants are also retrofitted for carbon capture and sequestration (CCS), a technology that captures and removes carbon from a power plant’s exhaust stream and prepares it for permanent disposal. Thus far, CCS has not been used in India. This study specifies that 90 percent of all carbon in the power plant exhaust is captured.

Ding and her team investigated power system planning under each of those grid-evolution scenarios and four assumptions about carbon caps: no cap, which is the current situation; 1,000 million tons (Mt) of carbon dioxide (CO₂) emissions, which reflects India’s announced targets for 2035; and two more-ambitious targets, namely 800 Mt and 500 Mt. For context, CO₂ emissions from India’s power sector totaled about 1,100 Mt in 2021. (Note that transmission network expansion is allowed in all scenarios.)

Key findings

Assuming the adoption of carbon caps under the four scenarios generated a vast array of detailed numerical results. But taken together, the results show interesting trends in the cost-optimal mix of generating capacity and the cost of electricity under the different scenarios.

Even without any limits on carbon emissions, most new capacity additions will be wind and solar generators — the lowest-cost option for expanding India’s electricity-generation capacity. Indeed, this is observed to be the case now in India. However, the increasing demand for electricity will still require some new coal plants to be built. Model results show a 10 to 20 percent increase in coal plant capacity by 2035 relative to 2020.

Under the baseline scenario, renewables are expanded up to the maximum allowed under the assumptions, implying that more deployment would be economical. More coal capacity is built, and as the cap on emissions tightens, there is also investment in natural gas power plants, as well as batteries to help compensate for the now-large amount of intermittent solar and wind generation. When a 500 Mt cap on carbon is imposed, the cost of electricity generation is twice as high as it was with no cap.

The high renewable capacity scenario reduces the development of new coal capacity and produces the lowest electricity cost of the four scenarios. Under the most stringent cap — 500 Mt — onshore wind farms play an important role in bringing the cost down. “Otherwise, it’ll be very expensive to reach such stringent carbon constraints,” notes Ding. “Certain coal plants that remain run only a few hours per year, so are inefficient as well as financially unviable. But they still need to be there to support wind and solar.” She explains that other backup sources of electricity, such as batteries, are even more costly. 

The biomass co-firing scenario assumes the same capacity limit on renewables as in the baseline scenario, and the results are much the same, in part because the biomass replaces such a low fraction — just 20 percent — of the coal in the fuel feedstock. “This scenario would be most similar to the current situation in India,” says Ding. “It won’t bring down the cost of electricity, so we’re basically saying that adding this technology doesn’t contribute effectively to decarbonization.”

But CCS plus biomass co-firing is a different story. It also assumes the limits on renewables development, yet it is the second-best option in terms of reducing costs. Under the 500 Mt cap on CO₂ emissions, retrofitting for both CCS and biomass co-firing produces a 22 percent reduction in the cost of electricity compared to the baseline scenario. In addition, as the carbon cap tightens, this option reduces the extent of deployment of natural gas plants and significantly improves overall coal plant utilization. That increased utilization “means that coal plants have switched from just meeting the peak demand to supplying part of the baseline load, which will lower the cost of coal generation,” explains Ding.

Some concerns

While those trends are enlightening, the analyses also uncovered some concerns for India to consider, in particular, with the two approaches that yielded the lowest electricity costs.

The high renewables scenario is, Ding notes, “very ideal.” It assumes that there will be little limiting the development of wind and solar capacity, so there won’t be any issues with supply chains, which is unrealistic. More importantly, the analyses showed that implementing the high renewables approach would create uneven investment in renewables across the 30 regions. Resources for onshore and offshore wind farms are mainly concentrated in a few regions in western and southern India. “So all the wind farms would be put in those regions, near where the rich cities are,” says Ding. “The poorer cities on the eastern side, where the coal power plants are, will have little renewable investment.”

So the approach that’s best in terms of cost is not best in terms of social welfare, because it tends to benefit the rich regions more than the poor ones. “It’s like [the government will] need to consider the trade-off between energy justice and cost,” says Ding. Enacting state-level renewable generation targets could encourage a more even distribution of renewable capacity installation. Also, as transmission expansion is planned, coordination among power system operators and renewable energy investors in different regions could help in achieving the best outcome.

CCS plus biomass co-firing — the second-best option for reducing prices — solves the equity problem posed by high renewables, and it assumes a more realistic level of renewable power adoption. However, CCS hasn’t been used in India, so there is no precedent in terms of costs. The researchers therefore based their cost estimates on the cost of CCS in China and then increased the required investment by 10 percent, the “first-of-a-kind” index developed by the U.S. Energy Information Administration. Based on those costs and other assumptions, the researchers conclude that coal plants with CCS could come into use by 2035 when the carbon cap for power generation is less than 1,000 Mt.

But will CCS actually be implemented in India? While there’s been discussion about using CCS in heavy industry, the Indian government has not announced any plans for implementing the technology in coal-fired power plants. Indeed, India is currently “very conservative about CCS,” says Ding. “Some researchers say CCS won’t happen because it’s so expensive, and as long as there’s no direct use for the captured carbon, the only thing you can do is put it in the ground.” She adds, "It’s really controversial to talk about whether CCS will be implemented in India in the next 10 years.”

Ding and her colleagues hope that other researchers and policymakers — especially those working in developing countries — may benefit from gaining access to their datasets and learning about their methods. Based on their findings for India, she stresses the importance of understanding the detailed geographical situation in a country in order to design plans and policies that are both realistic and equitable.

What would a behind-the-scenes look at a video generated by an artificial intelligence model be like? You might think the process is similar to stop-motion animation, where many images are created and stitched together, but that’s not quite the case for “diffusion models” like OpenAl's SORA and Google's VEO 2.

Instead of producing a video frame-by-frame (or “autoregressively”), these systems process the entire sequence at once. The resulting clip is often photorealistic, but the process is slow and doesn’t allow for on-the-fly changes. 

Scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Adobe Research have now developed a hybrid approach, called “CausVid,” to create videos in seconds. Much like a quick-witted student learning from a well-versed teacher, a full-sequence diffusion model trains an autoregressive system to swiftly predict the next frame while ensuring high quality and consistency. CausVid’s student model can then generate clips from a simple text prompt, turning a photo into a moving scene, extending a video, or altering its creations with new inputs mid-generation.

This dynamic tool enables fast, interactive content creation, cutting a 50-step process into just a few actions. It can craft many imaginative and artistic scenes, such as a paper airplane morphing into a swan, woolly mammoths venturing through snow, or a child jumping in a puddle. Users can also make an initial prompt, like “generate a man crossing the street,” and then make follow-up inputs to add new elements to the scene, like “he writes in his notebook when he gets to the opposite sidewalk.”

The CSAIL researchers say that the model could be used for different video editing tasks, like helping viewers understand a livestream in a different language by generating a video that syncs with an audio translation. It could also help render new content in a video game or quickly produce training simulations to teach robots new tasks.

Tianwei Yin SM ’25, PhD ’25, a recently graduated student in electrical engineering and computer science and CSAIL affiliate, attributes the model’s strength to its mixed approach.

“CausVid combines a pre-trained diffusion-based model with autoregressive architecture that’s typically found in text generation models,” says Yin, co-lead author of a new paper about the tool. “This AI-powered teacher model can envision future steps to train a frame-by-frame system to avoid making rendering errors.”

Yin’s co-lead author, Qiang Zhang, is a research scientist at xAI and a former CSAIL visiting researcher. They worked on the project with Adobe Research scientists Richard Zhang, Eli Shechtman, and Xun Huang, and two CSAIL principal investigators: MIT professors Bill Freeman and Frédo Durand.

Caus(Vid) and effect

Many autoregressive models can create a video that’s initially smooth, but the quality tends to drop off later in the sequence. A clip of a person running might seem lifelike at first, but their legs begin to flail in unnatural directions, indicating frame-to-frame inconsistencies (also called “error accumulation”).

Error-prone video generation was common in prior causal approaches, which learned to predict frames one by one on their own. CausVid instead uses a high-powered diffusion model to teach a simpler system its general video expertise, enabling it to create smooth visuals, but much faster.

CausVid displayed its video-making aptitude when researchers tested its ability to make high-resolution, 10-second-long videos. It outperformed baselines like “OpenSORA” and “MovieGen,” working up to 100 times faster than its competition while producing the most stable, high-quality clips.

Then, Yin and his colleagues tested CausVid’s ability to put out stable 30-second videos, where it also topped comparable models on quality and consistency. These results indicate that CausVid may eventually produce stable, hours-long videos, or even an indefinite duration.

A subsequent study revealed that users preferred the videos generated by CausVid’s student model over its diffusion-based teacher.

“The speed of the autoregressive model really makes a difference,” says Yin. “Its videos look just as good as the teacher’s ones, but with less time to produce, the trade-off is that its visuals are less diverse.”

CausVid also excelled when tested on over 900 prompts using a text-to-video dataset, receiving the top overall score of 84.27. It boasted the best metrics in categories like imaging quality and realistic human actions, eclipsing state-of-the-art video generation models like “Vchitect” and “Gen-3.”

While an efficient step forward in AI video generation, CausVid may soon be able to design visuals even faster — perhaps instantly — with a smaller causal architecture. Yin says that if the model is trained on domain-specific datasets, it will likely create higher-quality clips for robotics and gaming.

Experts say that this hybrid system is a promising upgrade from diffusion models, which are currently bogged down by processing speeds. “These models are way slower than LLMs [large language models] or generative image models,” says Carnegie Mellon University Assistant Professor Jun-Yan Zhu, who was not involved in the paper. “This new work changes that, making video generation much more efficient. That means better streaming speed, more interactive applications, and lower carbon footprints.”

The team’s work was supported, in part, by the Amazon Science Hub, the Gwangju Institute of Science and Technology, Adobe, Google, the U.S. Air Force Research Laboratory, and the U.S. Air Force Artificial Intelligence Accelerator. CausVid will be presented at the Conference on Computer Vision and Pattern Recognition in June.

As the saying goes, time is money. That’s certainly evident in the transportation sector, where people will pay more for direct flights, express trains, and other ways to get somewhere quickly.

Still, it is difficult to measure precisely how much people value their time. Now, a paper co-authored by an MIT economist uses ride-sharing data to reveal multiple implications of personalized pricing.

By focusing on a European ride-sharing platform that auctions its rides, the researchers found that people are more responsive to prices than to wait times. They also found that people pay more to save time during the workday, and that when people pay more to avoid waiting, it notably increases business revenues. And some segments of consumers are distinctly more willing than others to pay higher prices.

Specifically, when people can bid for rides that arrive sooner, the amount above the minimum price the platform can charge increases by 5.2 percent. Meanwhile, the gap between offered prices and the maximum that consumers are willing to pay decreases by 2.5 percent. In economics terms, this creates additional “surplus” value for firms, while lowering the “consumer surplus” in these transactions.

“One of the important quantities in transportation is the value of time,” says MIT economist Tobias Salz, co-author of a new paper detailing the study’s findings. “We came across a setting that offered a very clean way of examining this quantity, where the value of time is revealed by people’s transportation choices.”

The paper, “Personalized Pricing and the Value of Time: Evidence from Auctioned Cab Rides,” is being published in Econometrica. The authors are Nicholas Buchholz, an assistant professor of economics at Princeton University; Laura Doval, a professor at Columbia Business School; Jakub Kastl, a professor of economics at Princeton University; Filip Matejka, a professor at Charles University in Prague; and Salz, the Castle Krob Career Development Associate Professor of Economics in MIT’s Department of Economics.

It is not easy to study how much money people will spend to save time — and time alone. Transportation is one sector where it is possible to do so, though not the only one. People will also pay more for, say, an express pass to avoid long lines at an amusement park. But data for those scenarios, even when available, may contain complicating factors. (Also, the value of time shouldn’t be confused with how much people pay for services charged by the hour, from accountants to tuba lessons.)

In this case, however, the researchers were provided data from Liftago, a ride-sharing platform in Prague with a distinctive feature: It lets drivers bid on a passenger’s business, with the wait time until the auto arrives as one of the factors involved. Drivers can also indicate when they will be available. In studying how passengers compare offers with different wait times and prices, the researchers see exactly how much people are paying not to wait, other things being equal. All told, they examined 1.9 million ride requests and 5.2 million bids.

“It’s like an eBay for taxis,” Salz says. “Instead of assigning the driver to you, drivers bid for the passengers’ business. With this, we can very directly observe how people make their choices. How they value time is revealed by the wait and the prices attached to that. In many settings we don’t observe that directly, so it’s a very clean comparison that rids the data of a lot of confounds.”

The data set allows the researchers to examine many aspects of personalized pricing and the way it affects the transportation market in this setting. That produces a set of insights on its own, along with the findings on time valuation. 

Ultimately, the researchers found that the elasticity of prices — how much they change — ranged from four to 10 times as much as the elasticity of wait times, meaning people are more keen on avoiding high prices.

The team found the overall value of time in this context is $13.21 per hour for users of the ride-share platform, though the researchers note that is not a universal measure of the value of time and is dependent on this setting. The study also shows that bids increase during work hours.

Additionally, the research reveals a split among consumers: The top quartile of bids placed a value on time that is 3.5 times higher than the value of the bids in the bottom quartile.

Then there is still the question of how much personalized pricing benefits consumers, providers, or both. The numbers, again, show that the overall surplus increases — meaning business benefits — while the consumer surplus is reduced. However, the data show an even more nuanced picture. Because the top quartile of bidders are paying substantially more to avoid longer waits, they are the ones who absorb the brunt of the costs in this kind of system.

“The majority of consumers still benefit,” Salz says. “The consumers hurt by this have a very high willingness to pay. The source of welfare gains is that most consumers can be brought into the market. But the flip side is that the firm, by knowing every consumer’s choke point, can extract the surplus. Welfare goes up, the ride-sharing platform captures most of that, and drivers — interestingly — also benefit from the system, although they do not have access to the data.”

Economic theory and other transportation studies alone would not necessarily have predicted the study’s results and various nuances.

“It was not clear a priori whether consumers benefit,” Salz observes. “That is not something you would know without going to the data.”

While this study might hold particular interest for firms and others interested in transportation, mobility, and ride-sharing, it also fits into a larger body of economics research about information in markets and how its presence, or absence, influences consumer behavior, consumer welfare, and the functioning of markets.

“The [research] umbrella here is really information about where to find trading partners and what their willingness to pay is,” Salz says. “What I’m broadly interested in is these types of information frictions and how they determine market outcomes, how they might impact consumers, and be used by firms.”

The research was supported, in part, by the National Bureau of Economic Research, the U.S. Department of Transportation, and the National Science Foundation. 

What if data could help predict a patient’s prognosis, streamline hospital operations, or optimize human resources in medicine? A book fresh off the shelves, “The Analytics Edge in Healthcare,” shows that this is already happening, and demonstrates how to scale it. 

Authored by Dimitris Bertsimas, MIT’s vice provost for open learning, along with two of Bertsimas’ former students — Agni Orfanoudaki PhD ’21, associate professor of operations management at University of Oxford’s Saïd Business School, and Holly Wiberg PhD ’22, assistant professor of public policy and operations research at Carnegie Mellon University — the book provides a practical introduction to the field of health care analytics. With an emphasis on real-world applications, the first part of the book establishes technical foundations — spanning machine learning and optimization — while the second part of the book presents integrated case studies that cover various clinical specialties and problem types using descriptive, predictive, and prescriptive analytics. 

Part of a broader series, “The Analytics Edge in Healthcare” demonstrates how to leverage data and models to make better decisions within the health care sector, while its predecessor, “The Analytics Edge,” dives into the science of using data to build models, improve decisions, and add value to institutions and individuals. 

Bertsimas, who is also the associate dean of business analytics and the Boeing Leaders for Global Operations Professor of Management at the MIT Sloan School of Management, is the innovator behind 15.071 (The Analytics Edge), a course on MIT Open Learning’s MITx that has attracted hundreds of thousands of online learners and served as the inspiration behind the book series. Bertsimas took a break from research and his work at MIT Open Learning to discuss how the field of analytics is transforming the health care system and share some surprising ways analytics are already being used in hospitals. 

Q: How is the field of analytics changing the way hospitals provide care and manage their operations?

A: As an academic, I’ve always aspired to educate, write publications, and utilize what we do in practice. Therefore, I founded Holistic Hospital Optimization (H20) with the goal of optimizing hospital operations with machine learning to improve patient care. We have developed a variety of tools at MIT and implemented them at hospitals around the world. For example, we manage patients’ length of stay and their deterioration indexes (a computerized tool that predicts a patient’s risk of clinical deterioration); we manage nurse optimization and how hospitals can allocate human resources appropriately; and we optimize blocks for surgeries. This is the beginning of a change where analytics and AI methods are now being utilized quite widely. My hope would be that this work and this book will accelerate the effect of using these tools. 

Additionally, I have taught a nine-lecture course twice with Agni and Holly at the Hartford Hospital System, where I realized that these analytics methods — which are typically not taught in medical schools — can be demonstrated for health care practitioners, including physicians, nurses, and administrators. To have an impact, you need to have appropriate methods, implement them, and apply them, but you also need to educate people on how to use them. This links well with my role at Open Learning, where our objective is to educate learners globally. In fact, Open Learning is launching this fall Universal AI, a dynamic online learning experience that provides comprehensive knowledge on artificial intelligence, preparing a global audience of learners for employment in our rapidly evolving job market. 

Q: What are some surprising ways analytics are being used in health care that most people wouldn’t expect?

A: Using analytics, we have reduced patients’ length of stay at Hartford Hospital from 5.67 days to five days. We have an algorithm that predicts patients’ probability of being released; therefore, doctors prioritize the patients with the highest probability, preparing them for discharge. This means that the hospital can treat far more patients, and the patients stay in the hospital less time.

Furthermore, when hospitals saw an increase in nurse turnover during the Covid-19 pandemic, we developed an analytics system that takes into account equity and fairness and decreases overtime costs, giving preferred slots to nurses and decreasing overall turnover substantially. These are just two examples; there are many others where an analytical perspective to health care and medicine has made a material difference. 

Q: Looking ahead, how do you see artificial intelligence shaping the future of health care?

A: In a very significant way — we use machine learning to make better predictions, but generative AI can explain them. I already see a movement in that direction. It’s really the evolution of AI that made this possible, and it is exciting. It’s also important for the world, because of its capabilities to improve care and save lives. 

For example, through our program at the Hartford Hospital System, we discovered that a patient was getting worse and predicted through analytics that they would get even worse. After our prediction, the doctors examined the patient more closely and discovered the patient had an early case of sepsis, a life-threatening condition in which the body responds improperly to an infection. If we hadn’t detected sepsis earlier, the patient might have died. This made an actual difference in saving a person’s life. 

Q: If you had to describe “The Analytics Edge in Healthcare” in one or two words, what would they be, and why? 

A: The book is a phased transition in health care because it is capable of affecting the health care sector in a way that has not been done before. The book really outlines my work in health care and its applications in the last decade.

If there’s one thing that characterizes driving in any major city, it’s the constant stop-and-go as traffic lights change and as cars and trucks merge and separate and turn and park. This constant stopping and starting is extremely inefficient, driving up the amount of pollution, including greenhouse gases, that gets emitted per mile of driving. 

One approach to counter this is known as eco-driving, which can be installed as a control system in autonomous vehicles to improve their efficiency.

How much of a difference could that make? Would the impact of such systems in reducing emissions be worth the investment in the technology? Addressing such questions is one of a broad category of optimization problems that have been difficult for researchers to address, and it has been difficult to test the solutions they come up with. These are problems that involve many different agents, such as the many different kinds of vehicles in a city, and different factors that influence their emissions, including speed, weather, road conditions, and traffic light timing.

“We got interested a few years ago in the question: Is there something that automated vehicles could do here in terms of mitigating emissions?” says Cathy Wu, the Thomas D. and Virginia W. Cabot Career Development Associate Professor in the Department of Civil and Environmental Engineering and the Institute for Data, Systems, and Society (IDSS) at MIT, and a principal investigator in the Laboratory for Information and Decision Systems. “Is it a drop in the bucket, or is it something to think about?,” she wondered.

To address such a question involving so many components, the first requirement is to gather all available data about the system, from many sources. One is the layout of the network’s topology, Wu says, in this case a map of all the intersections in each city. Then there are U.S. Geological Survey data showing the elevations, to determine the grade of the roads. There are also data on temperature and humidity, data on the mix of vehicle types and ages, and on the mix of fuel types.

Eco-driving involves making small adjustments to minimize unnecessary fuel consumption. For example, as cars approach a traffic light that has turned red, “there’s no point in me driving as fast as possible to the red light,” she says. By just coasting, “I am not burning gas or electricity in the meantime.” If one car, such as an automated vehicle, slows down at the approach to an intersection, then the conventional, non-automated cars behind it will also be forced to slow down, so the impact of such efficient driving can extend far beyond just the car that is doing it.

That’s the basic idea behind eco-driving, Wu says. But to figure out the impact of such measures, “these are challenging optimization problems” involving many different factors and parameters, “so there is a wave of interest right now in how to solve hard control problems using AI.” 

The new benchmark system that Wu and her collaborators developed based on urban eco-driving, which they call “IntersectionZoo,” is intended to help address part of that need. The benchmark was described in detail in a paper presented at the 2025 International Conference on Learning Representation in Singapore.

Looking at approaches that have been used to address such complex problems, Wu says an important category of methods is multi-agent deep reinforcement learning (DRL), but a lack of adequate standard benchmarks to evaluate the results of such methods has hampered progress in the field.

The new benchmark is intended to address an important issue that Wu and her team identified two years ago, which is that with most existing deep reinforcement learning algorithms, when trained for one specific situation (e.g., one particular intersection), the result does not remain relevant when even small modifications are made, such as adding a bike lane or changing the timing of a traffic light, even when they are allowed to train for the modified scenario.

In fact, Wu points out, this problem of non-generalizability “is not unique to traffic,” she says. “It goes back down all the way to canonical tasks that the community uses to evaluate progress in algorithm design.” But because most such canonical tasks do not involve making modifications, “it’s hard to know if your algorithm is making progress on this kind of robustness issue, if we don’t evaluate for that.”

While there are many benchmarks that are currently used to evaluate algorithmic progress in DRL, she says, “this eco-driving problem features a rich set of characteristics that are important in solving real-world problems, especially from the generalizability point of view, and that no other benchmark satisfies.” This is why the 1 million data-driven traffic scenarios in IntersectionZoo uniquely position it to advance the progress in DRL generalizability.  As a result, “this benchmark adds to the richness of ways to evaluate deep RL algorithms and progress.”

And as for the initial question about city traffic, one focus of ongoing work will be applying this newly developed benchmarking tool to address the particular case of how much impact on emissions would come from implementing eco-driving in automated vehicles in a city, depending on what percentage of such vehicles are actually deployed.

But Wu adds that “rather than making something that can deploy eco-driving at a city scale, the main goal of this study is to support the development of general-purpose deep reinforcement learning algorithms, that can be applied to this application, but also to all these other applications — autonomous driving, video games, security problems, robotics problems, warehousing, classical control problems.”

Wu adds that “the project’s goal is to provide this as a tool for researchers, that’s openly available.” IntersectionZoo, and the documentation on how to use it, are freely available at GitHub.

Wu is joined on the paper by lead authors Vindula Jayawardana, a graduate student in MIT’s Department of Electrical Engineering and Computer Science (EECS); Baptiste Freydt, a graduate student from ETH Zurich; and co-authors Ao Qu, a graduate student in transportation; Cameron Hickert, an IDSS graduate student; and Zhongxia Yan PhD ’24. 

Tuberculosis, the world’s deadliest infectious disease, is estimated to infect around 10 million people each year, and kills more than 1 million annually. Once established in the lungs, the bacteria’s thick cell wall helps it to fight off the host immune system.

Much of that cell wall is made from complex sugar molecules known as glycans, but it’s not well-understood how those glycans help to defend the bacteria. One reason for that is that there hasn’t been an easy way to label them inside cells.

MIT chemists have now overcome that obstacle, demonstrating that they can label a glycan called ManLAM using an organic molecule that reacts with specific sulfur-containing sugars. These sugars are found in only three bacterial species, the most notorious and prevalent of which is Mycobacterium tuberculosis, the microbe that causes TB.

After labeling the glycan, the researchers were able to visualize where it is located within the bacterial cell wall, and to study what happens to it throughout the first few days of tuberculosis infection of host immune cells.

The researchers now hope to use this approach to develop a diagnostic that could detect TB-associated glycans, either in culture or in a urine sample, which could offer a cheaper and faster alternative to existing diagnostics. Chest X-rays and molecular diagnostics are very accurate but are not always available in developing nations where TB rates are high. In those countries, TB is often diagnosed by culturing microbes from a sputum sample, but that test has a high false negative rate, and it can be difficult for some patients, especially children, to provide a sputum sample. This test also requires many weeks for the bacteria to grow, delaying diagnosis.

“There aren’t a lot of good diagnostic options, and there are some patient populations, including children, who have a hard time giving samples that can be analyzed. There’s a lot of impetus to develop very simple, fast tests,” says Laura Kiessling, the Novartis Professor of Chemistry at MIT and the senior author of the study.

MIT graduate student Stephanie Smelyansky is the lead author of the paper, which appears this week in the Proceedings of the National Academy of Sciences. Other authors include Chi-Wang Ma, an MIT postdoc; Victoria Marando PhD ’23; Gregory Babunovic, a postdoc at the Harvard T.H. Chan School of Public Health; So Young Lee, an MIT graduate student; and Bryan Bryson, an associate professor of biological engineering at MIT.

Labeling glycans

Glycans are found on the surfaces of most cells, where they perform critical functions such as mediating communication between cells.In bacteria, glycans help the microbes to enter host cells, and they also appear to communicate with the host immune system, in some cases blocking the immune response.

“Mycobacterium tuberculosis has a really elaborate cell envelope compared to other bacteria, and it’s a rich structure that’s composed of a lot of different glycans,” Smelyansky says. “Something that’s often underappreciated is the fact that these glycans can also interact with our host cells. When our immune cells recognize these glycans, instead of sending out a danger signal, it can send the opposite message, that there’s no danger.”

Glycans are notoriously difficult to tag with any kind of probe, because unlike proteins or DNA, they don’t have distinctive sequences or chemical reactivities that can be targeted. And unlike proteins, they are not genetically encoded, so cells can’t be genetically engineered to produce sugars labeled with fluorescent tags such as green fluorescent protein.

One of the key glycans in M. tuberculosis, known as ManLAM, contains a rare sugar known as MTX, which is unusual in that it has a thioether — a sulfur atom sandwiched between two carbon atoms. This chemical group presented an opportunity to use a small-molecule tag that had been previously developed for labeling methionine, an  amino acid that contains a similar group.

The researchers showed that they could use this tag, known as an oxaziridine, to label ManLAM in M. tuberculosis. The researchers linked the oxaziridine to a fluorescent probe and showed that in M. tuberculosis, this tag showed up in the outer layer of the cell wall. When the researchers exposed the label to Mycobacterium smegmatis, a related bacterium that does not cause disease and does not have the sugar MTX, they saw no fluorescent signal.

“This is the first approach that really selectively allows us to visualize one glycan in particular,” Smelyansky says.

Better diagnostics

The researchers also showed that after labeling ManLAM in M. tuberculosis cells, they could track the cells as they infected immune cells called macrophages. Some tuberculosis researchers had hypothesized that the bacterial cells shed ManLAM once inside a host cell, and that those free glycans then interact with the host immune system. However, the MIT team found that the glycan appears to remain in the bacterial cell walls for at least the first few days of infection.

“The bacteria still have their cell walls attached to them. So it may be that some glycan is being released, but the majority of it is retained on the bacterial cell surface, which has never been shown before,” Smelyansky says.

The researchers now plan to use this approach to study what happens to the bacteria following treatment with different antibiotics, or immune stimulation of the macrophages. It could also be used to study in more detail how the bacterial cell wall is assembled, and how ManLAM helps bacteria get into macrophages and other cells.

“Having a handle to follow the bacteria is really valuable, and it will allow you to visualize processes, both in cells and in animal models, that were previously invisible,” Kiessling says.

She also hopes to use this approach to create new diagnostics for tuberculosis. There is currently a diagnostic in development that uses antibodies to detect ManLAM in a urine sample. However, this test only works well in patients with very active cases of TB, especially people who are immunosuppressed because of HIV or other conditions.

Using their small-molecule sensor instead of antibodies, the MIT team hopes to develop a more sensitive test that could detect ManLAM in the urine even when only small quantities are present.

“This is a beautifully elegant approach to selectively label the surface of mycobacteria, enabling real-time monitoring of cell wall dynamics in this important bacterial family. Such investigations will inform the development of novel strategies to diagnose, prevent, and treat mycobacterial disease, most notably tuberculosis, which remains a global health challenge,” says Todd Lowary, a distinguished research fellow at the Institute of Biological Chemistry, Academia Sinica, Taipei Taiwan, who was not involved in the research.

The research was funded by the National Institute of Allergy and Infectious Disease, the National Institutes of Health, the National Science Foundation, and the Croucher Fellowship.

MIT physicists have captured the first images of individual atoms freely interacting in space. The pictures reveal correlations among the “free-range” particles that until now were predicted but never directly observed. Their findings, appearing today in the journal Physical Review Letters, will help scientists visualize never-before-seen quantum phenomena in real space.

The images were taken using a technique developed by the team that first allows a cloud of atoms to move and interact freely. The researchers then turn on a lattice of light that briefly freezes the atoms in their tracks, and apply finely tuned lasers to quickly illuminate the suspended atoms, creating a picture of their positions before the atoms naturally dissipate.

The physicists applied the technique to visualize clouds of different types of atoms, and snapped a number of imaging firsts. The researchers directly observed atoms known as “bosons,” which bunched up in a quantum phenomenon to form a wave. They also captured atoms known as “fermions” in the act of pairing up in free space — a key mechanism that enables superconductivity.

“We are able to see single atoms in these interesting clouds of atoms and what they are doing in relation to each other, which is beautiful,” says Martin Zwierlein, the Thomas A. Frank Professor of Physics at MIT.

