Health

‘Surprise and relief’ from homeless patients: ‘This works for me’

Doctors report ‘fascinating and counterintuitive’ results delivering healthcare to hard-to-reach population via telehealth

Alvin Powell

Harvard Staff Writer

September 4, 2025 8 min read

The first comprehensive map of mouse brain activity has been unveiled by a large international collaboration of neuroscientists. 

Researchers from the International Brain Laboratory (IBL), including MIT neuroscientist Ila Fiete, published their open-access findings today in two papers in Nature, revealing insights into how decision-making unfolds across the entire brain in mice at single-cell resolution. This brain-wide activity map challenges the traditional hierarchical view of information processing in the brain and shows that decision-making is distributed across many regions in a highly coordinated way.

“This is the first time anyone has produced a full, brain-wide map of the activity of single neurons during decision-making,” explains co-founder of IBL Alexandre Pouget. “The scale is unprecedented as we recorded from over half-a-million neurons across mice in 12 labs, covering 279 brain areas, which together represent 95 percent of the mouse brain volume. The decision-making activity, and particularly reward, lit up the brain like a Christmas tree,” adds Pouget, who is also a group leader at the University of Geneva in Switzerland.

Modeling decision-making

The brain map was made possible by a major international collaboration of neuroscientists from multiple universities, including MIT. Researchers across 12 labs used state-of-the-art silicon electrodes, called neuropixels probes, for simultaneous neural recordings to measure brain activity while mice were carrying out a decision-making task.

“Participating in the International Brain Laboratory has added new ways for our group to contribute to science,” says Fiete, who is also a professor of brain and cognitive sciences, an associate investigator at the McGovern Institute for Brain Research, and director of the K. Lisa Yang ICoN Center at MIT. “Our lab has helped standardize methods to analyze and generate robust conclusions from data. As computational neuroscientists interested in building models of how the brain works, access to brain-wide recordings is incredible: the traditional approach of recording from one or a few brain areas limited our ability to build and test theories, resulting in fragmented models. Now, we have the delightful but formidable task to make sense of how all parts of the brain coordinate to perform a behavior. Surprisingly, having a full view of the brain leads to simplifications in the models of decision-making,” says Fiete.

The labs collected data from mice performing a decision-making task with sensory, motor, and cognitive components. In the task, a mouse sits in front of a screen and a light appears on the left or right side. If the mouse then responds by moving a small wheel in the correct direction, it receives a reward.

In some trials, the light is so faint that the animal must guess which way to turn the wheel, for which it can use prior knowledge: the light tends to appear more frequently on one side for a number of trials, before the high-frequency side switches. Well-trained mice learn to use this information to help them make correct guesses. These challenging trials therefore allowed the researchers to study how prior expectations influence perception and decision-making.

Brain-wide results

The first paper, “A brain-wide map of neural activity during complex behaviour,” showed that decision-making signals are surprisingly distributed across the brain, not localized to specific regions. This adds brain-wide evidence to a growing number of studies that challenge the traditional hierarchical model of brain function, and emphasizes that there is constant communication across brain areas during decision-making, movement onset, and even reward. This means that neuroscientists will need to take a more holistic, brain-wide approach when studying complex behaviors in the future.

“The unprecedented breadth of our recordings pulls back the curtain on how the entire brain performs the whole arc of sensory processing, cognitive decision-making, and movement generation,” says Fiete. “Structuring a collaboration that collects a large standardized dataset which single labs could not assemble is a revolutionary new direction for systems neuroscience, initiating the field into the hyper-collaborative mode that has contributed to leaps forward in particle physics and human genetics. Beyond our own conclusions, the dataset and associated technologies, which were released much earlier as part of the IBL mission, have already become a massively used resource for the entire neuroscience community.”

The second paper, “Brain-wide representations of prior information,” showed that prior expectations — our beliefs about what is likely to happen based on our recent experience — are encoded throughout the brain. Surprisingly, these expectations are not only found in cognitive areas, but also brain areas that process sensory information and control actions. For example, expectations are even encoded in early sensory areas such as the thalamus, the brain’s first relay for visual input from the eye. This supports the view that the brain acts as a prediction machine, but with expectations encoded across multiple brain structures playing a central role in guiding behavior responses. These findings could have implications for understanding conditions such as schizophrenia and autism, which are thought to be caused by differences in the way expectations are updated in the brain.

“Much remains to be unpacked: If it is possible to find a signal in a brain area, does it mean that this area is generating the signal, or simply reflecting a signal generated somewhere else? How strongly is our perception of the world shaped by our expectations? Now we can generate some quantitative answers and begin the next phase experiments to learn about the origins of the expectation signals by intervening to modulate their activity,” says Fiete.

Looking ahead, the team at IBL plan to expand beyond their initial focus on decision-making to explore a broader range of neuroscience questions. With renewed funding in hand, IBL aims to expand its research scope and continue to support large-scale, standardized experiments.

New model of collaborative neuroscience

Officially launched in 2017, IBL introduced a new model of collaboration in neuroscience that uses a standardized set of tools and data processing pipelines shared across multiple labs, enabling the collection of massive datasets while ensuring data alignment and reproducibility. This approach to democratize and accelerate science draws inspiration from large-scale collaborations in physics and biology, such as CERN and the Human Genome Project.

All data from these studies, along with detailed specifications of the tools and protocols used for data collection, are openly accessible to the global scientific community for further analysis and research. Summaries of these resources can be viewed and downloaded on the IBL website under the sections: Data, Tools, Protocols.

This research was supported by grants from Wellcome, the Simons Foundation, the National Institutes of Health, the National Science Foundation, the Gatsby Charitable Foundation, and by the Max Planck Society and the Humboldt Foundation.

© Image: Dan Birman

The first comprehensive map of mouse brain activity has been unveiled by a large international collaboration of neuroscientists. 

Researchers from the International Brain Laboratory (IBL), including MIT neuroscientist Ila Fiete, published their open-access findings today in two papers in Nature, revealing insights into how decision-making unfolds across the entire brain in mice at single-cell resolution. This brain-wide activity map challenges the traditional hierarchical view of information processing in the brain and shows that decision-making is distributed across many regions in a highly coordinated way.

“This is the first time anyone has produced a full, brain-wide map of the activity of single neurons during decision-making,” explains co-founder of IBL Alexandre Pouget. “The scale is unprecedented as we recorded from over half-a-million neurons across mice in 12 labs, covering 279 brain areas, which together represent 95 percent of the mouse brain volume. The decision-making activity, and particularly reward, lit up the brain like a Christmas tree,” adds Pouget, who is also a group leader at the University of Geneva in Switzerland.

Modeling decision-making

The brain map was made possible by a major international collaboration of neuroscientists from multiple universities, including MIT. Researchers across 12 labs used state-of-the-art silicon electrodes, called neuropixels probes, for simultaneous neural recordings to measure brain activity while mice were carrying out a decision-making task.

“Participating in the International Brain Laboratory has added new ways for our group to contribute to science,” says Fiete, who is also a professor of brain and cognitive sciences, an associate investigator at the McGovern Institute for Brain Research, and director of the K. Lisa Yang ICoN Center at MIT. “Our lab has helped standardize methods to analyze and generate robust conclusions from data. As computational neuroscientists interested in building models of how the brain works, access to brain-wide recordings is incredible: the traditional approach of recording from one or a few brain areas limited our ability to build and test theories, resulting in fragmented models. Now, we have the delightful but formidable task to make sense of how all parts of the brain coordinate to perform a behavior. Surprisingly, having a full view of the brain leads to simplifications in the models of decision-making,” says Fiete.

The labs collected data from mice performing a decision-making task with sensory, motor, and cognitive components. In the task, a mouse sits in front of a screen and a light appears on the left or right side. If the mouse then responds by moving a small wheel in the correct direction, it receives a reward.

In some trials, the light is so faint that the animal must guess which way to turn the wheel, for which it can use prior knowledge: the light tends to appear more frequently on one side for a number of trials, before the high-frequency side switches. Well-trained mice learn to use this information to help them make correct guesses. These challenging trials therefore allowed the researchers to study how prior expectations influence perception and decision-making.

Brain-wide results

The first paper, “A brain-wide map of neural activity during complex behaviour,” showed that decision-making signals are surprisingly distributed across the brain, not localized to specific regions. This adds brain-wide evidence to a growing number of studies that challenge the traditional hierarchical model of brain function, and emphasizes that there is constant communication across brain areas during decision-making, movement onset, and even reward. This means that neuroscientists will need to take a more holistic, brain-wide approach when studying complex behaviors in the future.

“The unprecedented breadth of our recordings pulls back the curtain on how the entire brain performs the whole arc of sensory processing, cognitive decision-making, and movement generation,” says Fiete. “Structuring a collaboration that collects a large standardized dataset which single labs could not assemble is a revolutionary new direction for systems neuroscience, initiating the field into the hyper-collaborative mode that has contributed to leaps forward in particle physics and human genetics. Beyond our own conclusions, the dataset and associated technologies, which were released much earlier as part of the IBL mission, have already become a massively used resource for the entire neuroscience community.”

The second paper, “Brain-wide representations of prior information,” showed that prior expectations — our beliefs about what is likely to happen based on our recent experience — are encoded throughout the brain. Surprisingly, these expectations are not only found in cognitive areas, but also brain areas that process sensory information and control actions. For example, expectations are even encoded in early sensory areas such as the thalamus, the brain’s first relay for visual input from the eye. This supports the view that the brain acts as a prediction machine, but with expectations encoded across multiple brain structures playing a central role in guiding behavior responses. These findings could have implications for understanding conditions such as schizophrenia and autism, which are thought to be caused by differences in the way expectations are updated in the brain.

“Much remains to be unpacked: If it is possible to find a signal in a brain area, does it mean that this area is generating the signal, or simply reflecting a signal generated somewhere else? How strongly is our perception of the world shaped by our expectations? Now we can generate some quantitative answers and begin the next phase experiments to learn about the origins of the expectation signals by intervening to modulate their activity,” says Fiete.

Looking ahead, the team at IBL plan to expand beyond their initial focus on decision-making to explore a broader range of neuroscience questions. With renewed funding in hand, IBL aims to expand its research scope and continue to support large-scale, standardized experiments.

New model of collaborative neuroscience

Officially launched in 2017, IBL introduced a new model of collaboration in neuroscience that uses a standardized set of tools and data processing pipelines shared across multiple labs, enabling the collection of massive datasets while ensuring data alignment and reproducibility. This approach to democratize and accelerate science draws inspiration from large-scale collaborations in physics and biology, such as CERN and the Human Genome Project.

All data from these studies, along with detailed specifications of the tools and protocols used for data collection, are openly accessible to the global scientific community for further analysis and research. Summaries of these resources can be viewed and downloaded on the IBL website under the sections: Data, Tools, Protocols.

This research was supported by grants from Wellcome, the Simons Foundation, the National Institutes of Health, the National Science Foundation, the Gatsby Charitable Foundation, and by the Max Planck Society and the Humboldt Foundation.

© Image: Dan Birman

3D printing has come a long way since its invention in 1983 by Chuck Hull, who pioneered stereolithography, a technique that solidifies liquid resin into solid objects using ultraviolet lasers. Over the decades, 3D printers have evolved from experimental curiosities into tools capable of producing everything from custom prosthetics to complex food designs, architectural models, and even functioning human organs. 

But as the technology matures, its environmental footprint has become increasingly difficult to set aside. The vast majority of consumer and industrial 3D printing still relies on petroleum-based plastic filament. And while “greener” alternatives made from biodegradable or recycled materials exist, they come with a serious trade-off: they’re often not as strong. These eco-friendly filaments tend to become brittle under stress, making them ill-suited for structural applications or load-bearing parts — exactly where strength matters most.

This trade-off between sustainability and mechanical performance prompted researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Hasso Plattner Institute to ask: Is it possible to build objects that are mostly eco-friendly, but still strong where it counts?

Their answer is SustainaPrint, a new software and hardware toolkit designed to help users strategically combine strong and weak filaments to get the best of both worlds. Instead of printing an entire object with high-performance plastic, the system analyzes a model through finite element analysis simulations, predicts where the object is most likely to experience stress, and then reinforces just those zones with stronger material. The rest of the part can be printed using greener, weaker filament, reducing plastic use while preserving structural integrity.

“Our hope is that SustainaPrint can be used in industrial and distributed manufacturing settings one day, where local material stocks may vary in quality and composition,” says MIT PhD student and CSAIL researcher Maxine Perroni-Scharf, who is a lead author on a paper presenting the project. “In these contexts, the testing toolkit could help ensure the reliability of available filaments, while the software’s reinforcement strategy could reduce overall material consumption without sacrificing function.” 

For their experiments, the team used Polymaker’s PolyTerra PLA as the eco-friendly filament, and standard or Tough PLA from Ultimaker for reinforcement. They used a 20 percent reinforcement threshold to show that even a small amount of strong plastic goes a long way. Using this ratio, SustainaPrint was able to recover up to 70 percent of the strength of an object printed entirely with high-performance plastic.

They printed dozens of objects, from simple mechanical shapes like rings and beams to more functional household items such as headphone stands, wall hooks, and plant pots. Each object was printed three ways: once using only eco-friendly filament, once using only strong PLA, and once with the hybrid SustainaPrint configuration. The printed parts were then mechanically tested by pulling, bending, or otherwise breaking them to measure how much force each configuration could withstand. 

In many cases, the hybrid prints held up nearly as well as the full-strength versions. For example, in one test involving a dome-like shape, the hybrid version outperformed the version printed entirely in Tough PLA. The team believes this may be due to the reinforced version’s ability to distribute stress more evenly, avoiding the brittle failure sometimes caused by excessive stiffness.

“This indicates that in certain geometries and loading conditions, mixing materials strategically may actually outperform a single homogenous material,” says Perroni-Scharf. “It’s a reminder that real-world mechanical behavior is full of complexity, especially in 3D printing, where interlayer adhesion and tool path decisions can affect performance in unexpected ways.”

A lean, green, eco-friendly printing machine

SustainaPrint starts off by letting a user upload their 3D model into a custom interface. By selecting fixed regions and areas where forces will be applied, the software then uses an approach called “Finite Element Analysis” to simulate how the object will deform under stress. It then creates a map showing pressure distribution inside the structure, highlighting areas under compression or tension, and applies heuristics to segment the object into two categories: those that need reinforcement, and those that don’t.

Recognizing the need for accessible and low-cost testing, the team also developed a DIY testing toolkit to help users assess strength before printing. The kit has a 3D-printable device with modules for measuring both tensile and flexural strength. Users can pair the device with common items like pull-up bars or digital scales to get rough, but reliable performance metrics. The team benchmarked their results against manufacturer data and found that their measurements consistently fell within one standard deviation, even for filaments that had undergone multiple recycling cycles.

Although the current system is designed for dual-extrusion printers, the researchers believe that with some manual filament swapping and calibration, it could be adapted for single-extruder setups, too. In current form, the system simplifies the modeling process by allowing just one force and one fixed boundary per simulation. While this covers a wide range of common use cases, the team sees future work expanding the software to support more complex and dynamic loading conditions. The team also sees potential in using AI to infer the object’s intended use based on its geometry, which could allow for fully automated stress modeling without manual input of forces or boundaries.

3D for free

The researchers plan to release SustainaPrint open-source, making both the software and testing toolkit available for public use and modification. Another initiative they aspire to bring to life in the future: education. “In a classroom, SustainaPrint isn’t just a tool, it’s a way to teach students about material science, structural engineering, and sustainable design, all in one project,” says Perroni-Scharf. “It turns these abstract concepts into something tangible.”

As 3D printing becomes more embedded in how we manufacture and prototype everything from consumer goods to emergency equipment, sustainability concerns will only grow. With tools like SustainaPrint, those concerns no longer need to come at the expense of performance. Instead, they can become part of the design process: built into the very geometry of the things we make.

Co-author Patrick Baudisch, who is a professor at the Hasso Plattner Institute, adds that “the project addresses a key question: What is the point of collecting material for the purpose of recycling, when there is no plan to actually ever use that material? Maxine presents the missing link between the theoretical/abstract idea of 3D printing material recycling and what it actually takes to make this idea relevant.”

Perroni-Scharf and Baudisch wrote the paper with CSAIL research assistant Jennifer Xiao; MIT Department of Electrical Engineering and Computer Science master’s student Cole Paulin ’24; master’s student Ray Wang SM ’25 and PhD student Ticha Sethapakdi SM ’19 (both CSAIL members); Hasso Plattner Institute PhD student Muhammad Abdullah; and Associate Professor Stefanie Mueller, lead of the Human-Computer Interaction Engineering Group at CSAIL.

The researchers’ work was supported by a Designing for Sustainability Grant from the Designing for Sustainability MIT-HPI Research Program. Their work will be presented at the ACM Symposium on User Interface Software and Technology in September.

© Image: Alex Shipps/MIT CSAIL, using assets from Pixabay and the researchers.

3D printing has come a long way since its invention in 1983 by Chuck Hull, who pioneered stereolithography, a technique that solidifies liquid resin into solid objects using ultraviolet lasers. Over the decades, 3D printers have evolved from experimental curiosities into tools capable of producing everything from custom prosthetics to complex food designs, architectural models, and even functioning human organs. 

But as the technology matures, its environmental footprint has become increasingly difficult to set aside. The vast majority of consumer and industrial 3D printing still relies on petroleum-based plastic filament. And while “greener” alternatives made from biodegradable or recycled materials exist, they come with a serious trade-off: they’re often not as strong. These eco-friendly filaments tend to become brittle under stress, making them ill-suited for structural applications or load-bearing parts — exactly where strength matters most.

This trade-off between sustainability and mechanical performance prompted researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Hasso Plattner Institute to ask: Is it possible to build objects that are mostly eco-friendly, but still strong where it counts?

Their answer is SustainaPrint, a new software and hardware toolkit designed to help users strategically combine strong and weak filaments to get the best of both worlds. Instead of printing an entire object with high-performance plastic, the system analyzes a model through finite element analysis simulations, predicts where the object is most likely to experience stress, and then reinforces just those zones with stronger material. The rest of the part can be printed using greener, weaker filament, reducing plastic use while preserving structural integrity.

“Our hope is that SustainaPrint can be used in industrial and distributed manufacturing settings one day, where local material stocks may vary in quality and composition,” says MIT PhD student and CSAIL researcher Maxine Perroni-Scharf, who is a lead author on a paper presenting the project. “In these contexts, the testing toolkit could help ensure the reliability of available filaments, while the software’s reinforcement strategy could reduce overall material consumption without sacrificing function.” 

For their experiments, the team used Polymaker’s PolyTerra PLA as the eco-friendly filament, and standard or Tough PLA from Ultimaker for reinforcement. They used a 20 percent reinforcement threshold to show that even a small amount of strong plastic goes a long way. Using this ratio, SustainaPrint was able to recover up to 70 percent of the strength of an object printed entirely with high-performance plastic.

They printed dozens of objects, from simple mechanical shapes like rings and beams to more functional household items such as headphone stands, wall hooks, and plant pots. Each object was printed three ways: once using only eco-friendly filament, once using only strong PLA, and once with the hybrid SustainaPrint configuration. The printed parts were then mechanically tested by pulling, bending, or otherwise breaking them to measure how much force each configuration could withstand. 

In many cases, the hybrid prints held up nearly as well as the full-strength versions. For example, in one test involving a dome-like shape, the hybrid version outperformed the version printed entirely in Tough PLA. The team believes this may be due to the reinforced version’s ability to distribute stress more evenly, avoiding the brittle failure sometimes caused by excessive stiffness.

“This indicates that in certain geometries and loading conditions, mixing materials strategically may actually outperform a single homogenous material,” says Perroni-Scharf. “It’s a reminder that real-world mechanical behavior is full of complexity, especially in 3D printing, where interlayer adhesion and tool path decisions can affect performance in unexpected ways.”

A lean, green, eco-friendly printing machine

SustainaPrint starts off by letting a user upload their 3D model into a custom interface. By selecting fixed regions and areas where forces will be applied, the software then uses an approach called “Finite Element Analysis” to simulate how the object will deform under stress. It then creates a map showing pressure distribution inside the structure, highlighting areas under compression or tension, and applies heuristics to segment the object into two categories: those that need reinforcement, and those that don’t.

Recognizing the need for accessible and low-cost testing, the team also developed a DIY testing toolkit to help users assess strength before printing. The kit has a 3D-printable device with modules for measuring both tensile and flexural strength. Users can pair the device with common items like pull-up bars or digital scales to get rough, but reliable performance metrics. The team benchmarked their results against manufacturer data and found that their measurements consistently fell within one standard deviation, even for filaments that had undergone multiple recycling cycles.

Although the current system is designed for dual-extrusion printers, the researchers believe that with some manual filament swapping and calibration, it could be adapted for single-extruder setups, too. In current form, the system simplifies the modeling process by allowing just one force and one fixed boundary per simulation. While this covers a wide range of common use cases, the team sees future work expanding the software to support more complex and dynamic loading conditions. The team also sees potential in using AI to infer the object’s intended use based on its geometry, which could allow for fully automated stress modeling without manual input of forces or boundaries.

3D for free

The researchers plan to release SustainaPrint open-source, making both the software and testing toolkit available for public use and modification. Another initiative they aspire to bring to life in the future: education. “In a classroom, SustainaPrint isn’t just a tool, it’s a way to teach students about material science, structural engineering, and sustainable design, all in one project,” says Perroni-Scharf. “It turns these abstract concepts into something tangible.”

As 3D printing becomes more embedded in how we manufacture and prototype everything from consumer goods to emergency equipment, sustainability concerns will only grow. With tools like SustainaPrint, those concerns no longer need to come at the expense of performance. Instead, they can become part of the design process: built into the very geometry of the things we make.

Co-author Patrick Baudisch, who is a professor at the Hasso Plattner Institute, adds that “the project addresses a key question: What is the point of collecting material for the purpose of recycling, when there is no plan to actually ever use that material? Maxine presents the missing link between the theoretical/abstract idea of 3D printing material recycling and what it actually takes to make this idea relevant.”

Perroni-Scharf and Baudisch wrote the paper with CSAIL research assistant Jennifer Xiao; MIT Department of Electrical Engineering and Computer Science master’s student Cole Paulin ’24; master’s student Ray Wang SM ’25 and PhD student Ticha Sethapakdi SM ’19 (both CSAIL members); Hasso Plattner Institute PhD student Muhammad Abdullah; and Associate Professor Stefanie Mueller, lead of the Human-Computer Interaction Engineering Group at CSAIL.

The researchers’ work was supported by a Designing for Sustainability Grant from the Designing for Sustainability MIT-HPI Research Program. Their work will be presented at the ACM Symposium on User Interface Software and Technology in September.

© Image: Alex Shipps/MIT CSAIL, using assets from Pixabay and the researchers.

The Pre-read Assembly focused on Gordin's 2021 book “On the Fringe,” which incoming students read over the summer as their introduction to the intellectual life of the University. 

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José Sánchez doesn’t just design buildings. He designs games that teach people how to build a better world.

The Abdul Latif Jameel Water and Food Systems Lab (J-WAFS) announced that Daniela Giardina has been named the new J-WAFS executive director. Giardina stepped into the role at the start of the fall semester, replacing founding executive director Renee J. Robins ’83, who is retiring after leading the program since its launch in 2014.

“Daniela brings a deep background in water and food security, along with excellent management and leadership skills,” says Robins. “Since I first met her nearly 10 years ago, I have been impressed with her commitment to working on global water and food challenges through research and innovation. I am so happy to know that I will be leaving J-WAFS in her experienced and capable hands.”

A decade of impact

J-WAFS fuels research, innovation, and collaboration to solve global water and food systems challenges. The mission of J-WAFS is to ensure safe and resilient supplies of water and food to meet the local and global needs of a dramatically growing population on a rapidly changing planet. J-WAFS funding opportunities are open to researchers in every MIT department, lab, and center, spanning all disciplines. Supported research projects include those involving engineering, science, technology, business, social science, economics, architecture, urban planning, and more. J-WAFS research and related activities include early-stage projects, sponsored research, commercialization efforts, student activities and mentorship, events that convene local and global experts, and international-scale collaborations.

The global water, food, and climate emergency makes J-WAFS’ work both timely and urgent. J-WAFS-funded researchers are achieving tangible, real-time solutions and results. Since its inception, J-WAFS has distributed nearly $26 million in grants, fellowships, and awards to the MIT community, supporting roughly 10 percent of MIT’s faculty and 300 students, postdocs, and research staff from 40 MIT departments, labs, and centers. J-WAFS grants have also helped researchers launch 13 startups and receive over $25 million in follow-on funding.

Giardina joins J-WAFS at an exciting time in the program’s history; in the spring, J-WAFS celebrated 10 years of supporting water and food research at MIT. The milestone was commemorated at a special event attended by MIT leadership, researchers, students, staff, donors, and others in the J-WAFS community. As J-WAFS enters its second decade, interest and opportunities for water and food research continue to grow. “I am truly honored to join J-WAFS at such a pivotal moment,” Giardina says.

Putting research into real-world practice

Giardina has nearly two decades of experience working with nongovernmental organizations and research institutions on humanitarian and development projects. Her work has taken her to Africa, Latin America, the Caribbean, and Central and Southeast Asia, where she has focused on water and food security projects. She has conducted technical trainings and assessments, and managed projects from design to implementation, including monitoring and evaluation.

Giardina comes to MIT from Oxfam America, where she directed disaster risk reduction and climate resilience initiatives, working on approaches to strengthen local leadership, community-based disaster risk reduction, and anticipatory action. Her role at Oxfam required her to oversee multimillion-dollar initiatives, supervising international teams, managing complex donor portfolios, and ensuring rigorous monitoring across programs. She connected hands-on research with community-oriented implementation, for example, by partnering with MIT’s D-Lab to launch an innovation lab in rural El Salvador. Her experience will help guide J-WAFS as it pursues impactful research that will make a difference on the ground.