In the same journal issue, two other groups report using similar imaging techniques, including a team led by Nobel laureate Wolfgang Ketterle, the John D. MacArthur Professor of Physics at MIT. Ketterle’s group visualized enhanced pair correlations among bosons, while the other group, from École Normale Supérieure in Paris, led by Tarik Yefsah, imaged a cloud of noninteracting fermions.

The study by Zwierlein and his colleagues is co-authored by MIT graduate students Ruixiao Yao, Sungjae Chi, and Mingxuan Wang, and MIT assistant professor of physics Richard Fletcher.

Inside the cloud

A single atom is about one-tenth of a nanometer in diameter, which is one-millionth of the thickness of a strand of human hair. Unlike hair, atoms behave and interact according to the rules of quantum mechanics; it is their quantum nature that makes atoms difficult to understand. For example, we cannot simultaneously know precisely where an atom is and how fast it is moving.

Scientists can apply various methods to image individual atoms, including absorption imaging, where laser light shines onto the atom cloud and casts its shadow onto a camera screen.

“These techniques allow you to see the overall shape and structure of a cloud of atoms, but not the individual atoms themselves,” Zwierlein notes. “It’s like seeing a cloud in the sky, but not the individual water molecules that make up the cloud.”

He and his colleagues took a very different approach in order to directly image atoms interacting in free space. Their technique, called “atom-resolved microscopy,” involves first corralling a cloud of atoms in a loose trap formed by a laser beam. This trap contains the atoms in one place where they can freely interact. The researchers then flash on a lattice of light, which freezes the atoms in their positions. Then, a second laser illuminates the suspended atoms, whose fluorescence reveals their individual positions.

“The hardest part was to gather the light from the atoms without boiling them out of the optical lattice,” Zwierlein says. “You can imagine if you took a flamethrower to these atoms, they would not like that. So, we’ve learned some tricks through the years on how to do this. And it’s the first time we do it in-situ, where we can suddenly freeze the motion of the atoms when they’re strongly interacting, and see them, one after the other. That’s what makes this technique more powerful than what was done before.”

Bunches and pairs

The team applied the imaging technique to directly observe interactions among both bosons and fermions. Photons are an example of a boson, while electrons are a type of fermion. Atoms can be bosons or fermions, depending on their total spin, which is determined by whether the total number of their protons, neutrons, and electrons is even or odd. In general, bosons attract, whereas fermions repel.

Zwierlein and his colleagues first imaged a cloud of bosons made up of sodium atoms. At low temperatures, a cloud of bosons forms what’s known as a Bose-Einstein condensate — a state of matter where all bosons share one and the same quantum state. MIT’s Ketterle was one of the first to produce a Bose-Einstein condensate, of sodium atoms, for which he shared the 2001 Nobel Prize in Physics.

Zwierlein’s group now is able to image the individual sodium atoms within the cloud, to observe their quantum interactions. It has long been predicted that bosons should “bunch” together, having an increased probability to be near each other. This bunching is a direct consequence of their ability to share one and the same quantum mechanical wave. This wave-like character was first predicted by physicist Louis de Broglie. It is the “de Broglie wave” hypothesis that in part sparked the beginning of modern quantum mechanics.

“We understand so much more about the world from this wave-like nature,” Zwierlein says. “But it’s really tough to observe these quantum, wave-like effects. However, in our new microscope, we can visualize this wave directly.”

In their imaging experiments, the MIT team were able to see, for the first time in situ, bosons bunch together as they shared one quantum, correlated de Broglie wave. The team also imaged a cloud of two types of lithium atoms. Each type of atom is a fermion, that naturally repels its own kind, but that can strongly interact with other particular fermion types. As they imaged the cloud, the researchers observed that indeed, the opposite fermion types did interact, and formed fermion pairs — a coupling that they could directly see for the first time.

“This kind of pairing is the basis of a mathematical construction people came up with to explain experiments. But when you see pictures like these, it’s showing in a photograph, an object that was discovered in the mathematical world,” says study co-author Richard Fletcher. “So it’s a very nice reminder that physics is about physical things. It’s real.”

Going forward, the team will apply their imaging technique to visualize more exotic and less understood phenomena, such as “quantum Hall physics” — situations when interacting electrons display novel correlated behaviors in the presence of a magnetic field.

“That’s where theory gets really hairy — where people start drawing pictures instead of being able to write down a full-fledged theory because they can’t fully solve it,” Zwierlein says. “Now we can verify whether these cartoons of quantum Hall states are actually real. Because they are pretty bizarre states.”

This work was supported, in part, by National Science Foundation through the MIT-Harvard Center for Ultracold Atoms, as well as by the Air Force Office of Scientific Research, the Army Research Office, the Department of Energy, the Defense Advanced Projects Research Agency, a Vannevar Bush Faculty Fellowship, and the David and Lucile Packard Foundation.

Caring well for the elderly is a familiar challenge. Some elderly people need close medical attention in facilities; others struggle with reduced capabilities while not wanting to leave their homes. For families, finding good care is hard and expensive, and already-burdened family members often pick up the slack.

The problem is expanding as birthrates drop while some segments of the population live longer, meaning that a growing portion of the population is elderly. In the U.S., there are currently three states currently where at least 20 percent of the population is 65 and older. (Yes, Florida is one.) But by 2050, demographic trends suggest, there will be 43 states with that profile.

In age terms, “America is becoming Florida,” quips MIT economist Jonathan Gruber. “And it’s not just America. The whole world is aging rapidly. The share of the population over 65 is growing rapidly everywhere, and within that, the share of the elderly that are over 85 is growing rapidly.”

In a new edited volume, Gruber and several other scholars explore the subject from a global perspective. The book, “Long-Term Care around the World,” is published this month by the University of Chicago Press. The co-editors are Gruber, the Ford Professor of Economics and chair of the Department of Economics at MIT; and Kathleen McGarry, a professor of economics at Stony Brook University.

The book looks at 10 relatively wealthy countries and how they approach the problem of long-term care. In their chapter about the U.S., Gruber and McGarry emphasize a remarkable fact: About one-third of long-term care for the elderly in the U.S. is informal, provided by family and friends, despite limited time and resources. Overall, long-term care is 2 percent of U.S. GDP.

“We have two fundamental long-term care problems in the U.S.,” Gruber says. “Too much informal care at home, and, relatedly, not enough options for elders to live with effective care in ‘congregate housing’ [or elder communities], even if they’re not sick enough for a nursing facility.”

The nature of the problem

The needs of the elderly sit in plain sight. In the U.S., about 30 percent of people 65 and over, and 60 percent of people 85 and over report limitations in basic activities. Getting dressed and taking baths are among the most common daily problems; shopping for groceries and managing money are also widely reported issues. Additionally, these limitations have mental health implications. About 10 percent of the elderly report depression, rising to 30 percent among those who struggle with three or more types of basic daily tasks.

Even so, the U.S. is not actually heavily dotted with nursing homes. In a country of about 330 million people, with 62 million being 65 and over, it’s unusual for an elderly person to be in one.

“We all think of nursing homes as where you go when you’re old, but there are only about 1.2 million people in nursing homes in America,” Gruber observes. “Which is a lot, but tiny compared to the share of people who are elderly in the U.S. and who have needs. Most people who have needs get them met at home.”

And while nursing homes can be costly, home care is too. Given an average U.S. salary of $23 per hour for a home health care aide, annual costs can reach six figures even with half-time care. As a result, many families simply help their elderly relatives as best they can.

Therefore, Gruber has found, we must account for the informal costs of elder care, too. Ultimately, Gruber says, informal help represents “an inefficient system of people taking care of their elderly parents at home, which is a stress on the family, and the elders don’t get enough care.”

To be sure, some people buy private long-term care insurance to defray these costs. But this is a tricky market, where insurers are concerned about “adverse selection,” people buying policies with a distinct need for them (beyond what insurers can detect). Rates therefore can seem high, and for limited, conditional benefits. Research by MIT economist Amy Finkelstein has shown that only 18 percent of long-term insurance policies are used.

“Private long-term care insurance is a market that just hasn’t worked well,” Gruber says. “It’s basically a fixed amount of money, should you meet certain conditions. And people are surprised by that, and it doesn’t meet their needs, and it’s expensive. We need a public solution.”

Congregate housing, a possible solution

Looking at long-term care internationally helps identify what those solutions might be. The U.S. does not neglect elder care, but could clearly broaden its affordable options.

“On the one hand, what jumped out at me is how normal the U.S. is,” Gruber says. “We’re in the middle of the pack in terms of the share of GDP we spend on long-term care.” However, some European countries that spend a similar share and also rely heavily on informal elder care, including Italy and Spain, have notably lower levels of GDP per capita.

Some other European countries with income levels closer to the U.S., including Germany and the Netherlands, do spend more on long-term elder care. The Netherlands tops the list by devoting about 4 percent of its GDP to this area.

However, in the U.S., the issue is not so much drastically changing how much it spends on long-term elder care, but how it spends. The Dutch have a relatively more extensive system of elder communities — the “congregate housing” for the elderly who are not desperately unwell, but simply find self-reliance increasingly hard.

“That’s the huge missing hole in the U.S. long-term care system, what do we do with people who aren’t sick enough for a nursing home, but probably shouldn’t be at home,” Gruber says. “Right now they stay at home, they’re lonely, they’re not getting services, their kids are super-stressed out, and they’re pulling millions of people out of the labor force, especially women. Everyone is unhappy about it, and they’re not growing GDP, so it’s hurting our economy and our well-being.”

Overall, then, Gruber thinks further investment in elder-care communities would be an example of effective government spending that can address the brewing crisis in long-term care — although it would require new federal legislation in a highly polarized political environment.

Could that happen? Could the U.S. invest more now and realize long-term financial benefits, while allowing working-age employees to spend more time at their jobs rather than acting as home caregivers? Making people more aware of the issue, Gruber thinks, is a necessary starting point.

“If anything might be bipartisan, it could be long-term care,” Gruber says. “Everybody has parents. A solution has to be bipartisan. Long-term care may be one of those areas where it’s possible.”

Support for the research was provided, in part, by the National Institute on Aging.

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a novel artificial intelligence model inspired by neural oscillations in the brain, with the goal of significantly advancing how machine learning algorithms handle long sequences of data.

AI often struggles with analyzing complex information that unfolds over long periods of time, such as climate trends, biological signals, or financial data. One new type of AI model, called "state-space models," has been designed specifically to understand these sequential patterns more effectively. However, existing state-space models often face challenges — they can become unstable or require a significant amount of computational resources when processing long data sequences.

To address these issues, CSAIL researchers T. Konstantin Rusch and Daniela Rus have developed what they call “linear oscillatory state-space models” (LinOSS), which leverage principles of forced harmonic oscillators — a concept deeply rooted in physics and observed in biological neural networks. This approach provides stable, expressive, and computationally efficient predictions without overly restrictive conditions on the model parameters.

"Our goal was to capture the stability and efficiency seen in biological neural systems and translate these principles into a machine learning framework," explains Rusch. "With LinOSS, we can now reliably learn long-range interactions, even in sequences spanning hundreds of thousands of data points or more."

The LinOSS model is unique in ensuring stable prediction by requiring far less restrictive design choices than previous methods. Moreover, the researchers rigorously proved the model’s universal approximation capability, meaning it can approximate any continuous, causal function relating input and output sequences.

Empirical testing demonstrated that LinOSS consistently outperformed existing state-of-the-art models across various demanding sequence classification and forecasting tasks. Notably, LinOSS outperformed the widely-used Mamba model by nearly two times in tasks involving sequences of extreme length.

Recognized for its significance, the research was selected for an oral presentation at ICLR 2025 — an honor awarded to only the top 1 percent of submissions. The MIT researchers anticipate that the LinOSS model could significantly impact any fields that would benefit from accurate and efficient long-horizon forecasting and classification, including health-care analytics, climate science, autonomous driving, and financial forecasting.

"This work exemplifies how mathematical rigor can lead to performance breakthroughs and broad applications," Rus says. "With LinOSS, we’re providing the scientific community with a powerful tool for understanding and predicting complex systems, bridging the gap between biological inspiration and computational innovation."

The team imagines that the emergence of a new paradigm like LinOSS will be of interest to machine learning practitioners to build upon. Looking ahead, the researchers plan to apply their model to an even wider range of different data modalities. Moreover, they suggest that LinOSS could provide valuable insights into neuroscience, potentially deepening our understanding of the brain itself.

Their work was supported by the Swiss National Science Foundation, the Schmidt AI2050 program, and the U.S. Department of the Air Force Artificial Intelligence Accelerator.

In the dim light of the lab, friends, family, and strangers watched the image of a pianist playing for them, the pianist’s fingers projected onto the moving keys of a real grand piano that filled the space with music.

Watching the ghostly musicians, faces and bodies blurred at their edges, several listeners shared one strong but strange conviction: “feeling someone’s presence” while “also knowing that I am the only one in the room.”

“It’s tough to explain,” another listener said. “It felt like they were in the room with me, but at the same time, not.”

That presence of absence is at the heart of TeleAbsence, a project by the MIT Media Lab’s Tangible Media group that focuses on technologies that create illusory communication with the dead and with past selves.

But rather than a “Black Mirror”-type scenario of synthesizing literal loved ones, the project led by Hiroshi Ishii, the Jerome B. Wiesner Professor of Media Arts and Sciences, instead seeks what it calls “poetic encounters” that reach across time and memory.

The project recently published a positioning paper in PRESENCE: Virtual and Augmented Reality that presents the design principles behind TeleAbsence, and how it could help people cope with loss and plan for how they might be remembered.

The phantom pianists of the MirrorFugue project, created by Tangible Media graduate Xiao Xiao ’09, SM ’11, PhD ’16, are one of the best-known examples of the project. On April 30, Xiao, now director and principal investigator at the Institute for Future Technologies of Da Vinci Higher Education in Paris, shared results from the first experimental study of TeleAbsence through MirrorFugue at the 2025 CHI conference on Human Factors in Computing Systems in Yokohama, Japan.

When Ishii spoke about TeleAbsence at the XPANSE 2024 conference in Abu Dhabi, “about 20 people came up to me after, and all of them told me they had tears in their eyes … the talk reminded them about a wife or a father who passed away,” he says. “One thing is clear: They want to see them again and talk to them again, metaphorically.”

Messages in bottles

As the director of the Tangible Media group, Ishii has been a world leader in telepresence, using technologies to connect people over physical distance. But when his mother died in 1998, Ishii says the pain of the loss prompted him to think about how much we long to connect across the distance of time.

His mother wrote poetry, and one of his first experiments in TeleAbsence was the creation of a Twitterbot that would post snippets of her poetry. Others watching the account online were so moved that they began posting photos of flowers to the feed to honor the mother and son.

“That was a turning point for TeleAbsence, and I wanted to expand this concept,” Ishii says.

Illusory communication, like the posted poems, is one key design principle of TeleAbsence. Even though users know the “conversation” is one-way, the researchers write, it can be comforting and cathartic to have a tangible way to reach out across time.

Finding ways to make memories material is another important design principle. One of the projects created by Ishii and colleagues is a series of glass bottles, reminiscent of the soy sauce bottles Ishii’s mother used while cooking. Open one of the bottles, and the sounds of chopping, of sizzling onions, of a radio playing quietly in the background, of a maternal voice, reunite a son with his mother.

Ishii says sight and sound are the primary modalities of TeleAbsence technologies for now, because although the senses of touch, smell, and taste are known to be powerful memory triggers, “it is a very big challenge to record that kind of multimodal moment.”

At the same time, one of the other pillars of TeleAbsence is the presence of absence. These are the physical markers, or traces, of a person that serve to remind us both of the person and that the person is gone. One of the most powerful examples, the researchers write, is the permanent “shadow” of Hiroshima Japanese resident Mitsuno Ochi, her silhouette transferred to stone steps 260 meters from where the atomic bomb detonated in 1945.

“Abstraction is very important,” Ishii says. “We want something to recall a moment, not physically recreate it.”

With the bottles, for instance, people have asked Ishii and his colleagues whether it might be more evocative to fill them with a perfume or drink. “But our philosophy is to make a bottle completely empty,” he explains. “The most important thing to let people imagine, based on the memory.”

Other important design principles within TeleAbsence include traces of reflection — the ephemera of faint pen scratches and blotted ink on a preserved letter, for instance — and the concept of remote time. TeleAbsence should go beyond dredging up a memory of a loved one, the researchers insist, and should instead produce a sense of being transported to spend a moment in the past with them.

Time travelers

For Xiao, who has played the piano her whole life, MirrorFugue is a “deeply personal project” that allowed her to travel to a time in her childhood that was almost lost to her.

Her parents moved from China to the United States when she was a baby — but it took eight years for Xiao to follow. “The piano, in a sense, was almost like my first language,” she recalls. “And then when I moved to America, my brain overwrote bits of my childhood where my operating system used to be in Chinese, and now it’s very much in English. But throughout this whole time, music and the piano stayed constant.”

MirrorFugue’s “sense of kind-of being there and not being there, and the wish to connect with oneself from the past, comes from my own desire to connect with my own past self,” she adds.

The new MirrorFugue study puts some empirical data behind the concept of TeleAbsence, she says. Its 28 participants were fitted with sensors to measure changes in their heart rate and hand movements during the experience. They were extensively interviewed about their perceptions and emotions afterward. The recorded images came from pianists ranging in experience from children early in their lessons to professional pianists like the late Ryuichi Sakamoto.

The researchers found that emotional experiences described by the listeners were significantly influenced by whether the listeners knew the pianist, as well as whether the pianist was known by the listeners to be alive or dead.

Some participants placed their own hands alongside the ghosts to play impromptu duets. One daughter, who said she had not paid close attention to her father’s playing when he was alive, was newly impressed by his talent. One person felt empathy watching his past self struggle through a new piece of music. A young girl, mouth slightly open in concentration and fingers small on the keys, showed her mother a past daughter that wasn’t possible to see in old photos.

The longing for past people and past selves can be “a deep sadness that will never go away,” says Xiao. “You’ll always carry it with you, but it also makes you sensitive to certain aesthetic experiences that’s also beautiful.”

“Once you’ve had that experience, it really resonates,” she adds, “And I think that’s why TeleAbsence resonates with so many people.”

Uncanny valleys and curated memory

Acutely aware of the potential ethical dangers of their research, the TeleAbsence scientists have worked with grief researchers and psychologists to better understand the implications of building these bridges through time.

For instance, “one thing we learned is that it depends on how long ago a person passed away,” says Ishii. “Right after death, when it’s very difficult for many people, this representation matters. But you have to make important informed decisions about whether this drags out the grief too long.”

TeleAbsence could comfort the dying, he says, by “knowing there is a means by which they are going to live on for their descendants.” He encourages people to consider curating “high-quality, condensed information,” such as their social media posts, that could be used for this purpose.

“But of course many families do not have ideal relationships, so I can easily think of the case where a descendant might not have any interest” in interacting with their ancestors through TeleAbsence, Ishii notes.

TeleAbsence should never fully recreate or generate new content for a loved one, he insists, pointing to the rise of “ghost bot” startups, companies that collect data on a person to create an “artificial, generative AI-based avatar that speaks what they never spoke, or do gestures or facial expressions.”

A recent viral video of a mother in Korea “reunited” in virtual reality with an avatar of her dead daughter, Ishii says, made him “very depressed, because they’re doing grief as entertainment, consumption for an audience.”

Xiao thinks there might still be some role for generative AI in the TeleAbsence space. She is writing a research proposal for MirrorFugue that would include representations of past pianists. “I think right now we’re getting to the point with generative AI that we can generate hand movements and we can transcribe the MIDI from the audio so that we can conjure up Franz Listz or Mozart or somebody, a really historical figure.”

“Now of course, it gets a little bit tricky, and we have discussed this, the role of AI and how to avoid the uncanny valley, how to avoid deceiving people,” she says. “But from a researcher’s perspective, it actually excites me a lot, the possibility to be able to empirically test these things.”

The importance of emptiness

Along with Ishii’s mother, the PRESENCE paper was also dedicated “in loving memory” to Elise O’Hara, a beloved Media Lab administrative assistant who worked with Tangible Media until her unexpected death in 2023. Her presence — and her absence — are felt deeply every day, says Ishii.

He wonders if TeleAbsence could someday become a common word “to describe something that was there, but is now gone.”

“When there is a place on a bookshelf where a book should be,” he says, “my students say, ‘oh, that’s a teleabsence.’”

Like a sudden silence in the middle of a song, or the empty white space of a painting, emptiness can hold important meaning. It’s an idea that we should make more room for in our lives, Ishii says.

“Because now we’re so busy, so many notification messages from your smartphone, and we are all distracted, always,” he suggests. “So emptiness and impermanence, presence of absence, if those concepts can be accepted, then people can think a bit more poetically.”

The composition of bacterial populations living on our faces plays a significant role in the development of acne and other skin conditions such as eczema. Two species of bacteria predominate in most people, but how they interact with each other, and how those interactions may contribute to disease, has been difficult to study.

MIT researchers have now revealed the dynamics of those interactions in more detail than previously possible, shedding light on when and how new bacterial strains emerge on the skin of the face. Their findings could help guide the development of new treatments for acne and other conditions, and may also help to optimize the timing of such treatments.

The researchers found that many new strains of Cutibacterium acnes, a species believed to contribute to the development of acne, are acquired during the early teenage years. But after that, the makeup of these populations becomes very stable and doesn’t change much even when exposed to new strains.

That suggests that this transitional stage could be the best window for introducing probiotic strains of C. acnes, says Tami Lieberman, an associate professor of civil and environmental engineering, a member of MIT’s Institute for Medical Engineering and Science, and the senior author of the study.

“We found that there are some surprising dynamics, and these dynamics provide insights for how to design probiotic therapy,” Lieberman says. “If we had a strain that we knew could prevent acne, these results would suggest we should make sure we apply them early during the transition to adulthood, to really get them to engraft.”

Jacob Baker PhD ’24, who is now the chief scientific officer at Taxa Technologies, is the lead author of the paper, which appears today in Cell Host and Microbe. Other authors include MIT graduate student Evan Qu, MIT postdoc Christopher Mancuso, Harvard University graduate student A. Delphine Tripp, and former MIT postdoc Arolyn Conwill PhD ’18.

Microbial dynamics

Although C. acnes has been implicated in the development of acne, it is still unclear exactly why acne develops in some people but not others — it may be that some strains are more likely to cause skin inflammation, or there may be differences in how the host immune system responds to the bacteria, Lieberman says. There are probiotic strains of C. acnes now available, which are thought to help prevent acne, but the benefits of these strains have not been proven.

Along with C. acnes, the other predominant bacterium found on the face is Staphylococcus epidermidis. Together, these two strains make up about 80 percent of the strains in the adult facial skin microbiome. Both of these species exist in different strains, or lineages, that vary by a small number of genetic mutations. However, until now, researchers had not been able to accurately measure this diversity or track how it changes over time.

Learning more about those dynamics could help researchers answer key questions that could help them develop new probiotic treatments for acne: How easy is it for new lineages to establish themselves on the skin, and what is the best time to introduce them?

To study these population shifts, the researchers had to measure how individual cells evolve over time. To do that, they began by obtaining microbiome samples from 30 children at a Boston-area school and from 27 of their parents. Studying members of the same family enabled the researchers to analyze the likelihood of different lineages being transferred between people in close contact.

For about half of the individuals, the researchers were able to take samples at multiple time points, and for the rest, only once. For each sample, they isolated individual cells and grew them into colonies, then sequenced their genomes.

This allowed the researchers to learn how many lineages were found on each person, how they changed over time, and how different cells from the same lineage were. From that information, the researchers could infer what had happened to those lineages in the recent past and how long they had been present on the individual.

Overall, the researchers identified a total of 89 C. acnes lineages and 78 S. epidermidis lineages, with up to 11 of each found in each person’s microbiome. Previous work had suggested that in each person’s facial skin microbiome, lineages of these two skin bacteria remain stable over long periods of time, but the MIT team found that these populations are actually more dynamic than previously thought.

“We wanted to know if these communities were truly stable, and if there could be times where they weren’t stable. In particular, if the transition to an adult skin like microbiome would have a higher rate of acquisition of new lineages,” Lieberman says.

During the early teens, an increase in hormone production results in increased oil on the skin, which is a good food source for bacteria. It has previously been shown that during this time, the density of bacteria on the skin of the face increases by about 10,000-fold. In this study, the researchers found that while the composition of C. acnes populations tended to remain very stable over time, the early teenage years present an opportunity for many more lineages of C. acnes to appear.

“For C. acnes, what we were able to show was that people do get strains throughout life, but very rarely,” Lieberman says. “We see the highest rate of influx when teenagers are transitioning to a more adult-like skin microbiome.”

The findings suggest that for topical probiotic treatments for acne, the best time to apply them is during the early teenage years, when there could be more opportunity for probiotic strains to become established.

Population turnover

Later in adulthood, there is a little bit of sharing of C. acnes strains between parents living in the same household, but the rate of turnover in any individual person’s microbiome is still very low, Lieberman says.

The researchers found that S. epidermidis has a much higher turnover rate than C. acnes — each S. epidermidis strain lives on the face for an average of less than two years. However, there was not very much overlap in the S. epidermidis lineages shared by members of the same household, suggesting that transfer of strains between people is not causing the high turnover rate.

“That suggests that something is preventing homogenization between people,” Lieberman says. “It could be host genetics or host behavior, or people using different topicals or different moisturizers, or it could be active restriction of new migrants from the bacteria that are already there at that moment.”

Now that they’ve shown that new C. acnes strains can be acquired during the early teenage years, the researchers hope to study whether the timing of this acquisition affects how the immune system responds to them. They also hope to learn more about how people maintain such different microbiome populations even when exposed to new lineages through close contact with family members.

“We want to understand why we each have unique strain communities despite the fact that there is this constant accessibility and high turnover, specifically for S. epidermidis,” Lieberman says. “What’s driving this constant turnover in S. epidermidis, and what are the implications of these new colonizations for acne during adolescence?”

The research was funded by the MIT Center for Microbiome Informatics and Therapeutics, a Smith Family Foundation Award for Excellence in Biomedical Research, and the National Institutes of Health.

The ambiguity in medical imaging can present major challenges for clinicians who are trying to identify disease. For instance, in a chest X-ray, pleural effusion, an abnormal buildup of fluid in the lungs, can look very much like pulmonary infiltrates, which are accumulations of pus or blood.

An artificial intelligence model could assist the clinician in X-ray analysis by helping to identify subtle details and boosting the efficiency of the diagnosis process. But because so many possible conditions could be present in one image, the clinician would likely want to consider a set of possibilities, rather than only having one AI prediction to evaluate.

One promising way to produce a set of possibilities, called conformal classification, is convenient because it can be readily implemented on top of an existing machine-learning model. However, it can produce sets that are impractically large. 

MIT researchers have now developed a simple and effective improvement that can reduce the size of prediction sets by up to 30 percent while also making predictions more reliable.

Having a smaller prediction set may help a clinician zero in on the right diagnosis more efficiently, which could improve and streamline treatment for patients. This method could be useful across a range of classification tasks — say, for identifying the species of an animal in an image from a wildlife park — as it provides a smaller but more accurate set of options.

“With fewer classes to consider, the sets of predictions are naturally more informative in that you are choosing between fewer options. In a sense, you are not really sacrificing anything in terms of accuracy for something that is more informative,” says Divya Shanmugam PhD ’24, a postdoc at Cornell Tech who conducted this research while she was an MIT graduate student.

Shanmugam is joined on the paper by Helen Lu ’24; Swami Sankaranarayanan, a former MIT postdoc who is now a research scientist at Lilia Biosciences; and senior author John Guttag, the Dugald C. Jackson Professor of Computer Science and Electrical Engineering at MIT and a member of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). The research will be presented at the Conference on Computer Vision and Pattern Recognition in June.

Prediction guarantees

AI assistants deployed for high-stakes tasks, like classifying diseases in medical images, are typically designed to produce a probability score along with each prediction so a user can gauge the model’s confidence. For instance, a model might predict that there is a 20 percent chance an image corresponds to a particular diagnosis, like pleurisy.

But it is difficult to trust a model’s predicted confidence because much prior research has shown that these probabilities can be inaccurate. With conformal classification, the model’s prediction is replaced by a set of the most probable diagnoses along with a guarantee that the correct diagnosis is somewhere in the set.

But the inherent uncertainty in AI predictions often causes the model to output sets that are far too large to be useful.

For instance, if a model is classifying an animal in an image as one of 10,000 potential species, it might output a set of 200 predictions so it can offer a strong guarantee.

“That is quite a few classes for someone to sift through to figure out what the right class is,” Shanmugam says.

The technique can also be unreliable because tiny changes to inputs, like slightly rotating an image, can yield entirely different sets of predictions.

To make conformal classification more useful, the researchers applied a technique developed to improve the accuracy of computer vision models called test-time augmentation (TTA).

TTA creates multiple augmentations of a single image in a dataset, perhaps by cropping the image, flipping it, zooming in, etc. Then it applies a computer vision model to each version of the same image and aggregates its predictions.

“In this way, you get multiple predictions from a single example. Aggregating predictions in this way improves predictions in terms of accuracy and robustness,” Shanmugam explains.

Maximizing accuracy

To apply TTA, the researchers hold out some labeled image data used for the conformal classification process. They learn to aggregate the augmentations on these held-out data, automatically augmenting the images in a way that maximizes the accuracy of the underlying model’s predictions.

Then they run conformal classification on the model’s new, TTA-transformed predictions. The conformal classifier outputs a smaller set of probable predictions for the same confidence guarantee.

“Combining test-time augmentation with conformal prediction is simple to implement, effective in practice, and requires no model retraining,” Shanmugam says.

Compared to prior work in conformal prediction across several standard image classification benchmarks, their TTA-augmented method reduced prediction set sizes across experiments, from 10 to 30 percent.

Importantly, the technique achieves this reduction in prediction set size while maintaining the probability guarantee.