Beyond program delivery, Giardina has played a strategic leadership role in shaping Oxfam’s global disaster risk reduction strategy and representing the organization at high-level U.N. and academic forums. She is multilingual and adept at building partnerships across cultures, having worked with governments, funders, and community-based organizations to strengthen resilience and advance equitable access to water and food.

Giardina holds a PhD in sustainable development from the University of Brescia in Italy. She also holds a master’s degree in environmental engineering from the Politecnico of Milan in Italy and is a chartered engineer since 2005 (equivalent to a professional engineering license in the United States). She also serves as vice chair of the Boston Network for International Development, a nonprofit that connects and strengthens Boston’s global development community.

“I have seen first-hand how climate change, misuse of resources, and inequality are undermining water and food security around the globe,” says Giardina. “What particularly excites me about J-WAFS is its interdisciplinary approach in facilitating meaningful partnerships to solve many of these problems through research and innovation. I am eager to help expand J-WAFS’ impact by strengthening existing programs, developing new initiatives, and building strategic partnerships that translate MIT's groundbreaking research into real-world solutions,” she adds.

A legacy of leadership

Renee Robins will retire with over 23 years of service to MIT. Years before joining the staff, she graduated from MIT with dual bachelor’s degrees in both biology and humanities/anthropology. She then went on to earn a master’s degree in public policy from Carnegie Mellon University. In 1998, she came back to MIT to serve in various roles across campus, including with the Cambridge MIT Institute, the MIT Portugal Program, the Mexico City Program, the Program on Emerging Technologies, and the Technology and Policy Program. She also worked at the Harvard Graduate School of Education, where she managed a $15 million research program as it scaled from implementation in one public school district to 59 schools in seven districts across North Carolina.

In late 2014, Robins joined J-WAFS as its founding executive director, playing a pivotal role in building it from the ground up and expanding the team to six full-time professionals. She worked closely with J-WAFS founding director Professor John H. Lienhard V to develop and implement funding initiatives, develop, and shepherd corporate-sponsored research partnerships, and mentor students in the Water Club and Food and Agriculture Club, as well as numerous other students. Throughout the years, Robins has inspired a diverse range of researchers to consider how their capabilities and expertise can be applied to water and food challenges. Perhaps most importantly, her leadership has helped cultivate a vibrant community, bringing together faculty, students, and research staff to be exposed to unfamiliar problems and new methodologies, to explore how their expertise might be applied, to learn from one another, and to collaborate.

At the J-WAFS 10th anniversary event in May, Robins noted, “it has been a true privilege to work alongside John Lienhard, our dedicated staff, and so many others. It’s been particularly rewarding to see the growth of an MIT network of water and food researchers that J-WAFS has nurtured, which grew out of those few individuals who saw themselves to be working in solitude on these critical challenges.”

Lienhard also spoke, thanking Robins by saying she “was my primary partner in building J-WAFS and [she is] a strong leader and strategic thinker.”

Not only is Robins a respected leader, she is also a dear friend to so many at MIT and beyond. In 2021, she was recognized for her outstanding leadership and commitment to J-WAFS and the Institute with an MIT Infinite Mile Award in the area of the Offices of the Provost and Vice President for Research.

Outside of MIT, Robins has served on the Board of Trustees for the International Honors Program — a comparative multi-site study abroad program, where she previously studied comparative culture and anthropology in seven countries around the world. Robins has also acted as an independent consultant, including work on program design and strategy around the launch of the Université Mohammed VI Polytechnique in Morocco.

Continuing the tradition of excellence

Giardina will report to J-WAFS director Rohit Karnik, the Abdul Latif Jameel Professor of Water and Food in the MIT Department of Mechanical Engineering. Karnik was named the director of J-WAFS in January, succeeding John Lienhard, who retired earlier this year.

As executive director, Giardina will be instrumental in driving J-WAFS’ mission and impact. She will work with Karnik to help shape J-WAFS’ programs, long-term strategy, and goals. She will also be responsible for supervising J-WAFS staff, managing grant administration, and overseeing and advising on financial decisions.

“I am very grateful to John and Renee, who have helped to establish J-WAFS as the Institute’s preeminent program for water and food research and significantly expanded MIT’s research efforts and impact in the water and food space,” says Karnik. “I am confident that with Daniela as executive director, J-WAFS will continue in the tradition of excellence that Renee and John put into place, as we move into the program’s second decade,” he notes.

Giardina adds, “I am inspired by the lab’s legacy of Renee Robins and Professor Lienhard, and I look forward to working with Professor Karnik and the J-WAFS staff.”

© Photo courtesy of J-WAFS.

Science & Tech

A smell test for science

Sy Boles

Harvard Staff Writer

September 4, 2025 4 min read

Researcher outlines vision for implants that could help patients reclaim vital connection with their surroundings

By Prof Tan Huay Cheem, from the Dept of Medicine,Yong Loo Lin School of Medicine at NUS

• Lianhe Zaobao, 2 September 2025, Fukan, p2

By Dr Hanny Kusnadi, Senior Lecturer at the Dept of Accounting at NUS Business School

• The Business Times, 2 September 2025, p5

By Prof Danny Quah, Li Ka Shing Professor in Economics at the Lee Kuan Yew School of Public Policy at NUS

• The Straits Times, 2 September 2025, Opinion, pB2

• CNA Online, 1 September 2025
• 8world Online, 1 September 2025
• CNA938, 1 September 2025
• Tamil Murasu, 2 September 2025, p2

• Lianhe Zaobao, 2 September 2025, Singapore, p9

• The Straits Times Online, 2 September 2025
• Lianhe Zaobao Online, 2 September 2025
• CNA Online, 2 September 2025
• 8world Online, 2 September 2025

Nine researchers from ETH Zurich have just been awarded Starting Grants from the European Research Council (ERC). This is a positive signal for Zurich as a research location.

• Money 89.3FM, 27 August 2025
• The Business Times, 28 August 2025, p15
• Lianhe Zaobao, 2 September 2025, Singapore, p10

• CNA Online, 28 August 2025
• CNA, 28 August 2025
• Channel 5 News, 28 August 2025
• 8world Online, 28 August 2025
• CNA938, 28 August 2025
• Lianhe Zaobao, 29 August 2025, Singapore, p7

• 8world Online, 28 August 2025

• Suria News Online, 28 August 2025

By Elyssa Liu, Legal Frameworks and Governance Team Leader at the Duke-NUS Centre for Outbreak Preparedness

• CNA Online, 26 August 2025

Asian nations are today becoming more central to shaping global politics, economics and security. As Asia’s global influence grows, its countries must take responsibility for stability and peace, rather than allowing rivalries to define the century.

This point was underscored during the dialogue between veteran diplomat and Distinguished Fellow at the NUS Asia Research Institute (ARI) Mr Kishore Mahbubani and Singapore’s former foreign minister Mr George Yeo in conjunction with the launch of the book, Can Asians Think of Peace? Essays on Managing Conflict in the Asian Century, on 22 August 2025.

The book, co-edited by Mr Mahbubani and Dr Kesava Chandra Varigonda and Ms Kristen Tang from ARI, compiles 61 essays written by global scholars, policymakers and experts for the Asian Peace Programme (APP) from the time of its launch in July 2020 to December 2024. The essays examine possible areas of conflict in Asia, offering Asia-focused, pragmatic perspectives on conflict management and peacebuilding.

Published in July 2025 as an open-access volume to coincide with APP’s fifth anniversary, the book has already been downloaded 103,000 times and reached #3 on The Straits Times non-fiction bestseller list, reflecting its broad reach across academia, policy circles and the public.

In his opening address at the launch, ARI Director Professor Tim Bunnell recalled how the APP was conceived in the wake of the 2020 military clashes between Chinese and Indian troops, as there was a recognition of the need for a credible peace initiative that was “Asia-focused, Asia-centred and Asia-led”.

“This book stands as proof of the important work the APP has undertaken in addressing that need, with recent tensions along the Thai-Cambodian border serving as a reminder that such peace-making efforts remain as vital as ever for the region,” said Prof Bunnell.

Speaking at the launch event attended by about 250 guests from academia and policymaking, Mr Yeo lauded APP for demonstrating what he called “a bias for peace”, describing how the book contained a “treasure trove of perspectives” on the region’s most pressing challenges.

He highlighted the critical role of understanding one’s opponents in geopolitics, as effective peacekeeping demands strategic insight and a clear grasp of what motivates the opponents and where their interests lie.

“When we start seeing problems from the other person’s perspective, and we can only do it if we respect the interlocutor, then the possibility of win-win outcomes becomes possible,” Mr Yeo said.

The dangers of ‘demonising opponents’ were also discussed during the dialogue between Mr Yeo and Mr Mahbubani.

“When you demonise the other, you become fearful, you become self-righteous. And out of self-righteousness arises the greatest evil. Those who are evil…believe they are doing right by the people,” said Mr Yeo, adding that this troubling mindset had become increasingly evident, for example, in US-China relations. Rather than allowing fear and self-righteousness to shape interactions, he emphasised the value of building understanding through shared histories and cultures.

Mr Mahbubani pointed to the recent warming of China–India relations, which he described as critical to peace in Asia. Mr Yeo concurred, citing symbolic gestures on both sides: President Xi Jinping’s 2014 visit to Prime Minister Narendra Modi’s home state of Gujarat, where the Buddhist monk Xuanzang once spent time, and Mr Modi’s reciprocal visit to Xi’s hometown of Xi’an, where Xuanzang had returned with Buddhist scriptures.

Mr Yeo drew on a striking metaphor by eminent historian and NUS University Professor Wang Gungwu, who described Singapore as the place where the “mandalas” of India and China overlap.

Extending this imagery, he noted that Southeast Asia is where the spheres of influence of China, India and the West intersect at varying intensities across the 10 ASEAN nations – giving the region a unique vantage point to understand all three. “The work which the APP does, in a sense, can only be done in Southeast Asia, and perhaps only in Singapore, which makes it so special,” he said.

Indeed, it is Mr Mahbubani’s hope that the APP can make a meaningful difference by serving as “a small candle” that illuminates the path to peace in Asia and beyond.

Can Asians Think of Peace? Essays on Managing Conflict in the Asian Century is a culmination of the APP’s work, with its open-access format allowing anyone worldwide to read and cite these essays, amplifying their reach across academia, policy, and public discourse.

Download the book for free now at publisher Springer Nature’s website.

Many attempts have been made to harness the power of new artificial intelligence and large language models (LLMs) to try to predict the outcomes of new chemical reactions. These have had limited success, in part because until now they have not been grounded in an understanding of fundamental physical principles, such as the laws of conservation of mass. Now, a team of researchers at MIT has come up with a way of incorporating these physical constraints on a reaction prediction model, and thus greatly improving the accuracy and reliability of its outputs.

The new work was reported Aug. 20 in the journal Nature, in a paper by recent postdoc Joonyoung Joung (now an assistant professor at Kookmin University, South Korea); former software engineer Mun Hong Fong (now at Duke University); chemical engineering graduate student Nicholas Casetti; postdoc Jordan Liles; physics undergraduate student Ne Dassanayake; and senior author Connor Coley, who is the Class of 1957 Career Development Professor in the MIT departments of Chemical Engineering and Electrical Engineering and Computer Science.

“The prediction of reaction outcomes is a very important task,” Joung explains. For example, if you want to make a new drug, “you need to know how to make it. So, this requires us to know what product is likely” to result from a given set of chemical inputs to a reaction. But most previous efforts to carry out such predictions look only at a set of inputs and a set of outputs, without looking at the intermediate steps or considering the constraints of ensuring that no mass is gained or lost in the process, which is not possible in actual reactions.

Joung points out that while large language models such as ChatGPT have been very successful in many areas of research, these models do not provide a way to limit their outputs to physically realistic possibilities, such as by requiring them to adhere to conservation of mass. These models use computational “tokens,” which in this case represent individual atoms, but “if you don’t conserve the tokens, the LLM model starts to make new atoms, or deletes atoms in the reaction.” Instead of being grounded in real scientific understanding, “this is kind of like alchemy,” he says. While many attempts at reaction prediction only look at the final products, “we want to track all the chemicals, and how the chemicals are transformed” throughout the reaction process from start to end, he says.

In order to address the problem, the team made use of a method developed back in the 1970s by chemist Ivar Ugi, which uses a bond-electron matrix to represent the electrons in a reaction. They used this system as the basis for their new program, called FlowER (Flow matching for Electron Redistribution), which allows them to explicitly keep track of all the electrons in the reaction to ensure that none are spuriously added or deleted in the process.

The system uses a matrix to represent the electrons in a reaction, and uses nonzero values to represent bonds or lone electron pairs and zeros to represent a lack thereof. “That helps us to conserve both atoms and electrons at the same time,” says Fong. This representation, he says, was one of the key elements to including mass conservation in their prediction system.

The system they developed is still at an early stage, Coley says. “The system as it stands is a demonstration — a proof of concept that this generative approach of flow matching is very well suited to the task of chemical reaction prediction.” While the team is excited about this promising approach, he says, “we’re aware that it does have specific limitations as far as the breadth of different chemistries that it’s seen.” Although the model was trained using data on more than a million chemical reactions, obtained from a U.S. Patent Office database, those data do not include certain metals and some kinds of catalytic reactions, he says.

“We’re incredibly excited about the fact that we can get such reliable predictions of chemical mechanisms” from the existing system, he says. “It conserves mass, it conserves electrons, but we certainly acknowledge that there’s a lot more expansion and robustness to work on in the coming years as well.”

But even in its present form, which is being made freely available through the online platform GitHub, “we think it will make accurate predictions and be helpful as a tool for assessing reactivity and mapping out reaction pathways,” Coley says. “If we’re looking toward the future of really advancing the state of the art of mechanistic understanding and helping to invent new reactions, we’re not quite there. But we hope this will be a steppingstone toward that.”

“It’s all open source,” says Fong. “The models, the data, all of them are up there,” including a previous dataset developed by Joung that exhaustively lists the mechanistic steps of known reactions. “I think we are one of the pioneering groups making this dataset, and making it available open-source, and making this usable for everyone,” he says.

The FlowER model matches or outperforms existing approaches in finding standard mechanistic pathways, the team says, and makes it possible to generalize to previously unseen reaction types. They say the model could potentially be relevant for predicting reactions for medicinal chemistry, materials discovery, combustion, atmospheric chemistry, and electrochemical systems.

In their comparisons with existing reaction prediction systems, Coley says, “using the architecture choices that we’ve made, we get this massive increase in validity and conservation, and we get a matching or a little bit better accuracy in terms of performance.”

He adds that “what’s unique about our approach is that while we are using these textbook understandings of mechanisms to generate this dataset, we’re anchoring the reactants and products of the overall reaction in experimentally validated data from the patent literature.” They are inferring the underlying mechanisms, he says, rather than just making them up. “We’re imputing them from experimental data, and that’s not something that has been done and shared at this kind of scale before.”

The next step, he says, is “we are quite interested in expanding the model’s understanding of metals and catalytic cycles. We’ve just scratched the surface in this first paper,” and most of the reactions included so far don’t include metals or catalysts, “so that’s a direction we’re quite interested in.”

In the long term, he says, “a lot of the excitement is in using this kind of system to help discover new complex reactions and help elucidate new mechanisms. I think that the long-term potential impact is big, but this is of course just a first step.”

The work was supported by the Machine Learning for Pharmaceutical Discovery and Synthesis consortium and the National Science Foundation.

© Image courtesy of the researchers.

Many attempts have been made to harness the power of new artificial intelligence and large language models (LLMs) to try to predict the outcomes of new chemical reactions. These have had limited success, in part because until now they have not been grounded in an understanding of fundamental physical principles, such as the laws of conservation of mass. Now, a team of researchers at MIT has come up with a way of incorporating these physical constraints on a reaction prediction model, and thus greatly improving the accuracy and reliability of its outputs.

The new work was reported Aug. 20 in the journal Nature, in a paper by recent postdoc Joonyoung Joung (now an assistant professor at Kookmin University, South Korea); former software engineer Mun Hong Fong (now at Duke University); chemical engineering graduate student Nicholas Casetti; postdoc Jordan Liles; physics undergraduate student Ne Dassanayake; and senior author Connor Coley, who is the Class of 1957 Career Development Professor in the MIT departments of Chemical Engineering and Electrical Engineering and Computer Science.

“The prediction of reaction outcomes is a very important task,” Joung explains. For example, if you want to make a new drug, “you need to know how to make it. So, this requires us to know what product is likely” to result from a given set of chemical inputs to a reaction. But most previous efforts to carry out such predictions look only at a set of inputs and a set of outputs, without looking at the intermediate steps or considering the constraints of ensuring that no mass is gained or lost in the process, which is not possible in actual reactions.

Joung points out that while large language models such as ChatGPT have been very successful in many areas of research, these models do not provide a way to limit their outputs to physically realistic possibilities, such as by requiring them to adhere to conservation of mass. These models use computational “tokens,” which in this case represent individual atoms, but “if you don’t conserve the tokens, the LLM model starts to make new atoms, or deletes atoms in the reaction.” Instead of being grounded in real scientific understanding, “this is kind of like alchemy,” he says. While many attempts at reaction prediction only look at the final products, “we want to track all the chemicals, and how the chemicals are transformed” throughout the reaction process from start to end, he says.

In order to address the problem, the team made use of a method developed back in the 1970s by chemist Ivar Ugi, which uses a bond-electron matrix to represent the electrons in a reaction. They used this system as the basis for their new program, called FlowER (Flow matching for Electron Redistribution), which allows them to explicitly keep track of all the electrons in the reaction to ensure that none are spuriously added or deleted in the process.

The system uses a matrix to represent the electrons in a reaction, and uses nonzero values to represent bonds or lone electron pairs and zeros to represent a lack thereof. “That helps us to conserve both atoms and electrons at the same time,” says Fong. This representation, he says, was one of the key elements to including mass conservation in their prediction system.

The system they developed is still at an early stage, Coley says. “The system as it stands is a demonstration — a proof of concept that this generative approach of flow matching is very well suited to the task of chemical reaction prediction.” While the team is excited about this promising approach, he says, “we’re aware that it does have specific limitations as far as the breadth of different chemistries that it’s seen.” Although the model was trained using data on more than a million chemical reactions, obtained from a U.S. Patent Office database, those data do not include certain metals and some kinds of catalytic reactions, he says.

“We’re incredibly excited about the fact that we can get such reliable predictions of chemical mechanisms” from the existing system, he says. “It conserves mass, it conserves electrons, but we certainly acknowledge that there’s a lot more expansion and robustness to work on in the coming years as well.”

But even in its present form, which is being made freely available through the online platform GitHub, “we think it will make accurate predictions and be helpful as a tool for assessing reactivity and mapping out reaction pathways,” Coley says. “If we’re looking toward the future of really advancing the state of the art of mechanistic understanding and helping to invent new reactions, we’re not quite there. But we hope this will be a steppingstone toward that.”

“It’s all open source,” says Fong. “The models, the data, all of them are up there,” including a previous dataset developed by Joung that exhaustively lists the mechanistic steps of known reactions. “I think we are one of the pioneering groups making this dataset, and making it available open-source, and making this usable for everyone,” he says.

The FlowER model matches or outperforms existing approaches in finding standard mechanistic pathways, the team says, and makes it possible to generalize to previously unseen reaction types. They say the model could potentially be relevant for predicting reactions for medicinal chemistry, materials discovery, combustion, atmospheric chemistry, and electrochemical systems.

In their comparisons with existing reaction prediction systems, Coley says, “using the architecture choices that we’ve made, we get this massive increase in validity and conservation, and we get a matching or a little bit better accuracy in terms of performance.”

He adds that “what’s unique about our approach is that while we are using these textbook understandings of mechanisms to generate this dataset, we’re anchoring the reactants and products of the overall reaction in experimentally validated data from the patent literature.” They are inferring the underlying mechanisms, he says, rather than just making them up. “We’re imputing them from experimental data, and that’s not something that has been done and shared at this kind of scale before.”

The next step, he says, is “we are quite interested in expanding the model’s understanding of metals and catalytic cycles. We’ve just scratched the surface in this first paper,” and most of the reactions included so far don’t include metals or catalysts, “so that’s a direction we’re quite interested in.”

In the long term, he says, “a lot of the excitement is in using this kind of system to help discover new complex reactions and help elucidate new mechanisms. I think that the long-term potential impact is big, but this is of course just a first step.”

The work was supported by the Machine Learning for Pharmaceutical Discovery and Synthesis consortium and the National Science Foundation.

© Image courtesy of the researchers.

Best-selling writer and technology blogger Cory Doctorow will make the A.D. White Professor-at-Large program’s second dual-campus visit, ending his week at Cornell Tech in New York City. Four other professors will visit Cornell this fall.

Science fiction author, activist and journalist Cory Doctorow will visit Cornell Sept. 11-19 as an A.D. White Professor at Large, taking part in several events on campus and in the community. 

Health

Facing life-or-death call on who gets liver transplants

Surgeons, medical professionals apply risk calculus that gets even more complex for patients with drinking problems

Anna Lamb

Harvard Staff Writer

September 3, 2025 5 min read

Cornell University alumnus Scott Belsky ‘02, partner at entertainment studio A24 and founder of A24 Labs and Behance, has joined the Cornell Tech Council.

Best-selling writer and technology blogger Cory Doctorow will make the A.D. White Professor-at-Large program’s second dual-campus visit, ending his week at Cornell Tech in New York City. Four other professors will visit Cornell this fall.

Students at ETH Zurich have developed a laser power bed fusion machine that follows a circular tool path to print round components, thereby being able to process multiple metals at once. The system significantly reduces manufacturing time and opens up new possibilities for aerospace and industry. ETH has filed a patent application for the machine.

Synthetic data are artificially generated by algorithms to mimic the statistical properties of actual data, without containing any information from real-world sources. While concrete numbers are hard to pin down, some estimates suggest that more than 60 percent of data used for AI applications in 2024 was synthetic, and this figure is expected to grow across industries.

Because synthetic data don’t contain real-world information, they hold the promise of safeguarding privacy while reducing the cost and increasing the speed at which new AI models are developed. But using synthetic data requires careful evaluation, planning, and checks and balances to prevent loss of performance when AI models are deployed.       

To unpack some pros and cons of using synthetic data, MIT News spoke with Kalyan Veeramachaneni, a principal research scientist in the Laboratory for Information and Decision Systems and co-founder of DataCebo whose open-core platform, the Synthetic Data Vault, helps users generate and test synthetic data.

Q: How are synthetic data created?

A: Synthetic data are algorithmically generated but do not come from a real situation. Their value lies in their statistical similarity to real data. If we’re talking about language, for instance, synthetic data look very much as if a human had written those sentences. While researchers have created synthetic data for a long time, what has changed in the past few years is our ability to build generative models out of data and use them to create realistic synthetic data. We can take a little bit of real data and build a generative model from that, which we can use to create as much synthetic data as we want. Plus, the model creates synthetic data in a way that captures all the underlying rules and infinite patterns that exist in the real data.

There are essentially four different data modalities: language, video or images, audio, and tabular data. All four of them have slightly different ways of building the generative models to create synthetic data. An LLM, for instance, is nothing but a generative model from which you are sampling synthetic data when you ask it a question.      

A lot of language and image data are publicly available on the internet. But tabular data, which is the data collected when we interact with physical and social systems, is often locked up behind enterprise firewalls. Much of it is sensitive or private, such as customer transactions stored by a bank. For this type of data, platforms like the Synthetic Data Vault provide software that can be used to build generative models. Those models then create synthetic data that preserve customer privacy and can be shared more widely.      

One powerful thing about this generative modeling approach for synthesizing data is that enterprises can now build a customized, local model for their own data. Generative AI automates what used to be a manual process.

Q: What are some benefits of using synthetic data, and which use-cases and applications are they particularly well-suited for?

A: One fundamental application which has grown tremendously over the past decade is using synthetic data to test software applications. There is data-driven logic behind many software applications, so you need data to test that software and its functionality. In the past, people have resorted to manually generating data, but now we can use generative models to create as much data as we need.

Users can also create specific data for application testing. Say I work for an e-commerce company. I can generate synthetic data that mimics real customers who live in Ohio and made transactions pertaining to one particular product in February or March.

Because synthetic data aren’t drawn from real situations, they are also privacy-preserving. One of the biggest problems in software testing has been getting access to sensitive real data for testing software in non-production environments, due to privacy concerns. Another immediate benefit is in performance testing. You can create a billion transactions from a generative model and test how fast your system can process them.

Another application where synthetic data hold a lot of promise is in training machine-learning models. Sometimes, we want an AI model to help us predict an event that is less frequent. A bank may want to use an AI model to predict fraudulent transactions, but there may be too few real examples to train a model that can identify fraud accurately. Synthetic data provide data augmentation — additional data examples that are similar to the real data. These can significantly improve the accuracy of AI models.

Also, sometimes users don’t have time or the financial resources to collect all the data. For instance, collecting data about customer intent would require conducting many surveys. If you end up with limited data and then try to train a model, it won’t perform well. You can augment by adding synthetic data to train those models better.

Q. What are some of the risks or potential pitfalls of using synthetic data, and are there steps users can take to prevent or mitigate those problems?

A. One of the biggest questions people often have in their mind is, if the data are synthetically created, why should I trust them? Determining whether you can trust the data often comes down to evaluating the overall system where you are using them.

There are a lot of aspects of synthetic data we have been able to evaluate for a long time. For instance, there are existing methods to measure how close synthetic data are to real data, and we can measure their quality and whether they preserve privacy. But there are other important considerations if you are using those synthetic data to train a machine-learning model for a new use case. How would you know the data are going to lead to models that still make valid conclusions?

New efficacy metrics are emerging, and the emphasis is now on efficacy for a particular task. You must really dig into your workflow to ensure the synthetic data you add to the system still allow you to draw valid conclusions. That is something that must be done carefully on an application-by-application basis.

Bias can also be an issue. Since it is created from a small amount of real data, the same bias that exists in the real data can carry over into the synthetic data. Just like with real data, you would need to purposefully make sure the bias is removed through different sampling techniques, which can create balanced datasets. It takes some careful planning, but you can calibrate the data generation to prevent the proliferation of bias.

To help with the evaluation process, our group created the Synthetic Data Metrics Library. We worried that people would use synthetic data in their environment and it would give different conclusions in the real world. We created a metrics and evaluation library to ensure checks and balances. The machine learning community has faced a lot of challenges in ensuring models can generalize to new situations. The use of synthetic data adds a whole new dimension to that problem.

I expect that the old systems of working with data, whether to build software applications, answer analytical questions, or train models, will dramatically change as we get more sophisticated at building these generative models. A lot of things we have never been able to do before will now be possible.

© Image: MIT News; iStock

If your hand lotion is a bit runnier than usual coming out of the bottle, it might have something to do with the goop’s “mechanical memory.”

Soft gels and lotions are made by mixing ingredients until they form a stable and uniform substance. But even after a gel has set, it can hold onto “memories,” or residual stress, from the mixing process. Over time, the material can give in to these embedded stresses and slide back into its former, premixed state. Mechanical memory is, in part, why hand lotion separates and gets runny over time. 

Now, an MIT engineer has devised a simple way to measure the degree of residual stress in soft materials after they have been mixed, and found that common products like hair gel and shaving cream have longer mechanical memories, holding onto residual stresses for longer periods of time than manufacturers might have assumed.

In a study appearing today in Physical Review Letters, Crystal Owens, a postdoc in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), presents a new protocol for measuring residual stress in soft, gel-like materials, using a standard benchtop rheometer.

Applying this protocol to everyday soft materials, Owens found that if a gel is made by mixing it in one direction, once it settles into a stable and uniform state, it effectively holds onto the memory of the direction in which it is mixed. Even after several days, the gel will hold some internal stress that, if released, will cause the gel to shift in the direction opposite to how it was initially mixed, reverting back to its earlier state.

“This is one reason different batches of cosmetics or food behave differently even if they underwent ‘identical’ manufacturing,” Owens says. “Understanding and measuring these hidden stresses during processing could help manufacturers design better products that last longer and perform more predictably.”

A soft glass

Hand lotion, hair gel, and shaving cream all fall under the category of “soft glassy materials” — materials that exhibit properties of both solids and liquids.

“Anything you can pour into your hand and it forms a soft mound is going to be considered a soft glass,” Owens explains. “In materials science, it’s considered a soft version of something that has the same amorphous structure as glass.”

In other words, a soft glassy material is a strange amalgam of a solid and a liquid. It can be poured out like a liquid, and it can hold its shape like a solid. Once they are made, these materials exist in a delicate balance between solid and liquid. And Owens wondered: For how long?

“What happens to these materials after very long times? Do they finally relax or do they never relax?” Owens says. “From a physics perspective, that’s a very interesting concept: What is the essential state of these materials?”

Twist and hold

In the manufacturing of soft glassy materials such as hair gel and shampoo, ingredients are first mixed into a uniform product. Quality control engineers then let a sample sit for about a minute — a period of time that they assume is enough to allow any residual stresses from the mixing process dissipate. In that time, the material should settle into a steady, stable state, ready for use.

But Owens suspected that the materials may hold some degree of stress from the production process long after they’ve appeared to settle.

“Residual stress is a low level of stress that’s trapped inside a material after it’s come to a steady state,” Owens says. “This sort of stress has not been measured in these sorts of materials.”

To test her hypothesis, she carried out experiments with two common soft glassy materials: hair gel and shaving cream. She made measurements of each material in a rheometer — an instrument consisting of two rotating plates that can twist and press a material together at precisely controlled pressures and forces that relate directly to the material’s internal stresses and strains.

In her experiments, she placed each material in the rheometer and spun the instrument’s top plate around to mix the material. Then she let the material settle, and then settle some more — much longer than one minute. During this time, she observed the amount of force it took the rheometer to hold the material in place. She reasoned that the greater the rheometer’s force, the more it must be counteracting any stress within the material that would otherwise cause it to shift out of its current state.

Over multiple experiments using this new protocol, Owens found that different types of soft glassy materials held a significant amount of residual stress, long after most researchers would assume the stress had dissipated. What’s more, she found that the degree of stress that a material retained was a reflection of the direction in which it was initially mixed, and when it was mixed.

“The material can effectively ‘remember’ which direction it was mixed, and how long ago,” Owens says. “And it turns out they hold this memory of their past, a lot longer than we used to think.”

In addition to the protocol she has developed to measure residual stress, Owens has developed a model to estimate how a material will change over time, given the degree of residual stress that it holds. Using this model, she says scientists might design materials with “short-term memory,” or very little residual stress, such that they remain stable over longer periods.

One material where she sees room for such improvement is asphalt — a substance that is first mixed, then poured in molten form over a surface where it then cools and settles over time. She suspects that residual stresses from the mixing of asphalt may contribute to cracks forming in pavement over time. Reducing these stresses at the start of the process could lead to longer-lasting, more resilient roads.

“People are inventing new types of asphalt all the time to be more eco-friendly, and all of these will have different levels of residual stress that will need some control,” she says. “There’s plenty of room to explore.”

This research was supported, in part, by MIT’s Postdoctoral Fellowship for Engineering Excellence and an MIT Mathworks Fellowship.

© Credit: Courtesy of the researchers

Sixty-three years after their formal separation, NUS and Universiti Malaya (UM) continue to honour their shared origins and enduring relationship.

Late last month, the two universities came together in Ipoh, Malaysia, to celebrate their social and academic ties through the 54^th edition of the UM-NUS Inter-University Tunku Chancellor Golf Tournament, as well as the concurrent UM-NUS Joint Academic Symposium on Precision Health.

To mark the occasion, a royal dinner was held on 25 August 2025 at the Istana Iskandariah, Kuala Kangsar. The event was hosted by the UM Chancellor, His Royal Highness (HRH) Sultan Dr Nazrin Muizzuddin Shah, Sultan of Perak, and the Raja Permaisuri of Perak, Her Royal Highness Tuanku Zara Salim. It was attended by the NUS Chancellor and President of the Republic of Singapore, Mr Tharman Shanmugaratnam, and his spouse, Mrs Jane Ittogi Shanmugaratnam, as well as Singapore’s High Commissioner to Malaysia, Mr Vanu Gopala Menon (Business ’85) and his spouse, Mrs Jayanthi Menon (Law ’89). Other members of the NUS delegation included Mr Hsieh Fu Hua, Chairman of the NUS Board of Trustees; Professor Tan Eng Chye (Science ’85), NUS President; and Ms Ovidia Lim-Rajaram (Arts & Social Sciences ’89), NUS Chief Alumni Officer, alongside participating academics and golfers.

In remarks made at the dinner, Professor Tan stated, “This is the second year we have paired the tournament with an academic symposium, symbolising the diverse and multilayered nature of the UM-NUS relationship: one that values both personal connection and intellectual exchange, each deepening and enriching the other.”

The sentiment was echoed by the Vice-Chancellor of UM, Professor Dato' Seri Ir. Dr Noor Azuan bin Abu Osman, who observed, “What gives universities their unique power is that we do not compete for resources in the same way states or corporations do. Instead, we compete by collaborating, and we grow stronger by sharing.”

Partners in progress

This commitment to collaboration and knowledge exchange was evident at the joint academic symposium on 26 August, themed around precision health. The event featured presentations from 10 distinguished researchers and professors from both UM and NUS, with speakers highlighting the ongoing transformation of healthcare from a one-size-fits-all model to one driven by personalised medicine and data-driven solutions.

“It’s always meaningful when we can come together to leverage our complementary strengths and address shared challenges,” said Professor Tai E Shyong from the NUS Yong Loo Lin School of Medicine. “UM and NUS have tremendous potential to advance medical research focused on Asian populations, with events like this providing a valuable platform to exchange ideas, foster collaboration, and drive progress in ways that ultimately improve the healthcare outcomes of our communities.”

Friendship in full swing

Complementing the joint symposium, the UM-NUS Inter-University Tunku Chancellor Golf Tournament took place on 26 August 2025 at the Royal Perak Golf Club, following a social round held the previous day at the Meru Valley Golf & Country Club.  More than 100 golfers—including faculty, staff and alumni from both institutions—participated in the event.  

First held in 1968 as a symbol of the strong relationship between senior leaders of UM and NUS, the longstanding tournament is hosted alternately by the two universities, strengthening ties across their academic and alumni communities.

This year, the UM team emerged victorious, capping off an eventful two days reaffirming more than six decades of friendship and collaboration.

 

By NUS Office of Alumni Relations

By Dr Jean-Loup Samaan, Senior Research Fellow from the Middle East Institute at NUS

• CNA Online, 30 August 2025

Campus & Community

Why institutions thrive on disagreement

Jason Sweet

Harvard Staff Writer

September 2, 2025 3 min read

Garber points to value of argument in sustaining communities at first Morning Prayers of school year

Caroline Uhler is an Andrew (1956) and Erna Viterbi Professor of Engineering at MIT; a professor of electrical engineering and computer science in the Institute for Data, Science, and Society (IDSS); and director of the Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard, where she is also a core institute and scientific leadership team member. 

Uhler is interested in all the methods by which scientists can uncover causality in biological systems, ranging from causal discovery on observed variables to causal feature learning and representation learning. In this interview, she discusses machine learning in biology, areas that are ripe for problem-solving, and cutting-edge research coming out of the Schmidt Center.

Q: The Eric and Wendy Schmidt Center has four distinct areas of focus structured around four natural levels of biological organization: proteins, cells, tissues, and organisms. What, within the current landscape of machine learning, makes now the right time to work on these specific problem classes?

A: Biology and medicine are currently undergoing a “data revolution.” The availability of large-scale, diverse datasets — ranging from genomics and multi-omics to high-resolution imaging and electronic health records — makes this an opportune time. Inexpensive and accurate DNA sequencing is a reality, advanced molecular imaging has become routine, and single cell genomics is allowing the profiling of millions of cells. These innovations — and the massive datasets they produce — have brought us to the threshold of a new era in biology, one where we will be able to move beyond characterizing the units of life (such as all proteins, genes, and cell types) to understanding the `programs of life’, such as the logic of gene circuits and cell-cell communication that underlies tissue patterning and the molecular mechanisms that underlie the genotype-phenotype map.

At the same time, in the past decade, machine learning has seen remarkable progress with models like BERT, GPT-3, and ChatGPT demonstrating advanced capabilities in text understanding and generation, while vision transformers and multimodal models like CLIP have achieved human-level performance in image-related tasks. These breakthroughs provide powerful architectural blueprints and training strategies that can be adapted to biological data. For instance, transformers can model genomic sequences similar to language, and vision models can analyze medical and microscopy images.

Importantly, biology is poised to be not just a beneficiary of machine learning, but also a significant source of inspiration for new ML research. Much like agriculture and breeding spurred modern statistics, biology has the potential to inspire new and perhaps even more profound avenues of ML research. Unlike fields such as recommender systems and internet advertising, where there are no natural laws to discover and predictive accuracy is the ultimate measure of value, in biology, phenomena are physically interpretable, and causal mechanisms are the ultimate goal. Additionally, biology boasts genetic and chemical tools that enable perturbational screens on an unparalleled scale compared to other fields. These combined features make biology uniquely suited to both benefit greatly from ML and serve as a profound wellspring of inspiration for it.

Q: Taking a somewhat different tack, what problems in biology are still really resistant to our current tool set? Are there areas, perhaps specific challenges in disease or in wellness, which you feel are ripe for problem-solving?

A: Machine learning has demonstrated remarkable success in predictive tasks across domains such as image classification, natural language processing, and clinical risk modeling. However, in the biological sciences, predictive accuracy is often insufficient. The fundamental questions in these fields are inherently causal: How does a perturbation to a specific gene or pathway affect downstream cellular processes? What is the mechanism by which an intervention leads to a phenotypic change? Traditional machine learning models, which are primarily optimized for capturing statistical associations in observational data, often fail to answer such interventional queries.There is a strong need for biology and medicine to also inspire new foundational developments in machine learning. 

The field is now equipped with high-throughput perturbation technologies — such as pooled CRISPR screens, single-cell transcriptomics, and spatial profiling — that generate rich datasets under systematic interventions. These data modalities naturally call for the development of models that go beyond pattern recognition to support causal inference, active experimental design, and representation learning in settings with complex, structured latent variables. From a mathematical perspective, this requires tackling core questions of identifiability, sample efficiency, and the integration of combinatorial, geometric, and probabilistic tools. I believe that addressing these challenges will not only unlock new insights into the mechanisms of cellular systems, but also push the theoretical boundaries of machine learning.

With respect to foundation models, a consensus in the field is that we are still far from creating a holistic foundation model for biology across scales, similar to what ChatGPT represents in the language domain — a sort of digital organism capable of simulating all biological phenomena. While new foundation models emerge almost weekly, these models have thus far been specialized for a specific scale and question, and focus on one or a few modalities.

Significant progress has been made in predicting protein structures from their sequences. This success has highlighted the importance of iterative machine learning challenges, such as CASP (critical assessment of structure prediction), which have been instrumental in benchmarking state-of-the-art algorithms for protein structure prediction and driving their improvement.

The Schmidt Center is organizing challenges to increase awareness in the ML field and make progress in the development of methods to solve causal prediction problems that are so critical for the biomedical sciences. With the increasing availability of single-gene perturbation data at the single-cell level, I believe predicting the effect of single or combinatorial perturbations, and which perturbations could drive a desired phenotype, are solvable problems. With our Cell Perturbation Prediction Challenge (CPPC), we aim to provide the means to objectively test and benchmark algorithms for predicting the effect of new perturbations.

Another area where the field has made remarkable strides is disease diagnostic and patient triage. Machine learning algorithms can integrate different sources of patient information (data modalities), generate missing modalities, identify patterns that may be difficult for us to detect, and help stratify patients based on their disease risk. While we must remain cautious about potential biases in model predictions, the danger of models learning shortcuts instead of true correlations, and the risk of automation bias in clinical decision-making, I believe this is an area where machine learning is already having a significant impact.

Q: Let’s talk about some of the headlines coming out of the Schmidt Center recently. What current research do you think people should be particularly excited about, and why? 

A: In collaboration with Dr. Fei Chen at the Broad Institute, we have recently developed a method for the prediction of unseen proteins’ subcellular location, called PUPS. Many existing methods can only make predictions based on the specific protein and cell data on which they were trained. PUPS, however, combines a protein language model with an image in-painting model to utilize both protein sequences and cellular images. We demonstrate that the protein sequence input enables generalization to unseen proteins, and the cellular image input captures single-cell variability, enabling cell-type-specific predictions. The model learns how relevant each amino acid residue is for the predicted sub-cellular localization, and it can predict changes in localization due to mutations in the protein sequences. Since proteins’ function is strictly related to their subcellular localization, our predictions could provide insights into potential mechanisms of disease. In the future, we aim to extend this method to predict the localization of multiple proteins in a cell and possibly understand protein-protein interactions.

Together with Professor G.V. Shivashankar, a long-time collaborator at ETH Zürich, we have previously shown how simple images of cells stained with fluorescent DNA-intercalating dyes to label the chromatin can yield a lot of information about the state and fate of a cell in health and disease, when combined with machine learning algorithms. Recently, we have furthered this observation and proved the deep link between chromatin organization and gene regulation by developing Image2Reg, a method that enables the prediction of unseen genetically or chemically perturbed genes from chromatin images. Image2Reg utilizes convolutional neural networks to learn an informative representation of the chromatin images of perturbed cells. It also employs a graph convolutional network to create a gene embedding that captures the regulatory effects of genes based on protein-protein interaction data, integrated with cell-type-specific transcriptomic data. Finally, it learns a map between the resulting physical and biochemical representation of cells, allowing us to predict the perturbed gene modules based on chromatin images.

Furthermore, we recently finalized the development of a method for predicting the outcomes of unseen combinatorial gene perturbations and identifying the types of interactions occurring between the perturbed genes. MORPH can guide the design of the most informative perturbations for lab-in-a-loop experiments. Furthermore, the attention-based framework provably enables our method to identify causal relations among the genes, providing insights into the underlying gene regulatory programs. Finally, thanks to its modular structure, we can apply MORPH to perturbation data measured in various modalities, including not only transcriptomics, but also imaging. We are very excited about the potential of this method to enable the efficient exploration of the perturbation space to advance our understanding of cellular programs by bridging causal theory to important applications, with implications for both basic research and therapeutic applications.

© Photo: Jiin Kang

One in every eight people — 970 million globally — live with mental illness, according to the World Health Organization, with depression and anxiety being the most common mental health conditions worldwide. Existing therapies for complex psychiatric disorders like depression, anxiety, and schizophrenia have limitations, and federal funding to address these shortcomings is growing increasingly uncertain.

Patricia and James Poitras ’63 have committed $8 million to the Poitras Center for Psychiatric Disorders Research to launch pioneering research initiatives aimed at uncovering the brain basis of major mental illness and accelerating the development of novel treatments.

“Federal funding rarely supports the kind of bold, early-stage research that has the potential to transform our understanding of psychiatric illness. Pat and I want to help fill that gap — giving researchers the freedom to follow their most promising leads, even when the path forward isn’t guaranteed,” says James Poitras, who is chair of the McGovern Institute for Brain Research board.

Their latest gift builds upon their legacy of philanthropic support for psychiatric disorders research at MIT, which now exceeds $46 million.

“With deep gratitude for Jim and Pat’s visionary support, we are eager to launch a bold set of studies aimed at unraveling the neural and cognitive underpinnings of major mental illnesses,” says Professor Robert Desimone, director of the McGovern Institute, home to the Poitras Center. “Together, these projects represent a powerful step toward transforming how we understand and treat mental illness.”

A legacy of support

Soon after joining the McGovern Institute Leadership Board in 2006, the Poitrases made a $20 million commitment to establish the Poitras Center for Psychiatric Disorders Research at MIT. The center’s goal, to improve human health by addressing the root causes of complex psychiatric disorders, is deeply personal to them both.

“We had decided many years ago that our philanthropic efforts would be directed towards psychiatric research. We could not have imagined then that this perfect synergy between research at MIT’s McGovern Institute and our own philanthropic goals would develop,” recalls Patricia. 

The center supports research at the McGovern Institute and collaborative projects with institutions such as the Broad Institute of MIT and Harvard, McLean Hospital, Mass General Brigham, and other clinical research centers. Since its establishment in 2007, the center has enabled advances in psychiatric research including the development of a machine learning “risk calculator” for bipolar disorder, the use of brain imaging to predict treatment outcomes for anxiety, and studies demonstrating that mindfulness can improve mental health in adolescents.

For the past decade, the Poitrases have also fueled breakthroughs in the lab of McGovern investigator and MIT Professor Feng Zhang, backing the invention of powerful CRISPR systems and other molecular tools that are transforming biology and medicine. Their support has enabled the Zhang team to engineer new delivery vehicles for gene therapy, including vehicles capable of carrying genetic payloads that were once out of reach. The lab has also advanced innovative RNA-guided gene engineering tools such as NovaIscB, published in Nature Biotechnology in May 2025. These revolutionary genome editing and delivery technologies hold promise for the next generation of therapies needed for serious psychiatric illness.

In addition to fueling research in the center, the Poitras family has gifted two endowed professorships — the James and Patricia Poitras Professor of Neuroscience at MIT, currently held by Feng Zhang, and the James W. (1963) and Patricia T. Poitras Professor of Brain and Cognitive Sciences at MIT, held by Guoping Feng — and an annual postdoctoral fellowship at the McGovern Institute.

New initiatives at the Poitras Center

The Poitras family’s latest commitment to the Poitras Center will launch an ambitious set of new projects that bring together neuroscientists, clinicians, and computational experts to probe underpinnings of complex psychiatric disorders including schizophrenia, anxiety, and depression. These efforts reflect the center’s core mission: to speed scientific discovery and therapeutic innovation in the field of psychiatric brain disorders research.

McGovern cognitive neuroscientists Evelina Fedorenko PhD ’07, an associate professor, and Nancy Kanwisher ’80, PhD ’86, the Walter A. Rosenblith Professor of Cognitive Neuroscience — in collaboration with psychiatrist Ann Shinn of McLean Hospital — will explore how altered inner speech and reasoning contribute to the symptoms of schizophrenia. They will collect functional MRI data from individuals diagnosed with schizophrenia and matched controls as they perform reasoning tasks. The goal is to identify the brain activity patterns that underlie impaired reasoning in schizophrenia, a core cognitive disruption in the disorder.

A complementary line of investigation will focus on the role of inner speech — the “voice in our head” that shapes thought and self-awareness. The team will conduct a large-scale online behavioral study of neurotypical individuals to analyze how inner speech characteristics correlate with schizophrenia-spectrum traits. This will be followed by neuroimaging work comparing brain architecture among individuals with strong or weak inner voices and people with schizophrenia, with the aim of discovering neural markers linked to self-talk and disrupted cognition.

A different project led by McGovern neuroscientist and MIT Associate Professor Mark Harnett and 2024–2026 Poitras Center Postdoctoral Fellow Cynthia Rais focuses on how ketamine — an increasingly used antidepressant — alters brain circuits to produce rapid and sustained improvements in mood. Despite its clinical success, ketamine’s mechanisms of action remain poorly understood. The Harnett lab is using sophisticated tools to track how ketamine affects synaptic communication and large-scale brain network dynamics, particularly in models of treatment-resistant depression. By mapping these changes at both the cellular and systems levels, the team hopes to reveal how ketamine lifts mood so quickly — and inform the development of safer, longer-lasting antidepressants.

Guoping Feng is leveraging a new animal model of depression to uncover the brain circuits that drive major depressive disorder. The new animal model provides a powerful system for studying the intricacies of mood regulation. Feng’s team is using state-of-the-art molecular tools to identify the specific genes and cell types involved in this circuit, with the goal of developing targeted treatments that can fine-tune these emotional pathways.

“This is one of the most promising models we have for understanding depression at a mechanistic level,” says Feng, who is also associate director of the McGovern Institute. “It gives us a clear target for future therapies.”

Another novel approach to treating mood disorders comes from the lab of James DiCarlo, the Peter de Florez Professor of Neuroscience at MIT, who is exploring the brain’s visual-emotional interface as a therapeutic tool for anxiety. The amygdala, a key emotional center in the brain, is heavily influenced by visual input. DiCarlo’s lab is using advanced computational models to design visual scenes that may subtly shift emotional processing in the brain — essentially using sight to regulate mood. Unlike traditional therapies, this strategy could offer a noninvasive, drug-free option for individuals suffering from anxiety.

Together, these projects exemplify the kind of interdisciplinary, high-impact research that the Poitras Center was established to support.

“Mental illness affects not just individuals, but entire families who often struggle in silence and uncertainty,” adds Patricia Poitras. “Our hope is that Poitras Center scientists will continue to make important advancements and spark novel treatments for complex mental health disorders and, most of all, give families living with these conditions a renewed sense of hope for the future.”

© Photo: Justin Knight

One in every eight people — 970 million globally — live with mental illness, according to the World Health Organization, with depression and anxiety being the most common mental health conditions worldwide. Existing therapies for complex psychiatric disorders like depression, anxiety, and schizophrenia have limitations, and federal funding to address these shortcomings is growing increasingly uncertain.

Patricia and James Poitras ’63 have committed $8 million to the Poitras Center for Psychiatric Disorders Research to launch pioneering research initiatives aimed at uncovering the brain basis of major mental illness and accelerating the development of novel treatments.

“Federal funding rarely supports the kind of bold, early-stage research that has the potential to transform our understanding of psychiatric illness. Pat and I want to help fill that gap — giving researchers the freedom to follow their most promising leads, even when the path forward isn’t guaranteed,” says James Poitras, who is chair of the McGovern Institute for Brain Research board.

Their latest gift builds upon their legacy of philanthropic support for psychiatric disorders research at MIT, which now exceeds $46 million.

“With deep gratitude for Jim and Pat’s visionary support, we are eager to launch a bold set of studies aimed at unraveling the neural and cognitive underpinnings of major mental illnesses,” says Professor Robert Desimone, director of the McGovern Institute, home to the Poitras Center. “Together, these projects represent a powerful step toward transforming how we understand and treat mental illness.”

A legacy of support

Soon after joining the McGovern Institute Leadership Board in 2006, the Poitrases made a $20 million commitment to establish the Poitras Center for Psychiatric Disorders Research at MIT. The center’s goal, to improve human health by addressing the root causes of complex psychiatric disorders, is deeply personal to them both.

“We had decided many years ago that our philanthropic efforts would be directed towards psychiatric research. We could not have imagined then that this perfect synergy between research at MIT’s McGovern Institute and our own philanthropic goals would develop,” recalls Patricia. 

The center supports research at the McGovern Institute and collaborative projects with institutions such as the Broad Institute of MIT and Harvard, McLean Hospital, Mass General Brigham, and other clinical research centers. Since its establishment in 2007, the center has enabled advances in psychiatric research including the development of a machine learning “risk calculator” for bipolar disorder, the use of brain imaging to predict treatment outcomes for anxiety, and studies demonstrating that mindfulness can improve mental health in adolescents.