The researchers also found that, even though they are sacrificing some labeled data that would normally be used for the conformal classification procedure, TTA boosts accuracy enough to outweigh the cost of losing those data.

“It raises interesting questions about how we used labeled data after model training. The allocation of labeled data between different post-training steps is an important direction for future work,” Shanmugam says.

In the future, the researchers want to validate the effectiveness of such an approach in the context of models that classify text instead of images. To further improve the work, the researchers are also considering ways to reduce the amount of computation required for TTA.

This research is funded, in part, by the Wistron Corporation.

Since its founding 19 years ago as a pioneering collaboration with Portuguese universities, research institutions and corporations, the MIT-Portugal Program (MPP) has achieved a slew of successes — from enabling 47 entrepreneurial spinoffs and funding over 220 joint projects between MIT and Portuguese researchers to training a generation of exceptional researchers on both sides of the Atlantic.

In March, with nearly two decades of collaboration under their belts, MIT and the Portuguese Science and Technology Foundation (FCT) signed an agreement that officially launches the program’s next chapter. Running through 2030, MPP’s Phase 4 will support continued exploration of innovative ideas and solutions in fields ranging from artificial intelligence and nanotechnology to climate change — both on the MIT campus and with partners throughout Portugal.  

“One of the advantages of having a program that has gone on so long is that we are pretty well familiar with each other at this point. Over the years, we’ve learned each other’s systems, strengths and weaknesses and we’ve been able to create a synergy that would not have existed if we worked together for a short period of time,” says Douglas Hart, MIT mechanical engineering professor and MPP co-director.

Hart and John Hansman, the T. Wilson Professor of Aeronautics and Astronautics at MIT and MPP co-director, are eager to take the program’s existing research projects further, while adding new areas of focus identified by MIT and FCT. Known as the Fundação para a Ciência e Tecnologia in Portugal, FCT is the national public agency supporting research in science, technology and innovation under Portugal’s Ministry of Education, Science and Innovation.

“Over the past two decades, the partnership with MIT has built a foundation of trust that has fostered collaboration among researchers and the development of projects with significant scientific impact and contributions to the Portuguese economy,” Fernando Alexandre, Portugal’s minister for education, science, and innovation, says. “In this new phase of the partnership, running from 2025 to 2030, we expect even greater ambition and impact — raising Portuguese science and its capacity to transform the economy and improve our society to even higher levels, while helping to address the challenges we face in areas such as climate change and the oceans, digitalization, and space.”

“International collaborations like the MIT-Portugal Program are absolutely vital to MIT’s mission of research, education and service. I’m thrilled to see the program move into its next phase,” says MIT President Sally Kornbluth. “MPP offers our faculty and students opportunities to work in unique research environments where they not only make new findings and learn new methods but also contribute to solving urgent local and global problems. MPP’s work in the realm of ocean science and climate is a prime example of how international partnerships like this can help solve important human problems."

Sharing MIT’s commitment to academic independence and excellence, Kornbluth adds, “the institutions and researchers we partner with through MPP enhance MIT’s ability to achieve its mission, enabling us to pursue the exacting standards of intellectual and creative distinction that make MIT a cradle of innovation and world leader in scientific discovery.”

The epitome of an effective international collaboration, MPP has stayed true to its mission and continued to deliver results here in the U.S. and in Portugal for nearly two decades — prevailing amid myriad shifts in the political, social, and economic landscape. The multifaceted program encompasses an annual research conference and educational summits such as an Innovation Workshop at MIT each June and a Marine Robotics Summer School in the Azores in July, as well as student and faculty exchanges that facilitate collaborative research. During the third phase of the program alone, 59 MIT students and 53 faculty and researchers visited Portugal, and MIT hosted 131 students and 49 faculty and researchers from Portuguese universities and other institutions.

In each roughly five-year phase, MPP researchers focus on a handful of core research areas. For Phase 3, MPP advanced cutting-edge research in four strategic areas: climate science and climate change; Earth systems: oceans to near space; digital transformation in manufacturing; and sustainable cities. Within these broad areas, MIT and FCT researchers worked together on numerous small-scale projects and several large “flagship” ones, including development of Portugal’s CubeSat satellite, a collaboration between MPP and several Portuguese universities and companies that marked the country’s second satellite launch and the first in 30 years.

While work in the Phase 3 fields will continue during Phase 4, researchers will also turn their attention to four more areas: chips/nanotechnology, energy (a previous focus in Phase 2), artificial intelligence, and space.

“We are opening up the aperture for additional collaboration areas,” Hansman says.

In addition to focusing on distinct subject areas, each phase has emphasized the various parts of MPP’s mission to differing degrees. While Phase 3 accentuated collaborative research more than educational exchanges and entrepreneurship, those two aspects will be given more weight under the Phase 4 agreement, Hart said.

“We have approval in Phase 4 to bring a number of Portuguese students over, and our principal investigators will benefit from close collaborations with Portuguese researchers,” he says.

The longevity of MPP and the recent launch of Phase 4 are evidence of the program’s value. The program has played a role in the educational, technological and economic progress Portugal has achieved over the past two decades, as well.  

“The Portugal of today is remarkably stronger than the Portugal of 20 years ago, and many of the places where they are stronger have been impacted by the program,” says Hansman, pointing to sustainable cities and “green” energy, in particular. “We can’t take direct credit, but we’ve been part of Portugal’s journey forward.”

Since MPP began, Hart adds, “Portugal has become much more entrepreneurial. Many, many, many more start-up companies are coming out of Portuguese universities than there used to be.”  

A recent analysis of MPP and FCT’s other U.S. collaborations highlighted a number of positive outcomes. The report noted that collaborations with MIT and other US universities have enhanced Portuguese research capacities and promoted organizational upgrades in the national R&D ecosystem, while providing Portuguese universities and companies with opportunities to engage in complex projects that would have been difficult to undertake on their own.

Regarding MIT in particular, the report found that MPP’s long-term collaboration has spawned the establishment of sustained doctoral programs and pointed to a marked shift within Portugal’s educational ecosystem toward globally aligned standards. MPP, it reported, has facilitated the education of 198 Portuguese PhDs.

Portugal’s universities, students and companies are not alone in benefitting from the research, networks, and economic activity MPP has spawned. MPP also delivers unique value to MIT, as well as to the broader US science and research community. Among the program’s consistent themes over the years, for example, is “joint interest in the Atlantic,” Hansman says.

This summer, Faial Island in the Azores will host MPP’s fifth annual Marine Robotics Summer School, a two-week course open to 12 Portuguese Master’s and first year PhD students and 12 MIT upper-level undergraduates and graduate students. The course, which includes lectures by MIT and Portuguese faculty and other researchers, workshops, labs and hands-on experiences, “is always my favorite,” said Hart.

“I get to work with some of the best researchers in the world there, and some of the top students coming out of Woods Hole Oceanographic Institution, MIT, and Portugal,” he says, adding that some of his previous Marine Robotics Summer School students have come to study at MIT and then gone on to become professors in ocean science.

“So, it’s been exciting to see the growth of students coming out of that program, certainly a positive impact,” Hart says.

MPP provides one-of-a-kind opportunities for ocean research due to the unique marine facilities available in Portugal, including not only open ocean off the Azores but also Lisbon’s deep-water port and a Portuguese Naval facility just south of Lisbon that is available for collaborative research by international scientists. Like MIT, Portuguese universities are also strongly invested in climate change research — a field of study keenly related to ocean systems.

“The international collaboration has allowed us to test and further develop our research prototypes in different aquaculture environments both in the US and in Portugal, while building on the unique expertise of our Portuguese faculty collaborator Dr. Ricardo Calado from the University of Aveiro and our industry collaborators,” says Stefanie Mueller, the TIBCO Career Development Associate Professor in MIT’s departments of Electrical Engineering and Computer Science and Mechanical Engineering and leader of the Human-Computer Interaction Group at the MIT Computer Science and Artificial Intelligence Lab.

Mueller points to the work of MIT mechanical engineering PhD student Charlene Xia, a Marine Robotics Summer School participant, whose research is aimed at developing an economical system to monitor the microbiome of seaweed farms and halt the spread of harmful bacteria associated with ocean warming. In addition to participating in the summer school as a student, Xia returned to the Azores for two subsequent years as a teaching assistant.

“The MIT-Portugal Program has been a key enabler of our research on monitoring the aquatic microbiome for potential disease outbreaks,” Mueller says.

As MPP enters its next phase, Hart and Hansman are optimistic about the program’s continuing success on both sides of the Atlantic and envision broadening its impact going forward.

“I think, at this point, the research is going really well, and we’ve got a lot of connections. I think one of our goals is to expand not the science of the program necessarily, but the groups involved,” Hart says, noting that MPP could have a bigger presence in technical fields such as AI and micro-nano manufacturing, as well as in social sciences and humanities.

“We’d like to involve many more people and new people here at MIT, as well as in Portugal,” he says, “so that we can reach a larger slice of the population.” 

In the future, quantum computers could rapidly simulate new materials or help scientists develop faster machine-learning models, opening the door to many new possibilities.

But these applications will only be possible if quantum computers can perform operations extremely quickly, so scientists can make measurements and perform corrections before compounding error rates reduce their accuracy and reliability.

The efficiency of this measurement process, known as readout, relies on the strength of the coupling between photons, which are particles of light that carry quantum information, and artificial atoms, units of matter that are often used to store information in a quantum computer.

Now, MIT researchers have demonstrated what they believe is the strongest nonlinear light-matter coupling ever achieved in a quantum system. Their experiment is a step toward realizing quantum operations and readout that could be performed in a few nanoseconds.

The researchers used a novel superconducting circuit architecture to show nonlinear light-matter coupling that is about an order of magnitude stronger than prior demonstrations, which could enable a quantum processor to run about 10 times faster.

There is still much work to be done before the architecture could be used in a real quantum computer, but demonstrating the fundamental physics behind the process is a major step in the right direction, says Yufeng “Bright” Ye SM ’20, PhD ’24, lead author of a paper on this research.

“This would really eliminate one of the bottlenecks in quantum computing. Usually, you have to measure the results of your computations in between rounds of error correction. This could accelerate how quickly we can reach the fault-tolerant quantum computing stage and be able to get real-world applications and value out of our quantum computers,” says Ye.

He is joined on the paper by senior author Kevin O’Brien, an associate professor and principal investigator in the Research Laboratory of Electronics (RLE) at MIT who leads the Quantum Coherent Electronics Group in the Department of Electrical Engineering and Computer Science (EECS). Additional MIT co-authors, with affiliations in RLE and/or MIT Lincoln Laboratory, include Jeremy B. Kline, Alec Yen, Gregory Cunningham, Max Tan, Alicia Zang, Michael Gingras, Bethany M. Niedzielski, Hannah Stickler, Kyle Serniak, and Mollie E. Schwartz. The research appears today in Nature Communications.

A new coupler

This physical demonstration builds on years of theoretical research in the O’Brien group.

After Ye joined the lab as a PhD student in 2019, he began developing a specialized photon detector to enhance quantum information processing.

Through that work, he invented a new type of quantum coupler, which is a device that facilitates interactions between qubits. Qubits are the building blocks of a quantum computer. This so-called quarton coupler had so many potential applications in quantum operations and readout that it quickly became a focus of the lab.

This quarton coupler is a special type of superconducting circuit that has the potential to generate extremely strong nonlinear coupling, which is essential for running most quantum algorithms. As the researchers feed more current into the coupler, it creates an even stronger nonlinear interaction. In this sense, nonlinearity means a system behaves in a way that is greater than the sum of its parts, exhibiting more complex properties.

“Most of the useful interactions in quantum computing come from nonlinear coupling of light and matter. If you can get a more versatile range of different types of coupling, and increase the coupling strength, then you can essentially increase the processing speed of the quantum computer,” Ye explains.

For quantum readout, researchers shine microwave light onto a qubit and then, depending on whether that qubit is in state 0 or 1, there is a frequency shift on its associated readout resonator. They measure this shift to determine the qubit’s state.

Nonlinear light-matter coupling between the qubit and resonator enables this measurement process.

The MIT researchers designed an architecture with a quarton coupler connected to two superconducting qubits on a chip. They turn one qubit into a resonator and use the other qubit as an artificial atom which stores quantum information. This information is transferred in the form of microwave light particles called photons.

“The interaction between these superconducting artificial atoms and the microwave light that routes the signal is basically how an entire superconducting quantum computer is built,” Ye explains.

Enabling faster readout

The quarton coupler creates nonlinear light-matter coupling between the qubit and resonator that’s about an order of magnitude stronger than researchers had achieved before. This could enable a quantum system with lightning-fast readout.

“This work is not the end of the story. This is the fundamental physics demonstration, but there is work going on in the group now to realize really fast readout,” O’Brien says.

That would involve adding additional electronic components, such as filters, to produce a readout circuit that could be incorporated into a larger quantum system.

The researchers also demonstrated extremely strong matter-matter coupling, another type of qubit interaction that is important for quantum operations. This is another area they plan to explore with future work.

Fast operations and readout are especially important for quantum computers because qubits have finite lifespans, a concept known as coherence time.

Stronger nonlinear coupling enables a quantum processor to run faster and with lower error, so the qubits can perform more operations in the same amount of time. This means the qubits can run more rounds of error correction during their lifespans.

“The more runs of error correction you can get in, the lower the error will be in the results,” Ye says.

In the long run, this work could help scientists build a fault-tolerant quantum computer, which is essential for practical, large-scale quantum computation.

This research was supported, in part, by the Army Research Office, the AWS Center for Quantum Computing, and the MIT Center for Quantum Engineering.

The ocean’s deep-sea bed is scattered with ancient rocks, each about the size of a closed fist, called “polymetallic nodules.” Elsewhere, along active and inactive hydrothermal vents and the deep ocean’s ridges, volcanic arcs, and tectonic plate boundaries, and on the flanks of seamounts, lie other types of mineral-rich deposits containing high-demand minerals.

The minerals found in the deep ocean are used to manufacture products like the lithium-ion batteries used to power electric vehicles, cell phones, or solar cells. In some cases, the estimated resources of critical mineral deposits in parts of the abyssal ocean exceed global land-based reserves severalfold.

“Society wants electric-powered vehicles, solar cells for clean energy, but all of this requires resources,” says Thomas Peacock, professor of mechanical engineering at MIT, in a video discussing his research. “Land-based resources are getting depleted, or are more challenging to access. In parts of the ocean, there are much more of these resources than in land-based reserve. The question is: Can it be less impactful to mine some of these resources from the ocean, rather than from land?”

Deep-sea mining is a new frontier in mineral extraction, with potentially significant implications for industry and the global economy, and important environmental and societal considerations. Through research, scientists like Peacock study the impacts of deep-sea mining activity objectively and rigorously, and can bring evidence to bear on decision-making. 

Mining activities, whether on land or at sea, can have significant impacts on the environment at local, regional, and global scales. As interest in deep-seabed mining is increasing, driven by the surging demand for critical minerals, scientific inquiries help illuminate the trade-offs.

Peacock has long studied the potential impacts of deep-sea mining in a region of the Pacific Ocean known as the Clarion Clipperton Zone (CCZ), where polymetallic nodules abound. A decade ago, his research group began studying deep-sea mining, seeing a critical need to develop monitoring and modeling capabilities for assessing the scale of impact.

Today, his MIT Environmental Dynamics Laboratory (ENDLab) is at the forefront of advancing understanding for emerging ocean utilization technologies. With research anchored in fundamental fluid dynamics, the team is developing cutting-edge monitoring programs, novel sensors, and modeling tools.

“We are studying the form of suspended sediment from deep sea mining operations, testing a new sensor for sediment and another new sensor for turbulence, studying the initial phases of the sediment plume development, and analyzing data from the 2021 and 2022 technology trials in the Pacific Ocean,” he explains.

In deep-sea nodule mining, vehicles collect nodules from the ocean floor and convey them back to a vessel above. After the critical materials are collected on the vessel, some leftover sediment may be returned to the deep-water column. The resulting sediment plumes, and their potential impacts, are a key focus of the team’s work.

A 2022 study conducted in the CCZ investigated the dynamics of sediment plumes near a deep-seabed polymetallic nodule mining vehicle. The experiments reveal most of the released sediment-laden water, between 92 and 98 percent, stayed close to the sea-bed floor, spreading laterally. The results suggest that turbidity current dynamics set the fraction of sediment that remains suspended in the water, along with the scale of the subsequent ambient sediment plume. The implications of the process, which had been previously overlooked, are substantial for plume modeling and informative for environmental impact statements.

“New model breakthroughs can help us make increasingly trustworthy predictions,” he says. The team also contributed to a recent study, published in the journal Nature, which showed that sediment deposited away from a test mining site gets cleared away, most likely by ocean currents, and reported on any observed biological recovery.

Researchers observed a site four decades after a nodule test mining experiment. Although biological impacts in many groups of organisms were present, populations of several organisms, including sediment macrofauna, mobile deposit feeders, and even large-sized sessile fauna, had begun to reestablish despite persistent physical changes at the seafloor. The study was led by the National Oceanography Centre in the U.K.

“A great deal has been learned about the fluid mechanics of deep-sea mining, in particular when it comes to deep-sea mining sediment plumes,” says Peacock, adding that the scientific progress continues with more results on the way. The work is setting new standards for in-situ monitoring of suspended sediment properties, and for how to interpret field data from recent technical trials.

Whether you are a person about town or a worm in a dish, life can throw all kinds of circumstances your way. What you need is a nervous system flexible enough to cope. In a new study, MIT neuroscientists show how even a simple animal can repurpose brain circuits and the chemical signals, or “neuromodulators,” in its brain to muster an adaptive response to an infection. The study therefore may provide a model for understanding how brains in more complex organisms, including ourselves, manage to use what they have to cope with shifting internal states. 

“Neuromodulators play pivotal roles in coupling changes in animals’ internal states to their behavior,” the scientists write in their paper, recently published in Nature Communications. “How combinations of neuromodulators released from different neuronal sources control the diverse internal states that animals exhibit remains an open question.”

When C. elegans worms fed on infectious Pseudomonas bacteria, they ate less and became more lethargic. When the researchers looked across the nervous system to see how that behavior happened, they discovered that the worm had completely revamped the roles of several of its 302 neurons and some of the peptides they secrete across the brain to modulate behavior. Systems that responded to stress in one case or satiety in another became reconfigured to cope with the infection.

“This is a question of, how do you adapt to your environment with the highest level of flexibility given the set of neurons and neuromodulators you have,” says postdoc Sreeparna Pradhan, co-lead author of the new study in Nature Communications. “How do you make the maximum set of options available to you?”

The research to find out took place in the lab of senior author Steve Flavell, an associate professor in The Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences and an investigator of the Howard Hughes Medical Institute. Pradhan, who was supported by a fellowship from MIT’s K. Lisa Yang Brain-Body Center during the work, teamed up with former Flavell Lab graduate student Gurrein Madan to lead the research.

Pradhan says the team discovered several surprises in the course of the study, including that a neuropeptide called FLP-13 completely flipped its function in infected animals versus animals experiencing other forms of stress. Previous research had shown that when worms are stressed by heat, a neuron called ALA releases FLP-13 to cause the worms to go into quiescence, a sleep-like state. But when the worms in the new study ate Pseudomonas bacteria, a band of other neurons released FLP-13 to fight off quiescence, enabling the worms to survive longer. Meanwhile, ALA took on a completely different role during sickness: leading the charge to suppress feeding by emitting a different group of peptides.

A comprehensive approach

To understand how the worms responded to infection, the team tracked many features of the worms’ behavior for days and made genetic manipulations to probe the underlying mechanisms at play. They also recorded activity across the worms' whole brains. This kind of a comprehensive observation and experimentation is difficult to achieve in more complex animals, but C. elegans’ relative simplicity makes it a tractable testbed, Pradhan says. The team’s approach also is what allowed it to make so many unexpected findings.

For instance, Pradhan didn’t suspect that the ALA neuron would turn out to be the neuron that suppressed feeding, but when she observed their behavior for long enough, she started to realize the reduced feeding arose from the worms taking little breaks that they wouldn’t normally take. As she and Madan were manipulating more than a dozen genes they thought might be affecting behavior and feeding in the worm, she included another called ceh-17 that she had read about years ago that seemed to promote bouts of “microsleep” in the worms. When they knocked out ceh-17, they found that those worms didn’t reduce feeding when they got infected, unlike normal animals. It just so happens that ceh-17 is specifically needed for ALA to function properly, so that’s when the team realized ALA might be involved in the feeding-reduction behavior.

To know for sure, they then knocked out the various peptides that ALA releases and saw that when they knocked out three in particular, flp-24, nlp-8 and flp-7, infected worms didn’t exhibit reduced feeding upon infection. That clinched that ALA drives the reduced feeding behavior by emitting those three peptides.

Meanwhile, Pradhan and Madan’s screens also revealed that when infected worms were missing flp-13, they would go into a quiescence state much sooner than infected worms with the peptide available. Notably, the worms that fought off the quiescence state lived longer. They found that fighting off quiescence depended on the FLP-13 coming from four neurons (I5, I1, ASH and OLL), but not from ALA. Further experiments showed that FLP-13 acted on a widespread neuropeptide receptor called DMSR-1 to prevent quiescence.

Having a little nap

The last major surprise of the study was that the quiescence that Pseudomonas infection induces in worms is not the same as other forms of sleepiness that show up in other contexts, such as after satiety or heat stress. In those cases, worms don’t wake easily (with a little poke), but amid infection their quiescence was readily reversible. It seemed more like lethargy than sleep. Using the lab’s ability image all neural activity during behavior, Pradhan and Madan discerned that a neuron called ASI was particularly active during the bouts of lethargy. That observation solidified further when they showed that ASI’s secretion of the peptide DAF-7 was required for the quiescence to emerge in infected animals.

In all, the study showed that the worms repurpose and reconfigure — sometimes to the point of completely reversing — the functions of neurons and peptides to mount an adaptive response to infection, versus a different problem like stress. The results therefore shed light on what has been a tricky question to resolve. How do brains use their repertoire of cells, circuits, and neuromodulators to deal with what life hands them? At least part of the answer seems to be by reshuffling existing components, rather than creating unique ones for each situation.

“The states of stress, satiety, and infection are not induced by unique sets of neuromodulators," the authors wrote in their paper. "Instead, one larger set of neuromodulators may be deployed from different sources and in different combinations to specify these different internal states.”

In addition to Pradhan, Madan, and Flavell, the paper’s other authors are Di Kang, Eric Bueno, Adam Atanas, Talya Kramer, Ugur Dag, Jessica Lage, Matthew Gomes, Alicia Kun-Yang Lu, and Jungyeon Park.

Support for the research came from the the Picower Institute, the Freedom Together Foundation, the K. Lisa Yang Brain-Body Center, and the Yang Tan Collective at MIT; the National Institutes of Health; the McKnight Foundation; the Alfred P. Sloan Foundation; and the Howard Hughes Medical Institute.

For Mingmar Sherpa, a senior research support associate in the Martin Lab in the Department of Biology, community is more than just his colleagues in the lab, where he studies how mechanical forces affect cell division timing during embryogenesis. On his long and winding path to MIT, he never left behind the people he grew up among in Nepal. Sherpa has been dedicated, every step of his career — from rural Solukhumbu to Kathmandu to Alabama to Cambridge — to advancing education and health care among his people in any way he can.

Despite working more than 7,000 miles away from home, Mingmar Sherpa makes every effort to keep himself connected to his community in Nepal. Every month, for example, he sends home money to support a computer lab that he established in his hometown in rural Solukhumbu, the district of Nepal that houses Mount Everest — just $250 a month covers the costs of a teacher’s salary, electricity, internet, and a space to teach. In this lab, almost 250 students thus far have learned computer skills essential to working in today’s digitally driven world. In college, Sherpa also started The Bright Vision Foundation (The Bright Future), an organization to support health and education in Nepal, and during the pandemic raised funds to provide personal protective equipment (PPE) and health care services across his home country. 

While Sherpa’s ambition to help his home can be traced back to his childhood, he didn’t have it all figured out from the start, and found inspiration at each step of his career.

“This mindset of giving back to the community, helping policymakers or establishing an organization to help people do science, helping the scientific community to find cures for diseases — all these ideas came to me along the way,” Sherpa says. “It is the journey that matters.”

A journey driven by hope and optimism

“Sherpa” is a reference to the ethnic group native to the mountainous regions of Nepal and Tibet, whose members are well-known for their mountaineering skills, which they use to guide and assist tourists who want to climb Mount Everest. Growing up in rural Solukhumbu, Sherpa was surrounded by people working in the tourism industry; few other occupations appeared feasible. There was just one hospital for the whole district, requiring locals to walk for days to get medical assistance.

The youngest of seven siblings, Sherpa went to an English-language middle school, which he had to walk for over an hour to get to. He excelled there, soon becoming the top student in his class and passing the national exam with distinction — success that allowed him to both dream of and accomplish a move to Kathmandu, the capital city of Nepal, to study in the best school in the country. 

It was an overwhelming transition, surrounded as he was for the first time by people from a very different social class, privileged with far more technological resources. The gaps between this well-equipped community and the one he left back home became increasingly obvious and left a strong impression on Sherpa.

There, he started thinking about how to use his newly acquired access to education and technology to uplift his community at home. He was especially fascinated by questions surrounding biology and human health, and next set his sights on attending college in the United States. 

“If I came to the U.S., I could learn skills which I could not learn in Nepal,” he says. “I could prepare myself to solve the problems that I want to solve.” 

At the University of Alabama in Birmingham, Sherpa continued to deepen his passion for biological science and joined a research lab. Through that work, he discovered the joys of basic research and the diverse set of skills it fosters. 

“I joined the lab to learn science, but to do science, you need other skills, like research communication,” he says. “I was learning unintentionally from being in a research position.” 

When Covid-19 spread around the globe, Sherpa wanted to apply the expertise and resources he had gained to help his people address the crisis. It was then that he started The Bright Vision Foundation, an organization aiming to raise the standards of health care and education in underserved communities in Nepal. Through the foundation, he raised funds to distribute PPE, provide health care services, and set up the computer lab in his childhood home. 

“Today’s world is all about technology and innovation, but here are good people in my community who don’t even know about computers,” he says. 

With the help of his brother, who serves as the lab instructor, and his parents, who provide the space and support the lab, and Sherpa’s own fundraising, he aims to help youths from backgrounds similar to his own be better prepared for the technologically advanced, globalized world of today.

The MIT chapter

Now, at MIT, Sherpa speaks with deep appreciation of the opportunities that the university has opened up for him — the people he has been meeting here, and the skills he has been learning. 

Professor of biology Adam C. Martin, Sherpa’s principal investigator, views making sure that international trainees like Mingmar are aware of the wide range of opportunities MIT offers — whether it be workshops, collaborations, networking and funding possibilities, or help with the pathway toward graduate school — as a key part of creating a supportive environment. 

Understanding the additional burdens on international trainees gives Martin extra appreciation for Sherpa’s perseverance, motivation, and desire to share his culture with the lab, sharing Nepalese food and providing context for Nepalese customs.

Being at such a research-intensive institution as MIT has helped Sherpa further clarify his goals and his view of the paths he can take to achieve them. Since college, his three passions have been intertwined: leadership, research, and human health. 

Sherpa will pursue a PhD in biomedical and biological sciences with a focus in cancer biology at Cornell University in the fall. In the longer term, he plans to focus on developing policy to improve public health.

Although Sherpa recognizes that Nepal is not the only place that might need his help, he has a sharp focus and an acute sense of what he is best positioned to do now. Sherpa is gearing up to organize a health camp in the spring to bring doctors to rural areas in Nepal, not only to provide care, but also to gather data on nutrition and health in different regions of the country.                        

“I cannot, in a day, or even a year, bring the living conditions of people in vulnerable communities up to a higher level, but I can slowly increase the living standard of people in less-developed communities, especially in Nepal,” he says. “There might be other parts of the world which are even more vulnerable than Nepal, but I haven’t explored them yet. But I know my community in Nepal, so I want to help improve people’s lives there.” 

When farmers apply pesticides to their crops, 30 to 50 percent of the chemicals end up in the air or soil instead of on the plants. Now, a team of researchers from MIT and Singapore has developed a much more precise way to deliver substances to plants: tiny needles made of silk.

In a study published today in Nature Nanotechnology, the researchers developed a way to produce large amounts of these hollow silk microneedles. They used them to inject agrochemicals and nutrients into plants, and to monitor their health.

“There’s a big need to make agriculture more efficient,” says Benedetto Marelli, the study’s senior author and an associate professor of civil and environmental engineering at MIT. “Agrochemicals are important for supporting our food system, but they’re also expensive and bring environmental side effects, so there’s a big need to deliver them precisely.”

Yunteng Cao PhD ’22, currently a postdoc Yale University, and Doyoon Kim, a former postdoc in the Marelli lab, led the study, which included a collaboration with the Disruptive and Sustainable Technologies for Agricultural Precision (DiSTAP) interdisciplinary research group at the Singapore-MIT Alliance for Research and Technology (SMART).

In demonstrations, the team used the technique to give plants iron to treat a disease known as chlorosis, and to add vitamin B12 to tomato plants to make them more nutritious. The researchers also showed the microneedles could be used to monitor the quality of fluids flowing into plants and to detect when the surrounding soil contained heavy metals.

Overall, the researchers believe the microneedles could serve as a new kind of plant interface for real-time health monitoring and biofortification.

“These microneedles could be a tool for plant scientists so they can understand more about plant health and how they grow,” Marelli says. “But they can also be used to add value to crops, making them more resilient and possibly even increasing yields.”

The inner workings of plants

Accessing the inner tissues of living plants requires scientists to get through the plants’ waxy skin without causing too much stress. In previous work, the researchers used silk-based microneedles to deliver agrochemicals to plants in lab environments and to detect pH changes in living plants. But these initial efforts involved small payloads, limiting their applications in commercial agriculture.

“Microneedles were originally developed for the delivery of vaccines or other drugs in humans,” Marelli explains. “Now we’ve adapted it so that the technology can work with plants, but initially we could not deliver sufficient doses of agrochemicals and nutrients to mitigate stressors or enhance crop nutritional values.”