For the past decade, the Poitrases have also fueled breakthroughs in the lab of McGovern investigator and MIT Professor Feng Zhang, backing the invention of powerful CRISPR systems and other molecular tools that are transforming biology and medicine. Their support has enabled the Zhang team to engineer new delivery vehicles for gene therapy, including vehicles capable of carrying genetic payloads that were once out of reach. The lab has also advanced innovative RNA-guided gene engineering tools such as NovaIscB, published in Nature Biotechnology in May 2025. These revolutionary genome editing and delivery technologies hold promise for the next generation of therapies needed for serious psychiatric illness.

In addition to fueling research in the center, the Poitras family has gifted two endowed professorships — the James and Patricia Poitras Professor of Neuroscience at MIT, currently held by Feng Zhang, and the James W. (1963) and Patricia T. Poitras Professor of Brain and Cognitive Sciences at MIT, held by Guoping Feng — and an annual postdoctoral fellowship at the McGovern Institute.

New initiatives at the Poitras Center

The Poitras family’s latest commitment to the Poitras Center will launch an ambitious set of new projects that bring together neuroscientists, clinicians, and computational experts to probe underpinnings of complex psychiatric disorders including schizophrenia, anxiety, and depression. These efforts reflect the center’s core mission: to speed scientific discovery and therapeutic innovation in the field of psychiatric brain disorders research.

McGovern cognitive neuroscientists Evelina Fedorenko PhD ’07, an associate professor, and Nancy Kanwisher ’80, PhD ’86, the Walter A. Rosenblith Professor of Cognitive Neuroscience — in collaboration with psychiatrist Ann Shinn of McLean Hospital — will explore how altered inner speech and reasoning contribute to the symptoms of schizophrenia. They will collect functional MRI data from individuals diagnosed with schizophrenia and matched controls as they perform reasoning tasks. The goal is to identify the brain activity patterns that underlie impaired reasoning in schizophrenia, a core cognitive disruption in the disorder.

A complementary line of investigation will focus on the role of inner speech — the “voice in our head” that shapes thought and self-awareness. The team will conduct a large-scale online behavioral study of neurotypical individuals to analyze how inner speech characteristics correlate with schizophrenia-spectrum traits. This will be followed by neuroimaging work comparing brain architecture among individuals with strong or weak inner voices and people with schizophrenia, with the aim of discovering neural markers linked to self-talk and disrupted cognition.

A different project led by McGovern neuroscientist and MIT Associate Professor Mark Harnett and 2024–2026 Poitras Center Postdoctoral Fellow Cynthia Rais focuses on how ketamine — an increasingly used antidepressant — alters brain circuits to produce rapid and sustained improvements in mood. Despite its clinical success, ketamine’s mechanisms of action remain poorly understood. The Harnett lab is using sophisticated tools to track how ketamine affects synaptic communication and large-scale brain network dynamics, particularly in models of treatment-resistant depression. By mapping these changes at both the cellular and systems levels, the team hopes to reveal how ketamine lifts mood so quickly — and inform the development of safer, longer-lasting antidepressants.

Guoping Feng is leveraging a new animal model of depression to uncover the brain circuits that drive major depressive disorder. The new animal model provides a powerful system for studying the intricacies of mood regulation. Feng’s team is using state-of-the-art molecular tools to identify the specific genes and cell types involved in this circuit, with the goal of developing targeted treatments that can fine-tune these emotional pathways.

“This is one of the most promising models we have for understanding depression at a mechanistic level,” says Feng, who is also associate director of the McGovern Institute. “It gives us a clear target for future therapies.”

Another novel approach to treating mood disorders comes from the lab of James DiCarlo, the Peter de Florez Professor of Neuroscience at MIT, who is exploring the brain’s visual-emotional interface as a therapeutic tool for anxiety. The amygdala, a key emotional center in the brain, is heavily influenced by visual input. DiCarlo’s lab is using advanced computational models to design visual scenes that may subtly shift emotional processing in the brain — essentially using sight to regulate mood. Unlike traditional therapies, this strategy could offer a noninvasive, drug-free option for individuals suffering from anxiety.

Together, these projects exemplify the kind of interdisciplinary, high-impact research that the Poitras Center was established to support.

“Mental illness affects not just individuals, but entire families who often struggle in silence and uncertainty,” adds Patricia Poitras. “Our hope is that Poitras Center scientists will continue to make important advancements and spark novel treatments for complex mental health disorders and, most of all, give families living with these conditions a renewed sense of hope for the future.”

© Photo: Justin Knight

Science & Tech

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September 2, 2025 4 min read

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A new and powerful particle detector just passed a critical test in its goal to decipher the ingredients of the early universe.

The sPHENIX detector is the newest experiment at Brookhaven National Laboratory’s Relativistic Heavy Ion Collider (RHIC) and is designed to precisely measure products of high-speed particle collisions. From the aftermath, scientists hope to reconstruct the properties of quark-gluon plasma (QGP) — a white-hot soup of subatomic particles known as quarks and gluons that is thought to have sprung into existence in the few microseconds following the Big Bang. Just as quickly, the mysterious plasma disappeared, cooling and combining to form the protons and neutrons that make up today’s ordinary matter.

Now, the sPHENIX detector has made a key measurement that proves it has the precision to help piece together the primordial properties of quark-gluon plasma.

In a paper in the Journal of High Energy Physics, scientists including physicists at MIT report that sPHENIX precisely measured the number and energy of particles that streamed out from gold ions that collided at close to the speed of light.

Straight ahead

This test is considered in physics to be a “standard candle,” meaning that the measurement is a well-established constant that can be used to gauge a detector’s precision.

In particular, sPHENIX successfully measured the number of charged particles that are produced when two gold ions collide, and determined how this number changes when the ions collide head-on, versus just glancing by. The detector’s measurements revealed that head-on collisions produced 10 times more charged particles, which were also 10 times more energetic, compared to less straight-on collisions.

“This indicates the detector works as it should,” says Gunther Roland, professor of physics at MIT, who is a member and former spokesperson for the sPHENIX Collaboration. “It’s as if you sent a new telescope up in space after you’ve spent 10 years building it, and it snaps the first picture. It’s not necessarily a picture of something completely new, but it proves that it’s now ready to start doing new science.”

“With this strong foundation, sPHENIX is well-positioned to advance the study of the quark-gluon plasma with greater precision and improved resolution,” adds Hao-Ren Jheng, a graduate student in physics at MIT and a lead co-author of the new paper. “Probing the evolution, structure, and properties of the QGP will help us reconstruct the conditions of the early universe.”

The paper’s co-authors are all members of the sPHENIX Collaboration, which comprises over 300 scientists from multiple institutions around the world, including Roland, Jheng, and physicists at MIT’s Bates Research and Engineering Center.

“Gone in an instant”

Particle colliders such as Brookhaven’s RHIC are designed to accelerate particles at “relativistic” speeds, meaning close to the speed of light. When these particles are flung around in opposite, circulating beams and brought back together, any smash-ups that occur can release an enormous amount of energy. In the right conditions, this energy can very briefly exist in the form of quark-gluon plasma — the same stuff that sprung out of the Big Bang.

Just as in the early universe, quark-gluon plasma doesn’t hang around for very long in particle colliders. If and when QGP is produced, it exists for just 10 to the minus 22, or about a sextillionth, of a second. In this moment, quark-gluon plasma is incredibly hot, up to several trillion degrees Celsius, and behaves as a “perfect fluid,” moving as one entity rather than as a collection of random particles. Almost immediately, this exotic behavior disappears, and the plasma cools and transitions into more ordinary particles such as protons and neutrons, which stream out from the main collision.

“You never see the QGP itself — you just see its ashes, so to speak, in the form of the particles that come from its decay,” Roland says. “With sPHENIX, we want to measure these particles to reconstruct the properties of the QGP, which is essentially gone in an instant.”

“One in a billion”

The sPHENIX detector is the next generation of Brookhaven’s original Pioneering High Energy Nuclear Interaction eXperiment, or PHENIX, which measured collisions of heavy ions generated by RHIC. In 2021, sPHENIX was installed in place of its predecessor, as a faster and more powerful version, designed to detect quark-gluon plasma’s more subtle and ephemeral signatures.

The detector itself is about the size of a two-story house and weighs around 1,000 tons. It sits at the intersection of RHIC’s two main collider beams, where relativistic particles, accelerated from opposite directions, meet and collide, producing particles that fly out into the detector. The sPHENIX detector is able to catch and measure 15,000 particle collisions per second, thanks to its novel, layered components, including the MVTX, or micro-vertex — a subdetector that was designed, built, and installed by scientists at MIT’s Bates Research and Engineering Center.

Together, the detector’s systems enable sPHENIX to act as a giant 3D camera that can track the number, energy, and paths of individual particles during an explosion of particles generated by a single collision.

“SPHENIX takes advantage of developments in detector technology since RHIC switched on 25 years ago, to collect data at the fastest possible rate,” says MIT postdoc Cameron Dean, who was a main contributor to the new study’s analysis. “This allows us to probe incredibly rare processes for the first time.”

In the fall of 2024, scientists ran the detector through the “standard candle” test to gauge its speed and precision. Over three weeks, they gathered data from sPHENIX as the main collider accelerated and smashed together beams of gold ions traveling at the speed of light. Their analysis of the data showed that sPHENIX accurately measured the number of charged particles produced in individual gold ion collisions, as well as the particles’ energies. What’s more, the detector was sensitive to a collision’s “head-on-ness,” and could observe that head-on collisions produced more particles with greater energy, compared to less direct collisions.

“This measurement provides clear evidence that the detector is functioning as intended,” Jheng says.

“The fun for sPHENIX is just beginning,” Dean adds. “We are currently back colliding particles and expect to do so for several more months. With all our data, we can look for the one-in-a-billion rare process that could give us insights on things like the density of QGP, the diffusion of particles through ultra-dense matter, and how much energy it takes to bind different particles together.”

This work was supported, in part, by the U.S. Department of Energy Office of Science, and the National Science Foundation.

© Credit: Courtesy of Brookhaven National Laboratory

For a long time, Satik Movsesyan envisioned a future of working in finance and also pursuing a full-time master’s degree program at the MIT Sloan School of Management. She says the MITx MicroMasters Program in Finance provides her with the ideal opportunity to directly enhance her career with courses developed and delivered by MIT Sloan faculty.

Movsesyan first began actively pursuing ways to connect with the MIT community as a first-year student in her undergraduate program at the American University of Armenia, where she majored in business with a concentration in accounting and finance. That’s when she discovered the MicroMasters Program in Finance. Led by MIT Open Learning and MIT Sloan, the program offers learners an opportunity to advance in the finance field through a rigorous, comprehensive online curriculum comprising foundational courses, mathematical methods, and advanced modeling. During her senior year, she started taking courses in the program, beginning with 15.516x (Financial Accounting).

“I saw completing the MicroMasters program as a way to accelerate my time at MIT offline, as well as to prepare me for the academic rigor,” says Movsesyan. “The program provides a way for me to streamline my studies, while also working toward transforming capital markets here in Armenia — in a way, also helping me to streamline my career.”

Movsesyan initially started as an intern at C-Quadrat Ampega Asset Management Armenia and was promoted to her current role of financial analyst. The firm is one of two pension asset managers in Armenia. Movsesyan credits the MicroMasters program with helping her to make deeper inferences in terms of analytical tasks and empowering her to create more enhanced dynamic models to support the efficient allocation of assets. Her learning has enabled her to build different valuation models for financial instruments. She is currently developing a portfolio management tool for her company.

“Although the courses are grounded deeply in theory, they never lack a perfect applicability component, which makes them very useful,” says Movsesyan. “Having MIT’s MicroMasters on a CV adds credibility as a professional, and your input becomes more valued by the employer.”

Movsesyan says that the program has helped her to develop resilience, as well as critical and analytical thinking. Her long-term goal is to become a portfolio manager and ultimately establish an asset management company, targeted at offering an extensive range of funds based on diverse risk-return preferences of investors, while promoting transparent and sustainable investment practices. 

“The knowledge I’ve gained from the variety of courses is a perfect blend which supports me day-to-day in building solutions to existing problems in asset management,” says Movsesyan.

In addition to being a learner in the program, Movsesyan serves as a community teaching assistant (CTA). After taking 15.516x, she became a CTA for that course, working with learners around the world. She says that this role of helping and supporting others requires constantly immersing herself in the course content, which also results in challenging herself and mastering the material.

“I think my story with the MITx MicroMasters Program is proof that no matter where you are — even if you’re in a small, developing country with limited resources — if you truly want to do something, you can achieve what you want,” says Movsesyan. “It’s an example for students around the world who also have transformative ideas and determination to take action. They can be a part of the MIT community.”

© Photo courtesy of Satik Movsesyan.

Measurements analysed by an international research team led by ETH Zurich show that the global ocean absorbed significantly less CO₂ than anticipated during the unprecedented marine heatwave in 2023.

• Lianhe Zaobao, 24 August 2025, News, p10

By Prof Lawrence Loh, Director of the Centre for Governance and Sustainability at NUS Business School

• The Business Times, 23 August 2025, p2

• CNA, 20 August 2025
• CNA Online, 20 August 2025
• Channel 5 News, 20 August 2025
• Vasantham News Online, 20 August 2025
• The Straits Times, 21 August 2025, The Big Story, Front Page & pA2
• Lianhe Zaobao, 21 August 2025, Singapore, Front Page & p6
• CNA938, 21 August 2025
• Money 89.3FM, 21 August 2025
• Kiss92FM, 21 August 2025
• One FM 91.3, 21 August 2025
• The New Paper, 21 August 2025
• Berita Harian, 21 August 2025, Front Page
• Tamil Murasu, 21 August 2025, Front Page
• Tabla!, 22 August 2025, p4
• Lianhe Zaobao, 25 August 2025, Opinion, p19

EPFL, ETH Zurich and the Swiss National Supercomputing Centre (CSCS) released Apertus 2 September, Switzerland’s first large-scale, open, multilingual language model — a milestone in generative AI for transparency and diversity.

• The Straits Times, 25 August 2025, Singapore, pA16

• Lianhe Zaobao, 25 August 2025, Singapore, p6

• Lianhe Zaobao, 1 September 2025, Singapore, p8

• Tamil Murasu Online, 31 August 2025
• The Straits Times, 1 September 2025, Singapore, pA15

By Dr Azhar Ibrahim Alwee, Senior Lecturer from the Dept of Malay Studies, Faculty of Arts and Social Sciences at NUS

• Suria News Online, 31 August 2025

For university students, summer break is more than a time to relax – it is also an opportunity for learning beyond the classroom.

Jazmine Lin, a third-year NUS Political Science undergraduate, participated in the Temasek-Foundation – NUS Leadership Enrichment and Regional Networking (TF - NUS LEaRN) Programme 2025 during the recent summer break, discovering that it offered her the best of both worlds: time to recharge combined with enriching learning experiences. She was among the 59 university students from across Southeast Asia who were able to learn more about the region, develop their leadership capabilities collaboratively and form new friendships through the programme.

Immersing in Chiang Mai’s culture

The programme kicked off in May with a two-week immersion in Chiang Mai, Thailand, hosted by the Language Institute Chiang Mai University (CMU). Thirty students from NUS, Singapore Institute of Technology and Singapore University of Social Sciences had the opportunity to interact with local community leaders and participate in various leadership development workshops. Through field trips, they were able to learn more about the different communities, their unique cultural practices and identities, as well as how the locals tapped into their surrounding resources to make a living.

One such visit took students to Nai Suan (which means ‘In the Garden’ in Thai), a community enterprise in the Mae Rim district that upcycles fallen leaves into biodegradable bowls and plates. This initiative not only promotes sustainable living, but it is also a source of income for the locals who collect the leaves. Students were invited to experience the process themselves – from washing the leaves and removing their veins, to moulding them into the final products by using a hydraulic press.

In the Doi Saket district, students visited the Ban Baiboon Thai-Tai Lue Wisdom Learning Center, which aims to preserve the indigenous Tai Lue culture by offering homestays and tours, as well as workshops where visitors can try their hand at various traditional crafts. Through touring traditional Tai Lue houses, observing the process of handicraft-making and sampling local snacks, students gained first-hand experience of the community’s authentic way of life.

The Chiang Mai leg concluded with a Sustainable Environment Hackathon, where students applied their learnings to develop solutions to address regional environmental challenges. They pitched their ideas to the faculty members from CMU’s Faculty of Science, sparking a lively exchange of innovation and teamwork.

Deepening regional understanding in Singapore

Upon returning to Singapore in July for the next leg of the programme, the students were joined by 29 peers from 19 universities across Southeast Asia.

In the first week, the students focused on community leadership, which emphasised skills such as teamwork and active listening. Students from the Nanyang Technological University and Singapore Management University TF-LEaRN programme also participated in the activities, creating a more dynamic environment.

For the second week, students explored the three key themes of greenery, water and racial harmony, all of which are hallmarks of Singapore’s identity and intrinsically important to national development. By participating in leadership seminars, fireside chats with NUS students and alumni who shared their experiences in community leadership, as well as learning journeys to Marina Barrage, Ba’alwie Mosque and a Veggie Rescue at Little India activity, they gained insights into Singapore’s approach to urban sustainability and multiculturalism. Many overseas students shared how this approach contrasted with those from their home countries, engendering enriching discussions on the region’s shared challenges and diverse approaches.

In the final week, students took part in a futures thinking segment conducted by the Lee Kuan Yew School of Public Policy at NUS, where they were introduced to tools such as horizon scanning and scenario communication, which are useful in anticipating trends and challenges, as well as developing effective strategies.

They were later divided into groups and tasked with identifying and addressing a community development issue in a Southeast Asian country of their choice. Through prototyping, brainstorming and presenting their ideas, students honed their ability to collaborate across cultural and academic disciplinary boundaries.

The entire programme has been an eye-opening experience for Jazmine. “Through the various talks, lectures and learning journeys, I saw how community leadership can come in many different forms. It was interesting to witness how different ideas came to life in both countries and the experience was made richer with the perspectives and insights from our Southeast Asian buddies. But beyond all the learnings, what stayed with me the most were the friendships built, and I believe this will endure well beyond the programme.”

By NUS Global Relations Office

Campus & Community

‘We mark your belonging here’

Christina Pazzanese

Harvard Staff Writer

September 1, 2025 4 min read

Garber urges Class of 2029 to teach, learn from one another, reject viewing world in simple binaries

• The Straits Times, 22 August 2025, Singapore, pA15
• Tamil Murasu, 22 August 2025, p3

By Dr Chew Han Ei, Senior Research Fellow and Head of Governance and Economy Cluster at the Institute of Policy Studies, Lee Kuan Yew School of Public Policy at NUS

• CNA Online, 22 August 2025

After 25 years at the NASA Jet Propulsion Laboratory, Richard Kornfeld is returning to his alma mater. Starting in September, he will take over the operational management of ETH Zurich Space, bringing extensive experience of space missions to his new role.

Princeton celebrated the the students' accomplishments at Opening Exercises.

Opening Exercises is part of Orientation programming introducing students to academic, co-curricular and extracurricular life at Princeton. Classes begin Tuesday, Sept. 2.

Wright emphasized the role of national laboratories in advancing science research priorities. 

Work & Economy

Why employers want workers with high EQs

Liz Mineo

Harvard Staff Writer

August 29, 2025 6 min read

‘Future of Jobs’ report highlights value of emotional intelligence

Arts & Culture

Viewing art like an expert

Sy Boles

Harvard Staff Writer

August 29, 2025 long read

Curators and conservators at the Harvard Art Museums zoom in on the tiny details that tell big stories about some of their favorite works

“Research and education require not only that we speak, but that we listen to and learn from one another,” the president told students at his annual Orientation talk on free expression.

After ten years, the Future Resilient Systems programme at the Singapore-ETH Centre (SEC) is drawing to a close. In our interview, Programme Director Jonas Jörin talks about the programme's successes and the future of resilience research.

• The Straits Times, 27 August 2025, The Big Story, pA5

• Lianhe Zaobao, 27 August 2025, Singapore, p6
• Berita Harian Online, 27 August 2025
• 8world Online, 27 August 2025

• Vasantham News Online, 26 August 2025
• CNA Online, 27 August 2025
• Money 89.3FM, 27 August 2025
• The Straits Times, 28 August 2025, Life, pC8

By Chen Jiani, a Master’s student from the Dept of Chinese Studies, Faculty of Arts and Social Sciences at NUS

• Lianhe Zaobao, 27 August 2025, Opinion, p19

Cancer cells provide healthy neighbouring cells with additional cell powerhouses to put them to work. This has been demonstrated by researchers at ETH Zurich in a new study. In this way, cancer is exploiting a mechanism that frequently serves to repair damaged cells.

How likely you think something is to happen depends on what you already believe about the circumstances. That is the simple concept behind Bayes’ rule, an approach to calculating probabilities, first proposed in 1763. Now, an international team of researchers has shown how Bayes’ rule operates in the quantum world. 
 
“I would say it is a breakthrough in mathematical physics,” said Professor Valerio Scarani, Deputy Director and Principal Investigator at the Centre for Quantum Technologies, and member of the team. His co-authors on the work published on 28 August 2025 in Physical Review Letters are Assistant Professor Ge Bai at the Hong Kong University of Science and Technology in China, and Professor Francesco Buscemi at Nagoya University in Japan. 

“Bayes’ rule has been helping us make smarter guesses for 250 years. Now we have taught it some quantum tricks,” said Prof Buscemi.

While researchers before them had proposed quantum analogues for Bayes’ rule, they are the first to derive a quantum Bayes’ rule from a fundamental principle.  

Conditional probability 

Bayes’ rule is named for Thomas Bayes, who first defined his rules for conditional probabilities in ‘An Essay Towards Solving a Problem in the Doctrine of Chances’.  

Consider a case in which a person tests positive for flu. They may have suspected they were sick, but this new information would change how they think about their health. Bayes’ rule provides a method to calculate the probability of flu conditioned not only on the test result and the chances of the test giving a wrong answer, but also on the individual’s initial beliefs. 

Bayes’ rule interprets probabilities as expressing degrees of belief in an event. This has been long debated, since some statisticians think that probabilities should be “objective” and not based on beliefs. However, in situations where beliefs are involved, Bayes’ rule is accepted as a guide for reasoning. This is why it has found widespread use from medical diagnosis and weather prediction to data science and machine learning. 

Principle of minimum change 

When calculating probabilities with Bayes’ rule, the principle of minimum change is obeyed. Mathematically, the principle of minimum change minimises the distance between the joint probability distributions of the initial and updated belief. Intuitively, this is the idea that for any new piece of information, beliefs are updated in the smallest possible way that is compatible with the new facts. In the case of the flu test, for example, a negative test would not imply that the person is healthy, but rather that they are less likely to have the flu. 

In their work, Prof Scarani, who is also from NUS Department of Physics, Asst Prof Bai, and Prof Buscemi began with a quantum analogue to the minimum change principle. They quantified change in terms of quantum fidelity, which is a measure of the closeness between quantum states.  

Researchers always thought a quantum Bayes’ rule should exist because quantum states define probabilities. For example, the quantum state of a particle provides the probability of it being found at different locations. The goal is to determine the whole quantum state, but the particle is only found at one location when a measurement is performed. This new information will then update the belief, boosting the probability around that location.  

The team derived their quantum Bayes’ rule by maximising the fidelity between two objects that represent the forward and the reverse process, in analogy with a classical joint probability distribution. Maximising fidelity is equivalent to minimising change. They found in some cases their equations matched the Petz recovery map, which was proposed by Dénes Petz in the 1980s and was later identified as one of the most likely candidates for the quantum Bayes’ rule based just on its properties.  

“This is the first time we have derived it from a higher principle, which could be a validation for using the Petz map,” said Prof Scarani. The Petz map has potential applications in quantum computing for tasks such as quantum error correction and machine learning. The team plans to explore whether applying the minimum change principle to other quantum measures might reveal other solutions.  

In an unhappy coincidence, the Covid-19 pandemic and Angie Jo’s doctoral studies in political science both began in 2019. Paradoxically, this global catastrophe helped define her primary research thrust.

As countries reacted with unprecedented fiscal measures to protect their citizens from economic collapse, Jo MCP ’19 discerned striking patterns among these interventions: Nations typically seen as the least generous on social welfare were suddenly deploying the most dramatic emergency responses.

“I wanted to understand why countries like the U.S., which famously offer minimal state support, suddenly mobilize an enormous emergency response to a crisis — only to let it vanish after the crisis passes,” says Jo.

Driven by this interest, Jo launched into a comparative exploration of welfare states that forms the backbone of her doctoral research. Her work examines how different types of welfare regimes respond to collective crises, and whether these responses lead to lasting institutional reforms or merely temporary patches.

A mismatch in investments

Jo’s research focuses on a particular subset of advanced industrialized democracies — countries like the United States, United Kingdom, Canada, and Australia — that political economists classify as “liberal welfare regimes.” These nations stand in contrast to the “social democratic welfare regimes” exemplified by Scandinavian countries.

“In everyday times, citizens in countries like Denmark or Sweden are already well-protected by a deep and comprehensive welfare state,” Jo explains. “When something like Covid hits, these countries were largely able to use the social policy tools and administrative infrastructure they already had, such as subsidized childcare and short-time work schemes that prevent mass layoffs.”

Liberal welfare regimes, however, exhibit a different pattern. During normal periods, "government assistance is viewed by many as the last resort,” Jo observes. “It’s means-tested and minimal, and the responsibility to manage risk is put on the individual.”