Hollow structures could increase the amount of chemicals microneedles can deliver, but Marelli says creating those structures at scale has historically required clean rooms and expensive facilities like the ones found inside the MIT.nano building.

For this study, Cao and Kim created a new way to manufacture hollow silk microneedles by combining silk fibroin protein with a salty solution inside tiny, cone-shaped molds. As water evaporated from the solution, the silk solidified into the mold while the salt forms crystalline structures inside the molds. When the salt was removed, it left behind in each needle a hollow structure or tiny pores, depending on the salt concentration and the separation of the organic and inorganic phases.

“It’s a pretty simple fabrication process. It can be done outside of a clean room — you could do it in your kitchen if you wanted,” Kim says. “It doesn’t require any expensive machinery.”

The researchers then tested their microneedles’ ability to deliver iron to iron-deficient tomato plants, which can cause a disease known as chlorosis. Chlorosis can decrease yields, but treating it by spraying crops is inefficient and can have environmental side effects. The researchers showed that their hollow microneedles could be used for the sustained delivery of iron without harming the plants.

The researchers also showed their microneedles could be used to fortify crops while they grow. Historically, crop fortification efforts have focused on minerals like zinc or iron, with vitamins only added after the food is harvested.

In each case, the researchers applied the microneedles to the stalks of plants by hand, but Marelli envisions equipping autonomous vehicles and other equipment already used in farms to automate and scale the process.

As part of the study, the researchers used microneedles to deliver vitamin B12, which is primarily found naturally in animal products, into the stalks of growing tomatoes, showing that vitamin B12 moved into the tomato fruits before harvest. The researchers propose their method could be used to fortify more plants with the vitamin.

Co-author Daisuke Urano, a plant scientist with DiSTAP, explains that “through a comprehensive assessment, we showed minimal adverse effects from microneedle injections in plants, with no observed short- or long-term negative impacts.”

“This new delivery mechanism opens up a lot of potential applications, so we wanted to do something nobody had done before,” Marelli explains.

Finally, the researchers explored the use of their microneedles to monitor the health of plants by studying tomatoes growing in hydroponic solutions contaminated with cadmium, a toxic metal commonly found in farms close to industrial and mining sites. They showed their microneedles absorbed the toxin within 15 minutes of being injected into the tomato stalks, offering a path to rapid detection.

Current advanced techniques for monitoring plant health, such as colorimetric and hyperspectral lead analyses, can only detect problems after plants growth is already being stunted. Other methods, such as sap sampling, can be too time-consuming.

Microneedles, in contrast, could be used to more easily collect sap for ongoing chemical analysis. For instance, the researchers showed they could monitor cadmium levels in tomatoes over the course of 18 hours.

A new platform for farming

The researchers believe the microneedles could be used to complement existing agricultural practices like spraying. The researchers also note the technology has applications beyond agriculture, such as in biomedical engineering.

“This new polymeric microneedle fabrication technique may also benefit research in microneedle-mediated transdermal and intradermal drug delivery and health monitoring,” Cao says.

For now, though, Marelli believes the microneedles offer a path to more precise, sustainable agriculture practices.

“We want to maximize the growth of plants without negatively affecting the health of the farm or the biodiversity of surrounding ecosystems,” Marelli says. “There shouldn’t be a trade-off between the agriculture industry and the environment. They should work together.”

This work was supported, in part, by the U.S. Office of Naval Research, the U.S. National Science Foundation, SMART, the National Research Foundation of Singapore, and the Singapore Prime Minister’s Office.

As demand grows for more powerful and efficient microelectronics systems, industry is turning to 3D integration — stacking chips on top of each other. This vertically layered architecture could allow high-performance processors, like those used for artificial intelligence, to be packaged closely with other highly specialized chips for communication or imaging. But technologists everywhere face a major challenge: how to prevent these stacks from overheating.

Now, MIT Lincoln Laboratory has developed a specialized chip to test and validate cooling solutions for packaged chip stacks. The chip dissipates extremely high power, mimicking high-performance logic chips, to generate heat through the silicon layer and in localized hot spots. Then, as cooling technologies are applied to the packaged stack, the chip measures temperature changes. When sandwiched in a stack, the chip will allow researchers to study how heat moves through stack layers and benchmark progress in keeping them cool. 

"If you have just a single chip, you can cool it from above or below. But if you start stacking several chips on top of each other, the heat has nowhere to escape. No cooling methods exist today that allow industry to stack multiples of these really high-performance chips," says Chenson Chen, who led the development of the chip with Ryan Keech, both of the laboratory’s Advanced Materials and Microsystems Group.

The benchmarking chip is now being used at HRL Laboratories, a research and development company co-owned by Boeing and General Motors, as they develop cooling systems for 3D heterogenous integrated (3DHI) systems. Heterogenous integration refers to the stacking of silicon chips with non-silicon chips, such as III-V semiconductors used in radio-frequency (RF) systems.   

"RF components can get very hot and run at very high powers — it adds an extra layer of complexity to 3D integration, which is why having this testing capability is so needed," Keech says.

The Defense Advanced Research Projects Agency (DARPA) funded the laboratory's development of the benchmarking chip to support the HRL program. All of this research stems from DARPA's Miniature Integrated Thermal Management Systems for 3D Heterogeneous Integration (Minitherms3D) program.

For the Department of Defense, 3DHI opens new opportunities for critical systems. For example, 3DHI could increase the range of radar and communication systems, enable the integration of advanced sensors on small platforms such as uncrewed aerial vehicles, or allow artificial intelligence data to be processed directly in fielded systems instead of remote data centers.

The test chip was developed through collaboration between circuit designers, electrical testing experts, and technicians in the laboratory's Microelectronics Laboratory. 

The chip serves two functions: generating heat and sensing temperature. To generate heat, the team designed circuits that could operate at very high power densities, in the kilowatts-per-square-centimeter range, comparable to the projected power demands of high-performance chips today and into the future. They also replicated the layout of circuits in those chips, allowing the test chip to serve as a realistic stand-in. 

"We adapted our existing silicon technology to essentially design chip-scale heaters," says Chen, who brings years of complex integration and chip design experience to the program. In the 2000s, he helped the laboratory pioneer the fabrication of two- and three-tier integrated circuits, leading early development of 3D integration.

The chip's heaters emulate both the background levels of heat within a stack and localized hot spots. Hot spots often occur in the most buried and inaccessible areas of a chip stack, making it difficult for 3D-chip developers to assess whether cooling schemes, such as microchannels delivering cold liquid, are reaching those spots and are effective enough.

That's where temperature-sensing elements come in. The chip is distributed with what Chen likens to "tiny thermometers" that read out the temperature in multiple locations across the chip as coolants are applied.

These thermometers are actually diodes, or switches that allow current to flow through a circuit as voltage is applied. As the diodes heat up, the current-to-voltage ratio changes. "We're able to check a diode's performance and know that it's 200 degrees C, or 100 degrees C, or 50 degrees C, for example," Keech says. "We thought creatively about how devices could fail from overheating, and then used those same properties to design useful measurement tools."

Chen and Keech — along with other design, fabrication, and electrical test experts across the laboratory — are now collaborating with HRL Laboratories researchers as they couple the chip with novel cooling technologies, and integrate those technologies into a 3DHI stack that could boost RF signal power. "We need to cool the heat equivalent of more than 190 laptop CPUs [central processing units], but in the size of a single CPU package," Christopher Roper, co-principal investigator at HRL, said in a recent press release announcing their program.

According to Keech, the rapid timeline for delivering the chip was a challenge overcome by teamwork through all phases of the chip's design, fabrication, test, and 3D heterogenous integration.

"Stacked architectures are considered the next frontier for microelectronics," he says. "We want to help the U.S. government get ahead in finding ways to integrate them effectively and enable the highest performance possible for these chips."

The laboratory team presented this work at the annual Government Microcircuit Applications and Critical Technology Conference (GOMACTech), held March 17-20.

To understand what drives disease progression in tissues, scientists need more than just a snapshot of cells in isolation — they need to see where the cells are, how they interact, and how that spatial organization shifts across disease states. A new computational method called MESA (Multiomics and Ecological Spatial Analysis), detailed in a study published in Nature Genetics, is helping researchers study diseased tissues in more meaningful ways.

The work details the results of a collaboration between researchers from MIT, Stanford University, Weill Cornell Medicine, the Ragon Institute of MGH, MIT, and Harvard, and the Broad Institute of MIT and Harvard, and was led by the Stanford team.

MESA brings an ecology-inspired lens to tissue analysis. It offers a pipeline to interpret spatial omics data — the product of cutting-edge technology that captures molecular information along with the location of cells in tissue samples. These data provide a high-resolution map of tissue “neighborhoods,” and MESA helps make sense of the structure of that map.

“By integrating approaches from traditionally distinct disciplines, MESA enables researchers to better appreciate how tissues are locally organized and how that organization changes in different disease contexts, powering new diagnostics and the identification of new targets for preventions and cures,” says Alex K. Shalek, the director of the Institute for Medical Engineering and Science (IMES), the J. W. Kieckhefer Professor in IMES and the Department of Chemistry, and an extramural member of the Koch Institute for Integrative Cancer Research at MIT, as well as an institute member of the Broad Institute and a member of the Ragon Institute.

“In ecology, people study biodiversity across regions — how animal species are distributed and interact,” explains Bokai Zhu, MIT postdoc and author on the study. “We realized we could apply those same ideas to cells in tissues. Instead of rabbits and snakes, we analyze T cells and B cells.”

By treating cell types like ecological species, MESA quantifies “biodiversity” within tissues and tracks how that diversity changes in disease. For example, in liver cancer samples, the method revealed zones where tumor cells consistently co-occurred with macrophages, suggesting these regions may drive unique disease outcomes.

“Our method reads tissues like ecosystems, uncovering cellular ‘hotspots’ that mark early signs of disease or treatment response,” Zhu adds. “This opens new possibilities for precision diagnostics and therapy design.”

MESA also offers another major advantage: It can computationally enrich tissue data without the need for more experiments. Using publicly available single-cell datasets, the tool transfers additional information — such as gene expression profiles — onto existing tissue samples. This approach deepens understanding of how spatial domains function, especially when comparing healthy and diseased tissue.

In tests across multiple datasets and tissue types, MESA uncovered spatial structures and key cell populations that were previously overlooked. It integrates different types of omics data, such as transcriptomics and proteomics, and builds a multilayered view of tissue architecture.

Currently available as a Python package, MESA is designed for academic and translational research. Although spatial omics is still too resource-intensive for routine in-hospital clinical use, the technology is gaining traction among pharmaceutical companies, particularly for drug trials where understanding tissue responses is critical.

“This is just the beginning,” says Zhu. “MESA opens the door to using ecological theory to unravel the spatial complexity of disease — and ultimately, to better predict and treat it.”

Many diseases are caused by a missing or defective copy of a single gene. For decades, scientists have been working on gene therapy treatments that could cure such diseases by delivering a new copy of the missing genes to the affected cells.

Despite those efforts, very few gene therapy treatments have been approved by the FDA. One of the challenges to developing these treatments has been achieving control over how much the new gene is expressed in cells — too little and it won’t succeed, too much and it could cause serious side effects.

To help achieve more precise control of gene therapy, MIT engineers have tuned and applied a control circuit that can keep expression levels within a target range. In human cells, they showed that they could use this method to deliver genes that could help treat diseases including fragile X syndrome, a disorder that leads to intellectual disability and other developmental problems.

“In theory, gene supplementation can solve monogenic disorders that are very diverse but have a relatively straightforward gene therapy fix if you could control the therapy well enough,” says Katie Galloway, the W. M. Keck Career Development Professor in Biomedical Engineering and Chemical Engineering and the senior author of the new study.

MIT graduate student Kasey Love is the lead author of the paper, which appears today in Cell Systems. Other authors of the paper include MIT graduate students Christopher Johnstone, Emma Peterman, and Stephanie Gaglione, and Michael Birnbaum, an associate professor of biological engineering at MIT.

Delivering genes

While gene therapy holds promise for treating a variety of diseases, including hemophilia and sickle cell anemia, only a handful of treatments have been approved so far, for an inherited retinal disease and certain blood cancers.

Most gene therapy approaches use a virus to deliver a new copy of a gene, which is then integrated into the DNA of host cells. Some cells may take up many copies of the gene, while others don’t receive any.

“Simple overexpression of that payload can result in a really wide range of expression levels in the target genes as they take up different numbers of copies of those genes or just have different expression levels,” Love says. “If it's not expressing enough, that defeats the purpose of the therapy. But on the other hand, expressing at too high levels is also a problem, as that payload can be toxic.”

To try to overcome this, scientists have experimented with different types of control circuits that constrain expression of the therapeutic gene. In this study, the MIT team decided to use a type of circuit called an incoherent feedforward loop (IFFL).

In an IFFL circuit, activation of the target gene simultaneously activates production of a molecule that suppresses gene expression. One type of molecule that can be used to achieve that suppression is microRNA — a short RNA sequence that binds to messenger RNA, preventing it from being translated into protein.

In this study, the MIT team designed an IFFL circuit, called “ComMAND” (Compact microRNA-mediated attenuator of noise and dosage), so that a microRNA strand that represses mRNA translation is encoded within the therapeutic gene. The microRNA is located within a short segment called an intron, which gets spliced out of the gene when it is transcribed into mRNA. This means that whenever the gene is turned on, both the mRNA and the microRNA that represses it are produced in roughly equal amounts.

This approach allows the researchers to control the entire ComMAND circuit with just one promoter — the DNA site where gene transcription is turned on. By swapping in promoters of different strengths, the researchers can tailor how much of the therapeutic gene will be produced.

In addition to offering tighter control, the circuit’s compact design allows it to be carried on a single delivery vehicle, such as a lentivirus or adeno-associated virus, which could improve the manufacturability of these therapies. Both of those viruses are frequently used to deliver therapeutic cargoes.

“Other people have developed microRNA based incoherent feed forward loops, but what Kasey has done is put it all on a single transcript, and she showed that this gives the best possible control when you have variable delivery to cells,” Galloway says.

Precise control

To demonstrate this system, the researchers designed ComMAND circuits that could deliver the gene FXN, which is mutated in Friedreich’s ataxia — a disorder that affects the heart and nervous system. They also delivered the gene Fmr1, whose dysfunction causes fragile X syndrome. In tests in human cells, they showed that they could tune gene expression levels to about eight times the levels normally seen in healthy cells.

Without ComMAND, gene expression was more than 50 times the normal level, which could pose safety risks. Further tests in animal models would be needed to determine the optimal levels, the researchers say.

The researchers also performed tests in rat neurons, mouse fibroblasts, and human T-cells. For those cells, they delivered a gene that encodes a fluorescent protein, so they could easily measure the gene expression levels. In those cells, too, the researchers found that they could control gene expression levels more precisely than without the circuit.

The researchers now plan to study whether they could use this approach to deliver genes at a level that would restore normal function and reverse signs of disease, either in cultured cells or animal models.

“There's probably some tuning that would need to be done to the expression levels, but we understand some of those design principles, so if we needed to tune the levels up or down, I think we'd know potentially how to go about that,” Love says.

Other diseases that this approach could be applied to include Rett syndrome, muscular dystrophy and spinal muscular atrophy, the researchers say.

“The challenge with a lot of those is they're also rare diseases, so you don't have large patient populations,” Galloway says. “We're trying to build out these tools that are robust so people can figure out how to do the tuning, because the patient populations are so small and there isn't a lot of funding for solving some of these disorders.”

The research was funded by the National Institute of General Medical Sciences, the National Science Foundation, the Institute for Collaborative Biotechnologies, and the Air Force Research Laboratory. 

Coordinating complicated interactive systems, whether it’s the different modes of transportation in a city or the various components that must work together to make an effective and efficient robot, is an increasingly important subject for software designers to tackle. Now, researchers at MIT have developed an entirely new way of approaching these complex problems, using simple diagrams as a tool to reveal better approaches to software optimization in deep-learning models.

They say the new method makes addressing these complex tasks so simple that it can be reduced to a drawing that would fit on the back of a napkin.

The new approach is described in the journal Transactions of Machine Learning Research, in a paper by incoming doctoral student Vincent Abbott and Professor Gioele Zardini of MIT’s Laboratory for Information and Decision Systems (LIDS).

“We designed a new language to talk about these new systems,” Zardini says. This new diagram-based “language” is heavily based on something called category theory, he explains.

It all has to do with designing the underlying architecture of computer algorithms — the programs that will actually end up sensing and controlling the various different parts of the system that’s being optimized. “The components are different pieces of an algorithm, and they have to talk to each other, exchange information, but also account for energy usage, memory consumption, and so on.” Such optimizations are notoriously difficult because each change in one part of the system can in turn cause changes in other parts, which can further affect other parts, and so on.

The researchers decided to focus on the particular class of deep-learning algorithms, which are currently a hot topic of research. Deep learning is the basis of the large artificial intelligence models, including large language models such as ChatGPT and image-generation models such as Midjourney. These models manipulate data by a “deep” series of matrix multiplications interspersed with other operations. The numbers within matrices are parameters, and are updated during long training runs, allowing for complex patterns to be found. Models consist of billions of parameters, making computation expensive, and hence improved resource usage and optimization invaluable.

Diagrams can represent details of the parallelized operations that deep-learning models consist of, revealing the relationships between algorithms and the parallelized graphics processing unit (GPU) hardware they run on, supplied by companies such as NVIDIA. “I’m very excited about this,” says Zardini, because “we seem to have found a language that very nicely describes deep learning algorithms, explicitly representing all the important things, which is the operators you use,” for example the energy consumption, the memory allocation, and any other parameter that you’re trying to optimize for.

Much of the progress within deep learning has stemmed from resource efficiency optimizations. The latest DeepSeek model showed that a small team can compete with top models from OpenAI and other major labs by focusing on resource efficiency and the relationship between software and hardware. Typically, in deriving these optimizations, he says, “people need a lot of trial and error to discover new architectures.” For example, a widely used optimization program called FlashAttention took more than four years to develop, he says. But with the new framework they developed, “we can really approach this problem in a more formal way.” And all of this is represented visually in a precisely defined graphical language.

But the methods that have been used to find these improvements “are very limited,” he says. “I think this shows that there’s a major gap, in that we don’t have a formal systematic method of relating an algorithm to either its optimal execution, or even really understanding how many resources it will take to run.” But now, with the new diagram-based method they devised, such a system exists.

Category theory, which underlies this approach, is a way of mathematically describing the different components of a system and how they interact in a generalized, abstract manner. Different perspectives can be related. For example, mathematical formulas can be related to algorithms that implement them and use resources, or descriptions of systems can be related to robust “monoidal string diagrams.” These visualizations allow you to directly play around and experiment with how the different parts connect and interact. What they developed, he says, amounts to “string diagrams on steroids,” which incorporates many more graphical conventions and many more properties.

“Category theory can be thought of as the mathematics of abstraction and composition,” Abbott says. “Any compositional system can be described using category theory, and the relationship between compositional systems can then also be studied.” Algebraic rules that are typically associated with functions can also be represented as diagrams, he says. “Then, a lot of the visual tricks we can do with diagrams, we can relate to algebraic tricks and functions. So, it creates this correspondence between these different systems.”

As a result, he says, “this solves a very important problem, which is that we have these deep-learning algorithms, but they’re not clearly understood as mathematical models.” But by representing them as diagrams, it becomes possible to approach them formally and systematically, he says.

One thing this enables is a clear visual understanding of the way parallel real-world processes can be represented by parallel processing in multicore computer GPUs. “In this way,” Abbott says, “diagrams can both represent a function, and then reveal how to optimally execute it on a GPU.”

The “attention” algorithm is used by deep-learning algorithms that require general, contextual information, and is a key phase of the serialized blocks that constitute large language models such as ChatGPT. FlashAttention is an optimization that took years to develop, but resulted in a sixfold improvement in the speed of attention algorithms.

Applying their method to the well-established FlashAttention algorithm, Zardini says that “here we are able to derive it, literally, on a napkin.” He then adds, “OK, maybe it’s a large napkin.” But to drive home the point about how much their new approach can simplify dealing with these complex algorithms, they titled their formal research paper on the work “FlashAttention on a Napkin.”

This method, Abbott says, “allows for optimization to be really quickly derived, in contrast to prevailing methods.” While they initially applied this approach to the already existing FlashAttention algorithm, thus verifying its effectiveness, “we hope to now use this language to automate the detection of improvements,” says Zardini, who in addition to being a principal investigator in LIDS, is the Rudge and Nancy Allen Assistant Professor of Civil and Environmental Engineering, and an affiliate faculty with the Institute for Data, Systems, and Society.

The plan is that ultimately, he says, they will develop the software to the point that “the researcher uploads their code, and with the new algorithm you automatically detect what can be improved, what can be optimized, and you return an optimized version of the algorithm to the user.”

In addition to automating algorithm optimization, Zardini notes that a robust analysis of how deep-learning algorithms relate to hardware resource usage allows for systematic co-design of hardware and software. This line of work integrates with Zardini’s focus on categorical co-design, which uses the tools of category theory to simultaneously optimize various components of engineered systems.

Abbott says that “this whole field of optimized deep learning models, I believe, is quite critically unaddressed, and that’s why these diagrams are so exciting. They open the doors to a systematic approach to this problem.”

“I’m very impressed by the quality of this research. ... The new approach to diagramming deep-learning algorithms used by this paper could be a very significant step,” says Jeremy Howard, founder and CEO of Answers.ai, who was not associated with this work. “This paper is the first time I’ve seen such a notation used to deeply analyze the performance of a deep-learning algorithm on real-world hardware. ... The next step will be to see whether real-world performance gains can be achieved.”

“This is a beautifully executed piece of theoretical research, which also aims for high accessibility to uninitiated readers — a trait rarely seen in papers of this kind,” says Petar Velickovic, a senior research scientist at Google DeepMind and a lecturer at Cambridge University, who was not associated with this work. These researchers, he says, “are clearly excellent communicators, and I cannot wait to see what they come up with next!”

The new diagram-based language, having been posted online, has already attracted great attention and interest from software developers. A reviewer from Abbott’s prior paper introducing the diagrams noted that “The proposed neural circuit diagrams look great from an artistic standpoint (as far as I am able to judge this).” “It’s technical research, but it’s also flashy!” Zardini says.

For a robot, the real world is a lot to take in. Making sense of every data point in a scene can take a huge amount of computational effort and time. Using that information to then decide how to best help a human is an even thornier exercise.

Now, MIT roboticists have a way to cut through the data noise, to help robots focus on the features in a scene that are most relevant for assisting humans.

Their approach, which they aptly dub “Relevance,” enables a robot to use cues in a scene, such as audio and visual information, to determine a human’s objective and then quickly identify the objects that are most likely to be relevant in fulfilling that objective. The robot then carries out a set of maneuvers to safely offer the relevant objects or actions to the human.

The researchers demonstrated the approach with an experiment that simulated a conference breakfast buffet. They set up a table with various fruits, drinks, snacks, and tableware, along with a robotic arm outfitted with a microphone and camera. Applying the new Relevance approach, they showed that the robot was able to correctly identify a human’s objective and appropriately assist them in different scenarios.

In one case, the robot took in visual cues of a human reaching for a can of prepared coffee, and quickly handed the person milk and a stir stick. In another scenario, the robot picked up on a conversation between two people talking about coffee, and offered them a can of coffee and creamer.

Overall, the robot was able to predict a human’s objective with 90 percent accuracy and to identify relevant objects with 96 percent accuracy. The method also improved a robot’s safety, reducing the number of collisions by more than 60 percent, compared to carrying out the same tasks without applying the new method.

“This approach of enabling relevance could make it much easier for a robot to interact with humans,” says Kamal Youcef-Toumi, professor of mechanical engineering at MIT. “A robot wouldn’t have to ask a human so many questions about what they need. It would just actively take information from the scene to figure out how to help.”

Youcef-Toumi’s group is exploring how robots programmed with Relevance can help in smart manufacturing and warehouse settings, where they envision robots working alongside and intuitively assisting humans.

Youcef-Toumi, along with graduate students Xiaotong Zhang and Dingcheng Huang, will present their new method at the IEEE International Conference on Robotics and Automation (ICRA) in May. The work builds on another paper presented at ICRA the previous year.

Finding focus

The team’s approach is inspired by our own ability to gauge what’s relevant in daily life. Humans can filter out distractions and focus on what’s important, thanks to a region of the brain known as the Reticular Activating System (RAS). The RAS is a bundle of neurons in the brainstem that acts subconsciously to prune away unnecessary stimuli, so that a person can consciously perceive the relevant stimuli. The RAS helps to prevent sensory overload, keeping us, for example, from fixating on every single item on a kitchen counter, and instead helping us to focus on pouring a cup of coffee.

“The amazing thing is, these groups of neurons filter everything that is not important, and then it has the brain focus on what is relevant at the time,” Youcef-Toumi explains. “That’s basically what our proposition is.”

He and his team developed a robotic system that broadly mimics the RAS’s ability to selectively process and filter information. The approach consists of four main phases. The first is a watch-and-learn “perception” stage, during which a robot takes in audio and visual cues, for instance from a microphone and camera, that are continuously fed into an AI “toolkit.” This toolkit can include a large language model (LLM) that processes audio conversations to identify keywords and phrases, and various algorithms that detect and classify objects, humans, physical actions, and task objectives. The AI toolkit is designed to run continuously in the background, similarly to the subconscious filtering that the brain’s RAS performs.

The second stage is a “trigger check” phase, which is a periodic check that the system performs to assess if anything important is happening, such as whether a human is present or not. If a human has stepped into the environment, the system’s third phase will kick in. This phase is the heart of the team’s system, which acts to determine the features in the environment that are most likely relevant to assist the human.

To establish relevance, the researchers developed an algorithm that takes in real-time predictions made by the AI toolkit. For instance, the toolkit’s LLM may pick up the keyword “coffee,” and an action-classifying algorithm may label a person reaching for a cup as having the objective of “making coffee.” The team’s Relevance method would factor in this information to first determine the “class” of objects that have the highest probability of being relevant to the objective of “making coffee.” This might automatically filter out classes such as “fruits” and “snacks,” in favor of “cups” and “creamers.” The algorithm would then further filter within the relevant classes to determine the most relevant “elements.” For instance, based on visual cues of the environment, the system may label a cup closest to a person as more relevant — and helpful — than a cup that is farther away.

In the fourth and final phase, the robot would then take the identified relevant objects and plan a path to physically access and offer the objects to the human.

Helper mode

The researchers tested the new system in experiments that simulate a conference breakfast buffet. They chose this scenario based on the publicly available Breakfast Actions Dataset, which comprises videos and images of typical activities that people perform during breakfast time, such as preparing coffee, cooking pancakes, making cereal, and frying eggs. Actions in each video and image are labeled, along with the overall objective (frying eggs, versus making coffee).

Using this dataset, the team tested various algorithms in their AI toolkit, such that, when receiving actions of a person in a new scene, the algorithms could accurately label and classify the human tasks and objectives, and the associated relevant objects.

In their experiments, they set up a robotic arm and gripper and instructed the system to assist humans as they approached a table filled with various drinks, snacks, and tableware. They found that when no humans were present, the robot’s AI toolkit operated continuously in the background, labeling and classifying objects on the table.

When, during a trigger check, the robot detected a human, it snapped to attention, turning on its Relevance phase and quickly identifying objects in the scene that were most likely to be relevant, based on the human’s objective, which was determined by the AI toolkit.

“Relevance can guide the robot to generate seamless, intelligent, safe, and efficient assistance in a highly dynamic environment,” says co-author Zhang.

Going forward, the team hopes to apply the system to scenarios that resemble workplace and warehouse environments, as well as to other tasks and objectives typically performed in household settings.

“I would want to test this system in my home to see, for instance, if I’m reading the paper, maybe it can bring me coffee. If I’m doing laundry, it can bring me a laundry pod. If I’m doing repair, it can bring me a screwdriver,” Zhang says. “Our vision is to enable human-robot interactions that can be much more natural and fluent.”

This research was made possible by the support and partnership of King Abdulaziz City for Science and Technology (KACST) through the Center for Complex Engineering Systems at MIT and KACST.

Nearly 150 years ago, scientists began to imagine how information might flow through the brain based on the shapes of neurons they had seen under the microscopes of the time. With today’s imaging technologies, scientists can zoom in much further, seeing the tiny synapses through which neurons communicate with one another, and even the molecules the cells use to relay their messages. These inside views can spark new ideas about how healthy brains work and reveal important changes that contribute to disease.

This sharper view of biology is not just about the advances that have made microscopes more powerful than ever before. Using methodology developed in the lab of MIT McGovern Institute for Brain Research investigator Edward Boyden, researchers around the world are imaging samples that have been swollen to as much as 20 times their original size so their finest features can be seen more clearly.

“It’s a very different way to do microscopy,” says Boyden, who is also a Howard Hughes Medical Institute (HHMI) investigator, a professor of brain and cognitive sciences and biological engineering, and a member of the Yang Tan Collective at MIT. “In contrast to the last 300 years of bioimaging, where you use a lens to magnify an image of light from an object, we physically magnify objects themselves.” Once a tissue is expanded, Boyden says, researchers can see more even with widely available, conventional microscopy hardware.

Boyden’s team introduced this approach, which they named expansion microscopy (ExM), in 2015. Since then, they have been refining the method and adding to its capabilities, while researchers at MIT and beyond deploy it to learn about life on the smallest of scales.

“It’s spreading very rapidly throughout biology and medicine,” Boyden says. “It’s being applied to kidney disease, the fruit fly brain, plant seeds, the microbiome, Alzheimer’s disease, viruses, and more.”

Origins of ExM 

To develop expansion microscopy, Boyden and his team turned to hydrogel, a material with remarkable water-absorbing properties that had already been put to practical use; it’s layered inside disposable diapers to keep babies dry. Boyden’s lab hypothesized that hydrogels could retain their structure while they absorbed hundreds of times their original weight in water, expanding the space between their chemical components as they swell.