Yet when Covid struck, these same governments “spent historically unprecedented amounts on emergency aid to citizens, including stimulus checks, expanded unemployment insurance, child tax credits, grants, and debt forbearance that might normally have faced backlash from many Americans as government ‘handouts.’”

This stark contrast — minimal investment in social safety nets during normal times followed by massive crisis spending — lies at the heart of Jo’s inquiry. “What struck me was the mismatch: The U.S. invests so little in social welfare at baseline, but when crisis hits, it can suddenly unleash massive aid — just not in ways that stick. So what happens when the next crisis comes?”

From architecture to political economy

Jo took a winding path to studying welfare states in crisis. Born in South Korea, she moved with her family to California at age 3 as her parents sought an American education for their children. After moving back to Korea for high school, she attended Harvard University, where she initially focused on art and architecture.

“I thought I’d be an artist,” Jo recalls, “but I always had many interests, and I was very aware of different countries and different political systems, because we were moving around a lot.”

While studying architecture at Harvard, Jo’s academic focus pivoted.

“I realized that most of the decisions around how things get built, whether it’s a building or a city or infrastructure, are made by the government or by powerful private actors,” she explains. “The architect is the artist’s hand that is commissioned to execute, but the decisions behind it, I realized, were what interested me more.”

After a year working in macroeconomics research at a hedge fund, Jo found herself drawn to questions in political economy. “While I didn’t find the zero-sum game of finance compelling, I really wanted to understand the interactions between markets and governments that lay behind the trades,” she says.

Jo decided to pursue a master’s degree in city planning at MIT, where she studied the political economy of master-planning new cities as a form of industrial policy in China and South Korea, before transitioning to the political science PhD program. Her research focus shifted dramatically when the Covid-19 pandemic struck.

“It was the first time I realized, wow, these wealthy Western democracies have serious problems, too,” Jo says. “They are not dealing well with this pandemic and the structural inequalities and the deep tensions that have always been part of some of these societies, but are being tested even further by the enormity of this shock.”

The costs of crisis response

One of Jo’s key insights challenges conventional wisdom about fiscal conservatism. The assumption that keeping government small saves money in the long run may be fundamentally flawed when considering crisis response.

“What I’m exploring in my research is the irony that the less you invest in a capable, effective and well-resourced government, the more that backfires when a crisis inevitably hits and you have to patch up the holes,” Jo argues. “You’re not saving money; you’re deferring the cost.”

This inefficiency becomes particularly apparent when examining how different countries deployed aid during Covid. Countries like Denmark, with robust data systems connecting health records, employment information, and family data, could target assistance with precision. The United States, by contrast, relied on blunter instruments.

“If your system isn’t built to deliver aid in normal times, it won’t suddenly work well under pressure,” Jo explains. “The U.S. had to invent entire programs from scratch overnight — and many were clumsy, inefficient, or regressive.”

There is also a political aspect to this constraint. “Not only do liberal welfare countries lack the infrastructure to address crises, they are often governed by powerful constituencies that do not want to build it — they deliberately choose to enact temporary benefits that are precisely designed to fade,” Jo argues. “This perpetuates a cycle where short-term compensations are employed from crisis to crisis, constraining the permanent expansion of the welfare state.”

Missed opportunities

Jo’s dissertation also examines whether crises provide opportunities for institutional reform. Her second paper focuses on the 2008 financial crisis in the United States, and the Hardest Hit Fund, a program that allocated federal money to state housing finance agencies to prevent foreclosures.

“I ask why, with hundreds of millions in federal aid and few strings attached, state agencies ultimately helped so few underwater homeowners shed unmanageable debt burdens,” Jo says. “The money and the mandate were there — the transformative capacity wasn’t.”

Some states used the funds to pursue ambitious policy interventions, such as restructuring mortgage debt to permanently reduce homeowners’ principal and interest burdens. However, most opted for temporary solutions like helping borrowers make up missed payments, while preserving their original contract. Partisan politics, financial interests, and status quo bias are most likely responsible for these varying state strategies, Jo believes.

She sees this as “another case of the choice that governments have between throwing money at the problem as a temporary Band-Aid solution, or using a crisis as an opportunity to pursue more ambitious, deeper reforms that help people more sustainably in the long run.”

The significance of crisis response research

For Jo, understanding how welfare states respond to crises is not just an academic exercise, but a matter of profound human consequence.

“When there’s an event like the financial crisis or Covid, the scale of suffering and the welfare gap that emerges is devastating,” Jo emphasizes. “I believe political science should be actively studying these rare episodes, rather than disregarding them as once-in-a-century anomalies.”

Her research carries implications for how we think about welfare state design and crisis preparedness. As Jo notes, the most vulnerable members of society — “people who are unbanked, undocumented, people who have low or no tax liability because they don’t make enough, immigrants or those who don’t speak English or don’t have access to the internet or are unhoused” — are often invisible to relief systems.

As Jo prepares for her career in academia, she is motivated to apply her political science training to address such failures. “We’re going to have more crises, whether pandemics, AI, climate disasters, or financial shocks,” Jo warns. “Finding better ways to cover those people is essential, and is not something that our current welfare state — or our politics — are designed to handle.”

© Photo: Minda de Gunzburg Center for European Studies/Harvard University

Every year, global health experts are faced with a high-stakes decision: Which influenza strains should go into the next seasonal vaccine? The choice must be made months in advance, long before flu season even begins, and it can often feel like a race against the clock. If the selected strains match those that circulate, the vaccine will likely be highly effective. But if the prediction is off, protection can drop significantly, leading to (potentially preventable) illness and strain on health care systems.

This challenge became even more familiar to scientists in the years during the Covid-19 pandemic. Think back to the time (and time and time again), when new variants emerged just as vaccines were being rolled out. Influenza behaves like a similar, rowdy cousin, mutating constantly and unpredictably. That makes it hard to stay ahead, and therefore harder to design vaccines that remain protective.

To reduce this uncertainty, scientists at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the MIT Abdul Latif Jameel Clinic for Machine Learning in Health set out to make vaccine selection more accurate and less reliant on guesswork. They created an AI system called VaxSeer, designed to predict dominant flu strains and identify the most protective vaccine candidates, months ahead of time. The tool uses deep learning models trained on decades of viral sequences and lab test results to simulate how the flu virus might evolve and how the vaccines will respond.

Traditional evolution models often analyze the effect of single amino acid mutations independently. “VaxSeer adopts a large protein language model to learn the relationship between dominance and the combinatorial effects of mutations,” explains Wenxian Shi, a PhD student in MIT’s Department of Electrical Engineering and Computer Science, researcher at CSAIL, and lead author of a new paper on the work. “Unlike existing protein language models that assume a static distribution of viral variants, we model dynamic dominance shifts, making it better suited for rapidly evolving viruses like influenza.”

An open-access report on the study was published today in Nature Medicine.

The future of flu

VaxSeer has two core prediction engines: one that estimates how likely each viral strain is to spread (dominance), and another that estimates how effectively a vaccine will neutralize that strain (antigenicity). Together, they produce a predicted coverage score: a forward-looking measure of how well a given vaccine is likely to perform against future viruses.

The scale of the score could be from an infinite negative to 0. The closer the score to 0, the better the antigenic match of vaccine strains to the circulating viruses. (You can imagine it as the negative of some kind of “distance.”)

In a 10-year retrospective study, the researchers evaluated VaxSeer’s recommendations against those made by the World Health Organization (WHO) for two major flu subtypes: A/H3N2 and A/H1N1. For A/H3N2, VaxSeer’s choices outperformed the WHO’s in nine out of 10 seasons, based on retrospective empirical coverage scores (a surrogate metric of the vaccine effectiveness, calculated from the observed dominance from past seasons and experimental HI test results). The team used this to evaluate vaccine selections, as the effectiveness is only available for vaccines actually given to the population. 

For A/H1N1, it outperformed or matched the WHO in six out of 10 seasons. In one notable case, for the 2016 flu season, VaxSeer identified a strain that wasn’t chosen by the WHO until the following year. The model’s predictions also showed strong correlation with real-world vaccine effectiveness estimates, as reported by the CDC, Canada’s Sentinel Practitioner Surveillance Network, and Europe’s I-MOVE program. VaxSeer’s predicted coverage scores aligned closely with public health data on flu-related illnesses and medical visits prevented by vaccination.

So how exactly does VaxSeer make sense of all these data? Intuitively, the model first estimates how rapidly a viral strain spreads over time using a protein language model, and then determines its dominance by accounting for competition among different strains.

Once the model has calculated its insights, they’re plugged into a mathematical framework based on something called ordinary differential equations to simulate viral spread over time. For antigenicity, the system estimates how well a given vaccine strain will perform in a common lab test called the hemagglutination inhibition assay. This measures how effectively antibodies can inhibit the virus from binding to human red blood cells, which is a widely used proxy for antigenic match/antigenicity. 

Outpacing evolution

“By modeling how viruses evolve and how vaccines interact with them, AI tools like VaxSeer could help health officials make better, faster decisions — and stay one step ahead in the race between infection and immunity,” says Shi. 

VaxSeer currently focuses only on the flu virus’s HA (hemagglutinin) protein,the major antigen of influenza. Future versions could incorporate other proteins like NA (neuraminidase), and factors like immune history, manufacturing constraints, or dosage levels. Applying the system to other viruses would also require large, high-quality datasets that track both viral evolution and immune responses — data that aren’t always publicly available. The team, however is currently working on the methods that can predict viral evolution in low-data regimes building on relations between viral families

“Given the speed of viral evolution, current therapeutic development often lags behind. VaxSeer is our attempt to catch up,” says Regina Barzilay, the School of Engineering Distinguished Professor for AI and Health at MIT, AI lead of Jameel Clinic, and CSAIL principal investigator. 

“This paper is impressive, but what excites me perhaps even more is the team’s ongoing work on predicting viral evolution in low-data settings,” says Assistant Professor Jon Stokes of the Department of Biochemistry and Biomedical Sciences at McMaster University in Hamilton, Ontario. “The implications go far beyond influenza. Imagine being able to anticipate how antibiotic-resistant bacteria or drug-resistant cancers might evolve, both of which can adapt rapidly. This kind of predictive modeling opens up a powerful new way of thinking about how diseases change, giving us the opportunity to stay one step ahead and design clinical interventions before escape becomes a major problem.”

Shi and Barzilay wrote the paper with MIT CSAIL postdoc Jeremy Wohlwend ’16, MEng ’17, PhD ’25 and recent CSAIL affiliate Menghua Wu ’19, MEng ’20, PhD ’25. Their work was supported, in part, by the U.S. Defense Threat Reduction Agency and MIT Jameel Clinic.

© Image: Alex Gagne

Every year, global health experts are faced with a high-stakes decision: Which influenza strains should go into the next seasonal vaccine? The choice must be made months in advance, long before flu season even begins, and it can often feel like a race against the clock. If the selected strains match those that circulate, the vaccine will likely be highly effective. But if the prediction is off, protection can drop significantly, leading to (potentially preventable) illness and strain on health care systems.

This challenge became even more familiar to scientists in the years during the Covid-19 pandemic. Think back to the time (and time and time again), when new variants emerged just as vaccines were being rolled out. Influenza behaves like a similar, rowdy cousin, mutating constantly and unpredictably. That makes it hard to stay ahead, and therefore harder to design vaccines that remain protective.

To reduce this uncertainty, scientists at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the MIT Abdul Latif Jameel Clinic for Machine Learning in Health set out to make vaccine selection more accurate and less reliant on guesswork. They created an AI system called VaxSeer, designed to predict dominant flu strains and identify the most protective vaccine candidates, months ahead of time. The tool uses deep learning models trained on decades of viral sequences and lab test results to simulate how the flu virus might evolve and how the vaccines will respond.

Traditional evolution models often analyze the effect of single amino acid mutations independently. “VaxSeer adopts a large protein language model to learn the relationship between dominance and the combinatorial effects of mutations,” explains Wenxian Shi, a PhD student in MIT’s Department of Electrical Engineering and Computer Science, researcher at CSAIL, and lead author of a new paper on the work. “Unlike existing protein language models that assume a static distribution of viral variants, we model dynamic dominance shifts, making it better suited for rapidly evolving viruses like influenza.”

An open-access report on the study was published today in Nature Medicine.

The future of flu

VaxSeer has two core prediction engines: one that estimates how likely each viral strain is to spread (dominance), and another that estimates how effectively a vaccine will neutralize that strain (antigenicity). Together, they produce a predicted coverage score: a forward-looking measure of how well a given vaccine is likely to perform against future viruses.

The scale of the score could be from an infinite negative to 0. The closer the score to 0, the better the antigenic match of vaccine strains to the circulating viruses. (You can imagine it as the negative of some kind of “distance.”)

In a 10-year retrospective study, the researchers evaluated VaxSeer’s recommendations against those made by the World Health Organization (WHO) for two major flu subtypes: A/H3N2 and A/H1N1. For A/H3N2, VaxSeer’s choices outperformed the WHO’s in nine out of 10 seasons, based on retrospective empirical coverage scores (a surrogate metric of the vaccine effectiveness, calculated from the observed dominance from past seasons and experimental HI test results). The team used this to evaluate vaccine selections, as the effectiveness is only available for vaccines actually given to the population. 

For A/H1N1, it outperformed or matched the WHO in six out of 10 seasons. In one notable case, for the 2016 flu season, VaxSeer identified a strain that wasn’t chosen by the WHO until the following year. The model’s predictions also showed strong correlation with real-world vaccine effectiveness estimates, as reported by the CDC, Canada’s Sentinel Practitioner Surveillance Network, and Europe’s I-MOVE program. VaxSeer’s predicted coverage scores aligned closely with public health data on flu-related illnesses and medical visits prevented by vaccination.

So how exactly does VaxSeer make sense of all these data? Intuitively, the model first estimates how rapidly a viral strain spreads over time using a protein language model, and then determines its dominance by accounting for competition among different strains.

Once the model has calculated its insights, they’re plugged into a mathematical framework based on something called ordinary differential equations to simulate viral spread over time. For antigenicity, the system estimates how well a given vaccine strain will perform in a common lab test called the hemagglutination inhibition assay. This measures how effectively antibodies can inhibit the virus from binding to human red blood cells, which is a widely used proxy for antigenic match/antigenicity. 

Outpacing evolution

“By modeling how viruses evolve and how vaccines interact with them, AI tools like VaxSeer could help health officials make better, faster decisions — and stay one step ahead in the race between infection and immunity,” says Shi. 

VaxSeer currently focuses only on the flu virus’s HA (hemagglutinin) protein,the major antigen of influenza. Future versions could incorporate other proteins like NA (neuraminidase), and factors like immune history, manufacturing constraints, or dosage levels. Applying the system to other viruses would also require large, high-quality datasets that track both viral evolution and immune responses — data that aren’t always publicly available. The team, however is currently working on the methods that can predict viral evolution in low-data regimes building on relations between viral families

“Given the speed of viral evolution, current therapeutic development often lags behind. VaxSeer is our attempt to catch up,” says Regina Barzilay, the School of Engineering Distinguished Professor for AI and Health at MIT, AI lead of Jameel Clinic, and CSAIL principal investigator. 

“This paper is impressive, but what excites me perhaps even more is the team’s ongoing work on predicting viral evolution in low-data settings,” says Assistant Professor Jon Stokes of the Department of Biochemistry and Biomedical Sciences at McMaster University in Hamilton, Ontario. “The implications go far beyond influenza. Imagine being able to anticipate how antibiotic-resistant bacteria or drug-resistant cancers might evolve, both of which can adapt rapidly. This kind of predictive modeling opens up a powerful new way of thinking about how diseases change, giving us the opportunity to stay one step ahead and design clinical interventions before escape becomes a major problem.”

Shi and Barzilay wrote the paper with MIT CSAIL postdoc Jeremy Wohlwend ’16, MEng ’17, PhD ’25 and recent CSAIL affiliate Menghua Wu ’19, MEng ’20, PhD ’25. Their work was supported, in part, by the U.S. Defense Threat Reduction Agency and MIT Jameel Clinic.

© Image: Alex Gagne

After ten years, the Future Resilient Systems programme at the Singapore-ETH Centre (SEC) is drawing to a close. In our interview, Programme Director Jonas Jörin talks about the programme's successes and the future of resilience research.

Today’s electric vehicle boom is tomorrow’s mountain of electronic waste. And while myriad efforts are underway to improve battery recycling, many EV batteries still end up in landfills.

A research team from MIT wants to help change that with a new kind of self-assembling battery material that quickly breaks apart when submerged in a simple organic liquid. In a new paper published in Nature Chemistry, the researchers showed the material can work as the electrolyte in a functioning, solid-state battery cell and then revert back to its original molecular components in minutes.

The approach offers an alternative to shredding the battery into a mixed, hard-to-recycle mass. Instead, because the electrolyte serves as the battery’s connecting layer, when the new material returns to its original molecular form, the entire battery disassembles to accelerate the recycling process.

“So far in the battery industry, we’ve focused on high-performing materials and designs, and only later tried to figure out how to recycle batteries made with complex structures and hard-to-recycle materials,” says the paper’s first author Yukio Cho PhD ’23. “Our approach is to start with easily recyclable materials and figure out how to make them battery-compatible. Designing batteries for recyclability from the beginning is a new approach.”

Joining Cho on the paper are PhD candidate Cole Fincher, Ty Christoff-Tempesta PhD ’22, Kyocera Professor of Ceramics Yet-Ming Chiang, Visiting Associate Professor Julia Ortony, Xiaobing Zuo, and Guillaume Lamour.

Better batteries

There’s a scene in one of the “Harry Potter” films where Professor Dumbledore cleans a dilapidated home with the flick of the wrist and a spell. Cho says that image stuck with him as a kid. (What better way to clean your room?) When he saw a talk by Ortony on engineering molecules so that they could assemble into complex structures and then revert back to their original form, he wondered if it could be used to make battery recycling work like magic.

That would be a paradigm shift for the battery industry. Today, batteries require harsh chemicals, high heat, and complex processing to recycle. There are three main parts of a battery: the positively charged cathode, the negatively charged electrode, and the electrolyte that shuttles lithium ions between them. The electrolytes in most lithium-ion batteries are highly flammable and degrade over time into toxic byproducts that require specialized handling.

To simplify the recycling process, the researchers decided to make a more sustainable electrolyte. For that, they turned to a class of molecules that self-assemble in water, named aramid amphiphiles (AAs), whose chemical structures and stability mimic that of Kevlar. The researchers further designed the AAs to contain polyethylene glycol (PEG), which can conduct lithium ions, on one end of each molecule. When the molecules are exposed to water, they spontaneously form nanoribbons with ion-conducting PEG surfaces and bases that imitate the robustness of Kevlar through tight hydrogen bonding. The result is a mechanically stable nanoribbon structure that conducts ions across its surface.

“The material is composed of two parts,” Cho explains. “The first part is this flexible chain that gives us a nest, or host, for lithium ions to jump around. The second part is this strong organic material component that is used in the Kevlar, which is a bulletproof material. Those make the whole structure stable.”

When added to water, the nanoribbons self-assemble to form millions of nanoribbons that can be hot-pressed into a solid-state material.

“Within five minutes of being added to water, the solution becomes gel-like, indicating there are so many nanofibers formed in the liquid that they start to entangle each other,” Cho says. “What’s exciting is we can make this material at scale because of the self-assembly behavior.”

The team tested the material’s strength and toughness, finding it could endure the stresses associated with making and running the battery. They also constructed a solid-state battery cell that used lithium iron phosphate for the cathode and lithium titanium oxide as the anode, both common materials in today’s batteries. The nanoribbons moved lithium ions successfully between the electrodes, but a side-effect known as polarization limited the movement of lithium ions into the battery’s electrodes during fast bouts of charging and discharging, hampering its performance compared to today’s gold-standard commercial batteries.

“The lithium ions moved along the nanofiber all right, but getting the lithium ion from the nanofibers to the metal oxide seems to be the most sluggish point of the process,” Cho says.

When they immersed the battery cell into organic solvents, the material immediately dissolved, with each part of the battery falling away for easier recycling. Cho compared the materials’ reaction to cotton candy being submerged in water.

“The electrolyte holds the two battery electrodes together and provides the lithium-ion pathways,” Cho says. “So, when you want to recycle the battery, the entire electrolyte layer can fall off naturally and you can recycle the electrodes separately.”

Validating a new approach

Cho says the material is a proof of concept that demonstrates the recycle-first approach.

“We don’t want to say we solved all the problems with this material,” Cho says. “Our battery performance was not fantastic because we used only this material as the entire electrolyte for the paper, but what we’re picturing is using this material as one layer in the battery electrolyte. It doesn’t have to be the entire electrolyte to kick off the recycling process.”

Cho also sees a lot of room for optimizing the material’s performance with further experiments.

Now, the researchers are exploring ways to integrate these kinds of materials into existing battery designs as well as implementing the ideas into new battery chemistries.

“It’s very challenging to convince existing vendors to do something very differently,” Cho says. “But with new battery materials that may come out in five or 10 years, it could be easier to integrate this into new designs in the beginning.”

Cho also believes the approach could help reshore lithium supplies by reusing materials from batteries that are already in the U.S.

“People are starting to realize how important this is,” Cho says. “If we can start to recycle lithium-ion batteries from battery waste at scale, it’ll have the same effect as opening lithium mines in the U.S. Also, each battery requires a certain amount of lithium, so extrapolating out the growth of electric vehicles, we need to reuse this material to avoid massive lithium price spikes.”

The work was supported, in part, by the National Science Foundation and the U.S. Department of Energy. This work was performed, in part, using the MIT.nano Characterization facilities.

© Image: Courtesy of the researchers, edited by MIT News

In World War II, Britain was fighting for its survival against German aerial bombardment. Yet Britain was importing dyes from Germany at the same time. This sounds curious, to put it mildly. How can two countries at war with each other also be trading goods?

Examples of this abound, actually. Britain also traded with its enemies for almost all of World War I. India and Pakistan conducted trade with each other during the First Kashmir War, from 1947 to 1949, and during the India-Pakistan War of 1965. Croatia and then-Yugoslavia traded with each other while fighting in 1992.

“States do in fact trade with their enemies during wars,” says MIT political scientist Mariya Grinberg. “There is a lot of variation in which products get traded, and in which wars, and there are differences in how long trade lasts into a war. But it does happen.”

Indeed, as Grinberg has found, state leaders tend to calculate whether trade can give them an advantage by boosting their own economies while not supplying their enemies with anything too useful in the near term.

“At its heart, wartime trade is all about the tradeoff between military benefits and economic costs,” Grinberg says. “Severing trade denies the enemy access to your products that could increase their military capabilities, but it also incurs a cost to you because you’re losing trade and neutral states could take over your long-term market share.” Therefore, many countries try trading with their wartime foes.

Grinberg explores this topic in a groundbreaking new book, the first one on the subject, “Trade in War: Economic Cooperation Across Enemy Lines,” published this month by Cornell University Press. It is also the first book by Grinberg, an assistant professor of political science at MIT.

Calculating time and utility

“Trade in War” has its roots in research Grinberg started as a doctoral student at the University of Chicago, where she noticed that wartime trade was a phenomenon not yet incorporated into theories of state behavior.

Grinberg wanted to learn about it comprehensively, so, as she quips, “I did what academics usually do: I went to the work of historians and said, ‘Historians, what have you got for me?’”

Modern wartime trading began during the Crimean War, which pitted Russia against France, Britain, the Ottoman Empire, and other allies. Before the war’s start in 1854, France had paid for many Russian goods that could not be shipped because ice in the Baltic Sea was late to thaw. To rescue its produce, France then persuaded Britain and Russia to adopt “neutral rights,” codified in the 1856 Declaration of Paris, which formalized the idea that goods in wartime could be shipped via neutral parties (sometimes acting as intermediaries for warring countries).

“This mental image that everyone has, that we don’t trade with our enemies during war, is actually an artifact of the world without any neutral rights,” Grinberg says. “Once we develop neutral rights, all bets are off, and now we have wartime trade.”

Overall, Grinberg’s systematic analysis of wartime trade shows that it needs to be understood on the level of particular goods. During wartime, states calculate how much it would hurt their own economies to stop trade of certain items; how useful specific products would be to enemies during war, and in what time frame; and how long a war is going to last.

“There are two conditions under which we can see wartime trade,” Grinberg says. “Trade is permitted when it does not help the enemy win the war, and it’s permitted when ending it would damage the state’s long-term economic security, beyond the current war.”

Therefore a state might export diamonds, knowing an adversary would need to resell such products over time to finance any military activities. Conversely, states will not trade products that can quickly convert into military use.

“The tradeoff is not the same for all products,” Grinberg says. “All products can be converted into something of military utility, but they vary in how long that takes. If I’m expecting to fight a short war, things that take a long time for my opponent to convert into military capabilities won’t help them win the current war, so they’re safer to trade.” Moreover, she adds, “States tend to prioritize maintaining their long-term economic stability, as long as the stakes don’t hit too close to home.”

This calculus helps explain some seemingly inexplicable wartime trade decisions. In 1917, three years into World War I, Germany started trading dyes to Britain. As it happens, dyes have military uses, for example as coatings for equipment. And World War I, infamously, was lasting far beyond initial expectations. But as of 1917, German planners thought the introduction of unrestricted submarine warfare would bring the war to a halt in their favor within a few months, so they approved the dye exports. That calculation was wrong, but it fits the framework Grinberg has developed.

States: Usually wrong about the length of wars

“Trade in War” has received praise from other scholars in the field. Michael Mastanduno of Dartmouth College has said the book “is a masterful contribution to our understanding of how states manage trade-offs across economics and security in foreign policy.”

For her part, Grinberg notes that her work holds multiple implications for international relations — one being that trade relationships do not prevent hostilities from unfolding, as some have theorized.