After some experimentation, Boyden’s team settled on four key steps to enlarging tissue samples for better imaging. First, the tissue must be infused with a hydrogel. Components of the tissue, biomolecules, are anchored to the gel’s web-like matrix, linking them directly to the molecules that make up the gel. Then the tissue is chemically softened and water is added. As the hydrogel absorbs the water, it swells and the tissue expands, growing evenly so the relative positions of its components are preserved.

Boyden and graduate students Fei Chen and Paul Tillberg’s first report on expansion microscopy was published in the journal Science in 2015. In it, the team demonstrated that by spreading apart molecules that had been crowded inside cells, features that would have blurred together under a standard light microscope became separate and distinct. Light microscopes can discriminate between objects that are separated by about 300 nanometers — a limit imposed by the laws of physics. With expansion microscopy, Boyden’s group reported an effective resolution of about 70 nanometers, for a fourfold expansion.

Boyden says this is a level of clarity that biologists need. “Biology is fundamentally, in the end, a nanoscale science,” he says. “Biomolecules are nanoscale, and the interactions between biomolecules are over nanoscale distances. Many of the most important problems in biology and medicine involve nanoscale questions.” Several kinds of sophisticated microscopes, each with their own advantages and disadvantages, can bring this kind of detail to light. But those methods are costly and require specialized skills, making them inaccessible for most researchers. “Expansion microscopy democratizes nanoimaging,” Boyden says. “Now, anybody can go look at the building blocks of life and how they relate to each other.”

Empowering scientists

Since Boyden’s team introduced expansion microscopy in 2015, research groups around the world have published hundreds of papers reporting on discoveries they have made using expansion microscopy. For neuroscientists, the technique has lit up the intricacies of elaborate neural circuits, exposed how particular proteins organize themselves at and across synapses to facilitate communication between neurons, and uncovered changes associated with aging and disease.

It has been equally empowering for studies beyond the brain. Sabrina Absalon uses expansion microscopy every week in her lab at Indiana University School of Medicine to study the malaria parasite, a single-celled organism packed with specialized structures that enable it to infect and live inside its hosts. The parasite is so small, most of those structures can’t be seen with ordinary light microscopy. “So as a cell biologist, I’m losing the biggest tool to infer protein function, organelle architecture, morphology, linked to function, and all those things — which is my eye,” she says. With expansion, she can not only see the organelles inside a malaria parasite, she can watch them assemble and follow what happens to them when the parasite divides. Understanding those processes, she says, could help drug developers find new ways to interfere with the parasite’s life cycle.

Absalon adds that the accessibility of expansion microscopy is particularly important in the field of parasitology, where a lot of research is happening in parts of the world where resources are limited. Workshops and training programs in Africa, South America, and Asia are ensuring the technology reaches scientists whose communities are directly impacted by malaria and other parasites. “Now they can get super-resolution imaging without very fancy equipment,” Absalon says.

Always improving

Since 2015, Boyden’s interdisciplinary lab group has found a variety of creative ways to improve expansion microscopy and use it in new ways. Their standard technique today enables better labeling, bigger expansion factors, and higher-resolution imaging. Cellular features less than 20 nanometers from one another can now be separated enough to appear distinct under a light microscope.

They’ve also adapted their protocols to work with a range of important sample types, from entire roundworms (popular among neuroscientists, developmental biologists, and other researchers) to clinical samples. In the latter regard, they’ve shown that expansion can help reveal subtle signs of disease, which could enable earlier or less-costly diagnoses.

Originally, the group optimized its protocol for visualizing proteins inside cells, by labeling proteins of interest and anchoring them to the hydrogel prior to expansion. With a new way of processing samples, users can now re-stain their expanded samples with new labels for multiple rounds of imaging, so they can pinpoint the positions of dozens of different proteins in the same tissue. That means researchers can visualize how molecules are organized with respect to one another and how they might interact, or survey large sets of proteins to see, for example, what changes with disease.

But better views of proteins were just the beginning for expansion microscopy. “We want to see everything,” Boyden says. “We’d love to see every biomolecule there is, with precision down to atomic scale.” They’re not there yet — but with new probes and modified procedures, it’s now possible to see not just proteins, but also RNA and lipids in expanded tissue samples.

Labeling lipids, including those that form the membranes surrounding cells, means researchers can now see clear outlines of cells in expanded tissues. With the enhanced resolution afforded by expansion, even the slender projections of neurons can be traced through an image. Typically, researchers have relied on electron microscopy, which generates exquisitely detailed pictures but requires expensive equipment, to map the brain’s circuitry. “Now, you can get images that look a lot like electron microscopy images, but on regular old light microscopes — the kind that everybody has access to,” Boyden says.

Boyden says expansion can be powerful in combination with other cutting-edge tools. When expanded samples are used with an ultra-fast imaging method developed by Eric Betzig, an HHMI investigator at the University of California at Berkeley, called lattice light-sheet microscopy, the entire brain of a fruit fly can be imaged at high resolution in just a few days.

And when RNA molecules are anchored within a hydrogel network and then sequenced in place, scientists can see exactly where inside cells the instructions for building specific proteins are positioned, which Boyden’s team demonstrated in a collaboration with Harvard University geneticist George Church and then-MIT-professor Aviv Regev. “Expansion basically upgrades many other technologies’ resolutions,” Boyden says. “You’re doing mass-spec imaging, X-ray imaging, or Raman imaging? Expansion just improved your instrument.”

Expanding possibilities

Ten years past the first demonstration of expansion microscopy’s power, Boyden and his team are committed to continuing to make expansion microscopy more powerful. “We want to optimize it for different kinds of problems, and making technologies faster, better, and cheaper is always important,” he says. But the future of expansion microscopy will be propelled by innovators outside the Boyden lab, too. “Expansion is not only easy to do, it’s easy to modify — so lots of other people are improving expansion in collaboration with us, or even on their own,” Boyden says.

Boyden points to a group led by Silvio Rizzoli at the University Medical Center Göttingen in Germany that, collaborating with Boyden, has adapted the expansion protocol to discern the physical shapes of proteins. At the Korea Advanced Institute of Science and Technology, researchers led by Jae-Byum Chang, a former postdoc in Boyden’s group, have worked out how to expand entire bodies of mouse embryos and young zebra fish, collaborating with Boyden to set the stage for examining developmental processes and long-distance neural connections with a new level of detail. And mapping connections within the brain’s dense neural circuits could become easier with light-microscopy based connectomics, an approach developed by Johann Danzl and colleagues at the Institute of Science and Technology in Austria that takes advantage of both the high resolution and molecular information that expansion microscopy can reveal.

“The beauty of expansion is that it lets you see a biological system down to its smallest building blocks,” Boyden says.

His team is intent on pushing the method to its physical limits, and anticipates new opportunities for discovery as they do. “If you can map the brain or any biological system at the level of individual molecules, you might be able to see how they all work together as a network — how life really operates,” he says.

MIT engineers have developed a technique to grow and peel ultrathin “skins” of electronic material. The method could pave the way for new classes of electronic devices, such as ultrathin wearable sensors, flexible transistors and computing elements, and highly sensitive and compact imaging devices. 

As a demonstration, the team fabricated a thin membrane of pyroelectric material — a class of heat-sensing material that produces an electric current in response to changes in temperature. The thinner the pyroelectric material, the better it is at sensing subtle thermal variations.

With their new method, the team fabricated the thinnest pyroelectric membrane yet, measuring 10 nanometers thick, and demonstrated that the film is highly sensitive to heat and radiation across the far-infrared spectrum.

The newly developed film could enable lighter, more portable, and highly accurate far-infrared (IR) sensing devices, with potential applications for night-vision eyewear and autonomous driving in foggy conditions. Current state-of-the-art far-IR sensors require bulky cooling elements. In contrast, the new pyroelectric thin film requires no cooling and is sensitive to much smaller changes in temperature. The researchers are exploring ways to incorporate the film into lighter, higher-precision night-vision glasses.

“This film considerably reduces weight and cost, making it lightweight, portable, and easier to integrate,” Xinyuan Zhang, a graduate student in MIT’s Department of Materials Science and Engineering (DMSE). “For example, it could be directly worn on glasses.”

The heat-sensing film could also have applications in environmental and biological sensing, as well as imaging of astrophysical phenomena that emit far-infrared radiation.

What’s more, the new lift-off technique is generalizable beyond pyroelectric materials. The researchers plan to apply the method to make other ultrathin, high-performance semiconducting films.

Their results are reported today in a paper appearing in the journal Nature. The study’s MIT co-authors are first author Xinyuan Zhang, Sangho Lee, Min-Kyu Song, Haihui Lan, Jun Min Suh, Jung-El Ryu, Yanjie Shao, Xudong Zheng, Ne Myo Han, and Jeehwan Kim, associate professor of mechanical engineering and of materials science and engineering, along with researchers at the University Wisconsin at Madison led by Professor Chang-Beom Eom and authors from multiple other institutions.

Chemical peel

Kim’s group at MIT is finding new ways to make smaller, thinner, and more flexible electronics. They envision that such ultrathin computing “skins” can be incorporated into everything from smart contact lenses and wearable sensing fabrics to stretchy solar cells and bendable displays. To realize such devices, Kim and his colleagues have been experimenting with methods to grow, peel, and stack semiconducting elements, to fabricate ultrathin, multifunctional electronic thin-film membranes.

One method that Kim has pioneered is “remote epitaxy” — a technique where semiconducting materials are grown on a single-crystalline substrate, with an ultrathin layer of graphene in between. The substrate’s crystal structure serves as a scaffold along which the new material can grow. The graphene acts as a nonstick layer, similar to Teflon, making it easy for researchers to peel off the new film and transfer it onto flexible and stacked electronic devices. After peeling off the new film, the underlying substrate can be reused to make additional thin films.

Kim has applied remote epitaxy to fabricate thin films with various characteristics. In trying different combinations of semiconducting elements, the researchers happened to notice that a certain pyroelectric material, called PMN-PT, did not require an intermediate layer assist in order to separate from its substrate. Just by growing PMN-PT directly on a single-crystalline substrate, the researchers could then remove the grown film, with no rips or tears to its delicate lattice.

“It worked surprisingly well,” Zhang says. “We found the peeled film is atomically smooth.”

Lattice lift-off

In their new study, the MIT and UW Madison researchers took a closer look at the process and discovered that the key to the material’s easy-peel property was lead. As part of its chemical structure, the team, along with colleagues at the Rensselaer Polytechnic Institute, discovered that the pyroelectric film contains an orderly arrangement of lead atoms that have a large “electron affinity,” meaning that lead attracts electrons and prevents the charge carriers from traveling and connecting to another materials such as an underlying substrate. The lead acts as tiny nonstick units, allowing the material as a whole to peel away, perfectly intact.

The team ran with the realization and fabricated multiple ultrathin films of PMN-PT, each about 10 nanometers thin. They peeled off pyroelectric films and transfered them onto a small chip to form an array of 100 ultrathin heat-sensing pixels, each about 60 square microns (about .006 square centimeters). They exposed the films to ever-slighter changes in temperature and found the pixels were highly sensitive to small changes across the far-infrared spectrum.

The sensitivity of the pyroelectric array is comparable to that of state-of-the-art night-vision devices. These devices are currently based on photodetector materials, in which a change in temperature induces the material’s electrons to jump in energy and briefly cross an energy “band gap,” before settling back into their ground state. This electron jump serves as an electrical signal of the temperature change. However, this signal can be affected by noise in the environment, and to prevent such effects, photodetectors have to also include cooling devices that bring the instruments down to liquid nitrogen temperatures.

Current night-vision goggles and scopes are heavy and bulky. With the group’s new pyroelectric-based approach, NVDs could have the same sensitivity without the cooling weight.

The researchers also found that the films were sensitive beyond the range of current night-vision devices and could respond to wavelengths across the entire infrared spectrum. This suggests that the films could be incorporated into small, lightweight, and portable devices for various applications that require different infrared regions. For instance, when integrated into autonomous vehicle platforms, the films could enable cars to “see” pedestrians and vehicles in complete darkness or in foggy and rainy conditions. 

The film could also be used in gas sensors for real-time and on-site environmental monitoring, helping detect pollutants. In electronics, they could monitor heat changes in semiconductor chips to catch early signs of malfunctioning elements.

The team says the new lift-off method can be generalized to materials that may not themselves contain lead. In those cases, the researchers suspect that they can infuse Teflon-like lead atoms into the underlying substrate to induce a similar peel-off effect. For now, the team is actively working toward incorporating the pyroelectric films into a functional night-vision system.

“We envision that our ultrathin films could be made into high-performance night-vision goggles, considering its broad-spectrum infrared sensitivity at room-temperature, which allows for a lightweight design without a cooling system,” Zhang says. “To turn this into a night-vision system, a functional device array should be integrated with readout circuitry. Furthermore, testing in varied environmental conditions is essential for practical applications.”

This work was supported by the U.S. Air Force Office of Scientific Research.

When chemists design new chemical reactions, one useful piece of information involves the reaction’s transition state — the point of no return from which a reaction must proceed.

This information allows chemists to try to produce the right conditions that will allow the desired reaction to occur. However, current methods for predicting the transition state and the path that a chemical reaction will take are complicated and require a huge amount of computational power.

MIT researchers have now developed a machine-learning model that can make these predictions in less than a second, with high accuracy. Their model could make it easier for chemists to design chemical reactions that could generate a variety of useful compounds, such as pharmaceuticals or fuels.

“We’d like to be able to ultimately design processes to take abundant natural resources and turn them into molecules that we need, such as materials and therapeutic drugs. Computational chemistry is really important for figuring out how to design more sustainable processes to get us from reactants to products,” says Heather Kulik, the Lammot du Pont Professor of Chemical Engineering, a professor of chemistry, and the senior author of the new study.

Former MIT graduate student Chenru Duan PhD ’22, who is now at Deep Principle; former Georgia Tech graduate student Guan-Horng Liu, who is now at Meta; and Cornell University graduate student Yuanqi Du are the lead authors of the paper, which appears today in Nature Machine Intelligence.

Better estimates

For any given chemical reaction to occur, it must go through a transition state, which takes place when it reaches the energy threshold needed for the reaction to proceed. These transition states are so fleeting that they’re nearly impossible to observe experimentally.

As an alternative, researchers can calculate the structures of transition states using techniques based on quantum chemistry. However, that process requires a great deal of computing power and can take hours or days to calculate a single transition state.

“Ideally, we’d like to be able to use computational chemistry to design more sustainable processes, but this computation in itself is a huge use of energy and resources in finding these transition states,” Kulik says.

In 2023, Kulik, Duan, and others reported on a machine-learning strategy that they developed to predict the transition states of reactions. This strategy is faster than using quantum chemistry techniques, but still slower than what would be ideal because it requires the model to generate about 40 structures, then run those predictions through a “confidence model” to predict which states were most likely to occur.

One reason why that model needs to be run so many times is that it uses randomly generated guesses for the starting point of the transition state structure, then performs dozens of calculations until it reaches its final, best guess. These randomly generated starting points may be very far from the actual transition state, which is why so many steps are needed.

The researchers’ new model, React-OT, described in the Nature Machine Intelligence paper, uses a different strategy. In this work, the researchers trained their model to begin from an estimate of the transition state generated by linear interpolation — a technique that estimates each atom’s position by moving it halfway between its position in the reactants and in the products, in three-dimensional space.

“A linear guess is a good starting point for approximating where that transition state will end up,” Kulik says. “What the model’s doing is starting from a much better initial guess than just a completely random guess, as in the prior work.”

Because of this, it takes the model fewer steps and less time to generate a prediction. In the new study, the researchers showed that their model could make predictions with only about five steps, taking about 0.4 seconds. These predictions don’t need to be fed through a confidence model, and they are about 25 percent more accurate than the predictions generated by the previous model.

“That really makes React-OT a practical model that we can directly integrate to the existing computational workflow in high-throughput screening to generate optimal transition state structures,” Duan says.

“A wide array of chemistry”

To create React-OT, the researchers trained it on the same dataset that they used to train their older model. These data contain structures of reactants, products, and transition states, calculated using quantum chemistry methods, for 9,000 different chemical reactions, mostly involving small organic or inorganic molecules.

Once trained, the model performed well on other reactions from this set, which had been held out of the training data. It also performed well on other types of reactions that it hadn’t been trained on, and could make accurate predictions involving reactions with larger reactants, which often have side chains that aren’t directly involved in the reaction.

“This is important because there are a lot of polymerization reactions where you have a big macromolecule, but the reaction is occurring in just one part. Having a model that generalizes across different system sizes means that it can tackle a wide array of chemistry,” Kulik says.

The researchers are now working on training the model so that it can predict transition states for reactions between molecules that include additional elements, including sulfur, phosphorus, chlorine, silicon, and lithium.

“To quickly predict transition state structures is key to all chemical understanding,” says Markus Reiher, a professor of theoretical chemistry at ETH Zurich, who was not involved in the study. “The new approach presented in the paper could very much accelerate our search and optimization processes, bringing us faster to our final result. As a consequence, also less energy will be consumed in these high-performance computing campaigns. Any progress that accelerates this optimization benefits all sorts of computational chemical research.”

The MIT team hopes that other scientists will make use of their approach in designing their own reactions, and have created an app for that purpose.

“Whenever you have a reactant and product, you can put them into the model and it will generate the transition state, from which you can estimate the energy barrier of your intended reaction, and see how likely it is to occur,” Duan says.

The research was funded by the U.S. Army Research Office, the U.S. Department of Defense Basic Research Office, the U.S. Air Force Office of Scientific Research, the National Science Foundation, and the U.S. Office of Naval Research.

In metamaterials design, the name of the game has long been “stronger is better.”

Metamaterials are synthetic materials with microscopic structures that give the overall material exceptional properties. A huge focus has been in designing metamaterials that are stronger and stiffer than their conventional counterparts. But there’s a trade-off: The stiffer a material, the less flexible it is.

MIT engineers have now found a way to fabricate a metamaterial that is both strong and stretchy. The base material is typically highly rigid and brittle, but it is printed in precise, intricate patterns that form a structure that is both strong and flexible.

The key to the new material’s dual properties is a combination of stiff microscopic struts and a softer woven architecture. This microscopic “double network,” which is printed using a plexiglass-like polymer, produced a material that could stretch over four times its size without fully breaking. In comparison, the polymer in other forms has little to no stretch and shatters easily once cracked.

The researchers say the new double-network design can be applied to other materials, for instance to fabricate stretchy ceramics, glass, and metals. Such tough yet bendy materials could be made into tear-resistant textiles, flexible semiconductors, electronic chip packaging, and durable yet compliant scaffolds on which to grow cells for tissue repair.

“We are opening up this new territory for metamaterials,” says Carlos Portela, the Robert N. Noyce Career Development Associate Professor at MIT. “You could print a double-network metal or ceramic, and you could get a lot of these benefits, in that it would take more energy to break them, and they would be significantly more stretchable.”

Portela and his colleagues report their findings today in the journal Nature Materials. His MIT co-authors include first author James Utama Surjadi as well as Bastien Aymon and Molly Carton.

Inspired gel

Along with other research groups, Portela and his colleagues have typically designed metamaterials by printing or nanofabricating microscopic lattices using conventional polymers similar to plexiglass and ceramic. The specific pattern, or architecture, that they print can impart exceptional strength and impact resistance to the resulting metamaterial.

Several years ago, Portela was curious whether a metamaterial could be made from an inherently stiff material, but be patterned in a way that would turn it into a much softer, stretchier version.

“We realized that the field of metamaterials has not really tried to make an impact in the soft matter realm,” he says. “So far, we’ve all been looking for the stiffest and strongest materials possible.”

Instead, he looked for a way to synthesize softer, stretchier metamaterials. Rather than printing microscopic struts and trusses, similar to those of conventional lattice-based metamaterials, he and his team made an architecture of interwoven springs, or coils. They found that, while the material they used was itself stiff like plexiglass, the resulting woven metamaterial was soft and springy, like rubber.

“They were stretchy, but too soft and compliant,” Portela recalls.

In looking for ways to bulk up their softer metamaterial, the team found inspiration in an entirely different material: hydrogel. Hydrogels are soft, stretchy, Jell-O-like materials that are composed of mostly water and a bit of polymer structure. Researchers including groups at MIT have devised ways to make hydrogels that are both soft and stretchy, and also tough. They do so by combining polymer networks with very different properties, such as a network of molecules that is naturally stiff,  which gets chemically cross-linked with another molecular network that is inherently soft. Portela and his colleagues wondered whether such a double-network design could be adapted to metamaterials.

“That was our ‘aha’ moment,” Portela says. “We thought: Can we get inspiration from these hydrogels to create a metamaterial with similar stiff and stretchy properties?”

Strut and weave

For their new study, the team fabricated a metamaterial by combining two microscopic architectures. The first is a rigid, grid-like scaffold of struts and trusses. The second is a pattern of coils that weave around each strut and truss. Both networks are made from the same acrylic plastic and are printed in one go, using a high-precision, laser-based printing technique called two-photon lithography.

The researchers printed samples of the new double-network-inspired metamaterial, each measuring in size from several square microns to several square millimeters. They put the material through a series of stress tests, in which they attached either end of the sample to a specialized nanomechanical press and measured the force it took to pull the material apart. They also recorded high-resolution videos to observe the locations and ways in which the material stretched and tore as it was pulled apart.

They found their new double-network design was able stretch three times its own length, which also happened to be 10 times farther compared to a conventional lattice-patterned metamaterial printed with the same acrylic plastic. Portela says the new material’s stretchy resistance comes from the interactions between the material’s rigid struts and the messier, coiled weave as the material is stressed and pulled.

“Think of this woven network as a mess of spaghetti tangled around a lattice. As we break the monolithic lattice network, those broken parts come along for the ride, and now all this spaghetti gets entangled with the lattice pieces,” Portela explains. “That promotes more entanglement between woven fibers, which means you have more friction and more energy dissipation.”

In other words, the softer structure wound throughout the material’s rigid lattice takes on more stress thanks to multiple knots or entanglements promoted by the cracked struts. As this stress spreads unevenly through the material, an initial crack is unlikely to go straight through and quickly tear the material. What’s more, the team found that if they introduced strategic holes, or “defects,” in the metamaterial, they could further dissipate any stress that the material undergoes, making it even stretchier and more resistant to tearing apart.

“You might think this makes the material worse,” says study co-author Surjadi. “But we saw once we started adding defects, we doubled the amount of stretch we were able to do, and tripled the amount of energy that we dissipated. That gives us a material that’s both stiff and tough, which is usually a contradiction.”

The team has developed a computational framework that can help engineers estimate how a metamaterial will perform given the pattern of its stiff and stretchy networks. They envision such a blueprint will be useful in designing tear-proof textiles and fabrics.

“We also want to try this approach on more brittle materials, to give them multifunctionality,” Portela says. “So far we’ve talked of mechanical properties, but what if we could also make them conductive, or responsive to temperature? For that, the two networks could be made from different polymers, that respond to temperature in different ways, so that a fabric can open its pores or become more compliant when it’s warm and can be more rigid when it’s cold. That’s something we can explore now.”

This research was supported, in part, by the U.S. National Science Foundation, and the MIT MechE MathWorks Seed Fund. This work was performed, in part, through the use of MIT.nano’s facilities.

MIT researchers have created a periodic table that shows how more than 20 classical machine-learning algorithms are connected. The new framework sheds light on how scientists could fuse strategies from different methods to improve existing AI models or come up with new ones.

For instance, the researchers used their framework to combine elements of two different algorithms to create a new image-classification algorithm that performed 8 percent better than current state-of-the-art approaches.

The periodic table stems from one key idea: All these algorithms learn a specific kind of relationship between data points. While each algorithm may accomplish that in a slightly different way, the core mathematics behind each approach is the same.

Building on these insights, the researchers identified a unifying equation that underlies many classical AI algorithms. They used that equation to reframe popular methods and arrange them into a table, categorizing each based on the approximate relationships it learns.

Just like the periodic table of chemical elements, which initially contained blank squares that were later filled in by scientists, the periodic table of machine learning also has empty spaces. These spaces predict where algorithms should exist, but which haven’t been discovered yet.

The table gives researchers a toolkit to design new algorithms without the need to rediscover ideas from prior approaches, says Shaden Alshammari, an MIT graduate student and lead author of a paper on this new framework.

“It’s not just a metaphor,” adds Alshammari. “We’re starting to see machine learning as a system with structure that is a space we can explore rather than just guess our way through.”

She is joined on the paper by John Hershey, a researcher at Google AI Perception; Axel Feldmann, an MIT graduate student; William Freeman, the Thomas and Gerd Perkins Professor of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL); and senior author Mark Hamilton, an MIT graduate student and senior engineering manager at Microsoft. The research will be presented at the International Conference on Learning Representations.

An accidental equation

The researchers didn’t set out to create a periodic table of machine learning.

After joining the Freeman Lab, Alshammari began studying clustering, a machine-learning technique that classifies images by learning to organize similar images into nearby clusters.

She realized the clustering algorithm she was studying was similar to another classical machine-learning algorithm, called contrastive learning, and began digging deeper into the mathematics. Alshammari found that these two disparate algorithms could be reframed using the same underlying equation.

“We almost got to this unifying equation by accident. Once Shaden discovered that it connects two methods, we just started dreaming up new methods to bring into this framework. Almost every single one we tried could be added in,” Hamilton says.

The framework they created, information contrastive learning (I-Con), shows how a variety of algorithms can be viewed through the lens of this unifying equation. It includes everything from classification algorithms that can detect spam to the deep learning algorithms that power LLMs.

The equation describes how such algorithms find connections between real data points and then approximate those connections internally.

Each algorithm aims to minimize the amount of deviation between the connections it learns to approximate and the real connections in its training data.

They decided to organize I-Con into a periodic table to categorize algorithms based on how points are connected in real datasets and the primary ways algorithms can approximate those connections.

“The work went gradually, but once we had identified the general structure of this equation, it was easier to add more methods to our framework,” Alshammari says.

A tool for discovery

As they arranged the table, the researchers began to see gaps where algorithms could exist, but which hadn’t been invented yet.

The researchers filled in one gap by borrowing ideas from a machine-learning technique called contrastive learning and applying them to image clustering. This resulted in a new algorithm that could classify unlabeled images 8 percent better than another state-of-the-art approach.

They also used I-Con to show how a data debiasing technique developed for contrastive learning could be used to boost the accuracy of clustering algorithms.

In addition, the flexible periodic table allows researchers to add new rows and columns to represent additional types of datapoint connections.

Ultimately, having I-Con as a guide could help machine learning scientists think outside the box, encouraging them to combine ideas in ways they wouldn’t necessarily have thought of otherwise, says Hamilton.

“We’ve shown that just one very elegant equation, rooted in the science of information, gives you rich algorithms spanning 100 years of research in machine learning. This opens up many new avenues for discovery,” he adds.

“Perhaps the most challenging aspect of being a machine-learning researcher these days is the seemingly unlimited number of papers that appear each year. In this context, papers that unify and connect existing algorithms are of great importance, yet they are extremely rare. I-Con provides an excellent example of such a unifying approach and will hopefully inspire others to apply a similar approach to other domains of machine learning,” says Yair Weiss, a professor in the School of Computer Science and Engineering at the Hebrew University of Jerusalem, who was not involved in this research.

This research was funded, in part, by the Air Force Artificial Intelligence Accelerator, the National Science Foundation AI Institute for Artificial Intelligence and Fundamental Interactions, and Quanta Computer.

Essential for many industries ranging from Hollywood computer-generated imagery to product design, 3D modeling tools often use text or image prompts to dictate different aspects of visual appearance, like color and form. As much as this makes sense as a first point of contact, these systems are still limited in their realism due to their neglect of something central to the human experience: touch.

Fundamental to the uniqueness of physical objects are their tactile properties, such as roughness, bumpiness, or the feel of materials like wood or stone. Existing modeling methods often require advanced computer-aided design expertise and rarely support tactile feedback that can be crucial for how we perceive and interact with the physical world.

With that in mind, researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have created a new system for stylizing 3D models using image prompts, effectively replicating both visual appearance and tactile properties.

The CSAIL team’s “TactStyle” tool allows creators to stylize 3D models based on images while also incorporating the expected tactile properties of the textures. TactStyle separates visual and geometric stylization, enabling the replication of both visual and tactile properties from a single image input.

PhD student Faraz Faruqi, lead author of a new paper on the project, says that TactStyle could have far-reaching applications, extending from home decor and personal accessories to tactile learning tools. TactStyle enables users to download a base design — such as a headphone stand from Thingiverse — and customize it with the styles and textures they desire. In education, learners can explore diverse textures from around the world without leaving the classroom, while in product design, rapid prototyping becomes easier as designers quickly print multiple iterations to refine tactile qualities.

“You could imagine using this sort of system for common objects, such as phone stands and earbud cases, to enable more complex textures and enhance tactile feedback in a variety of ways,” says Faruqi, who co-wrote the paper alongside MIT Associate Professor Stefanie Mueller, leader of the Human-Computer Interaction (HCI) Engineering Group at CSAIL. “You can create tactile educational tools to demonstrate a range of different concepts in fields such as biology, geometry, and topography.”

Traditional methods for replicating textures involve using specialized tactile sensors — such as GelSight, developed at MIT — that physically touch an object to capture its surface microgeometry as a “heightfield.” But this requires having a physical object or its recorded surface for replication. TactStyle allows users to replicate the surface microgeometry by leveraging generative AI to generate a heightfield directly from an image of the texture.

On top of that, for platforms like the 3D printing repository Thingiverse, it’s difficult to take individual designs and customize them. Indeed, if a user lacks sufficient technical background, changing a design manually runs the risk of actually “breaking” it so that it can’t be printed anymore. All of these factors spurred Faruqi to wonder about building a tool that enables customization of downloadable models on a high level, but that also preserves functionality.

In experiments, TactStyle showed significant improvements over traditional stylization methods by generating accurate correlations between a texture’s visual image and its heightfield. This enables the replication of tactile properties directly from an image. One psychophysical experiment showed that users perceive TactStyle’s generated textures as similar to both the expected tactile properties from visual input and the tactile features of the original texture, leading to a unified tactile and visual experience.

TactStyle leverages a preexisting method, called “Style2Fab,” to modify the model’s color channels to match the input image’s visual style. Users first provide an image of the desired texture, and then a fine-tuned variational autoencoder is used to translate the input image into a corresponding heightfield. This heightfield is then applied to modify the model’s geometry to create the tactile properties.

The color and geometry stylization modules work in tandem, stylizing both the visual and tactile properties of the 3D model from a single image input. Faruqi says that the core innovation lies in the geometry stylization module, which uses a fine-tuned diffusion model to generate heightfields from texture images — something previous stylization frameworks do not accurately replicate.

Looking ahead, Faruqi says the team aims to extend TactStyle to generate novel 3D models using generative AI with embedded textures. This requires exploring exactly the sort of pipeline needed to replicate both the form and function of the 3D models being fabricated. They also plan to investigate “visuo-haptic mismatches” to create novel experiences with materials that defy conventional expectations, like something that appears to be made of marble but feels like it’s made of wood.

Faruqi and Mueller co-authored the new paper alongside PhD students Maxine Perroni-Scharf and Yunyi Zhu, visiting undergraduate student Jaskaran Singh Walia, visiting masters student Shuyue Feng, and assistant professor Donald Degraen of the Human Interface Technology (HIT) Lab NZ in New Zealand.

MIT astronomers have discovered a planet some 140 light-years from Earth that is rapidly crumbling to pieces.

The disintegrating world is about the mass of Mercury, although it circles about 20 times closer to its star than Mercury does to the sun, completing an orbit every 30.5 hours. At such close proximity to its star, the planet is likely covered in magma that is boiling off into space. As the roasting planet whizzes around its star, it is shedding an enormous amount of surface minerals and effectively evaporating away.

The astronomers spotted the planet using NASA’s Transiting Exoplanet Survey Satellite (TESS), an MIT-led mission that monitors the nearest stars for transits, or periodic dips in starlight that could be signs of orbiting exoplanets. The signal that tipped the astronomers off was a peculiar transit, with a dip that fluctuated in depth every orbit.

The scientists confirmed that the signal is of a tightly orbiting rocky planet that is trailing a long, comet-like tail of debris.

“The extent of the tail is gargantuan, stretching up to 9 million kilometers long, or roughly half of the planet’s entire orbit,” says Marc Hon, a postdoc in MIT’s Kavli Institute for Astrophysics and Space Research.

It appears that the planet is disintegrating at a dramatic rate, shedding an amount of material equivalent to one Mount Everest each time it orbits its star. At this pace, given its small mass, the researchers predict that the planet may completely disintegrate in about 1 million to 2 million years.

“We got lucky with catching it exactly when it’s really going away,” says Avi Shporer, a collaborator on the discovery who is also at the TESS Science Office. “It’s like on its last breath.”

Hon and Shporer, along with their colleagues, have published their results today in the Astrophysical Journal Letters. Their MIT co-authors include Saul Rappaport, Andrew Vanderburg, Jeroen Audenaert, William Fong, Jack Haviland, Katharine Hesse, Daniel Muthukrishna, Glen Petitpas, Ellie Schmelzer, Sara Seager, and George Ricker, along with collaborators from multiple other institutions.

Roasting away

The new planet, which scientists have tagged as BD+05 4868 Ab, was detected almost by happenstance.

“We weren’t looking for this kind of planet,” Hon says. “We were doing the typical planet vetting, and I happened to spot this signal that appeared very unusual.”

The typical signal of an orbiting exoplanet looks like a brief dip in a light curve, which repeats regularly, indicating that a compact body such as a planet is briefly passing in front of, and temporarily blocking, the light from its host star.

This typical pattern was unlike what Hon and his colleagues detected from the host star BD+05 4868 A, located in the constellation of Pegasus. Though a transit appeared every 30.5 hours, the brightness took much longer to return to normal, suggesting a long trailing structure still blocking starlight. Even more intriguing, the depth of the dip changed with each orbit, suggesting that whatever was passing in front of the star wasn’t always the same shape or blocking the same amount of light.

“The shape of the transit is typical of a comet with a long tail,” Hon explains. “Except that it’s unlikely that this tail contains volatile gases and ice as expected from a real comet — these would not survive long at such close proximity to the host star. Mineral grains evaporated from the planetary surface, however, can linger long enough to present such a distinctive tail.”

Given its proximity to its star, the team estimates that the planet is roasting at around 1,600 degrees Celsius, or close to 3,000 degrees Fahrenheit. As the star roasts the planet, any minerals on its surface are likely boiling away and escaping into space, where they cool into a long and dusty tail.

The dramatic demise of this planet is a consequence of its low mass, which is between that of Mercury and the moon. More massive terrestrial planets like the Earth have a stronger gravitational pull and therefore can hold onto their atmospheres. For BD+05 4868 Ab, the researchers suspect there is very little gravity to hold the planet together.

“This is a very tiny object, with very weak gravity, so it easily loses a lot of mass, which then further weakens its gravity, so it loses even more mass,” Shporer explains. “It’s a runaway process, and it’s only getting worse and worse for the planet.”

Mineral trail

Of the nearly 6,000 planets that astronomers have discovered to date, scientists know of only three other disintegrating planets beyond our solar system. Each of these crumbling worlds were spotted over 10 years ago using data from NASA’s Kepler Space Telescope. All three planets were spotted with similar comet-like tails. BD+05 4868 Ab has the longest tail and the deepest transits out of the four known disintegrating planets to date.

“That implies that its evaporation is the most catastrophic, and it will disappear much faster than the other planets,” Hon explains.

The planet’s host star is relatively close, and thus brighter than the stars hosting the other three disintegrating planets, making this system ideal for further observations using NASA’s James Webb Space Telescope (JWST), which can help determine the mineral makeup of the dust tail by identifying which colors of infrared light it absorbs.

This summer, Hon and graduate student Nicholas Tusay from Penn State University will lead observations of BD+05 4868 Ab using JWST. “This will be a unique opportunity to directly measure the interior composition of a rocky planet, which may tell us a lot about the diversity and potential habitability of terrestrial planets outside our solar system,” Hon says.

The researchers also will look through TESS data for signs of other disintegrating worlds.

“Sometimes with the food comes the appetite, and we are now trying to initiate the search for exactly these kinds of objects,” Shporer says. “These are weird objects, and the shape of the signal changes over time, which is something that’s difficult for us to find. But it’s something we’re actively working on.”

This work was supported, in part, by NASA.

One of the most exciting developments in cancer treatment is a wave of new cell therapies that train a patient’s immune system to attack cancer cells. Such therapies have saved the lives of patients with certain aggressive cancers and few other options. Most of these therapies work by teaching immune cells to recognize and attack specific proteins on the surface of cancer cells.

Unfortunately, most proteins found on cancer cells aren’t unique to tumors. They’re also often present on healthy cells, making it difficult to target cancer aggressively without triggering dangerous attacks on other tissue. The problem has limited the application of cell therapies to a small subset of cancers.

Now Senti Bio is working to create smarter cell therapies using synthetic biology. The company, which was founded by former MIT faculty member and current MIT Research Associate Tim Lu ’03, MEng ’03, PhD ’08 and Professor James Collins, is equipping cells with gene circuits that allow the cells to sense and respond to their environments.

Lu, who studied computer science as an undergraduate at MIT, describes Senti’s approach as programming living cells to behave more like computers — responding to specific biological cues with “if/then” logic, just like computer code.

“We have innovated a cell therapy that says, ‘Kill anything displaying the cancer target, but spare anything that has this healthy target,’” Lu explains. “Despite the promise of certain cancer targets, problems can arise when they are expressed on healthy cells that we want to protect. Our logic gating technology was designed to recognize and avoid killing those healthy cells, which introduces a whole spectrum of additional cancers that don’t have a single clean target that we can now potentially address. That’s the power of embedding these cells with logic.”

The company’s lead drug candidate aims to help patients with acute myeloid leukemia (AML) who have experienced a relapse or are unresponsive to other therapies. The prognosis for such patients is poor, but early data from the company’s first clinical trial showed that two of the first three patients Senti treated experienced complete remission, where subsequent bone marrow tests couldn’t detect a single cancer cell.

“It’s essentially one of the best responses you can get in this disease, so we were really excited to see that,” says Lu, who served on MIT’s faculty until leaving to lead Senti in 2022.

Senti is expecting to release more patient data at the upcoming American Association for Cancer Research (AACR) meeting at the end of April.

“Our groundbreaking work at Senti is showing that one can harness synthetic biology technologies to create programmable, smart medicines for treating patients with cancer,” says Collins, who is currently MIT’s Termeer Professor of Medical Engineering and Science. “This is tremendously exciting and demonstrates how one can utilize synthetic biological circuits, in this case logic gates, to design highly effective, next-generation living therapeutics.”

From computer science to cancer care

Lu was inspired as an undergraduate studying electrical engineering and computer science by the Human Genome Project, an international race to sequence the human genome. Later, he entered the Harvard-MIT Health Sciences and Technology (HST) program, through which he earned a PhD from MIT in electrical and biomedical imaging and an MD from Harvard. During that time, he worked in the lab of his eventual Senti co-founder James Collins, a synthetic biology pioneer.

In 2010, Lu joined MIT as an assistant professor with a joint appointment in the departments of Biological Engineering and of Electrical Engineering and Computer Science. Over the course of the next 14 years, Lu led the Synthetic Biology Group at MIT and started several biotech companies, including Engine Biosciences and Tango Therapeutics, which are also developing precision cancer treatments.

In 2015, a group of researchers including Lu and MIT Institute Professor Phillip Sharp published research showing they could use gene circuits to get immune cells to selectively respond to tumor cells in their environment.

“One of the first things we published focused on the idea of logic gates in living cells,” Lu says. “A computer has ‘and’ gates, ‘or’ gates, and ‘not’ gates that allow it to perform computations, and we started publishing gene circuits that implement logic into living cells. These allow cells to detect signals and then make logical decisions like, ‘Should we switch on or off?’”

Around that time, the first cell therapies and cancer immunotherapies began to be approved by the Food and Drug Administration, and the founders saw their technology as a way to take those approaches to the next level. They officially founded Senti Bio in 2016, with Lu taking a sabbatical from MIT to serve as CEO.

The company licensed technology from MIT and subsequently advanced the cellular logic gates so they could work with multiple types of engineered immune cells, including T cells and “natural killer” cells. Senti’s cells can respond to specific proteins that exist on the surface of both cancer and healthy cells to increase selectivity.

“We can now create a cell therapy where the cell makes a decision as to whether to kill a cancer cell or spare a healthy cell even when those cells are right next to each other,” Lu says. “If you can’t distinguish between cancerous and healthy cells, you get unwanted side effects, or you may not be able to hit the cancer as hard as you’d like. But once you can do that, there’s a lot of ways to maximize your firepower against the cancer cells.”

Hope for patients

Senti’s lead clinical trial is focusing on patients with relapsed or refractory blood cancers, including AML.

“Obviously the most important thing is getting a good response for patients,” Lu says. “But we’re also doing additional scientific work to confirm that the logic gates are working the way we expect them to in humans. Based on that information, we can then deploy logic gates into additional therapeutic indications such as solid tumors, where you have a lot of the same problems with finding a target.”

Another company that has partnered with Senti to use some of Senti’s technology also has an early clinical trial underway in liver cancer. Senti is also partnering with other companies to apply its gene circuit technology in areas like regenerative medicine and neuroscience.

“I think this is broader than just cell therapies,” Lu says. “We believe if we can prove this out in AML, it will lead to a fundamentally new way of diagnosing and treating cancer, where we’re able to definitively identify and target cancer cells and spare healthy cells. We hope it will become a whole new class of medicines moving forward.”

Programmers can now use large language models (LLMs) to generate computer code more quickly. However, this only makes programmers’ lives easier if that code follows the rules of the programming language and doesn’t cause a computer to crash.

Some methods exist for ensuring LLMs conform to the rules of whatever language they are generating text in, but many of these methods either distort the model’s intended meaning or are too time-consuming to be feasible for complex tasks.

A new approach developed by researchers at MIT and elsewhere automatically guides an LLM to generate text that adheres to the rules of the relevant language, such as a particular programming language, and is also error-free. Their method allows an LLM to allocate efforts toward outputs that are most likely to be valid and accurate, while discarding unpromising outputs early in the process. This probabilistic approach boosts computational efficiency.

Due to these efficiency gains, the researchers’ architecture enabled small LLMs to outperform much larger models in generating accurate, properly structured outputs for several real-world use cases, including molecular biology and robotics.

In the long run, this new architecture could help nonexperts control AI-generated content. For instance, it could allow businesspeople to write complex queries in SQL, a language for database manipulation, using only natural language prompts.

“This work has implications beyond research. It could improve programming assistants, AI-powered data analysis, and scientific discovery tools by ensuring that AI-generated outputs remain both useful and correct,” says João Loula, an MIT graduate student and co-lead author of a paper on this framework.

Loula is joined on the paper by co-lead authors Benjamin LeBrun, a research assistant at the Mila-Quebec Artificial Intelligence Institute, and Li Du, a graduate student at John Hopkins University; co-senior authors Vikash Mansinghka ’05, MEng ’09, PhD ’09, a principal research scientist and leader of the Probabilistic Computing Project in the MIT Department of Brain and Cognitive Sciences; Alexander K. Lew SM ’20, an assistant professor at Yale University; Tim Vieira, a postdoc at ETH Zurich; and Timothy J. O’Donnell, an associate professor at McGill University and a Canada CIFAR AI Chair at Mila, who led the international team; as well as several others. The research will be presented at the International Conference on Learning Representations.

Enforcing structure and meaning

One common approach for controlling the structured text generated by LLMs involves checking an entire output, like a block of computer code, to make sure it is valid and will run error-free. If not, the user must start again, racking up computational resources.

On the other hand, a programmer could stop to check the output along the way. While this can ensure the code adheres to the programming language and is structurally valid, incrementally correcting the code may cause it to drift from the meaning the user intended, hurting its accuracy in the long run.

“It is much easier to enforce structure than meaning. We can quickly check whether something is in the right programming language, but to check its meaning you have to execute the code. Our work is also about dealing with these different types of information,” Loula says.

The researchers’ approach involves engineering knowledge into the LLM to steer it toward the most promising outputs. These outputs are more likely to follow the structural constraints defined by a user, and to have the meaning the user intends.

“We are not trying to train an LLM to do this. Instead, we are engineering some knowledge that an expert would have and combining it with the LLM’s knowledge, which offers a very different approach to scaling than you see in deep learning,” Mansinghka adds.

They accomplish this using a technique called sequential Monte Carlo, which enables parallel generation from an LLM to compete with each other. The model dynamically allocates resources to different threads of parallel computation based on how promising their output appears.

Each output is given a weight that represents how likely it is to be structurally valid and semantically accurate. At each step in the computation, the model focuses on those with higher weights and throws out the rest.

In a sense, it is like the LLM has an expert looking over its shoulder to ensure it makes the right choices at each step, while keeping it focused on the overall goal. The user specifies their desired structure and meaning, as well as how to check the output, then the researchers’ architecture guides the LLM to do the rest.

“We’ve worked out the hard math so that, for any kinds of constraints you’d like to incorporate, you are going to get the proper weights. In the end, you get the right answer,” Loula says.

Boosting small models

To test their approach, they applied the framework to LLMs tasked with generating four types of outputs: Python code, SQL database queries, molecular structures, and plans for a robot to follow.

When compared to existing approaches, the researchers’ method performed more accurately while requiring less computation.

In Python code generation, for instance, the researchers’ architecture enabled a small, open-source model to outperform a specialized, commercial closed-source model that is more than double its size.

“We are very excited that we can allow these small models to punch way above their weight,” Loula says.

Moving forward, the researchers want to use their technique to control larger chunks of generated text, rather than working one small piece at a time. They also want to combine their method with learning, so that as they control the outputs a model generates, it learns to be more accurate.

In the long run, this project could have broader applications for non-technical users. For instance, it could be combined with systems for automated data modeling, and querying generative models of databases.

The approach could also enable machine-assisted data analysis systems, where the user can converse with software that accurately models the meaning of the data and the questions asked by the user, adds Mansinghka.

“One of the fundamental questions of linguistics is how the meaning of words, phrases, and sentences can be grounded in models of the world, accounting for uncertainty and vagueness in meaning and reference. LLMs, predicting likely token sequences, don’t address this problem. Our paper shows that, in narrow symbolic domains, it is technically possible to map from words to distributions on grounded meanings. It’s a small step towards deeper questions in cognitive science, linguistics, and artificial intelligence needed to understand how machines can communicate about the world like we do,” says O’Donnell.

This research is funded and supported, in part, by the Canada CIFAR AI Chairs Program, the MIT Quest for Intelligence, and Convergent Research. 

Cleft lip and cleft palate are among the most common birth defects, occurring in about one in 1,050 births in the United States. These defects, which appear when the tissues that form the lip or the roof of the mouth do not join completely, are believed to be caused by a mix of genetic and environmental factors.

In a new study, MIT biologists have discovered how a genetic variant often found in people with these facial malformations leads to the development of cleft lip and cleft palate.

Their findings suggest that the variant diminishes cells’ supply of transfer RNA, a molecule that is critical for assembling proteins. When this happens, embryonic face cells are unable to fuse to form the lip and roof of the mouth.

“Until now, no one had made the connection that we made. This particular gene was known to be part of the complex involved in the splicing of transfer RNA, but it wasn’t clear that it played such a crucial role for this process and for facial development. Without the gene, known as DDX1, certain transfer RNA can no longer bring amino acids to the ribosome to make new proteins. If the cells can’t process these tRNAs properly, then the ribosomes can’t make protein anymore,” says Michaela Bartusel, an MIT research scientist and the lead author of the study.

Eliezer Calo, an associate professor of biology at MIT, is the senior author of the paper, which appears today in the American Journal of Human Genetics.

Genetic variants

Cleft lip and cleft palate, also known as orofacial clefts, can be caused by genetic mutations, but in many cases, there is no known genetic cause.

“The mechanism for the development of these orofacial clefts is unclear, mostly because they are known to be impacted by both genetic and environmental factors,” Calo says. “Trying to pinpoint what might be affected has been very challenging in this context.”

To discover genetic factors that influence a particular disease, scientists often perform genome-wide association studies (GWAS), which can reveal variants that are found more often in people who have a particular disease than in people who don’t.

For orofacial clefts, some of the genetic variants that have regularly turned up in GWAS appeared to be in a region of DNA that doesn’t code for proteins. In this study, the MIT team set out to figure out how variants in this region might influence the development of facial malformations.

Their studies revealed that these variants are located in an enhancer region called e2p24.2. Enhancers are segments of DNA that interact with protein-coding genes, helping to activate them by binding to transcription factors that turn on gene expression.

The researchers found that this region is in close proximity to three genes, suggesting that it may control the expression of those genes. One of those genes had already been ruled out as contributing to facial malformations, and another had already been shown to have a connection. In this study, the researchers focused on the third gene, which is known as DDX1.

DDX1, it turned out, is necessary for splicing transfer RNA (tRNA) molecules, which play a critical role in protein synthesis. Each transfer RNA molecule transports a specific amino acid to the ribosome — a cell structure that strings amino acids together to form proteins, based on the instructions carried by messenger RNA.

While there are about 400 different tRNAs found in the human genome, only a fraction of those tRNAs require splicing, and those are the tRNAs most affected by the loss of DDX1. These tRNAs transport four different amino acids, and the researchers hypothesize that these four amino acids may be particularly abundant in proteins that embryonic cells that form the face need to develop properly.

When the ribosomes need one of those four amino acids, but none of them are available, the ribosome can stall, and the protein doesn’t get made.

The researchers are now exploring which proteins might be most affected by the loss of those amino acids. They also plan to investigate what happens inside cells when the ribosomes stall, in hopes of identifying a stress signal that could potentially be blocked and help cells survive.

Malfunctioning tRNA

While this is the first study to link tRNA to craniofacial malformations, previous studies have shown that mutations that impair ribosome formation can also lead to similar defects. Studies have also shown that disruptions of tRNA synthesis — caused by mutations in the enzymes that attach amino acids to tRNA, or in proteins involved in an earlier step in tRNA splicing — can lead to neurodevelopmental disorders.

“Defects in other components of the tRNA pathway have been shown to be associated with neurodevelopmental disease,” Calo says. “One interesting parallel between these two is that the cells that form the face are coming from the same place as the cells that form the neurons, so it seems that these particular cells are very susceptible to tRNA defects.”

The researchers now hope to explore whether environmental factors linked to orofacial birth defects also influence tRNA function. Some of their preliminary work has found that oxidative stress — a buildup of harmful free radicals — can lead to fragmentation of tRNA molecules. Oxidative stress can occur in embryonic cells upon exposure to ethanol, as in fetal alcohol syndrome, or if the mother develops gestational diabetes.

“I think it is worth looking for mutations that might be causing this on the genetic side of things, but then also in the future, we would expand this into which environmental factors have the same effects on tRNA function, and then see which precautions might be able to prevent any effects on tRNAs,” Bartusel says.

The research was funded by the National Science Foundation Graduate Research Program, the National Cancer Institute, the National Institute of General Medical Sciences, and the Pew Charitable Trusts.

At any given time, about 100,000 people in the U.S. are waiting to become kidney transplant recipients. Roughly one-fifth of those get a new kidney each year, but others die while waiting. In short, the demand for kidneys makes it important to think about how we use the limited supply.

A study co-authored by an MIT economist brings new data to this issue, providing nuanced estimates of the lifespan-lengthening effect of kidney transplants. That can be hard to measure well, but the study is the first to account for some of the complexities involved, including the decisions patients make when accepting kidney transplants, and some of their pre-existing health factors.

The research concludes the system in use produces an additional 9.29 life-years from transplantation (LYFT) for kidney recipients. (LYFT is the difference in median survival for those with and without transplants.) If the organs were assigned randomly to patients, the study finds, that LYFT average would only be 7.54 overall. From that perspective, the current transplant system is a net positive for patients. However, the study also finds that the LYFT figure could potentially be raised as high as 14.08, depending on how the matching system is structured.

In any case, more precise estimates about the benefits of kidney transplants can help inform policymakers about the dynamics of the matching system in use.

“There’s always this question about how to take the scarce number of organs being donated and place them efficiently, and place them well,” says MIT economist Nikhil Agarwal, co-author of a newly published paper detailing the study’s results. As he emphasizes, the point of the paper is to inform the ongoing refinement of the matching system, rather than advocate one viewpoint or another.

The paper, “Choices and Outcomes in Assignment Mechanisms: The Allocation of Deceased Donor Kidneys,” is published in the latest issue of Econometrica. The authors are Agarwal, who is a professor in MIT’s Department of Economics; Charles Hodgson, an assistant professor of economics at Yale University; and Paulo Somaini, an associate professor of economics in Stanford University’s Graduate School of Business.

After people die, there is a period lasting up to 48 hours when they could be viable organ donors. Potential kidney recipients are prioritized by time spent on wait-lists as well as tissue-type similarity, and can accept or reject any given transplant offer.

Over the last decade-plus, Agarwal has conducted significant empirical research on matching systems for organ donations, especially kidney transplants. To conduct this study, the researchers used comprehensive data about patients on the kidney wait-list from 2000-2010, made available by the Organ Procurement and Transplantation Network, the national U.S. registry. This allowed the scholars to analyze both the matching system and the health effects of transplants; they track patient survival until February 2020.

The work is the first quasiexperimental study of kidney transplants; by carefully examining the decision-making tendencies of kidney recipients, along with many other health factors, the scholars are able to evaluate the effects of a transplant, other things being equal. Recipients are more likely to select kidney offers from donors who are younger, lacked hypertension, died of head trauma (suggesting their internal organs were healthy), and with whom they have perfect tissue-type matches.

“The [previous] methodology of estimating what are the life-years benefits was not incorporating this selection issue,” Agarwal says.

Additionally, overall, a key empirical feature of kidney transplants is that recipients who are healthier overall tend to have the largest realized life-years benefits from a transplant, meaning that the greatest increase in LYFT is not found in the set of patients with the worst health.

“You might think people who are the sickest and who are most likely to die without an organ are going to benefit the most from it [in added life-years],” Agarwal says. “But there might be some other comorbidity or factor that made them sick, and their body’s going to take a toll on the new organ, so the benefits might not be as large.”

With this in mind, the maximal LYFT number of 14.08 in the study comes from, broadly, a hypothetical scenario in which an increased number of otherwise healthy people receive transplants. Again, the current system tends to prioritize time spent on a wait-list. And some observers might advocate for a system that prioritizes those who are sickest. With all that in mind, the policymaking process for kidney transplants may still involve recognition that the biggest gains in patient life-years are not necessarily aligned with other prioritization factors.

“Our results indicate … a dilemma rooted in the tension between these two goals,” the authors write in the paper.

To be clear, Agarwal is not advocating for any one system over another, but conducting data-driven research so that policy officials can make more fully informed decisions in the ongoing, long-term process of trying to refine valuable transplant networks.

“I don’t necessarily think it’s my comparative advantage to make the ethical decisions, but we can at least think about and quantify what some of the tradeoffs are,” Agarwal adds.

Support for the research was provided in part by the National Science Foundation and by the Alfred P. Sloan Foundation. 

When some commuter trains arrive at the end of the line, they must travel to a switching platform to be turned around so they can depart the station later, often from a different platform than the one at which they arrived.

Engineers use software programs called algorithmic solvers to plan these movements, but at a station with thousands of weekly arrivals and departures, the problem becomes too complex for a traditional solver to unravel all at once.

Using machine learning, MIT researchers have developed an improved planning system that reduces the solve time by up to 50 percent and produces a solution that better meets a user’s objective, such as on-time train departures. The new method could also be used for efficiently solving other complex logistical problems, such as scheduling hospital staff, assigning airline crews, or allotting tasks to factory machines.

Engineers often break these kinds of problems down into a sequence of overlapping subproblems that can each be solved in a feasible amount of time. But the overlaps cause many decisions to be needlessly recomputed, so it takes the solver much longer to reach an optimal solution.

The new, artificial intelligence-enhanced approach learns which parts of each subproblem should remain unchanged, freezing those variables to avoid redundant computations. Then a traditional algorithmic solver tackles the remaining variables.

“Often, a dedicated team could spend months or even years designing an algorithm to solve just one of these combinatorial problems. Modern deep learning gives us an opportunity to use new advances to help streamline the design of these algorithms. We can take what we know works well, and use AI to accelerate it,” says Cathy Wu, the Thomas D. and Virginia W. Cabot Career Development Associate Professor in Civil and Environmental Engineering (CEE) and the Institute for Data, Systems, and Society (IDSS) at MIT, and a member of the Laboratory for Information and Decision Systems (LIDS).

She is joined on the paper by lead author Sirui Li, an IDSS graduate student; Wenbin Ouyang, a CEE graduate student; and Yining Ma, a LIDS postdoc. The research will be presented at the International Conference on Learning Representations.

Eliminating redundance

One motivation for this research is a practical problem identified by a master’s student Devin Camille Wilkins in Wu’s entry-level transportation course. The student wanted to apply reinforcement learning to a real train-dispatch problem at Boston’s North Station. The transit organization needs to assign many trains to a limited number of platforms where they can be turned around well in advance of their arrival at the station.

This turns out to be a very complex combinatorial scheduling problem — the exact type of problem Wu’s lab has spent the past few years working on.

When faced with a long-term problem that involves assigning a limited set of resources, like factory tasks, to a group of machines, planners often frame the problem as Flexible Job Shop Scheduling.

In Flexible Job Shop Scheduling, each task needs a different amount of time to complete, but tasks can be assigned to any machine. At the same time, each task is composed of operations that must be performed in the correct order.

Such problems quickly become too large and unwieldy for traditional solvers, so users can employ rolling horizon optimization (RHO) to break the problem into manageable chunks that can be solved faster.

With RHO, a user assigns an initial few tasks to machines in a fixed planning horizon, perhaps a four-hour time window. Then, they execute the first task in that sequence and shift the four-hour planning horizon forward to add the next task, repeating the process until the entire problem is solved and the final schedule of task-machine assignments is created.

A planning horizon should be longer than any one task’s duration, since the solution will be better if the algorithm also considers tasks that will be coming up.

But when the planning horizon advances, this creates some overlap with operations in the previous planning horizon. The algorithm already came up with preliminary solutions to these overlapping operations.

“Maybe these preliminary solutions are good and don’t need to be computed again, but maybe they aren’t good. This is where machine learning comes in,” Wu explains.

For their technique, which they call learning-guided rolling horizon optimization (L-RHO), the researchers teach a machine-learning model to predict which operations, or variables, should be recomputed when the planning horizon rolls forward.

L-RHO requires data to train the model, so the researchers solve a set of subproblems using a classical algorithmic solver. They took the best solutions — the ones with the most operations that don’t need to be recomputed — and used these as training data.

Once trained, the machine-learning model receives a new subproblem it hasn’t seen before and predicts which operations should not be recomputed. The remaining operations are fed back into the algorithmic solver, which executes the task, recomputes these operations, and moves the planning horizon forward. Then the loop starts all over again.

“If, in hindsight, we didn’t need to reoptimize them, then we can remove those variables from the problem. Because these problems grow exponentially in size, it can be quite advantageous if we can drop some of those variables,” she adds.

An adaptable, scalable approach

To test their approach, the researchers compared L-RHO to several base algorithmic solvers, specialized solvers, and approaches that only use machine learning. It outperformed them all, reducing solve time by 54 percent and improving solution quality by up to 21 percent.

In addition, their method continued to outperform all baselines when they tested it on more complex variants of the problem, such as when factory machines break down or when there is extra train congestion. It even outperformed additional baselines the researchers created to challenge their solver.

“Our approach can be applied without modification to all these different variants, which is really what we set out to do with this line of research,” she says.

L-RHO can also adapt if the objectives change, automatically generating a new algorithm to solve the problem — all it needs is a new training dataset.