“We can’t expect even strong trade relations to deter a conflict,” Grinberg says. “On the other hand, when we learn our assumptions about the world are not necessarily correct, we can try to find different levers to deter war.”

Grinberg has also observed that states are not good, by any measure, at projecting how long they will be at war.

“States very infrequently get forecasts about the length of war right,” Grinberg says. That fact has formed the basis of a second, ongoing Grinberg book project.

“Now I’m studying why states go to war unprepared, why they think their wars are going to end quickly,” Grinberg says. “If people just read history, they will learn almost all of human history works against this assumption.”

At the same time, Grinberg thinks there is much more that scholars could learn specifically about trade and economic relations among warring countries — and hopes her book will spur additional work on the subject.

“I’m almost certain that I’ve only just begun to scratch the surface with this book,” she says. 

© Credit: Courtesy of Cornell University Press, and MIT Political Science

From bustling hawker centres to iconic historical architecture, Singapore’s rich cultural heritage holds powerful potential for storytelling. In celebration of the nation’s 60th year of independence, Pitch It! 2025 shines a spotlight on the theme “The Singapore Story”. Organised annually by the NUS Communications and New Media Society (CNM Society) since 2013, Pitch It! is a nationwide competition that encourages tertiary students to unleash their creativity and inspire social change through the use of diverse forms of media.

This year’s edition, held from April to July, was organised in partnership with Mediacorp’s Bloomr.SG (a content creator network to foster creative talents), the Singapore Heritage Society, and the Singapore Film Society. Featuring the problem statement “How can we enhance the telling of The Singapore Story – celebrating its colonial landmarks, traditional music, kampong life, and hawker food – in a way that fosters unity, pride and cross-cultural appreciation?”, Pitch It! 2025 challenged participants to develop compelling advertising or social media campaigns to bring these stories to life.

Learning to tell cultural stories

The four-month journey saw 24 student teams from polytechnics and universities competing for up to S$3000 worth of cash prizes. To kickstart the creative process, participants attended two highly anticipated masterclasses.

The first, co-led by Mr Han Ming Guang from the Singapore Heritage Society and Ms Priyanka Nair from the Singapore Film Society, highlighted the importance of authenticity in presenting Singapore’s cultural narratives and the role of youth in shaping national memory. Participants were encouraged to think critically about how campaigns could move beyond nostalgia to foster inclusivity, representation, and long-term relevance. Drawing from their own experiences in heritage conservation, Mr Han and Ms Nair shared the challenges they faced in raising public awareness and offered practical insights into the strategies and campaign approaches they employed to address them.

In the second masterclass, Mr Diogo Martins and Ms Denise Tan from Mediacorp’s Bloomr.SG introduced participants to the anatomy of a successful digital campaign. Using real-life case studies, participants explored how to integrate social media trends with emotive storytelling to drive public engagement. They came away with a deeper understanding of how creativity, data, and empathy intersect to create impactful media campaigns in today’s fast-paced digital landscape. Renee Chew, a Year 2 Communications and New Media student from the NUS Faculty of Arts and Social Sciences (FASS), said, “The masterclasses were incredibly engaging and helped us to develop our campaign effectively.”

From concept to campaign

Armed with new insights, teams conducted research and fieldwork and developed campaign concepts ranging from a video series to interactive heritage trails. Following the preliminary round, five standout teams were shortlisted to present their campaigns at the Grand Finals, where they received constructive feedback from industry judges and had the opportunity to refine their proposals ahead of the final pitch.

The Pitch It! 2025 Grand Finals took place in July, with teams Ixora, Wing Wing, Need Compass and Singapore FM presenting their campaigns to a distinguished panel of industry judges comprising the speakers from the earlier masterclasses – Mr Han, Ms Nair, Mr Martins and Ms Lisa Low from Mediacorp’s Bloomr.SG.

The top prize went to Team Wing Wing for their campaign #HearOurSingaporeStory, which centred on the power of sound to connect generations. At unique phone booths located near historic sites such as the Victoria Concert Hall, Clarke Quay and the Esplanade, seniors and youths can share personal stories about Singapore’s iconic landmarks and traditions – creating more authentic and emotionally resonant stories which can be shared and appreciated across generations. Mr Han highlighted how the team had a long-term vision to sustain their campaign beyond their initial proposal and had good environmental scanning. This was one of many reasons why their pitch stood out from the rest.

“It was really fun coming up with bold, creative ideas to bring the Singapore Story to life,” shared Shernice Feng, a second-year Communication Studies student from the Nanyang Technological University’s Wee Kim Wee School of Communication and Information and a member of the winning team. “I found the experience very fulfilling as it gave us a platform to explore our own interests and perspectives.”

Also featured at the Grand Finals was a panel discussion on “Heritage, Media and Inclusive Narratives” with Mr Han, Ms Nair and Dr Jinna Tay, Senior Lecturer from the FASS CNM Department. The panel explored key challenges in heritage communication, including how to represent culture responsibly amid commercial interests, adapting to shifting media consumption patterns, sustaining long-term interest in heritage beyond one-off campaigns, and finding a balance between data-driven content strategies and meaningful storytelling.

As Pitch It! 2025 drew to a close, students had the opportunity to network with speakers and partners, engaging in meaningful conversations that extended well beyond the formal programme.

Preparing our youth for the real world

The competition not only showcased the creativity of Singapore’s youth but also provided a meaningful learning experience – connecting students with industry mentors and giving them a glimpse into the realities of campaign development.

Chieng Josiah, CNM Society Vice-President in charge of external relations and events, and Year 2 CNM Student, was proud of his organising team, which included Project Directors Year 4 CNM student Ho Ee Hsuen and Year 2 CNM student Tran Nguyen Thao Anh, for persevering through the academic year to plan this major competition.

He shared, “It has been a memorable experience planning and improving on this year’s edition to commemorate SG60 with our culture-focused and media-focused masterclasses and Grand Finals. It was also heartwarming to see our participants from various schools connect with industry experts and experienced individuals in the local cultural and heritage scene. We hope Pitch It! continues to inspire our aspiring communications professionals!”

“Pitch It! is a great opportunity for students to tackle real-world industry challenges,” noted Mr Martins. “Through the process, they gain valuable experience in aggregating insights to form coherent solutions. It also helps build their confidence – in public speaking, pitching ideas effectively, and competing at a professional level.”

By the CNM Society at the NUS Faculty of Arts and Social Sciences

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Our cells produce a variety of proteins, each with a specific role that, in many cases, means that they need to be in a particular part of the cell where that role is needed. One of the ways that cells ensure certain proteins end up in the right location at the right time is through localized translation, a process that ensures that proteins are made — or translated — close to where they will be needed. MIT professor of biology and Whitehead Institute for Biomedical Research member Jonathan Weissman and colleagues have studied localized translation in order to understand how it affects cell functions and allows cells to quickly respond to changing conditions.

Now, Weissman, who is also a Howard Hughes Medical Institute Investigator, and postdoc in his lab Jingchuan Luo have expanded our knowledge of localized translation at mitochondria, structures that generate energy for the cell. In an open-access paper published today in Cell, they share a new tool, LOCL-TL, for studying localized translation in close detail, and describe the discoveries it enabled about two classes of proteins that are locally translated at mitochondria.

The importance of localized translation at mitochondria relates to their unusual origin. Mitochondria were once bacteria that lived within our ancestors’ cells. Over time, the bacteria lost their autonomy and became part of the larger cells, which included migrating most of their genes into the larger cell’s genome in the nucleus. Cells evolved processes to ensure that proteins needed by mitochondria that are encoded in genes in the larger cell’s genome get transported to the mitochondria. Mitochondria retain a few genes in their own genome, so production of proteins from the mitochondrial genome and that of the larger cell’s genome must be coordinated to avoid mismatched production of mitochondrial parts. Localized translation may help cells to manage the interplay between mitochondrial and nuclear protein production — among other purposes.

How to detect local protein production

For a protein to be made, genetic code stored in DNA is read into RNA, and then the RNA is read or translated by a ribosome, a cellular machine that builds a protein according to the RNA code. Weissman’s lab previously developed a method to study localized translation by tagging ribosomes near a structure of interest, and then capturing the tagged ribosomes in action and observing the proteins they are making. This approach, called proximity-specific ribosome profiling, allows researchers to see what proteins are being made where in the cell. The challenge that Luo faced was how to tweak this method to capture only ribosomes at work near mitochondria.

Ribosomes work quickly, so a ribosome that gets tagged while making a protein at the mitochondria can move on to making other proteins elsewhere in the cell in a matter of minutes. The only way researchers can guarantee that the ribosomes they capture are still working on proteins made near the mitochondria is if the experiment happens very quickly.

Weissman and colleagues had previously solved this time sensitivity problem in yeast cells with a ribosome-tagging tool called BirA that is activated by the presence of the molecule biotin. BirA is fused to the cellular structure of interest, and tags ribosomes it can touch — but only once activated. Researchers keep the cell depleted of biotin until they are ready to capture the ribosomes, to limit the time when tagging occurs. However, this approach does not work with mitochondria in mammalian cells because they need biotin to function normally, so it cannot be depleted.

Luo and Weissman adapted the existing tool to respond to blue light instead of biotin. The new tool, LOV-BirA, is fused to the mitochondrion’s outer membrane. Cells are kept in the dark until the researchers are ready. Then they expose the cells to blue light, activating LOV-BirA to tag ribosomes. They give it a few minutes and then quickly extract the ribosomes. This approach proved very accurate at capturing only ribosomes working at mitochondria.

The researchers then used a method originally developed by the Weissman lab to extract the sections of RNA inside of the ribosomes. This allows them to see exactly how far along in the process of making a protein the ribosome is when captured, which can reveal whether the entire protein is made at the mitochondria, or whether it is partly produced elsewhere and only gets completed at the mitochondria.

“One advantage of our tool is the granularity it provides,” Luo says. “Being able to see what section of the protein is locally translated helps us understand more about how localized translation is regulated, which can then allow us to understand its dysregulation in disease and to control localized translation in future studies.”

Two protein groups are made at mitochondria

Using these approaches, the researchers found that about 20 percent of the genes needed in mitochondria that are located in the main cellular genome are locally translated at mitochondria. These proteins can be divided into two distinct groups with different evolutionary histories and mechanisms for localized translation.

One group consists of relatively long proteins, each containing more than 400 amino acids or protein building blocks. These proteins tend to be of bacterial origin — present in the ancestor of mitochondria — and they are locally translated in both mammalian and yeast cells, suggesting that their localized translation has been maintained through a long evolutionary history.

Like many mitochondrial proteins encoded in the nucleus, these proteins contain a mitochondrial targeting sequence (MTS), a ZIP code that tells the cell where to bring them. The researchers discovered that most proteins containing an MTS also contain a nearby inhibitory sequence that prevents transportation until they are done being made. This group of locally translated proteins lacks the inhibitory sequence, so they are brought to the mitochondria during their production.

Production of these longer proteins begins anywhere in the cell, and then after approximately the first 250 amino acids are made, they get transported to the mitochondria. While the rest of the protein gets made, it is simultaneously fed into a channel that brings it inside the mitochondrion. This ties up the channel for a long time, limiting import of other proteins, so cells can only afford to do this simultaneous production and import for select proteins. The researchers hypothesize that these bacterial-origin proteins are given priority as an ancient mechanism to ensure that they are accurately produced and placed within mitochondria.

The second locally translated group consists of short proteins, each less than 200 amino acids long. These proteins are more recently evolved, and correspondingly, the researchers found that the mechanism for their localized translation is not shared by yeast. Their mitochondrial recruitment happens at the RNA level. Two sequences within regulatory sections of each RNA molecule that do not encode the final protein instead code for the cell’s machinery to recruit the RNAs to the mitochondria.

The researchers searched for molecules that might be involved in this recruitment, and identified the RNA binding protein AKAP1, which exists at mitochondria. When they eliminated AKAP1, the short proteins were translated indiscriminately around the cell. This provided an opportunity to learn more about the effects of localized translation, by seeing what happens in its absence. When the short proteins were not locally translated, this led to the loss of various mitochondrial proteins, including those involved in oxidative phosphorylation, our cells’ main energy generation pathway.

In future research, Weissman and Luo will delve deeper into how localized translation affects mitochondrial function and dysfunction in disease. The researchers also intend to use LOCL-TL to study localized translation in other cellular processes, including in relation to embryonic development, neural plasticity, and disease.

“This approach should be broadly applicable to different cellular structures and cell types, providing many opportunities to understand how localized translation contributes to biological processes,” Weissman says. “We’re particularly interested in what we can learn about the roles it may play in diseases including neurodegeneration, cardiovascular diseases, and cancers.”

© Image: Devin Powell/Whitehead Institute

Our cells produce a variety of proteins, each with a specific role that, in many cases, means that they need to be in a particular part of the cell where that role is needed. One of the ways that cells ensure certain proteins end up in the right location at the right time is through localized translation, a process that ensures that proteins are made — or translated — close to where they will be needed. MIT professor of biology and Whitehead Institute for Biomedical Research member Jonathan Weissman and colleagues have studied localized translation in order to understand how it affects cell functions and allows cells to quickly respond to changing conditions.

Now, Weissman, who is also a Howard Hughes Medical Institute Investigator, and postdoc in his lab Jingchuan Luo have expanded our knowledge of localized translation at mitochondria, structures that generate energy for the cell. In an open-access paper published today in Cell, they share a new tool, LOCL-TL, for studying localized translation in close detail, and describe the discoveries it enabled about two classes of proteins that are locally translated at mitochondria.

The importance of localized translation at mitochondria relates to their unusual origin. Mitochondria were once bacteria that lived within our ancestors’ cells. Over time, the bacteria lost their autonomy and became part of the larger cells, which included migrating most of their genes into the larger cell’s genome in the nucleus. Cells evolved processes to ensure that proteins needed by mitochondria that are encoded in genes in the larger cell’s genome get transported to the mitochondria. Mitochondria retain a few genes in their own genome, so production of proteins from the mitochondrial genome and that of the larger cell’s genome must be coordinated to avoid mismatched production of mitochondrial parts. Localized translation may help cells to manage the interplay between mitochondrial and nuclear protein production — among other purposes.

How to detect local protein production

For a protein to be made, genetic code stored in DNA is read into RNA, and then the RNA is read or translated by a ribosome, a cellular machine that builds a protein according to the RNA code. Weissman’s lab previously developed a method to study localized translation by tagging ribosomes near a structure of interest, and then capturing the tagged ribosomes in action and observing the proteins they are making. This approach, called proximity-specific ribosome profiling, allows researchers to see what proteins are being made where in the cell. The challenge that Luo faced was how to tweak this method to capture only ribosomes at work near mitochondria.

Ribosomes work quickly, so a ribosome that gets tagged while making a protein at the mitochondria can move on to making other proteins elsewhere in the cell in a matter of minutes. The only way researchers can guarantee that the ribosomes they capture are still working on proteins made near the mitochondria is if the experiment happens very quickly.

Weissman and colleagues had previously solved this time sensitivity problem in yeast cells with a ribosome-tagging tool called BirA that is activated by the presence of the molecule biotin. BirA is fused to the cellular structure of interest, and tags ribosomes it can touch — but only once activated. Researchers keep the cell depleted of biotin until they are ready to capture the ribosomes, to limit the time when tagging occurs. However, this approach does not work with mitochondria in mammalian cells because they need biotin to function normally, so it cannot be depleted.

Luo and Weissman adapted the existing tool to respond to blue light instead of biotin. The new tool, LOV-BirA, is fused to the mitochondrion’s outer membrane. Cells are kept in the dark until the researchers are ready. Then they expose the cells to blue light, activating LOV-BirA to tag ribosomes. They give it a few minutes and then quickly extract the ribosomes. This approach proved very accurate at capturing only ribosomes working at mitochondria.

The researchers then used a method originally developed by the Weissman lab to extract the sections of RNA inside of the ribosomes. This allows them to see exactly how far along in the process of making a protein the ribosome is when captured, which can reveal whether the entire protein is made at the mitochondria, or whether it is partly produced elsewhere and only gets completed at the mitochondria.

“One advantage of our tool is the granularity it provides,” Luo says. “Being able to see what section of the protein is locally translated helps us understand more about how localized translation is regulated, which can then allow us to understand its dysregulation in disease and to control localized translation in future studies.”

Two protein groups are made at mitochondria

Using these approaches, the researchers found that about 20 percent of the genes needed in mitochondria that are located in the main cellular genome are locally translated at mitochondria. These proteins can be divided into two distinct groups with different evolutionary histories and mechanisms for localized translation.

One group consists of relatively long proteins, each containing more than 400 amino acids or protein building blocks. These proteins tend to be of bacterial origin — present in the ancestor of mitochondria — and they are locally translated in both mammalian and yeast cells, suggesting that their localized translation has been maintained through a long evolutionary history.

Like many mitochondrial proteins encoded in the nucleus, these proteins contain a mitochondrial targeting sequence (MTS), a ZIP code that tells the cell where to bring them. The researchers discovered that most proteins containing an MTS also contain a nearby inhibitory sequence that prevents transportation until they are done being made. This group of locally translated proteins lacks the inhibitory sequence, so they are brought to the mitochondria during their production.

Production of these longer proteins begins anywhere in the cell, and then after approximately the first 250 amino acids are made, they get transported to the mitochondria. While the rest of the protein gets made, it is simultaneously fed into a channel that brings it inside the mitochondrion. This ties up the channel for a long time, limiting import of other proteins, so cells can only afford to do this simultaneous production and import for select proteins. The researchers hypothesize that these bacterial-origin proteins are given priority as an ancient mechanism to ensure that they are accurately produced and placed within mitochondria.

The second locally translated group consists of short proteins, each less than 200 amino acids long. These proteins are more recently evolved, and correspondingly, the researchers found that the mechanism for their localized translation is not shared by yeast. Their mitochondrial recruitment happens at the RNA level. Two sequences within regulatory sections of each RNA molecule that do not encode the final protein instead code for the cell’s machinery to recruit the RNAs to the mitochondria.

The researchers searched for molecules that might be involved in this recruitment, and identified the RNA binding protein AKAP1, which exists at mitochondria. When they eliminated AKAP1, the short proteins were translated indiscriminately around the cell. This provided an opportunity to learn more about the effects of localized translation, by seeing what happens in its absence. When the short proteins were not locally translated, this led to the loss of various mitochondrial proteins, including those involved in oxidative phosphorylation, our cells’ main energy generation pathway.

In future research, Weissman and Luo will delve deeper into how localized translation affects mitochondrial function and dysfunction in disease. The researchers also intend to use LOCL-TL to study localized translation in other cellular processes, including in relation to embryonic development, neural plasticity, and disease.

“This approach should be broadly applicable to different cellular structures and cell types, providing many opportunities to understand how localized translation contributes to biological processes,” Weissman says. “We’re particularly interested in what we can learn about the roles it may play in diseases including neurodegeneration, cardiovascular diseases, and cancers.”

© Image: Devin Powell/Whitehead Institute

The MIT School of Humanities, Arts, and Social Sciences announced leadership changes in three of its academic units for the 2025-26 academic year.

“We have an excellent cohort of leaders coming in,” says Agustín Rayo, the Kenan Sahin Dean of the School of Humanities, Arts, and Social Sciences. “I very much look forward to working with them and welcoming them into the school's leadership team.”

Sandy Alexandre will serve as head of MIT Literature. Alexandre is an associate professor of literature and served as co-head of the section in 2024-25. Her research spans the late 19th-century to present-day Black American literature and culture. Her first book, “The Properties of Violence: Claims to Ownership in Representations of Lynching,” uses the history of American lynching violence as a framework to understand matters concerning displacement, property ownership, and the American pastoral ideology in a literary context. Her work thoughtfully explores how literature envisions ecologies of people, places, and objects as recurring echoes of racial violence, resonating across the long arc of U.S. history. She earned a bachelor’s degree in English language and literature from Dartmouth College and a master’s and PhD in English from the University of Virginia.

Manduhai Buyandelger will serve as director of the Program in Women’s and Gender Studies. A professor of anthropology, Buyandelger’s research seeks to find solutions for achieving more-integrated (and less-violent) lives for humans and non-humans by examining the politics of multi-species care and exploitation, urbanization, and how diverse material and spiritual realities interact and shape the experiences of different beings. By examining urban multi-species coexistence in different places in Mongolia, the United States, Japan, and elsewhere, her study probes possibilities for co-cultivating an integrated multi-species existence. She is also developing an anthro-engineering project with the MIT Department of Nuclear Science and Engineering (NSE) to explore pathways to decarbonization in Mongolia by examining user-centric design and responding to political and cultural constraints on clean-energy issues. She offers a transdisciplinary course with NSE, 21A.S01 (Anthro-Engineering: Decarbonization at the Million Person Scale), in collaboration with her colleagues in Mongolia’s capital, Ulaanbaatar. She has written two books on religion, gender, and politics in post-socialist Mongolia: “Tragic Spirits: Shamanism, Gender, and Memory in Contemporary Mongolia” (University of Chicago Press, 2013) and “A Thousand Steps to the Parliament: Constructing Electable Women in Mongolia” (University of Chicago Press, 2022). Her essays have appeared in American Ethnologist, Journal of Royal Anthropological Association, Inner Asia, and Annual Review of Anthropology. She earned a BA in literature and linguistics and an MA in philology from the National University of Mongolia, and a PhD in social anthropology from Harvard University.

Eden Medina PhD ’05 will serve as head of the Program in Science, Technology, and Society. A professor of science, technology, and society, Medina studies the relationship of science, technology, and processes of political change in Latin America. She is the author of “Cybernetic Revolutionaries: Technology and Politics in Allende's Chile” (MIT Press, 2011), which won the 2012 Edelstein Prize for best book on the history of technology and the 2012 Computer History Museum Prize for best book on the history of computing. Her co-edited volume “Beyond Imported Magic: Essays on Science, Technology, and Society in Latin America” (MIT Press, 2014) received the Amsterdamska Award from the European Society for the Study of Science and Technology (2016). In addition to her writings, Medina co-curated the exhibition “How to Design a Revolution: The Chilean Road to Design,” which opened in 2023 at the Centro Cultural La Moneda in Santiago, Chile, and is currently on display at the design museum Disseny Hub in Barcelona, Spain. She holds a PhD in the history and social study of science and technology from MIT and a master’s degree in studies of law from Yale Law School. She worked as an electrical engineer prior to starting her graduate studies.

Fikile R. Brushett, a Ralph Landau Professor of Chemical Engineering Practice, was named director of MIT’s David H. Koch School of Chemical Engineering Practice, effective July 1. In this role, Brushett will lead one of MIT’s most innovative and distinctive educational programs.

Brushett joined the chemical engineering faculty in 2012 and has been a deeply engaged member of the department. An internationally recognized leader in the field of energy storage, his research advances the science and engineering of electrochemical technologies for a sustainable energy economy. He is particularly interested in the fundamental processes that define the performance, cost, and lifetime of present-day and next-generation electrochemical systems. In addition to his research, Brushett has served as a first-year undergraduate advisor, as a member of the department’s graduate admissions committee, and on MIT’s Committee on the Undergraduate Program.

“Fik’s scholarly excellence and broad service position him perfectly to take on this new challenge,” says Kristala L. J. Prather, the Arthur D. Little Professor and head of the Department of Chemical Engineering (ChemE). “His role as practice school director reflects not only his technical expertise, but his deep commitment to preparing students for meaningful, impactful careers. I’m confident he will lead the practice school with the same spirit of excellence and innovation that has defined the program for generations.”

Brushett succeeds T. Alan Hatton, a Ralph Landau Professor of Chemical Engineering Practice Post-Tenure, who directed the practice school for 36 years. For many, Hatton’s name is synonymous with the program. When he became director in 1989, only a handful of major chemical companies hosted stations.

“I realized that focusing on one industry segment was not sustainable and did not reflect the breadth of a chemical engineering education,” Hatton recalls. “So I worked to modernize the experience for students and have it reflect the many ways chemical engineers practice in the modern world.”

Under Hatton’s leadership, the practice school expanded globally and across industries, providing students with opportunities to work on diverse technologies in a wide range of locations. He pioneered the model of recruiting new companies each year, allowing many more firms to participate while also spreading costs across a broader sponsor base. He also introduced an intensive, hands-on project management course at MIT during Independent Activities Period, which has become a valuable complement to students’ station work and future careers.

Value for students and industry

The practice school benefits not only students, but also the companies that host them. By embedding teams directly into manufacturing plants and R&D centers, businesses gain fresh perspectives on critical technical challenges, coupled with the analytical rigor of MIT-trained problem-solvers. Many sponsors report that projects completed by practice school students have yielded measurable cost savings, process improvements, and even new opportunities for product innovation.

For manufacturing industries, where efficiency, safety, and sustainability are paramount, the program provides actionable insights that help companies strengthen competitiveness and accelerate growth. The model creates a unique partnership: students gain true real-world training, while companies benefit from MIT expertise and the creativity of the next generation of chemical engineers.

A century of hands-on learning

Founded in 1916 by MIT chemical engineering alumnus Arthur D. Little and Professor William Walker, with funding from George Eastman of Eastman Kodak, the practice school was designed to add a practical dimension to chemical engineering education. The first five sites — all in the Northeast — focused on traditional chemical industries working on dyes, abrasives, solvents, and fuels.

Today, the program remains unique in higher education. Students consult with companies worldwide across fields ranging from food and pharmaceuticals to energy and finance, tackling some of industry’s toughest challenges. More than a hundred years after its founding, the practice school continues to embody MIT’s commitment to hands-on, problem-driven learning that transforms both students and the industries they serve.