In the future, the researchers want to better understand the logic behind their model’s decision to freeze some variables, but not others. They also want to integrate their approach into other types of complex optimization problems like inventory management or vehicle routing.

This work was supported, in part, by the National Science Foundation, MIT’s Research Support Committee, an Amazon Robotics PhD Fellowship, and MathWorks.

When visual information enters the brain, it travels through two pathways that process different aspects of the input. For decades, scientists have hypothesized that one of these pathways, the ventral visual stream, is responsible for recognizing objects, and that it might have been optimized by evolution to do just that.

Consistent with this, in the past decade, MIT scientists have found that when computational models of the anatomy of the ventral stream are optimized to solve the task of object recognition, they are remarkably good predictors of the neural activities in the ventral stream.

However, in a new study, MIT researchers have shown that when they train these types of models on spatial tasks instead, the resulting models are also quite good predictors of the ventral stream’s neural activities. This suggests that the ventral stream may not be exclusively optimized for object recognition.

“This leaves wide open the question about what the ventral stream is being optimized for. I think the dominant perspective a lot of people in our field believe is that the ventral stream is optimized for object recognition, but this study provides a new perspective that the ventral stream could be optimized for spatial tasks as well,” says MIT graduate student Yudi Xie.

Xie is the lead author of the study, which will be presented at the International Conference on Learning Representations. Other authors of the paper include Weichen Huang, a visiting student through MIT’s Research Science Institute program; Esther Alter, a software engineer at the MIT Quest for Intelligence; Jeremy Schwartz, a sponsored research technical staff member; Joshua Tenenbaum, a professor of brain and cognitive sciences; and James DiCarlo, the Peter de Florez Professor of Brain and Cognitive Sciences, director of the Quest for Intelligence, and a member of the McGovern Institute for Brain Research at MIT.

Beyond object recognition

When we look at an object, our visual system can not only identify the object, but also determine other features such as its location, its distance from us, and its orientation in space. Since the early 1980s, neuroscientists have hypothesized that the primate visual system is divided into two pathways: the ventral stream, which performs object-recognition tasks, and the dorsal stream, which processes features related to spatial location.

Over the past decade, researchers have worked to model the ventral stream using a type of deep-learning model known as a convolutional neural network (CNN). Researchers can train these models to perform object-recognition tasks by feeding them datasets containing thousands of images along with category labels describing the images.

The state-of-the-art versions of these CNNs have high success rates at categorizing images. Additionally, researchers have found that the internal activations of the models are very similar to the activities of neurons that process visual information in the ventral stream. Furthermore, the more similar these models are to the ventral stream, the better they perform at object-recognition tasks. This has led many researchers to hypothesize that the dominant function of the ventral stream is recognizing objects.

However, experimental studies, especially a study from the DiCarlo lab in 2016, have found that the ventral stream appears to encode spatial features as well. These features include the object’s size, its orientation (how much it is rotated), and its location within the field of view. Based on these studies, the MIT team aimed to investigate whether the ventral stream might serve additional functions beyond object recognition.

“Our central question in this project was, is it possible that we can think about the ventral stream as being optimized for doing these spatial tasks instead of just categorization tasks?” Xie says.

To test this hypothesis, the researchers set out to train a CNN to identify one or more spatial features of an object, including rotation, location, and distance. To train the models, they created a new dataset of synthetic images. These images show objects such as tea kettles or calculators superimposed on different backgrounds, in locations and orientations that are labeled to help the model learn them.

The researchers found that CNNs that were trained on just one of these spatial tasks showed a high level of “neuro-alignment” with the ventral stream — very similar to the levels seen in CNN models trained on object recognition.

The researchers measure neuro-alignment using a technique that DiCarlo’s lab has developed, which involves asking the models, once trained, to predict the neural activity that a particular image would generate in the brain. The researchers found that the better the models performed on the spatial task they had been trained on, the more neuro-alignment they showed.

“I think we cannot assume that the ventral stream is just doing object categorization, because many of these other functions, such as spatial tasks, also can lead to this strong correlation between models’ neuro-alignment and their performance,” Xie says. “Our conclusion is that you can optimize either through categorization or doing these spatial tasks, and they both give you a ventral-stream-like model, based on our current metrics to evaluate neuro-alignment.”

Comparing models

The researchers then investigated why these two approaches — training for object recognition and training for spatial features — led to similar degrees of neuro-alignment. To do that, they performed an analysis known as centered kernel alignment (CKA), which allows them to measure the degree of similarity between representations in different CNNs. This analysis showed that in the early to middle layers of the models, the representations that the models learn are nearly indistinguishable.

“In these early layers, essentially you cannot tell these models apart by just looking at their representations,” Xie says. “It seems like they learn some very similar or unified representation in the early to middle layers, and in the later stages they diverge to support different tasks.”

The researchers hypothesize that even when models are trained to analyze just one feature, they also take into account “non-target” features — those that they are not trained on. When objects have greater variability in non-target features, the models tend to learn representations more similar to those learned by models trained on other tasks. This suggests that the models are using all of the information available to them, which may result in different models coming up with similar representations, the researchers say.

“More non-target variability actually helps the model learn a better representation, instead of learning a representation that’s ignorant of them,” Xie says. “It’s possible that the models, although they’re trained on one target, are simultaneously learning other things due to the variability of these non-target features.”

In future work, the researchers hope to develop new ways to compare different models, in hopes of learning more about how each one develops internal representations of objects based on differences in training tasks and training data.

“There could be still slight differences between these models, even though our current way of measuring how similar these models are to the brain tells us they’re on a very similar level. That suggests maybe there’s still some work to be done to improve upon how we can compare the model to the brain, so that we can better understand what exactly the ventral stream is optimized for,” Xie says.

The research was funded by the Semiconductor Research Corporation and the U.S. Defense Advanced Research Projects Agency.

As we mature from childhood, our vocabulary — as well as the ways we use it — grows, and our experiences become richer, allowing us to think, reason, and interact with others with specificity and intention. Accordingly, our word choices evolve to align with our personal values, ethics, cultural norms, and views. Over time, most of us develop an internal “guide” that enables us to learn context behind conversation; it also frequently directs us away from sharing information and sentiments that are, or could be, harmful or inappropriate. As it turns out, large language models (LLMs) — which are trained on extensive, public datasets and therefore often have biases and toxic language baked in — can gain a similar capacity to moderate their own language.

A new method from MIT, the MIT-IBM Watson AI Lab, and IBM Research, called self-disciplined autoregressive sampling (SASA), allows LLMs to detoxify their own outputs, without sacrificing fluency. 

Unlike other detoxifying methods, this decoding algorithm learns a boundary between toxic/nontoxic subspaces within the LLM’s own internal representation, without altering the parameters of the model, the need for retraining, or an external reward model. Then, during inference, the algorithm assesses the toxicity value of the partially generated phrase: tokens (words) already generated and accepted, along with each potential new token that could reasonably be chosen for proximity to the classifier boundary. Next, it selects a word option that places the phrase in the nontoxic space, ultimately offering a fast and efficient way to generate less-toxic language.

“We wanted to find out a way with any existing language model [that], during the generation process, the decoding can be subject to some human values; the example here we are taking is toxicity,” says the study’s lead author Ching-Yun “Irene” Ko PhD ’24, a former graduate intern with the MIT-IBM Watson AI Lab and a current research scientist at IBM’s Thomas J. Watson Research Center in New York.

Ko’s co-authors include Luca Daniel, professor in the MIT Department of Electrical Engineering and Computer Science (EECS), a member of the MIT-IBM Watson AI Lab, and Ko’s graduate advisor; and several members of the MIT-IBM Watson AI Lab and/or IBM Research — Pin-Yu Chen, Payel Das, Youssef Mroueh, Soham Dan, Georgios Kollias, Subhajit Chaudhury, and Tejaswini Pedapati. The work will be presented at the International Conference on Learning Representations.

Finding the “guardrails”

The training resources behind LLMs almost always include content collected from public spaces like the internet and other readily available datasets. As such, curse words and bullying/unpalatable language is a component, although some of it is in the context of literary works. It then follows that LLMs can innately produce — or be tricked into generating — dangerous and/or biased content, which often contains disagreeable words or hateful language, even from innocuous prompts. Further, it’s been found that they can learn and amplify language that’s not preferred or even detrimental for many applications and downstream tasks — leading to the need for mitigation or correction strategies.

There are many ways to achieve robust language generation that’s fair and value-aligned. Some methods use LLM retraining with a sanitized dataset, which is costly, takes time, and may alter the LLM’s performance; others employ decoding external reward models, like sampling or beam search, which take longer to run and require more memory. In the case of SASA, Ko, Daniel, and the IBM Research team developed a method that leverages the autoregressive nature of LLMs, and using a decoding-based strategy during the LLM’s inference, gradually steers the generation — one token at a time — away from unsavory or undesired outputs and toward better language.

The research group achieved this by building a linear classifier that operates on the learned subspace from the LLM’s embedding. When LLMs are trained, words with similar meanings are placed closely together in vector space and further away from dissimilar words; the researchers hypothesized that an LLM’s embedding would therefore also capture contextual information, which could be used for detoxification. The researchers used datasets that contained sets of a prompt (first half of a sentence or thought), a response (the completion of that sentence), and human-attributed annotation, like toxic or nontoxic, preferred or not preferred, with continuous labels from 0-1, denoting increasing toxicity. A Bayes-optimal classifier was then applied to learn and figuratively draw a line between the binary subspaces within the sentence embeddings, represented by positive values (nontoxic space) and negative numbers (toxic space). 

The SASA system then works by re-weighting the sampling probabilities of newest potential token based on the value of it and the generated phrase’s distance to the classifier, with the goal of remaining close to the original sampling distribution.

To illustrate, if a user is generating a potential token #12 in a sentence, the LLM will look over its full vocabulary for a reasonable word, based on the 11 words that came before it, and using top-k, top-p, it will filter and produce roughly 10 tokens to select from. SASA then evaluates each of those tokens in the partially completed sentence for its proximity to the classifier (i.e., the value of tokens 1-11, plus each potential token 12). Tokens that produce sentences in the positive space are encouraged, while those in the negative space are penalized. Additionally, the further away from the classifier, the stronger the impact.

“The goal is to change the autoregressive sampling process by re-weighting the probability of good tokens. If the next token is likely to be toxic given the context, then we are going to reduce the sampling probability for those prone to be toxic tokens,” says Ko. The researchers chose to do it this way “because the things we say, whether it’s benign or not, is subject to the context.”

Tamping down toxicity for value matching

The researchers evaluated their method against several baseline interventions with three LLMs of increasing size; all were transformers and autoregressive-based: GPT2-Large, Llama2-7b, and Llama 3.1-8b-Instruct, with 762 million, 7 billion, and 8 billion parameters respectively. For each prompt, the LLM was tasked with completing the sentence/phrase 25 times, and PerspectiveAPI scored them from 0 to 1, with anything over 0.5 being toxic. The team looked at two metrics: the average maximum toxicity score over the 25 generations for all the prompts, and the toxic rate, which was the probability of producing at least one toxic phrase over 25 generations. Reduced fluency (and therefore increased perplexity) were also analyzed. SASA was tested to complete RealToxicityPrompts (RPT), BOLD, and AttaQ datasets, which contained naturally occurring, English sentence prompts.

The researchers ramped up the complexity of their trials for detoxification by SASA, beginning with nontoxic prompts from the RPT dataset, looking for harmful sentence completions. Then, they escalated it to more challenging prompts from RPT that were more likely to produce concerning results, and as well applied SASA to the instruction-tuned model to assess if their technique could further reduce unwanted ouputs. They also used the BOLD and AttaQ benchmarks to examine the general applicability of SASA in detoxification. With the BOLD dataset, the researchers further looked for gender bias in language generations and tried to achieve a balanced toxic rate between the genders. Lastly, the team looked at runtime, memory usage, and how SASA could be combined with word filtering to achieve healthy and/or helpful language generation.

“If we think about how human beings think and react in the world, we do see bad things, so it’s not about allowing the language model to see only the good things. It’s about understanding the full spectrum — both good and bad,” says Ko, “and choosing to uphold our values when we speak and act.”

Overall, SASA achieved significant toxic language generation reductions, performing on par with RAD, a state-of-the-art external reward model technique. However, it was universally observed that stronger detoxification accompanied a decrease in fluency. Before intervention, the LLMs produced more toxic responses for female labeled prompts than male; however, SASA was able to also significantly cut down harmful responses, making them more equalized. Similarly, word filtering on top of SASA did markedly lower toxicity levels, but it also hindered the ability of the LLM to respond coherently.

A great aspect of this work is that it’s a well-defined, constrained optimization problem, says Ko, meaning that balance between open language generation that sounds natural and the need to reduce unwanted language can be achieved and tuned.

Further, Ko says, SASA could work well for multiple attributes in the future: “For human beings, we have multiple human values. We don’t want to say toxic things, but we also want to be truthful, helpful, and loyal … If you were to fine-tune a model for all of these values, it would require more computational resources and, of course, additional training.” On account of the lightweight manner of SASA, it could easily be applied in these circumstances: “If you want to work with multiple values, it’s simply checking the generation’s position in multiple subspaces. It only adds marginal overhead in terms of the compute and parameters,” says Ko, leading to more positive, fair, and principle-aligned language.

This work was supported, in part, by the MIT-IBM Watson AI Lab and the National Science Foundation.

Tropical cyclones are hurricanes that brew over the tropical ocean and can travel over land, inundating coastal regions. The most extreme cyclones can generate devastating storm tides — seawater that is heightened by the tides and swells onto land, causing catastrophic flood events in coastal regions. A new study by MIT scientists finds that, as the planet warms, the recurrence of destructive storm tides will increase tenfold for one of the hardest-hit regions of the world.

In a study appearing today in One Earth, the scientists report that, for the highly populated coastal country of Bangladesh, what was once a 100-year event could now strike every 10 years — or more often — by the end of the century. 

In a future where fossil fuels continue to burn as they do today, what was once considered a catastrophic, once-in-a-century storm tide will hit Bangladesh, on average, once per decade. And the kind of storm tides that have occurred every decade or so will likely batter the country’s coast more frequently, every few years.

Bangladesh is one of the most densely populated countries in the world, with more than 171 million people living in a region roughly the size of New York state. The country has been historically vulnerable to tropical cyclones, as it is a low-lying delta that is easily flooded by storms and experiences a seasonal monsoon. Some of the most destructive floods in the world have occurred in Bangladesh, where it’s been increasingly difficult for agricultural economies to recover.

The study also finds that Bangladesh will likely experience tropical cyclones that overlap with the months-long monsoon season. Until now, cyclones and the monsoon have occurred at separate times during the year. But as the planet warms, the scientists’ modeling shows that cyclones will push into the monsoon season, causing back-to-back flooding events across the country.

“Bangladesh is very active in preparing for climate hazards and risks, but the problem is, everything they’re doing is more or less based on what they’re seeing in the present climate,” says study co-author Sai Ravela, principal research scientist in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS). “We are now seeing an almost tenfold rise in the recurrence of destructive storm tides almost anywhere you look in Bangladesh. This cannot be ignored. So, we think this is timely, to say they have to pause and revisit how they protect against these storms.”

Ravela’s co-authors are Jiangchao Qiu, a postdoc in EAPS, and Kerry Emanuel, professor emeritus of atmospheric science at MIT.

Height of tides

In recent years, Bangladesh has invested significantly in storm preparedness, for instance in improving its early-warning system, fortifying village embankments, and increasing access to community shelters. But such preparations have generally been based on the current frequency of storms.

In this new study, the MIT team aimed to provide detailed projections of extreme storm tide hazards, which are flooding events where tidal effects amplify cyclone-induced storm surge, in Bangladesh under various climate-warming scenarios and sea-level rise projections.

“A lot of these events happen at night, so tides play a really strong role in how much additional water you might get, depending on what the tide is,” Ravela explains.

To evaluate the risk of storm tide, the team first applied a method of physics-based downscaling, which Emanuel’s group first developed over 20 years ago and has been using since to study hurricane activity in different parts of the world. The technique involves a low-resolution model of the global ocean and atmosphere that is embedded with a finer-resolution model that simulates weather patterns as detailed as a single hurricane. The researchers then scatter hurricane “seeds” in a region of interest and run the model forward to observe which seeds grow and make landfall over time.

To the downscaled model, the researchers incorporated a hydrodynamical model, which simulates the height of a storm surge, given the pattern and strength of winds at the time of a given storm. For any given simulated storm, the team also tracked the tides, as well as effects of sea level rise, and incorporated this information into a numerical model that calculated the storm tide, or the height of the water, with tidal effects as a storm makes landfall.

Extreme overlap

With this framework, the scientists simulated tens of thousands of potential tropical cyclones near Bangladesh, under several future climate scenarios, ranging from one that resembles the current day to one in which the world experiences further warming as a result of continued fossil fuel burning. For each simulation, they recorded the maximum storm tides along the coast of Bangladesh and noted the frequency of storm tides of various heights in a given climate scenario.

“We can look at the entire bucket of simulations and see, for this storm tide of say, 3 meters, we saw this many storms, and from that you can figure out the relative frequency of that kind of storm,” Qiu says. “You can then invert that number to a return period.”

A return period is the time it takes for a storm of a particular type to make landfall again. A storm that is considered a “100-year event” is typically more powerful and destructive, and in this case, creates more extreme storm tides, and therefore more catastrophic flooding, compared to a 10-year event.

From their modeling, Ravela and his colleagues found that under a scenario of increased global warming, the storms that previously were considered 100-year events, producing the highest storm tide values, can recur every decade or less by late-century. They also observed that, toward the end of this century, tropical cyclones in Bangladesh will occur across a broader seasonal window, potentially overlapping in certain years with the seasonal monsoon season.

“If the monsoon rain has come in and saturated the soil, a cyclone then comes in and it makes the problem much worse,” Ravela says. “People won’t have any reprieve between the extreme storm and the monsoon. There are so many compound and cascading effects between the two. And this only emerges because warming happens.”

Ravela and his colleagues are using their modeling to help experts in Bangladesh better evaluate and prepare for a future of increasing storm risk. And he says that the climate future for Bangladesh is in some ways not unique to this part of the world.

“This climate change story that is playing out in Bangladesh in a certain way will be playing out in a different way elsewhere,” Ravela notes. “Maybe where you are, the story is about heat stress, or amplifying droughts, or wildfires. The peril is different. But the underlying catastrophe story is not that different.”

This research is supported in part by the MIT Climate Resilience Early Warning Systems Climate Grand Challenges project, the Jameel Observatory JO-CREWSNet project; MIT Weather and Climate Extremes Climate Grand Challenges project; and Schmidt Sciences, LLC. 

The MIT Health and Life Sciences Collaborative (MIT HEALS) has announced the establishment of the Hood Pediatric Innovation Hub, an ambitious effort designed to drive cutting-edge innovation in children’s health care. Launched in collaboration with the Charles H. Hood Foundation, the hub will focus on addressing unmet needs in pediatric medicine by developing technologies and treatments tailored specifically for children.

Leveraging the Institute’s strengths in the life sciences, the hub will provide seed funding and strategic support for bold, high-impact research projects with the potential to transform health care for children. It will also act as a springboard for emerging scientific leaders, empowering them to help shape the future of pediatric health.

“The Hood Pediatric Innovation Hub represents an extraordinary opportunity to create meaningful and lasting change in the lives of children,” says Anantha Chandrakasan, dean of the MIT School of Engineering, MIT’s chief innovation and strategy officer, and head of MIT HEALS. “By collaborating with the Charles H. Hood Foundation, we’re harnessing MIT’s interdisciplinary strengths to tackle some of the most pressing challenges in pediatric health care.”

Addressing critical gaps in pediatric health care

Despite making up a significant portion of the global population, children have been largely underserved when it comes to medical innovation, leaving immense gaps in care. Pediatric conditions that shape a lifetime of health and well-being often lack dedicated solutions — forcing reliance on repurposed adult treatments or no solution at all. From 2008 to 2018, only 10 percent of U.S. Food and Drug Administration approvals were designated for individuals under the age of 18.

There is a massive opportunity to prioritize innovation for people during their formative years and drive breakthroughs that not only improve individual lives but also elevate health outcomes for generations to come. The Hood Pediatric Innovation Hub seeks to lead this transformation by creating a dedicated community for advancing technologies and research.

“We are thrilled to collaborate with MIT to launch the hub, a bold initiative that will drive groundbreaking science and technology for children. MIT’s unparalleled expertise in engineering and life sciences, combined with our deep commitment to pediatric innovation, creates a powerful force for change,” says Hood Foundation President Neil Smiley, on behalf of the foundation’s board of trustees. “We look forward to this catalytic gift igniting transformative programs that will shape the future of children’s health and well-being for generations to come.”

The Hood Foundation, based in Massachusetts, has committed $15 million over five years to support the creation and development of the hub, reinforcing its long-standing dedication to advancing groundbreaking pediatric research. Since its establishment in 1942, the Charles H. Hood Foundation has sought to fill gaps in the pediatric health care system by awarding research grants and supporting the development of pediatric related tools and treatments.

In addition to its established grant programs, over the course of the past decade the Hood Foundation has served as a pioneer in supporting young companies trying to bring pediatric innovations to the patients who need them, by way of program-related investments made via its venture arm, CH Innovations LLC.

“The Hood Foundation’s longstanding dedication to improving child health has led to the formation of an extensive and robust network of researchers, clinician-scientists, entrepreneurs, and other leaders in science and business who stand well-positioned to engage with and contribute to the hub’s efforts,” adds Smiley.

A central role in the MIT Health and Life Sciences Collaborative

The Hood Pediatric Innovation Hub, which will be administered in the MIT School of Engineering, will serve as a cornerstone of MIT HEALS, an Institute-wide initiative to address society’s most urgent health challenges. The hub’s cross-disciplinary approach underscores MIT’s commitment to inspiring, accelerating, and delivering solutions at scale to some of society’s most urgent and intractable health challenges.

Elazer R. Edelman will serve as faculty lead, with Joseph J. Frassica as the executive director of the hub. Edelman is the Edward J. Poitras Professor in Medical Engineering and Science in MIT’s Institute for Medical Engineering and Science (IMES) and director of MIT’s Center for Clinical and Translational Research. He also serves as a professor of medicine at Harvard Medical School and a cardiologist at Brigham and Women’s Hospital’s cardiac intensive care unit in Boston. Frassica serves as professor of the practice in IMES at MIT. He is also a member of the teaching and research staff of the Massachusetts General Hospital (pediatric critical care) and serves as pediatric editor for the Journal of Intensive Care Medicine.

“As scientists, engineers, and clinicians, we are obliged to ensure that what we learn and what we invent is available to all. Ironically, those most in need of innovation are least able to access and benefit from it — children especially. The support of the Hood Foundation and collaboration with our MIT and extended community can help address this gap and fill this vital void,” says Edelman.

"The Hood Pediatric Innovation Hub will serve as a catalyst, mentor, and advocate for pediatric innovation, harnessing MIT’s world-class expertise and Hood’s extensive network of pediatric innovators to tackle the most pressing challenges in pediatric care. Thanks to the generous support of the Hood Foundation, we plan to build the infrastructure and programs needed to transform groundbreaking ideas into real-world solutions that improve the lives of children and the providers who care for them," Frassica adds.

Driving research, advocacy, and education

Beyond supporting research, the hub seeks to bolster the broader pediatric research community through outreach, education, and advocacy. By working closely with key collaborators and leveraging relationships with other stakeholders such as hospitals, industry, patient advocates, and funders, the hub will identify, develop, and advance efforts to find economically viable pathways to bring treatments to young patients. 

The hub will also create the infrastructure to seamlessly share deep organizational understanding of the regulatory processes governing innovation for children with researchers and innovators in the hub community.

Bacteria can be engineered to sense a variety of molecules, such as pollutants or soil nutrients. In most cases, however, these signals can only be detected by looking at the cells under a microscope, making them impractical for large-scale use.

Using a new method that triggers cells to produce molecules that generate unique combinations of color, MIT engineers have shown that they can read out these bacterial signals from as far as 90 meters away. Their work could lead to the development of bacterial sensors for agricultural and other applications, which could be monitored by drones or satellites.

“It’s a new way of getting information out of the cell. If you’re standing next to it, you can’t see anything by eye, but from hundreds of meters away, using specific cameras, you can get the information when it turns on,” says Christopher Voigt, head of MIT’s Department of Biological Engineering and the senior author of the new study.

In a paper appearing today in Nature Biotechnology, the researchers showed that they could engineer two different types of bacteria to produce molecules that give off distinctive wavelengths of light across the visible and infrared spectra of light, which can be imaged with hyperspectral cameras. These reporting molecules were linked to genetic circuits that detect nearby bacteria, but this approach could also be combined with any existing sensor, such as those for arsenic or other contaminants, the researchers say.

“The nice thing about this technology is that you can plug and play whichever sensor you want,” says Yonatan Chemla, an MIT postdoc who is one of the lead authors of the paper. “There is no reason that any sensor would not be compatible with this technology.”

Itai Levin PhD ’24 is also a lead author of the paper. Other authors include former undergraduate students Yueyang Fan ’23 and Anna Johnson ’22, and Connor Coley, an associate professor of chemical engineering at MIT.

Hyperspectral imaging

There are many ways to engineer bacterial cells so that they can sense a particular chemical. Most of these work by connecting detection of a molecule to an output such as green fluorescent protein (GFP). These work well for lab studies, but such sensors can’t be measured from long distances.

For long-distance sensing, the MIT team came up with the idea to engineer cells to produce hyperspectral reporter molecules, which can be detected using hyperspectral cameras. These cameras, which were first invented in the 1970s, can determine how much of each color wavelength is present in any given pixel. Instead of showing up as simply red or green, each pixel contains information on hundreds different wavelengths of light.

Currently, hyperspectral cameras are used for applications such as detecting the presence of radiation. In the areas around Chernobyl, these cameras have been used to measure slight color changes that radioactive metals produce in the chlorophyll of plant cells. Hyperspectral cameras are also used to look for signs of malnutrition or pathogen invasion in plants.

That work inspired the MIT team to explore whether they could engineer bacterial cells to produce hyperspectral reporters when they detect a target molecule.

For a hyperspectral reporter to be most useful, it should have a spectral signature with peaks in multiple wavelengths of light, making it easier to detect. The researchers performed quantum calculations to predict the hyperspectral signatures of about 20,000 naturally occurring cell molecules, allowing them to identify those with the most unique patterns of light emission. Another key feature is the number of enzymes that would need to be engineered into a cell to get it to produce the reporter — a trait that will vary for different types of cells.

“The ideal molecule is one that’s really different from everything else, making it detectable, and requires the fewest number of enzymes to produce it in the cell,” Voigt says.

In this study, the researchers identified two different molecules that were best suited for two types of bacteria. For a soil bacterium called Pseudomonas putida, they used a reporter called biliverdin — a pigment that results from the breakdown of heme. For an aquatic bacterium called Rubrivivax gelatinosus, they used a type of bacteriochlorophyll. For each bacterium, the researchers engineered the enzymes necessary to produce the reporter into the host cell, then linked them to genetically engineered sensor circuits.

“You could add one of these reporters to a bacterium or any cell that has a genetically encoded sensor in its genome. So, it might respond to metals or radiation or toxins in the soil, or nutrients in the soil, or whatever it is you want it to respond to. Then the output of that would be the production of this molecule that can then be sensed from far away,” Voigt says.

Long-distance sensing

In this study, the researchers linked the hyperspectral reporters to circuits designed for quorum sensing, which allow cells to detect other nearby bacteria. They have also shown, in work done after this paper, that these reporting molecules can be linked to sensors for chemicals including arsenic.

When testing their sensors, the researchers deployed them in boxes so they would remain contained. The boxes were placed in fields, deserts, or on the roofs of buildings, and the cells produced signals that could be detected using hyperspectral cameras mounted on drones. The cameras take about 20 to 30 seconds to scan the field of view, and computer algorithms then analyze the signals to reveal whether the hyperspectral reporters are present.

In this paper, the researchers reported imaging from a maximum distance of 90 meters, but they are now working on extending those distances.

They envision that these sensors could be deployed for agricultural purposes such as sensing nitrogen or nutrient levels in soil. For those applications, the sensors could also be designed to work in plant cells. Detecting landmines is another potential application for this type of sensing.

Before being deployed, the sensors would need to undergo regulatory approval by the U.S. Environmental Protection Agency, as well as the U.S. Department of Agriculture if used for agriculture. Voigt and Chemla have been working with both agencies, the scientific community, and other stakeholders to determine what kinds of questions need to be answered before these technologies could be approved.

“We’ve been very busy in the past three years working to understand what are the regulatory landscapes and what are the safety concerns, what are the risks, what are the benefits of this kind of technology?” Chemla says.

The research was funded by the U.S. Department of Defense; the Army Research Office, a directorate of the U.S. Army Combat Capabilities Development Command Army Research Laboratory (the funding supported engineering of environmental strains and optimization of genetically-encoded sensors and hyperspectral reporter biosynthetic pathways); and the Ministry of Defense of Israel.

Data privacy comes with a cost. There are security techniques that protect sensitive user data, like customer addresses, from attackers who may attempt to extract them from AI models — but they often make those models less accurate.

MIT researchers recently developed a framework, based on a new privacy metric called PAC Privacy, that could maintain the performance of an AI model while ensuring sensitive data, such as medical images or financial records, remain safe from attackers. Now, they’ve taken this work a step further by making their technique more computationally efficient, improving the tradeoff between accuracy and privacy, and creating a formal template that can be used to privatize virtually any algorithm without needing access to that algorithm’s inner workings.

The team utilized their new version of PAC Privacy to privatize several classic algorithms for data analysis and machine-learning tasks.

They also demonstrated that more “stable” algorithms are easier to privatize with their method. A stable algorithm’s predictions remain consistent even when its training data are slightly modified. Greater stability helps an algorithm make more accurate predictions on previously unseen data.

The researchers say the increased efficiency of the new PAC Privacy framework, and the four-step template one can follow to implement it, would make the technique easier to deploy in real-world situations.

“We tend to consider robustness and privacy as unrelated to, or perhaps even in conflict with, constructing a high-performance algorithm. First, we make a working algorithm, then we make it robust, and then private. We’ve shown that is not always the right framing. If you make your algorithm perform better in a variety of settings, you can essentially get privacy for free,” says Mayuri Sridhar, an MIT graduate student and lead author of a paper on this privacy framework.

She is joined in the paper by Hanshen Xiao PhD ’24, who will start as an assistant professor at Purdue University in the fall; and senior author Srini Devadas, the Edwin Sibley Webster Professor of Electrical Engineering at MIT. The research will be presented at the IEEE Symposium on Security and Privacy.

Estimating noise

To protect sensitive data that were used to train an AI model, engineers often add noise, or generic randomness, to the model so it becomes harder for an adversary to guess the original training data. This noise reduces a model’s accuracy, so the less noise one can add, the better.

PAC Privacy automatically estimates the smallest amount of noise one needs to add to an algorithm to achieve a desired level of privacy.

The original PAC Privacy algorithm runs a user’s AI model many times on different samples of a dataset. It measures the variance as well as correlations among these many outputs and uses this information to estimate how much noise needs to be added to protect the data.

This new variant of PAC Privacy works the same way but does not need to represent the entire matrix of data correlations across the outputs; it just needs the output variances.

“Because the thing you are estimating is much, much smaller than the entire covariance matrix, you can do it much, much faster,” Sridhar explains. This means that one can scale up to much larger datasets.

Adding noise can hurt the utility of the results, and it is important to minimize utility loss. Due to computational cost, the original PAC Privacy algorithm was limited to adding isotropic noise, which is added uniformly in all directions. Because the new variant estimates anisotropic noise, which is tailored to specific characteristics of the training data, a user could add less overall noise to achieve the same level of privacy, boosting the accuracy of the privatized algorithm.

Privacy and stability

As she studied PAC Privacy, Sridhar hypothesized that more stable algorithms would be easier to privatize with this technique. She used the more efficient variant of PAC Privacy to test this theory on several classical algorithms.

Algorithms that are more stable have less variance in their outputs when their training data change slightly. PAC Privacy breaks a dataset into chunks, runs the algorithm on each chunk of data, and measures the variance among outputs. The greater the variance, the more noise must be added to privatize the algorithm.

Employing stability techniques to decrease the variance in an algorithm’s outputs would also reduce the amount of noise that needs to be added to privatize it, she explains.

“In the best cases, we can get these win-win scenarios,” she says.

The team showed that these privacy guarantees remained strong despite the algorithm they tested, and that the new variant of PAC Privacy required an order of magnitude fewer trials to estimate the noise. They also tested the method in attack simulations, demonstrating that its privacy guarantees could withstand state-of-the-art attacks.

“We want to explore how algorithms could be co-designed with PAC Privacy, so the algorithm is more stable, secure, and robust from the beginning,” Devadas says. The researchers also want to test their method with more complex algorithms and further explore the privacy-utility tradeoff.

“The question now is: When do these win-win situations happen, and how can we make them happen more often?” Sridhar says.

“I think the key advantage PAC Privacy has in this setting over other privacy definitions is that it is a black box — you don’t need to manually analyze each individual query to privatize the results. It can be done completely automatically. We are actively building a PAC-enabled database by extending existing SQL engines to support practical, automated, and efficient private data analytics,” says Xiangyao Yu, an assistant professor in the computer sciences department at the University of Wisconsin at Madison, who was not involved with this study.

This research is supported, in part, by Cisco Systems, Capital One, the U.S. Department of Defense, and a MathWorks Fellowship.

As the world moves to reduce carbon emissions, solar and wind power will play an increasing role on electricity grids. But those renewable sources only generate electricity when it’s sunny or windy. So to ensure a reliable power grid — one that can deliver electricity 24/7 — it’s crucial to have a means of storing electricity when supplies are abundant and delivering it later, when they’re not. And sometimes large amounts of electricity will need to be stored not just for hours, but for days, or even longer.

Some methods of achieving “long-duration energy storage” are promising. For example, with pumped hydro energy storage, water is pumped from a lake to another, higher lake when there’s extra electricity and released back down through power-generating turbines when more electricity is needed. But that approach is limited by geography, and most potential sites in the United States have already been used. Lithium-ion batteries could provide grid-scale storage, but only for about four hours. Longer than that and battery systems get prohibitively expensive.

A team of researchers from MIT and the Norwegian University of Science and Technology (NTNU) has been investigating a less-familiar option based on an unlikely-sounding concept: liquid air, or air that is drawn in from the surroundings, cleaned and dried, and then cooled to the point that it liquefies. 

“Liquid air energy storage” (LAES) systems have been built, so the technology is technically feasible. Moreover, LAES systems are totally clean and can be sited nearly anywhere, storing vast amounts of electricity for days or longer and delivering it when it’s needed. But there haven’t been conclusive studies of its economic viability. Would the income over time warrant the initial investment and ongoing costs? With funding from the MIT Energy Initiative’s Future Energy Systems Center, the researchers developed a model that takes detailed information on LAES systems and calculates when and where those systems would be economically viable, assuming future scenarios in line with selected decarbonization targets as well as other conditions that may prevail on future energy grids.

They found that under some of the scenarios they modeled, LAES could be economically viable in certain locations. Sensitivity analyses showed that policies providing a subsidy on capital expenses could make LAES systems economically viable in many locations. Further calculations showed that the cost of storing a given amount of electricity with LAES would be lower than with more familiar systems such as pumped hydro and lithium-ion batteries. They conclude that LAES holds promise as a means of providing critically needed long-duration storage when future power grids are decarbonized and dominated by intermittent renewable sources of electricity.

The researchers — Shaylin A. Cetegen, a PhD candidate in the MIT Department of Chemical Engineering (ChemE); Professor Emeritus Truls Gundersen of the NTNU Department of Energy and Process Engineering; and MIT Professor Emeritus Paul I. Barton of ChemE — describe their model and their findings in a new paper published in the journal Energy.

The LAES technology and its benefits

LAES systems consists of three steps: charging, storing, and discharging. When supply on the grid exceeds demand and prices are low, the LAES system is charged. Air is then drawn in and liquefied. A large amount of electricity is consumed to cool and liquefy the air in the LAES process. The liquid air is then sent to highly insulated storage tanks, where it’s held at a very low temperature and atmospheric pressure. When the power grid needs added electricity to meet demand, the liquid air is first pumped to a higher pressure and then heated, and it turns back into a gas. This high-pressure, high-temperature, vapor-phase air expands in a turbine that generates electricity to be sent back to the grid.

According to Cetegen, a primary advantage of LAES is that it’s clean. “There are no contaminants involved,” she says. “It takes in and releases only ambient air and electricity, so it’s as clean as the electricity that’s used to run it.” In addition, a LAES system can be built largely from commercially available components and does not rely on expensive or rare materials. And the system can be sited almost anywhere, including near other industrial processes that produce waste heat or cold that can be used by the LAES system to increase its energy efficiency.

Economic viability

In considering the potential role of LAES on future power grids, the first question is: Will LAES systems be attractive to investors? Answering that question requires calculating the technology’s net present value (NPV), which represents the sum of all discounted cash flows — including revenues, capital expenditures, operating costs, and other financial factors — over the project's lifetime. (The study assumed a cash flow discount rate of 7 percent.)

To calculate the NPV, the researchers needed to determine how LAES systems will perform in future energy markets. In those markets, various sources of electricity are brought online to meet the current demand, typically following a process called “economic dispatch:” The lowest-cost source that’s available is always deployed next. Determining the NPV of liquid air storage therefore requires predicting how that technology will fare in future markets competing with other sources of electricity when demand exceeds supply — and also accounting for prices when supply exceeds demand, so excess electricity is available to recharge the LAES systems.

For their study, the MIT and NTNU researchers designed a model that starts with a description of an LAES system, including details such as the sizes of the units where the air is liquefied and the power is recovered, and also capital expenses based on estimates reported in the literature. The model then draws on state-of-the-art pricing data that’s released every year by the National Renewable Energy Laboratory (NREL) and is widely used by energy modelers worldwide. The NREL dataset forecasts prices, construction and retirement of specific types of electricity generation and storage facilities, and more, assuming eight decarbonization scenarios for 18 regions of the United States out to 2050.

The new model then tracks buying and selling in energy markets for every hour of every day in a year, repeating the same schedule for five-year intervals. Based on the NREL dataset and details of the LAES system — plus constraints such as the system’s physical storage capacity and how often it can switch between charging and discharging — the model calculates how much money LAES operators would make selling power to the grid when it’s needed and how much they would spend buying electricity when it’s available to recharge their LAES system. In line with the NREL dataset, the model generates results for 18 U.S. regions and eight decarbonization scenarios, including 100 percent decarbonization by 2035 and 95 percent decarbonization by 2050, and other assumptions about future energy grids, including high-demand growth plus high and low costs for renewable energy and for natural gas.

Cetegen describes some of their results: “Assuming a 100-megawatt (MW) system — a standard sort of size — we saw economic viability pop up under the decarbonization scenario calling for 100 percent decarbonization by 2035.” So, positive NPVs (indicating economic viability) occurred only under the most aggressive — therefore the least realistic — scenario, and they occurred in only a few southern states, including Texas and Florida, likely because of how those energy markets are structured and operate.

The researchers also tested the sensitivity of NPVs to different storage capacities, that is, how long the system could continuously deliver power to the grid. They calculated the NPVs of a 100 MW system that could provide electricity supply for one day, one week, and one month. “That analysis showed that under aggressive decarbonization, weekly storage is more economically viable than monthly storage, because [in the latter case] we’re paying for more storage capacity than we need,” explains Cetegen.

Improving the NPV of the LAES system

The researchers next analyzed two possible ways to improve the NPV of liquid air storage: by increasing the system’s energy efficiency and by providing financial incentives. Their analyses showed that increasing the energy efficiency, even up to the theoretical limit of the process, would not change the economic viability of LAES under the most realistic decarbonization scenarios. On the other hand, a major improvement resulted when they assumed policies providing subsidies on capital expenditures on new installations. Indeed, assuming subsidies of between 40 percent and 60 percent made the NPVs for a 100 MW system become positive under all the realistic scenarios.

Thus, their analysis showed that financial incentives could be far more effective than technical improvements in making LAES economically viable. While engineers may find that outcome disappointing, Cetegen notes that from a broader perspective, it’s good news. “You could spend your whole life trying to optimize the efficiency of this process, and it wouldn’t translate to securing the investment needed to scale the technology,” she says. “Policies can take a long time to implement as well. But theoretically you could do it overnight. So if storage is needed [on a future decarbonized grid], then this is one way to encourage adoption of LAES right away.”

Cost comparison with other energy storage technologies

Calculating the economic viability of a storage technology is highly dependent on the assumptions used. As a result, a different measure — the “levelized cost of storage” (LCOS) — is typically used to compare the costs of different storage technologies. In simple terms, the LCOS is the cost of storing each unit of energy over the lifetime of a project, not accounting for any income that results.

On that measure, the LAES technology excels. The researchers’ model yielded an LCOS for liquid air storage of about $60 per megawatt-hour, regardless of the decarbonization scenario. That LCOS is about a third that of lithium-ion battery storage and half that of pumped hydro. Cetegen cites another interesting finding: the LCOS of their assumed LAES system varied depending on where it’s being used. The standard practice of reporting a single LCOS for a given energy storage technology may not provide the full picture.

Cetegen has adapted the model and is now calculating the NPV and LCOS for energy storage using lithium-ion batteries. But she’s already encouraged by the LCOS of liquid air storage. “While LAES systems may not be economically viable from an investment perspective today, that doesn’t mean they won’t be implemented in the future,” she concludes. “With limited options for grid-scale storage expansion and the growing need for storage technologies to ensure energy security, if we can't find economically viable alternatives, we’ll likely have to turn to least-cost solutions to meet storage needs. This is why the story of liquid air storage is far from over. We believe our findings justify the continued exploration of LAES as a key energy storage solution for the future.”

Insect-scale robots can squeeze into places their larger counterparts can’t, like deep into a collapsed building to search for survivors after an earthquake.

However, as they move through the rubble, tiny crawling robots might encounter tall obstacles they can’t climb over or slanted surfaces they will slide down. While aerial robots could avoid these hazards, the amount of energy required for flight would severely limit how far the robot can travel into the wreckage before it needs to return to base and recharge.

To get the best of both locomotion methods, MIT researchers developed a hopping robot that can leap over tall obstacles and jump across slanted or uneven surfaces, while using far less energy than an aerial robot.

The hopping robot, which is smaller than a human thumb and weighs less than a paperclip, has a springy leg that propels it off the ground, and four flapping-wing modules that give it lift and control its orientation.

The robot can jump about 20 centimeters into the air, or four times its height, at a lateral speed of about 30 centimeters per second, and has no trouble hopping across ice, wet surfaces, and uneven soil, or even onto a hovering drone. All the while, the hopping robot consumes about 60 percent less energy than its flying cousin.

Due to its light weight and durability, and the energy efficiency of the hopping process, the robot could carry about 10 times more payload than a similar-sized aerial robot, opening the door to many new applications.

“Being able to put batteries, circuits, and sensors on board has become much more feasible with a hopping robot than a flying one. Our hope is that one day this robot could go out of the lab and be useful in real-world scenarios,” says Yi-Hsuan (Nemo) Hsiao, an MIT graduate student and co-lead author of a paper on the hopping robot.

Hsiao is joined on the paper by co-lead authors Songnan Bai, a research assistant professor at The University of Hong Kong; and Zhongtao Guan, an incoming MIT graduate student who completed this work as a visiting undergraduate; as well as Suhan Kim and Zhijian Ren of MIT; and senior authors Pakpong Chirarattananon, an associate professor of the City University of Hong Kong; and Kevin Chen, an associate professor in the MIT Department of Electrical Engineering and Computer Science and head of the Soft and Micro Robotics Laboratory within the Research Laboratory of Electronics. The research appears today in Science Advances.

Maximizing efficiency

Jumping is common among insects, from fleas that leap onto new hosts to grasshoppers that bound around a meadow. While jumping is less common among insect-scale robots, which usually fly or crawl, hopping affords many advantages for energy efficiency.

When a robot hops, it transforms potential energy, which comes from its height off the ground, into kinetic energy as it falls. This kinetic energy transforms back to potential energy when it hits the ground, then back to kinetic as it rises, and so on.

To maximize efficiency of this process, the MIT robot is fitted with an elastic leg made from a compression spring, which is akin to the spring on a click-top pen. This spring converts the robot’s downward velocity to upward velocity when it strikes the ground.

“If you have an ideal spring, your robot can just hop along without losing any energy. But since our spring is not quite ideal, we use the flapping modules to compensate for the small amount of energy it loses when it makes contact with the ground,” Hsiao explains.

As the robot bounces back up into the air, the flapping wings provide lift, while ensuring the robot remains upright and has the correct orientation for its next jump. Its four flapping-wing mechanisms are powered by soft actuators, or artificial muscles, that are durable enough to endure repeated impacts with the ground without being damaged.

“We have been using the same robot for this entire series of experiments, and we never needed to stop and fix it,” Hsiao adds.

Key to the robot’s performance is a fast control mechanism that determines how the robot should be oriented for its next jump. Sensing is performed using an external motion-tracking system, and an observer algorithm computes the necessary control information using sensor measurements.

As the robot hops, it follows a ballistic trajectory, arcing through the air. At the peak of that trajectory, it estimates its landing position. Then, based on its target landing point, the controller calculates the desired takeoff velocity for the next jump. While airborne, the robot flaps its wings to adjust its orientation so it strikes the ground with the correct angle and axis to move in the proper direction and at the right speed.

Durability and flexibility

The researchers put the hopping robot, and its control mechanism, to the test on a variety of surfaces, including grass, ice, wet glass, and uneven soil — it successfully traversed all surfaces. The robot could even hop on a surface that was dynamically tilting.

“The robot doesn’t really care about the angle of the surface it is landing on. As long as it doesn’t slip when it strikes the ground, it will be fine,” Hsiao says.

Since the controller can handle multiple terrains, the robot can easily transition from one surface to another without missing a beat.

For instance, hopping across grass requires more thrust than hopping across glass, since blades of grass cause a damping effect that reduces its jump height. The controller can pump more energy to the robot’s wings during its aerial phase to compensate.

Due to its small size and light weight, the robot has an even smaller moment of inertia, which makes it more agile than a larger robot and better able to withstand collisions.

The researchers showcased its agility by demonstrating acrobatic flips. The featherweight robot could also hop onto an airborne drone without damaging either device, which could be useful in collaborative tasks.

In addition, while the team demonstrated a hopping robot that carried twice its weight, the maximum payload may be much higher. Adding more weight doesn’t hurt the robot’s efficiency. Rather, the efficiency of the spring is the most significant factor that limits how much the robot can carry.

Moving forward, the researchers plan to leverage its ability to carry heavy loads by installing batteries, sensors, and other circuits onto the robot, in the hopes of enabling it to hop autonomously outside the lab.

“Multimodal robots (those combining multiple movement strategies) are generally challenging and particularly impressive at such a tiny scale. The versatility of this tiny multimodal robot — flipping, jumping on rough or moving terrain, and even another robot — makes it even more impressive,” says Justin Yim, assistant professor at the University of Illinois at Urbana-Champagne, who was not involved with this work. “Continuous hopping shown in this research enables agile and efficient locomotion in environments with many large obstacles.”

This research is funded, in part, by the U.S. National Science Foundation and the MIT MISTI program. Chirarattananon was supported by the Research Grants Council of the Hong Kong Special Administrative Region of China. Hsiao is supported by a MathWorks Fellowship, and Kim is supported by a Zakhartchenko Fellowship.

The process of discovering molecules that have the properties needed to create new medicines and materials is cumbersome and expensive, consuming vast computational resources and months of human labor to narrow down the enormous space of potential candidates.

Large language models (LLMs) like ChatGPT could streamline this process, but enabling an LLM to understand and reason about the atoms and bonds that form a molecule, the same way it does with words that form sentences, has presented a scientific stumbling block.

Researchers from MIT and the MIT-IBM Watson AI Lab created a promising approach that augments an LLM with other machine-learning models known as graph-based models, which are specifically designed for generating and predicting molecular structures.

Their method employs a base LLM to interpret natural language queries specifying desired molecular properties. It automatically switches between the base LLM and graph-based AI modules to design the molecule, explain the rationale, and generate a step-by-step plan to synthesize it. It interleaves text, graph, and synthesis step generation, combining words, graphs, and reactions into a common vocabulary for the LLM to consume.

When compared to existing LLM-based approaches, this multimodal technique generated molecules that better matched user specifications and were more likely to have a valid synthesis plan, improving the success ratio from 5 percent to 35 percent.

It also outperformed LLMs that are more than 10 times its size and that design molecules and synthesis routes only with text-based representations, suggesting multimodality is key to the new system’s success.

“This could hopefully be an end-to-end solution where, from start to finish, we would automate the entire process of designing and making a molecule. If an LLM could just give you the answer in a few seconds, it would be a huge time-saver for pharmaceutical companies,” says Michael Sun, an MIT graduate student and co-author of a paper on this technique.

Sun’s co-authors include lead author Gang Liu, a graduate student at the University of Notre Dame; Wojciech Matusik, a professor of electrical engineering and computer science at MIT who leads the Computational Design and Fabrication Group within the Computer Science and Artificial Intelligence Laboratory (CSAIL); Meng Jiang, associate professor at the University of Notre Dame; and senior author Jie Chen, a senior research scientist and manager in the MIT-IBM Watson AI Lab. The research will be presented at the International Conference on Learning Representations.

Best of both worlds

Large language models aren’t built to understand the nuances of chemistry, which is one reason they struggle with inverse molecular design, a process of identifying molecular structures that have certain functions or properties.

LLMs convert text into representations called tokens, which they use to sequentially predict the next word in a sentence. But molecules are “graph structures,” composed of atoms and bonds with no particular ordering, making them difficult to encode as sequential text.

On the other hand, powerful graph-based AI models represent atoms and molecular bonds as interconnected nodes and edges in a graph. While these models are popular for inverse molecular design, they require complex inputs, can’t understand natural language, and yield results that can be difficult to interpret.

The MIT researchers combined an LLM with graph-based AI models into a unified framework that gets the best of both worlds.

Llamole, which stands for large language model for molecular discovery, uses a base LLM as a gatekeeper to understand a user’s query — a plain-language request for a molecule with certain properties.

For instance, perhaps a user seeks a molecule that can penetrate the blood-brain barrier and inhibit HIV, given that it has a molecular weight of 209 and certain bond characteristics.

As the LLM predicts text in response to the query, it switches between graph modules.

One module uses a graph diffusion model to generate the molecular structure conditioned on input requirements. A second module uses a graph neural network to encode the generated molecular structure back into tokens for the LLMs to consume. The final graph module is a graph reaction predictor which takes as input an intermediate molecular structure and predicts a reaction step, searching for the exact set of steps to make the molecule from basic building blocks.

The researchers created a new type of trigger token that tells the LLM when to activate each module. When the LLM predicts a “design” trigger token, it switches to the module that sketches a molecular structure, and when it predicts a “retro” trigger token, it switches to the retrosynthetic planning module that predicts the next reaction step.

“The beauty of this is that everything the LLM generates before activating a particular module gets fed into that module itself. The module is learning to operate in a way that is consistent with what came before,” Sun says.

In the same manner, the output of each module is encoded and fed back into the generation process of the LLM, so it understands what each module did and will continue predicting tokens based on those data.

Better, simpler molecular structures

In the end, Llamole outputs an image of the molecular structure, a textual description of the molecule, and a step-by-step synthesis plan that provides the details of how to make it, down to individual chemical reactions.

In experiments involving designing molecules that matched user specifications, Llamole outperformed 10 standard LLMs, four fine-tuned LLMs, and a state-of-the-art domain-specific method. At the same time, it boosted the retrosynthetic planning success rate from 5 percent to 35 percent by generating molecules that are higher-quality, which means they had simpler structures and lower-cost building blocks.

“On their own, LLMs struggle to figure out how to synthesize molecules because it requires a lot of multistep planning. Our method can generate better molecular structures that are also easier to synthesize,” Liu says.

To train and evaluate Llamole, the researchers built two datasets from scratch since existing datasets of molecular structures didn’t contain enough details. They augmented hundreds of thousands of patented molecules with AI-generated natural language descriptions and customized description templates.

The dataset they built to fine-tune the LLM includes templates related to 10 molecular properties, so one limitation of Llamole is that it is trained to design molecules considering only those 10 numerical properties.

In future work, the researchers want to generalize Llamole so it can incorporate any molecular property. In addition, they plan to improve the graph modules to boost Llamole’s retrosynthesis success rate.

And in the long run, they hope to use this approach to go beyond molecules, creating multimodal LLMs that can handle other types of graph-based data, such as interconnected sensors in a power grid or transactions in a financial market.

“Llamole demonstrates the feasibility of using large language models as an interface to complex data beyond textual description, and we anticipate them to be a foundation that interacts with other AI algorithms to solve any graph problems,” says Chen.

This research is funded, in part, by the MIT-IBM Watson AI Lab, the National Science Foundation, and the Office of Naval Research.

Well into the late 19th century, the U.S. retail sector was overwhelmingly local, consisting of small, independent merchants throughout the country. That started changing after Sears and Roebuck’s famous catalog became popular, allowing the firm to grow, while a rival, Montgomery Ward, also expanded. By the 1930s, the U.S. had 130,000 chain stores, topped by Atlantic and Pacific supermarkets (the A&P), with over 15,000 stores.

A century onward, the U.S. retail landscape is dominated by retail giants. Today, 90 percent of Americans live within 10 miles of a Walmart, while five of the country’s 10 biggest employers — Walmart, Amazon, Home Depot, Kroger, and Target— are retailers. Two others in the top 10, UPS and FedEx, are a major part of the retail economy.

The ubiquity of these big retailers, and the sheer extent of the U.S. shopping economy as a whole, is unusual compared to the country’s European counterparts. Domestic consumption plays an outsized role in driving growth in the United States, and credit plays a much larger role in supporting that consumption than in Europe. The U.S. has five times as much retail space per capita as Japan and the U.K., and 10 times as much as Germany. Unlike in Europe, shopping hours are largely unregulated.

How did this happen? To be sure, Walmart, Amazon, Target, and other massive chains have plenty of business acumen. But the full story involves a century or more of political tectonics and legal debates, which helped shape the size of U.S. retailing and the prominence of its large discount chains. 

“The markets that we take as given, that we think of as the natural outcome of supply and demand, are heavily shaped by policy and by politics,” says MIT political scientist Kathleen Thelen.

Thelen examines the subject in a new book, “Attention, Shoppers! American Retail Capitalism and the Origins of the Amazon Economy,” published today by Princeton University Press. In it, she examines the growth of the particular model of supersized, low-cost, low-wage retailing that now features so prominently in the U.S. economy.

Prioritizing prices

While a great deal has been written about specific American companies, Thelen’s book has some distinctive features. One is a comparison to the economies of Europe, where she has focused much of her scholarship. Another is her historical lens, extending back to the start of chain retailing.

“It seems like every time I set out to explain something in the present, I’m thrown back to the 19th century,” Thelen says.

For instance, as both Sears and Montgomery Ward grew, producers and consumers were still experimenting with alternate commercial arrangements, like cooperatives, which pooled suppliers together, but they ultimately ran into economic and legal headwinds. Especially, at the time, legal headwinds.

“Antitrust laws in the United States were very forbearing toward big multidivisional corporations and very punitive toward alternative types of arrangements like cooperatives, so big retailers got a real boost in that period,” Thelen says. Separately, the U.S. Postal Service was also crucial, since big mail order houses like Sears relied on not just on its delivery services but also its money order system, to sell goods to the company’s many customers who lacked bank accounts.

Smaller retailers fought large chains during the Depression, especially in the South and the West, which forms another phase of the story. But low-cost discounters worked around some laws through regulatory arbitrage, finding friendlier regulations in some states — and sometimes though outright rule-breaking. Ultimately, larger retailers have thrived again in the last half century, especially as antitrust law increasingly prioritized consumer prices as its leading measuring stick.

Most antitrust theorizing since the 1960s “valorizes consumer welfare, which is basically defined as price, so anything that delivers the lowest price to consumers is A-OK,” Thelen says. “We’re in this world where the large, low-cost retailers are delivering consumer welfare in the way the courts are defining it.”

That emphasis on prices, she notes, then spills over into other areas of the economy, especially wages and labor relations.

“If you prioritize prices, one of the main ways to reduce prices is to reduce labor costs,” Thelen says. “It’s no coincidence that low-cost discounters are often low-wage employers. Indeed, they often squeeze their vendors to deliver goods at ever-lower prices, and by extension they’re pressing down on wages in their supplier networks as well.”

As Thelen’s book explains, legal views supporting large chains were also common during the first U.S. wave of chain-retail growth. She writes, “large, low-cost retailers have almost always enjoyed a privileged position in the American antitrust regime.”

In the “deep equilibrium”

“Attention, Shoppers!” makes clear that this tendency toward lower prices, lower employee pay, and high consumer convenience is particularly pronounced in the U.S., where 22.6 percent of employees count as low-wage workers (making two-thirds or less of the country’s median wage). In the other countries that belong to the Organization for Economic Cooperation and Development, 13.9 percent of workers fit that description. About three-quarters of U.S. retail workers are in the low-wage category.

In other OECD countries, on aggregate, manufacturers and producers make up bigger chunks of the economy and, correspondingly, often have legal frameworks more friendly to manufacturers and to labor. But in the U.S., large retailers have gained more leverage, if anything, in the last half-century, Thelen notes.

“You might think mass retailers and manufacturers would have a symbiotic relationship, but historically there has been great tension between them, especially on price,” Thelen says. “In the postwar period, the balance of power became tilted toward retailers, and away from manufacturers and labor. Retailers also had consumers on their side, and had more power over data to dictate the terms on which their vendors would supply goods to them.”

Currently, as Thelen writes in the book, the U.S. is in a “deep equilibrium” on this front, in that many low-wage workers now rely on these low-cost retailers to make ends meet — and because Americans as a whole now find it normal to have their purchases delivered at lightning speed. Things might be different, Thelen suggests, if there are changes to U.S. antitrust enforcement, or, especially, major reforms to labor law, such as allowing workers to organize for higher wages across companies, not just at individual stores. Short of that, the equilibrium is likely to hold.

“Attention, Shoppers!” has received praise from other scholars. Louis Hyman, a historian at Johns Hopkins University, has called it a “pathbreaking study that provides insight into not only the past but also the future of online retail.”

For her part, Thelen hopes readers will learn more about an economic landscape we might take for granted, even while we shop at big chains, around us and online.

“The triumph of these types of retailers was not inevitable,” Thelen says. “It was a function of politics and political choice.”

Think of your most prized belongings. In an increasingly virtual world, wouldn’t it be great to save a copy of that precious item and all the memories it holds?

In mixed-reality settings, you can create a digital twin of a physical item, such as an old doll. But it’s hard to replicate interactive elements, like the way it moves or the sounds it makes — the sorts of unique interactive features that made the toy distinct in the first place.

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) sought to change that, and they have a potential solution. Their “InteRecon” program enables users to recapture real-world objects in a mobile app, and then animate them in mixed-reality environments. 

This prototype could recreate the interaction functions in the physical world, such as the head motions of your favorite bobblehead, or playing a classic video on a digital version of your vintage TV. It creates more lifelike and personal digital surroundings while preserving a memory.

InteRecon’s ability to reconstruct the interactive experience of different items could make it a useful tool for teachers explaining important concepts, like demonstrating how gravity pulls an object down. It could also add a new visual component to museum exhibits, such as animating a painting or bringing a historical mannequin to life (without the scares of characters from “Night at the Museum”). Eventually, InteRecon may be able to teach a doctor’s apprentice organ surgery or a cosmetic procedure by visualizing each motion needed to complete the task.