The practice school experience is part of ChemE’s MSCEP and PhD/ScDCEP programs. After coursework for each program is completed, a student attends practice school stations at host company sites. A group of six to 10 students spends two months each at two stations; each station experience includes teams of two or three students working on a month-long project, where they will prepare formal talks, scope of work, and a final report for the host company. Recent stations include Evonik in Marl, Germany; AstraZeneca in Gaithersburg, Maryland; EGA in Dubai, UAE; AspenTech in Bedford, Massachusetts; and Shell Technology Center and Dimensional Energy in Houston, Texas.

© Photo: Lillie Paquette

MIT researchers have developed a technique that enables real-time, 3D monitoring of corrosion, cracking, and other material failure processes inside a nuclear reactor environment.

This could allow engineers and scientists to design safer nuclear reactors that also deliver higher performance for applications like electricity generation and naval vessel propulsion.

During their experiments, the researchers utilized extremely powerful X-rays to mimic the behavior of neutrons interacting with a material inside a nuclear reactor.

They found that adding a buffer layer of silicon dioxide between the material and its substrate, and keeping the material under the X-ray beam for a longer period of time, improves the stability of the sample. This allows for real-time monitoring of material failure processes.

By reconstructing 3D image data on the structure of a material as it fails, researchers could design more resilient materials that can better withstand the stress caused by irradiation inside a nuclear reactor.

“If we can improve materials for a nuclear reactor, it means we can extend the life of that reactor. It also means the materials will take longer to fail, so we can get more use out of a nuclear reactor than we do now. The technique we’ve demonstrated here allows to push the boundary in understanding how materials fail in real-time,” says Ericmoore Jossou, who has shared appointments in the Department of Nuclear Science and Engineering (NSE), where he is the John Clark Hardwick Professor, and the Department of Electrical Engineering and Computer Science (EECS), and the MIT Schwarzman College of Computing.

Jossou, senior author of a study on this technique, is joined on the paper by lead author David Simonne, an NSE postdoc; Riley Hultquist, a graduate student in NSE; Jiangtao Zhao, of the European Synchrotron; and Andrea Resta, of Synchrotron SOLEIL. The research was published Tuesday by the journal Scripta Materiala.

“Only with this technique can we measure strain with a nanoscale resolution during corrosion processes. Our goal is to bring such novel ideas to the nuclear science community while using synchrotrons both as an X-ray probe and radiation source,” adds Simonne.

Real-time imaging

Studying real-time failure of materials used in advanced nuclear reactors has long been a goal of Jossou’s research group.

Usually, researchers can only learn about such material failures after the fact, by removing the material from its environment and imaging it with a high-resolution instrument.

“We are interested in watching the process as it happens. If we can do that, we can follow the material from beginning to end and see when and how it fails. That helps us understand a material much better,” he says.

They simulate the process by firing an extremely focused X-ray beam at a sample to mimic the environment inside a nuclear reactor. The researchers must use a special type of high-intensity X-ray, which is only found in a handful of experimental facilities worldwide.

For these experiments they studied nickel, a material incorporated into alloys that are commonly used in advanced nuclear reactors. But before they could start the X-ray equipment, they had to prepare a sample.

To do this, the researchers used a process called solid state dewetting, which involves putting a thin film of the material onto a substrate and heating it to an extremely high temperature in a furnace until it transforms into single crystals.

“We thought making the samples was going to be a walk in the park, but it wasn’t,” Jossou says.

As the nickel heated up, it interacted with the silicon substrate and formed a new chemical compound, essentially derailing the entire experiment. After much trial-and-error, the researchers found that adding a thin layer of silicon dioxide between the nickel and substrate prevented this reaction.

But when crystals formed on top of the buffer layer, they were highly strained. This means the individual atoms had moved slightly to new positions, causing distortions in the crystal structure.

Phase retrieval algorithms can typically recover the 3D size and shape of a crystal in real-time, but if there is too much strain in the material, the algorithms will fail.

However, the team was surprised to find that keeping the X-ray beam trained on the sample for a longer period of time caused the strain to slowly relax, due to the silicon buffer layer. After a few extra minutes of X-rays, the sample was stable enough that they could utilize phase retrieval algorithms to accurately recover the 3D shape and size of the crystal.

“No one had been able to do that before. Now that we can make this crystal, we can image electrochemical processes like corrosion in real time, watching the crystal fail in 3D under conditions that are very similar to inside a nuclear reactor. This has far-reaching impacts,” he says.

They experimented with a different substrate, such as niobium doped strontium titanate, and found that only a silicon dioxide buffered silicon wafer created this unique effect.

An unexpected result

As they fine-tuned the experiment, the researchers discovered something else.

They could also use the X-ray beam to precisely control the amount of strain in the material, which could have implications for the development of microelectronics.

In the microelectronics community, engineers often introduce strain to deform a material’s crystal structure in a way that boosts its electrical or optical properties.

“With our technique, engineers can use X-rays to tune the strain in microelectronics while they are manufacturing them. While this was not our goal with these experiments, it is like getting two results for the price of one,” he adds.

In the future, the researchers want to apply this technique to more complex materials like steel and other metal alloys used in nuclear reactors and aerospace applications. They also want to see how changing the thickness of the silicon dioxide buffer layer impacts their ability to control the strain in a crystal sample.

“This discovery is significant for two reasons. First, it provides fundamental insight into how nanoscale materials respond to radiation — a question of growing importance for energy technologies, microelectronics, and quantum materials. Second, it highlights the critical role of the substrate in strain relaxation, showing that the supporting surface can determine whether particles retain or release strain when exposed to focused X-ray beams,” says Edwin Fohtung, an associate professor at the Rensselaer Polytechnic Institute, who was not involved with this work.

This work was funded, in part, by the MIT Faculty Startup Fund and the U.S. Department of Energy. The sample preparation was carried out, in part, at the MIT.nano facilities.

© Image: iStock; MIT News

With three Sloan Fellows, three NSF CAREER Award winners, and a Forbes 30 Under 30 honoree among them, this year’s new faculty cohort arrives with notable accolades and ambitions.

In a groundbreaking study, researchers from the University of Bern and ETH Zurich have shown how climate change is intensifying supercell thunderstorms in Europe. At a global temperature increase of 3 degrees Celsius, these powerful storms are expected to occur more frequently, especially in the Alpine region.

His understanding of how forces like earthquakes and waves interact with buildings and bridges transformed the fields of structural reliability and risk assessment.

Science & Tech

Solving evolutionary mystery of how humans came to walk upright

Kermit Pattison

Harvard Staff Writer

August 27, 2025 6 min read

New study identifies genetic, developmental shifts that resculpted pelvis, setting ancestors apart from other primates

For the first time, chemists at ETH Zurich have successfully used extremely short, rotating flashes of light to measure and manipulate the different movements of electrons in mirror-image molecules. They showed that chirality of molecules is not just a structural but also an electronic phenomenon.

Nation & World

When global trade is about more than money

Economist’s new tool looks at how China is more effective than U.S. in exerting political power through import, export controls

Christy DeSmith

Harvard Staff Writer

August 27, 2025 6 min read

Health

Missing teens where they are

Analysts highlight a school-sized gap in mental health screening

Alvin Powell

Harvard Staff Writer

August 27, 2025 4 min read

The HIL building on the Hönggerberg campus is set to become a living lab. Now in need of renovation, the building will be remodelled and extended, with completion pencilled in for 2035. Professorships at ETH Zurich will engage with the project directly to research techniques and designs. Their aim is to advance sustainable redevelopment and retrofitting methods.

Following a one-year hiatus, NUS Team Bumblebee made a strong return to RoboSub 2025, reclaiming the championship title. The team had previously clinched its first championship in 2022 and successfully retained the top place in 2023.

Held from 11 to 17 August 2025 at the William Woollett Jr Aquatics Center in the City of Irvine in California, USA, RoboSub is a global competition that challenges teams to tackle real-world underwater robotics problems – from oceanographic exploration and mapping to object detection and manipulation. This year’s event brought together 58 teams from across the globe.

Team Bumblebee took a break from last year’s edition of RoboSub to focus on the Maritime RobotX Challenge, another international competition that advances autonomous robotic systems in the maritime domain. This year, the NUS team returned to RoboSub 2025 amid a record number of contenders, including Duke University, San Diego State University and Arizona State University – all of which were also finalists in this year’s competition.

After a week of intense competition – featuring technical presentations to judges and multiple rounds of pool testing - Team Bumblebee emerged as the overall champion and also swept awards for design documentation, namely Top Website, Top Video, Top Report and Top Assessment.

Team Lead Leong Deng Jun, Year 4 Computer Engineering undergraduate, reflected on the team’s effort, “I am very proud of what the team has achieved at this RoboSub. This year’s competition was especially challenging with a record number of teams participating. Many teams came better prepared, which meant we had limited testing time in the competition arena. Despite some unexpected setbacks and hardware issues, every member of our team stepped up and contributed to this victory.”

Based at the College of Design and Engineering in NUS, Team Bumblebee is a student-run multidisciplinary project comprising 55 undergraduates from Mechanical, Computer, and Electrical Engineering as well as NUS School of Computing, NUS Business School, and NUS College of Humanities and Sciences.

The NUS Business School marked its 60^th anniversary with a birthday bash on 14 August 2025, bringing together current students, staff and past and present leaders to celebrate its journey and look to the future.

Founded in 1965 as the Department of Business Administration, it began with just 21 students recruited from the Faculty of Arts by its first leader, Dr Andrew Zecha, who personally persuaded undergraduates to join the fledgling department. It went on to lay the foundation for business education in Singapore, setting up Master of Business Administration (MBA) and Executive MBA (EMBA) programmes in partnership with schools in China and the US and earning accreditations that helped it transform into a global business school.

Today, the Business School boasts more than 6,000 students across the undergraduate and postgraduate levels and an alumni network of more than 50,000. It is the top business school in Asia and ranks among the top ten globally.

NUS President Tan Eng Chye, NUS Business School co-founder Mr Tan Yam Pin, former deans Emeritus Professor Lee Soo Ann, Professor Wee Chow Hou, Professor Hum Sin Hoon and Professor Kulwant Singh, and Deputy Dean Associate Professor Jumana Zahalka joined Distinguished Professor Andrew Rose, Dean of the Business School, for a cake-cutting ceremony to open the event.

In his opening remarks, Prof Rose thanked the School’s founders and leaders for their contributions over the past six decades, as well as the faculty and staff whose education and research work form the core of the School’s reputation. “They are the reason why we’re attracting higher and higher quality students, and our reputation continues to grow,” he said.

The event included a fully subscribed masterclass on the future of sustainability in Asia, delivered by Professor Lawrence Loh, Director of the Centre for Governance and Sustainability, and an activity-packed carnival. Attendees enjoyed traditional snacks, arcade games, art booths and a dunk tank where their attempts to dunk Prof Rose and other faculty members raised money for a good cause.

Leading in Singapore and Asia

For former Business School leaders Mr Tan, Prof Lee and Prof Wee, the 60^th anniversary milestone was an opportunity to reflect on the stark changes that have taken place since their time. The School’s enrolment growth is especially impressive for Mr Tan, who was there when the initial cohort of 21 was enrolled. In comparison, the undergraduate intake in recent years has been around 4,000 students per year.

Mr Tan reflected: “In 1963, when we first conceived the concept of starting a department of business administration, it seemed like a baby step forward to me. Now looking back, it was actually a giant leap, like (Neil) Armstrong said.”

Back then, business subjects like accountancy were considered more suitable for polytechnic diplomas, rather than degrees, Prof Lee recalled. However, things changed when Singapore gained independence in 1965 and needed to train its own finance professionals.

Prof Lee said, “The teaching of accounting and business administration became essential for businesses to survive, because the accountants had returned to England. By teaching accounting here, we upgraded the capabilities of Singapore and the economy.”

He looks forward to continued innovations in how the School prepares students for the changing business environment, such as by offering more double degree programmes and leveraging new technologies like Generative AI to help students learn faster and more broadly.

Prof Wee was pleased to see that several initiatives he introduced during his tenure from 1990–1999 have become an integral part of the School’s offerings and shaped its global focus.

For instance, he was an early advocate of the modular system over the traditional year-long curriculum, as it would allow students to retake individual modules as needed and embark on exchange programmes more flexibly. The same system has now been implemented across the entire University.

To strengthen the School’s influence as an authority on Asian business, he encouraged faculty members to initiate collaborations with authors of well-known business textbooks to develop new editions with Asian case studies and contexts. In addition, Chinese MBA and EMBA programmes were launched under his leadership, offering the first such programmes outside mainland China.

A project that he would have liked to execute before he stepped down was the creation of a bilingual master’s programme, which he believes would be critical to the Business School’s mission of providing business education with an Asian lens. He still hopes that this vision will eventually come to fruition in the School’s next chapter.

“Singapore as a nation has survived because the West wants to listen to our views about China, and the Chinese come to us to know about the West,” Prof Wee said. “As a society, our strength lies in playing that bridging role. It’s important that the government is trying to cultivate bilingualism, and our universities should complement this.”

Campus & Community

‘We’re so happy to have you here’

Eileen O'Grady

Harvard Staff Writer

August 26, 2025 5 min read

Yard brims with voices and motion, excitement and nerves, sweat and tears on move-in day

The grants will support new editions of ancient Babylonian literature, workshops for educators on digital methods and resources, and the definitive scholarly edition of Jefferson’s correspondence and papers.

Nation & World

Global concerns rising about erosion of academic freedom

New paper suggests threats are more widespread, less obvious than some might think

Christina Pazzanese

Harvard Staff Writer

August 26, 2025 8 min read

Crime and public safety are among the most pressing concerns across communities in the United States. Violence fractures lives and carries staggering costs; the economic burden of gun violence alone tops $100 billion each year. More than 5 million people live under supervision through incarceration, probation, or parole, while countless more experience the collateral consequences of arrests and criminal charges. Achieving lasting public safety requires confronting both crime itself and the collateral consequences of the U.S. criminal justice system.

To help meet these dual challenges, J-PAL North America — a regional office of MIT’s Abdul Latif Jameel Poverty Action Lab (J-PAL) — with generous grant support from Arnold Ventures, launched the Initiative for Effective US Crime Policy (IECP). This initiative will generate rigorous evidence on strategies to make communities safer, reduce discrimination, and improve outcomes at every stage of the criminal justice process.

“There are a lot of open questions. We desperately need to be trying new solutions, but we need to try them in a way that enables us to learn whether they work,” notes Jennifer Doleac, executive vice president of criminal justice at Arnold Ventures. “There is a path forward for us to step up and make a concerted effort to make sure that we are being very strategic in how we spend our time and where we are directing our resources.”

Building on more than a decade of pioneering randomized evaluations, J‑PAL North America’s IECP will fund rigorous new studies in the criminal justice space, offer hands-on technical assistance, and connect researchers with practitioners. By reviewing both established and emerging evidence, the initiative will also help decision-makers focus resources on interventions that demonstrably improve public safety.

“Through this initiative, we aim to expand the use of rigorous existing evidence and help scale interventions that are proven to improve outcomes, from prevention to reintegration,” says Sara Heller, associate professor of economics at the University of Michigan and co-chair of IECP. “At the same time, IECP seeks to fill critical gaps in the evidence base by supporting new research on what works to improve the criminal justice system in the United States.”

A platform for collaboration

In June at the MIT Museum, IECP convened over 70 researchers, policymakers, and practitioners to identify research priorities and catalyze collaboration. Speakers explored the structural drivers of violence, effective pathways for translating evidence into policy, and strategies for establishing successful partnerships between researchers and practitioners.

Speakers also reflected on the value and limits of existing evidence and discussed areas in which randomized evaluations can help address the most pressing questions. Randomized evaluations have contributed powerful insights in areas such as summer youth employment programs, reminders to increase court appearances, hot-spot policing, and the use of body-worn cameras. Yet many important questions remain unanswered.

“We know randomized evaluations can answer hard policy questions, but only if we ask the right questions, with the right lens, at the right scale,” says Amanda Agan, associate professor at Cornell University and co-chair of IECP. “This convening was a call to push further: to design studies that are not only rigorous, but also relevant to the lived experiences of communities and the structural forces that shape public safety."

How to take part

Are you a practitioner with a promising idea in the criminal justice space, a policymaker planning a new program, a researcher developing a real-world intervention, or a funder investing in rigorous empirical evidence? IECP supports research partnerships to advance scalable, evidence-based solutions in the criminal legal system by funding impact evaluations, connecting researchers and practitioners, and supporting the design of randomized evaluations and the dissemination of evidence.   

To learn more about this initiative, please contact iecp@povertyactionlab.org.

© Photo courtesy of J-PAL North America.

Crime and public safety are among the most pressing concerns across communities in the United States. Violence fractures lives and carries staggering costs; the economic burden of gun violence alone tops $100 billion each year. More than 5 million people live under supervision through incarceration, probation, or parole, while countless more experience the collateral consequences of arrests and criminal charges. Achieving lasting public safety requires confronting both crime itself and the collateral consequences of the U.S. criminal justice system.

To help meet these dual challenges, J-PAL North America — a regional office of MIT’s Abdul Latif Jameel Poverty Action Lab (J-PAL) — with generous grant support from Arnold Ventures, launched the Initiative for Effective US Crime Policy (IECP). This initiative will generate rigorous evidence on strategies to make communities safer, reduce discrimination, and improve outcomes at every stage of the criminal justice process.

“There are a lot of open questions. We desperately need to be trying new solutions, but we need to try them in a way that enables us to learn whether they work,” notes Jennifer Doleac, executive vice president of criminal justice at Arnold Ventures. “There is a path forward for us to step up and make a concerted effort to make sure that we are being very strategic in how we spend our time and where we are directing our resources.”

Building on more than a decade of pioneering randomized evaluations, J‑PAL North America’s IECP will fund rigorous new studies in the criminal justice space, offer hands-on technical assistance, and connect researchers with practitioners. By reviewing both established and emerging evidence, the initiative will also help decision-makers focus resources on interventions that demonstrably improve public safety.

“Through this initiative, we aim to expand the use of rigorous existing evidence and help scale interventions that are proven to improve outcomes, from prevention to reintegration,” says Sara Heller, associate professor of economics at the University of Michigan and co-chair of IECP. “At the same time, IECP seeks to fill critical gaps in the evidence base by supporting new research on what works to improve the criminal justice system in the United States.”

A platform for collaboration

In June at the MIT Museum, IECP convened over 70 researchers, policymakers, and practitioners to identify research priorities and catalyze collaboration. Speakers explored the structural drivers of violence, effective pathways for translating evidence into policy, and strategies for establishing successful partnerships between researchers and practitioners.

Speakers also reflected on the value and limits of existing evidence and discussed areas in which randomized evaluations can help address the most pressing questions. Randomized evaluations have contributed powerful insights in areas such as summer youth employment programs, reminders to increase court appearances, hot-spot policing, and the use of body-worn cameras. Yet many important questions remain unanswered.

“We know randomized evaluations can answer hard policy questions, but only if we ask the right questions, with the right lens, at the right scale,” says Amanda Agan, associate professor at Cornell University and co-chair of IECP. “This convening was a call to push further: to design studies that are not only rigorous, but also relevant to the lived experiences of communities and the structural forces that shape public safety."

How to take part

Are you a practitioner with a promising idea in the criminal justice space, a policymaker planning a new program, a researcher developing a real-world intervention, or a funder investing in rigorous empirical evidence? IECP supports research partnerships to advance scalable, evidence-based solutions in the criminal legal system by funding impact evaluations, connecting researchers and practitioners, and supporting the design of randomized evaluations and the dissemination of evidence.   

To learn more about this initiative, please contact iecp@povertyactionlab.org.

© Photo courtesy of J-PAL North America.

Arts & Culture

Funny or failure? It’s a fine line.

Anna Lamb

Harvard Staff Writer

August 26, 2025 7 min read

‘Jimmy Kimmel Live!’ writer Will Burke on taking risks in comedy and why getting laughs is worth near-constant rejection

ETH Zurich researchers have found the holy grail of brewing: the formula for stable beer foam. But it's not just breweries that will benefit from these findings.

Environmental scientists are increasingly using enormous artificial intelligence models to make predictions about changes in weather and climate, but a new study by MIT researchers shows that bigger models are not always better.

The team demonstrates that, in certain climate scenarios, much simpler, physics-based models can generate more accurate predictions than state-of-the-art deep-learning models.

Their analysis also reveals that a benchmarking technique commonly used to evaluate machine-learning techniques for climate predictions can be distorted by natural variations in the data, like fluctuations in weather patterns. This could lead someone to believe a deep-learning model makes more accurate predictions when that is not the case.

The researchers developed a more robust way of evaluating these techniques, which shows that, while simple models are more accurate when estimating regional surface temperatures, deep-learning approaches can be the best choice for estimating local rainfall.

They used these results to enhance a simulation tool known as a climate emulator, which can rapidly simulate the effect of human activities onto a future climate.

The researchers see their work as a “cautionary tale” about the risk of deploying large AI models for climate science. While deep-learning models have shown incredible success in domains such as natural language, climate science contains a proven set of physical laws and approximations, and the challenge becomes how to incorporate those into AI models.

“We are trying to develop models that are going to be useful and relevant for the kinds of things that decision-makers need going forward when making climate policy choices. While it might be attractive to use the latest, big-picture machine-learning model on a climate problem, what this study shows is that stepping back and really thinking about the problem fundamentals is important and useful,” says study senior author Noelle Selin, a professor in the MIT Institute for Data, Systems, and Society (IDSS) and the Department of Earth, Atmospheric and Planetary Sciences (EAPS), and director of the Center for Sustainability Science and Strategy.

Selin’s co-authors are lead author Björn Lütjens, a former EAPS postdoc who is now a research scientist at IBM Research; senior author Raffaele Ferrari, the Cecil and Ida Green Professor of Oceanography in EAPS and co-director of the Lorenz Center; and Duncan Watson-Parris, assistant professor at the University of California at San Diego. Selin and Ferrari are also co-principal investigators of the Bringing Computation to the Climate Challenge project, out of which this research emerged. The paper appears today in the Journal of Advances in Modeling Earth Systems.

Comparing emulators

Because the Earth’s climate is so complex, running a state-of-the-art climate model to predict how pollution levels will impact environmental factors like temperature can take weeks on the world’s most powerful supercomputers.

Scientists often create climate emulators, simpler approximations of a state-of-the art climate model, which are faster and more accessible. A policymaker could use a climate emulator to see how alternative assumptions on greenhouse gas emissions would affect future temperatures, helping them develop regulations.

But an emulator isn’t very useful if it makes inaccurate predictions about the local impacts of climate change. While deep learning has become increasingly popular for emulation, few studies have explored whether these models perform better than tried-and-true approaches.

The MIT researchers performed such a study. They compared a traditional technique called linear pattern scaling (LPS) with a deep-learning model using a common benchmark dataset for evaluating climate emulators.

Their results showed that LPS outperformed deep-learning models on predicting nearly all parameters they tested, including temperature and precipitation.

“Large AI methods are very appealing to scientists, but they rarely solve a completely new problem, so implementing an existing solution first is necessary to find out whether the complex machine-learning approach actually improves upon it,” says Lütjens.

Some initial results seemed to fly in the face of the researchers’ domain knowledge. The powerful deep-learning model should have been more accurate when making predictions about precipitation, since those data don’t follow a linear pattern.

They found that the high amount of natural variability in climate model runs can cause the deep learning model to perform poorly on unpredictable long-term oscillations, like El Niño/La Niña. This skews the benchmarking scores in favor of LPS, which averages out those oscillations.

Constructing a new evaluation

From there, the researchers constructed a new evaluation with more data that address natural climate variability. With this new evaluation, the deep-learning model performed slightly better than LPS for local precipitation, but LPS was still more accurate for temperature predictions.

“It is important to use the modeling tool that is right for the problem, but in order to do that you also have to set up the problem the right way in the first place,” Selin says.

Based on these results, the researchers incorporated LPS into a climate emulation platform to predict local temperature changes in different emission scenarios.

“We are not advocating that LPS should always be the goal. It still has limitations. For instance, LPS doesn’t predict variability or extreme weather events,” Ferrari adds.

Rather, they hope their results emphasize the need to develop better benchmarking techniques, which could provide a fuller picture of which climate emulation technique is best suited for a particular situation.

“With an improved climate emulation benchmark, we could use more complex machine-learning methods to explore problems that are currently very hard to address, like the impacts of aerosols or estimations of extreme precipitation,” Lütjens says.

Ultimately, more accurate benchmarking techniques will help ensure policymakers are making decisions based on the best available information.

The researchers hope others build on their analysis, perhaps by studying additional improvements to climate emulation methods and benchmarks. Such research could explore impact-oriented metrics like drought indicators and wildfire risks, or new variables like regional wind speeds.

This research is funded, in part, by Schmidt Sciences, LLC, and is part of the MIT Climate Grand Challenges team for “Bringing Computation to the Climate Challenge.”

© Credit: MIT News; iStock

While sharing a single cup of coffee, Raul Radovitzky, the Jerome C. Hunsaker Professor in the Department of Aeronautics and Astronautics, and his wife Flavia Cardarelli, senior administrative assistant in the Institute for Data, Systems, and Society, recently discussed the love they have for their “nighttime jobs” living in McCormick Hall as faculty heads of house, and explained why it is so gratifying for them to be a part of this community.

The couple, married for 32 years, first met playing in a sandbox at the age of 3 in Argentina (but didn't start dating until they were in their 20s). Radovitzky has been a part of the MIT ecosystem since 2001, while Cardarelli began working at MIT in 2006. They became heads of house at McCormick Hall, the only all-female residence hall on campus, in 2015, and recently applied to extend their stay.

“Our head-of-house role is always full of surprises. We never know what we’ll encounter, but we love it. Students think we do this just for them, but in truth, it’s very rewarding for us as well. It keeps us on our toes and brings a lot of joy,” says Cardarelli. “We like to think of ourselves as the cool aunt and uncle for the students,” Radovitzky adds.

Heads of house at MIT influence many areas of students’ development by acting as advisors and mentors to their residents. Additionally, they work closely with the residence hall’s student government, as well as staff from the Division of Student Life, to foster their community’s culture.

Vice Chancellor for Student Life Suzy Nelson explains, “Our faculty heads of house have the long view at MIT and care deeply about students’ academic and personal growth. We are fortunate to have such dedicated faculty who serve in this way. The heads of house enhance the student experience in so many ways — whether it is helping a student with a personal problem, hosting Thanksgiving dinner for students who were not able to go home, or encouraging students to get involved in new activities, they are always there for students.”

“Our heads of house help our students fully participate in residential life. They model civil discourse at community dinners, mentor and tutor residents, and encourage residents to try new things. With great expertise and aplomb, they formally and informally help our students become their whole selves,” says Chancellor Melissa Nobles.

“I love teaching, I love conducting research with my group, and I enjoy serving as a head of house. The community aspect is deeply meaningful to me. MIT has become such a central part of our lives. Our kids are both MIT graduates, and we are incredibly proud of them. We do have a life outside of MIT — weekends with friends and family, personal activities — but MIT is a big part of who we are. It’s more than a job; it’s a community. We live on campus, and while it can be intense and demanding, we really love it,” says Radovitzky.

Jessica Quaye ’20, a former resident of McCormick Hall, says, “what sets McCormick apart is the way Raul and Flavia transform the four dorm walls into a home for everyone. You might come to McCormick alone, but you never leave alone. If you ran into them somewhere on campus, you could be sure that they would call you out and wave excitedly. You could invite Raul and Flavia to your concerts and they would show up to support your extracurricular endeavors. They built an incredible family that carries the fabric of MIT with a blend of academic brilliance, a warm open-door policy, and unwavering support for our extracurricular pursuits.”

Soundbytes

Q: What first drew you to the heads of house role?

Radovitzky: I had been aware of the role since I arrived at MIT, and over time, I started to wonder if it might be something we’d consider. When our kids were young, it didn’t seem feasible — we lived in the suburbs, and life there was good. But I always had an innate interest in building stronger connections with the student community.

Later, several colleagues encouraged us to apply. I discussed it with the family. Everyone was excited about it. Our teenagers were thrilled by the idea of living on a college campus. We applied together, submitting a letter as a family explaining why we were so passionate about it. We interviewed at McCormick, Baker, and McGregor. When we were offered McCormick, I’ll admit — I was nervous. I wasn’t sure I’d be the right fit for an all-female residence.

Cardarelli: We would have been nervous no matter where we ended up, but McCormick felt like home. It suited us in ways we didn’t anticipate. Raul, for instance, discovered he had a real rapport with the students, telling goofy jokes, making karaoke playlists, and learning about Taylor Swift and Nicki Minaj.

Radovitzky: It’s true! I never knew I’d become an expert at picking karaoke playlists. But we found our rhythm here, and it’s been deeply rewarding.

Q: What makes the McCormick community special?

Radovitzky: McCormick has a unique spirit. I can step out of our apartment and be greeted by 10 smiling faces. That energy is contagious. It’s not just about events or programming — it’s about building trust. We’ve built traditions around that, like our “make your own pizza” nights in our apartment, a wonderful McCormick event we inherited from our predecessors. We host four sessions each spring in which students roll out dough, choose toppings, and we chat as we cook and eat together. Everyone remembers the pizza nights — they’re mentioned in every testimonial.

Cardarelli: We’ve been lucky to have amazing graduate resident assistants and area directors every year. They’re essential partners in building community. They play a key role in creating community and supporting the students on their floors. They help with everything — from tutoring to events to walking students to urgent care if needed.

Radovitzky: In the fall, we take our residents to Crane Beach and host a welcome brunch. Karaoke in our apartment is a big hit too, and a unique way to make them comfortable coming to our apartment from day one. We do it three times a year — during orientation, and again each semester.

Cardarelli: We also host monthly barbecues open to all dorms and run McFast, our first-year tutoring program. Raul started by tutoring physics and math, four hours a week. Now, upperclass students lead most of the sessions. It’s great for both academic support and social connection.

Radovitzky: We also have an Independent Activities Period pasta night tradition. We cook for around 100 students, using four sauces that Flavia makes from scratch — bolognese, creamy mushroom, marinara, and pesto. Students love it.

Q: What’s unique about working in an all-female residence hall?

Cardarelli: I’ve helped students hem dresses, bake, and even apply makeup. It’s like having hundreds of daughters.

Radovitzky: The students here are incredibly mature and engaged. They show real interest in us as people. Many of the activities and connections we’ve built wouldn’t be possible in a different setting. Every year during “de-stress night,” I get my nails painted every color and have a face mask on. During “Are You Smarter Than an MIT Professor,” they dunk me in a water tank.

© Photo: Sarah Foote

• Lianhe Zaobao, 16 August 2025, Singapore, p8

• Lianhe Zaobao, 16 August 2025, Business, p24

By Dr Lin Jing, Research Fellow from the Middle East Institute at NUS

• Lianhe Zaobao, 16 August 2025, Opinion, p21

The National University of Singapore (NUS) has received a generous S$3 million pledge from global real estate powerhouse Mapletree Investments (Mapletree) to strengthen service-learning courses that will empower over 4,000 student volunteers annually to uplift more than 60,000 beneficiaries. This collaboration underscores that everyone has a role in building a "we-first" society where the government, community and corporate partners work together to create a more inclusive Singapore.

This milestone moment was graced by Guest-of-Honour Ms Low Yen Ling, Senior Minister of State, Ministry of Culture, Community and Youth & Ministry of Trade and Industry, on 25 August 2025 at Mapletree Business City.

Under the Communities and Engagement (C&E) pillar of the General Education curriculum, NUS undergraduate students across various disciplines can read service-learning courses as part of their graduation requirements. These courses encourage deep reflection and constructive actions on societal needs and real-world issues such as inequality and poverty which underprivileged and disadvantaged communities struggle with.

As the Principal Founding Donor and one of the largest donors towards the C&E Pillar with a focus on Seniors and Vulnerable Families, Mapletree’s contribution will sustain and expand NUS C&E courses such as GEN2060 Reconnect SeniorsSG, GEN2061 Support Healthy AgeingSG, GEN2062 Community Activities for Seniors with SG Cares, and GEN2070 Community Link (ComLink) Befrienders. Since the pilot launch of the C&E Pillar (Seniors and Vulnerable Families) in Academic Year 2022/2023, over 5,000 students have completed, or are currently enrolled in, these courses.

These service-learning courses run up to a year, encouraging students to take initiative in community service while developing critical thinking about complex social challenges.

Beyond volunteering, students also reflect, analyse and create solutions. The impact of this approach is multifaceted – beneficiaries find companionship and renewed hope; community partners gain extra help on the ground; students cultivate life-long values, and the ripple effects strengthen Singapore’s social fabric.

NUS President Professor Tan Eng Chye said, “We are grateful to Mapletree for this generous contribution, which will greatly enhance the impact of our service-learning courses. By empowering our students to serve the community, we are nurturing among the next generation empathy and a deeper awareness of societal needs among the disadvantaged and underprivileged. At the same time, we create opportunities for our students to do their part and support Singapore’s ageing population and lower-income families. These efforts reinforce our commitment to make a positive impact on society and community through our mission in education.”

Mr Edmund Cheng, Chairman of Mapletree, said, “Mapletree’s latest Corporate Social Responsibility (CSR) initiative with NUS aligns with two of our four CSR pillars – Healthcare and Education. Through our gift, as part of our US$10 million commitment to Temasek Trust’s Philanthropy Asia Alliance (PAA), these courses create beautiful bridges to facilitate relationships between students, seniors and vulnerable families, enriching the lives of all involved. We will continue to invest in the communities where we operate, strengthening the social fabric in meaningful ways.”

In 2023, Temasek Trust announced the launch of PAA to drive positive impact across Asia and mobilise collective philanthropic partnerships and strategies addressing global environmental and social challenges.

With Mapletree’s support, students will deepen their role as volunteers to implement hands-on initiatives to engage seniors and disadvantaged families. For example, as part of GEN2062, students facilitate thoughtfully designed activities at Active Ageing Centres and Senior Care Centres that stimulate cognitive function, enhance physical health, and improve the holistic well-being of elderly participants. Students in GEN2060 and GEN2070 conduct home visits to befriend seniors and vulnerable families, while students in GEN2061 share vital information about government assistance schemes with seniors through door-to-door visits. A small part of the gift will also go towards empowering student initiatives in other C&E courses to support these sectors.

Ms Cheryl Lim, Manager of Programmes at NTUC Health Senior Day Care, said, “We are heartened to witness the smiles on the seniors’ faces, made possible by the diverse range of activities organised by NUS students. The contributions of the students have been truly invaluable—their initiative in planning and leading these activities has not only enriched the lives of our seniors but also provided our team with interesting ideas. We truly appreciate the long-term, sustained collaboration and partnership with NUS that helps foster intergenerational bonds and nurture a continued sense of belonging, especially in our seniors, and within the broader community across generations.”

Complementing the efforts by community partners to promote better health and social engagement among seniors, the courses under the C&E pillar also enable NUS students to experience personal growth through their interaction and involvement with the seniors.

Mr Sean Ang Teng Han, a second-year student from the NUS Faculty of Science, who is currently reading the GEN2062 course, said, “It challenged me to step up and develop soft skills I did not have in the past, such as managing group dynamics, holding the attention of a crowd, and adapting quickly to different personalities. Learning how to engage and entertain a group of elderly participants has taught me a lot about communication and leadership in a more communal setting.”

Mapletree’s gift is the latest in a long-standing partnership with NUS that began over a decade ago with the establishment of the Mapletree Bursary in 2012. With a total endowment of S$900,000 to date, the Bursary has provided over 130 students with financial support, removing barriers that may otherwise pose challenges to their education journey. With this latest gift, Mapletree hopes to promote intergenerational bonding, a volunteering culture among youth, and opportunities to enhance age-in-place initiatives, one of the many efforts to address one of Singapore’s most pressing societal challenges – a rapidly ageing population, with one in four residents reaching the age of 65 by 2030.

Cognitive readiness denotes a person's ability to respond and adapt to the changes around them. This includes functions like keeping balance after tripping, or making the right decision in a challenging situation based on knowledge and past experiences. For military service members, cognitive readiness is crucial for their health and safety, as well as mission success. Injury to the brain is a major contributor to cognitive impairment, and between 2000 and 2024, more than 500,000 military service members were diagnosed with traumatic brain injury (TBI) — caused by anything from a fall during training to blast exposure on the battlefield. While impairment from factors like sleep deprivation can be treated through rest and recovery, others caused by injury may require more intense and prolonged medical attention.

"Current cognitive readiness tests administered to service members lack the sensitivity to detect subtle shifts in cognitive performance that may occur in individuals exposed to operational hazards," says Christopher Smalt, a researcher in the laboratory's Human Health and Performance Systems Group. "Unfortunately, the cumulative effects of these exposures are often not well-documented during military service or after transition to Veterans Affairs, making it challenging to provide effective support."

Smalt is part of a team at the laboratory developing a suite of portable diagnostic tests that provide near-real-time screening for brain injury and cognitive health. One of these tools, called READY, is a smartphone or tablet app that helps identify a potential change in cognitive performance in less than 90 seconds. Another tool, called MINDSCAPE — which is being developed in collaboration with Richard Fletcher, a visiting scientist in the Rapid Prototyping Group who leads the Mobile Technology Lab at the MIT Auto-ID Laboratory, and his students — uses virtual reality (VR) technology for a more in-depth analysis to pinpoint specific conditions such as TBI, post-traumatic stress disorder, or sleep deprivation. Using these tests, medical personnel on the battlefield can make quick and effective decisions for treatment triage.

Both READY and MINDSCAPE are a response to a series of Congressional legislation mandates, military program requirements, and mission-driven health needs to improve brain health screening capabilities for service members.

Cognitive readiness biomarkers

The READY and MINDSCAPE platforms incorporate more than a decade of laboratory research on finding the right indicators of cognitive readiness to build into rapid testing applications. Thomas Quatieri oversaw this work and identified balance, eye movement, and speech as three reliable biomarkers. He is leading the effort at Lincoln Laboratory to develop READY.

"READY stands for Rapid Evaluation of Attention for DutY, and is built on the premise that attention is the key to being 'ready' for a mission," he says. "In one view, we can think of attention as the mental state that allows you to focus on a task."

For someone to be attentive, their brain must continuously anticipate and process incoming sensory information and then instruct the body to respond appropriately. For example, if a friend yells "catch" and then throws a ball in your direction, in order to catch that ball, your brain must process the incoming auditory and visual data, predict in advance what may happen in the next few moments, and then direct your body to respond with an action that synchronizes those sensory data. The result? You realize from hearing the word "catch" and seeing the moving ball that your friend is throwing the ball to you, and you reach out a hand to catch it just in time.

"An unhealthy or fatigued brain — caused by TBI or sleep deprivation, for example — may have challenges within a neurosensory feed-forward [prediction] or feedback [error] system, thus hampering the person's ability to attend," Quatieri says.

READY's three tests measure a person’s ability to track a moving dot with their eye, balance, and hold a vowel fixed at one pitch. The app then uses the data to calculate a variability or "wobble" indicator, which represents changes from the test taker's baseline or from expected results based on others with similar demographics, or the general population. The results are displayed to the user as an indication of the patient's level of attention.

If the READY screen shows an impairment, the administrator can then direct the subject to follow up with MINDSCAPE, which stands for Mobile Interface for Neurological Diagnostic Situational Cognitive Assessment and Psychological Evaluation. MINDSCAPE uses VR technology to administer additional, in-depth tests to measure cognitive functions such as reaction time and working memory. These standard neurocognitive tests are recorded with multimodal physiological sensors, such as electroencephalography (EEG), photoplethysmography, and pupillometry, to better pinpoint diagnosis.

Holistic and adaptable

A key advantage of READY and MINDSCAPE is their ability to leverage existing technologies, allowing for rapid deployment in the field. By utilizing sensors and capabilities already integrated into smartphones, tablets, and VR devices, these assessment tools can be easily adapted for use in operational settings at a significantly reduced cost.

"We can immediately apply our advanced algorithms to the data collected from these devices, without the need for costly and time-consuming hardware development," Smalt says. "By harnessing the capabilities of commercially available technologies, we can quickly provide valuable insights and improve upon traditional assessment methods."

Bringing new capabilities and AI for brain-health sensing into operational environments is a theme across several projects at the laboratory. Another example is EYEBOOM (Electrooculography and Balance Blast Overpressure Monitoring System), a wearable technology developed for the U.S. Special Forces to monitor blast exposure. EYEBOOM continuously monitors a wearer's eye and body movements as they experience blast energy, and warns of potential harm. For this program, the laboratory developed an algorithm that could identify a potential change in physiology resulting from blast exposure during operations, rather than waiting for a check-in.

All three technologies are in development to be versatile, so they can be adapted for other relevant uses. For example, a workflow could pair EYEBOOM's monitoring capabilities with the READY and MINDSCAPE tests: EYEBOOM would continuously monitor for exposure risk and then prompt the wearer to seek additional assessment.

"A lot of times, research focuses on one specific modality, whereas what we do at the laboratory is search for a holistic solution that can be applied for many different purposes," Smalt says.

MINDSCAPE is undergoing testing at the Walter Reed National Military Center this year. READY will be tested with the U.S. Army Research Institute of Environmental Medicine (USARIEM) in 2026 in the context of sleep deprivation. Smalt and Quatieri also see the technologies finding use in civilian settings — on sporting event sidelines, in doctors' offices, or wherever else there is a need to assess brain readiness.

MINDSCAPE is being developed with clinical validation and support from Stefanie Kuchinsky at the Walter Reed National Military Medical Center. Quatieri and his team are developing the READY tests in collaboration with Jun Maruta and Jam Ghajar from the Brain Trauma Foundation (BTF), and Kristin Heaton from USARIEM. The tests are supported by concurrent evidence-based guidelines lead by the BTF and the Military TBI Initiative at Uniform Services University.

© Image: Tammy Ko/Lincoln Laboratory

Back in the 17th century, German astronomer Johannes Kepler figured out the laws of motion that made it possible to accurately predict where our solar system’s planets would appear in the sky as they orbit the sun. But it wasn’t until decades later, when Isaac Newton formulated the universal laws of gravitation, that the underlying principles were understood. Although they were inspired by Kepler’s laws, they went much further, and made it possible to apply the same formulas to everything from the trajectory of a cannon ball to the way the moon’s pull controls the tides on Earth — or how to launch a satellite from Earth to the surface of the moon or planets.

Today’s sophisticated artificial intelligence systems have gotten very good at making the kind of specific predictions that resemble Kepler’s orbit predictions. But do they know why these predictions work, with the kind of deep understanding that comes from basic principles like Newton’s laws? As the world grows ever-more dependent on these kinds of AI systems, researchers are struggling to try to measure just how they do what they do, and how deep their understanding of the real world actually is.

Now, researchers in MIT’s Laboratory for Information and Decision Systems (LIDS) and at Harvard University have devised a new approach to assessing how deeply these predictive systems understand their subject matter, and whether they can apply knowledge from one domain to a slightly different one. And by and large the answer at this point, in the examples they studied, is — not so much.

The findings were presented at the International Conference on Machine Learning, in Vancouver, British Columbia, last month by Harvard postdoc Keyon Vafa, MIT graduate student in electrical engineering and computer science and LIDS affiliate Peter G. Chang, MIT assistant professor and LIDS principal investigator Ashesh Rambachan, and MIT professor, LIDS principal investigator, and senior author Sendhil Mullainathan.

“Humans all the time have been able to make this transition from good predictions to world models,” says Vafa, the study’s lead author. So the question their team was addressing was, “have foundation models — has AI — been able to make that leap from predictions to world models? And we’re not asking are they capable, or can they, or will they. It’s just, have they done it so far?” he says.

“We know how to test whether an algorithm predicts well. But what we need is a way to test for whether it has understood well,” says Mullainathan, the Peter de Florez Professor with dual appointments in the MIT departments of Economics and Electrical Engineering and Computer Science and the senior author on the study. “Even defining what understanding means was a challenge.” 

In the Kepler versus Newton analogy, Vafa says, “they both had models that worked really well on one task, and that worked essentially the same way on that task. What Newton offered was ideas that were able to generalize to new tasks.” That capability, when applied to the predictions made by various AI systems, would entail having it develop a world model so it can “transcend the task that you’re working on and be able to generalize to new kinds of problems and paradigms.”

Another analogy that helps to illustrate the point is the difference between centuries of accumulated knowledge of how to selectively breed crops and animals, versus Gregor Mendel’s insight into the underlying laws of genetic inheritance.

“There is a lot of excitement in the field about using foundation models to not just perform tasks, but to learn something about the world,” for example in the natural sciences, he says. “It would need to adapt, have a world model to adapt to any possible task.”

Are AI systems anywhere near the ability to reach such generalizations? To test the question, the team looked at different examples of predictive AI systems, at different levels of complexity. On the very simplest of examples, the systems succeeded in creating a realistic model of the simulated system, but as the examples got more complex that ability faded fast.

The team developed a new metric, a way of measuring quantitatively how well a system approximates real-world conditions. They call the measurement inductive bias — that is, a tendency or bias toward responses that reflect reality, based on inferences developed from looking at vast amounts of data on specific cases.

The simplest level of examples they looked at was known as a lattice model. In a one-dimensional lattice, something can move only along a line. Vafa compares it to a frog jumping between lily pads in a row. As the frog jumps or sits, it calls out what it’s doing — right, left, or stay. If it reaches the last lily pad in the row, it can only stay or go back. If someone, or an AI system, can just hear the calls, without knowing anything about the number of lily pads, can it figure out the configuration? The answer is yes: Predictive models do well at reconstructing the “world” in such a simple case. But even with lattices, as you increase the number of dimensions, the systems no longer can make that leap.

“For example, in a two-state or three-state lattice, we showed that the model does have a pretty good inductive bias toward the actual state,” says Chang. “But as we increase the number of states, then it starts to have a divergence from real-world models.”

A more complex problem is a system that can play the board game Othello, which involves players alternately placing black or white disks on a grid. The AI models can accurately predict what moves are allowable at a given point, but it turns out they do badly at inferring what the overall arrangement of pieces on the board is, including ones that are currently blocked from play.

The team then looked at five different categories of predictive models actually in use, and again, the more complex the systems involved, the more poorly the predictive modes performed at matching the true underlying world model.

With this new metric of inductive bias, “our hope is to provide a kind of test bed where you can evaluate different models, different training approaches, on problems where we know what the true world model is,” Vafa says. If it performs well on these cases where we already know the underlying reality, then we can have greater faith that its predictions may be useful even in cases “where we don’t really know what the truth is,” he says.

People are already trying to use these kinds of predictive AI systems to aid in scientific discovery, including such things as properties of chemical compounds that have never actually been created, or of potential pharmaceutical compounds, or for predicting the folding behavior and properties of unknown protein molecules. “For the more realistic problems,” Vafa says, “even for something like basic mechanics, we found that there seems to be a long way to go.”

Chang says, “There’s been a lot of hype around foundation models, where people are trying to build domain-specific foundation models — biology-based foundation models, physics-based foundation models, robotics foundation models, foundation models for other types of domains where people have been collecting a ton of data” and training these models to make predictions, “and then hoping that it acquires some knowledge of the domain itself, to be used for other downstream tasks.”

This work shows there’s a long way to go, but it also helps to show a path forward. “Our paper suggests that we can apply our metrics to evaluate how much the representation is learning, so that we can come up with better ways of training foundation models, or at least evaluate the models that we’re training currently,” Chang says. “As an engineering field, once we have a metric for something, people are really, really good at optimizing that metric.”

© Image: iStock

Back in the 17th century, German astronomer Johannes Kepler figured out the laws of motion that made it possible to accurately predict where our solar system’s planets would appear in the sky as they orbit the sun. But it wasn’t until decades later, when Isaac Newton formulated the universal laws of gravitation, that the underlying principles were understood. Although they were inspired by Kepler’s laws, they went much further, and made it possible to apply the same formulas to everything from the trajectory of a cannon ball to the way the moon’s pull controls the tides on Earth — or how to launch a satellite from Earth to the surface of the moon or planets.

Today’s sophisticated artificial intelligence systems have gotten very good at making the kind of specific predictions that resemble Kepler’s orbit predictions. But do they know why these predictions work, with the kind of deep understanding that comes from basic principles like Newton’s laws? As the world grows ever-more dependent on these kinds of AI systems, researchers are struggling to try to measure just how they do what they do, and how deep their understanding of the real world actually is.

Now, researchers in MIT’s Laboratory for Information and Decision Systems (LIDS) and at Harvard University have devised a new approach to assessing how deeply these predictive systems understand their subject matter, and whether they can apply knowledge from one domain to a slightly different one. And by and large the answer at this point, in the examples they studied, is — not so much.

The findings were presented at the International Conference on Machine Learning, in Vancouver, British Columbia, last month by Harvard postdoc Keyon Vafa, MIT graduate student in electrical engineering and computer science and LIDS affiliate Peter G. Chang, MIT assistant professor and LIDS principal investigator Ashesh Rambachan, and MIT professor, LIDS principal investigator, and senior author Sendhil Mullainathan.

“Humans all the time have been able to make this transition from good predictions to world models,” says Vafa, the study’s lead author. So the question their team was addressing was, “have foundation models — has AI — been able to make that leap from predictions to world models? And we’re not asking are they capable, or can they, or will they. It’s just, have they done it so far?” he says.

“We know how to test whether an algorithm predicts well. But what we need is a way to test for whether it has understood well,” says Mullainathan, the Peter de Florez Professor with dual appointments in the MIT departments of Economics and Electrical Engineering and Computer Science and the senior author on the study. “Even defining what understanding means was a challenge.” 

In the Kepler versus Newton analogy, Vafa says, “they both had models that worked really well on one task, and that worked essentially the same way on that task. What Newton offered was ideas that were able to generalize to new tasks.” That capability, when applied to the predictions made by various AI systems, would entail having it develop a world model so it can “transcend the task that you’re working on and be able to generalize to new kinds of problems and paradigms.”

Another analogy that helps to illustrate the point is the difference between centuries of accumulated knowledge of how to selectively breed crops and animals, versus Gregor Mendel’s insight into the underlying laws of genetic inheritance.

“There is a lot of excitement in the field about using foundation models to not just perform tasks, but to learn something about the world,” for example in the natural sciences, he says. “It would need to adapt, have a world model to adapt to any possible task.”

Are AI systems anywhere near the ability to reach such generalizations? To test the question, the team looked at different examples of predictive AI systems, at different levels of complexity. On the very simplest of examples, the systems succeeded in creating a realistic model of the simulated system, but as the examples got more complex that ability faded fast.

The team developed a new metric, a way of measuring quantitatively how well a system approximates real-world conditions. They call the measurement inductive bias — that is, a tendency or bias toward responses that reflect reality, based on inferences developed from looking at vast amounts of data on specific cases.

The simplest level of examples they looked at was known as a lattice model. In a one-dimensional lattice, something can move only along a line. Vafa compares it to a frog jumping between lily pads in a row. As the frog jumps or sits, it calls out what it’s doing — right, left, or stay. If it reaches the last lily pad in the row, it can only stay or go back. If someone, or an AI system, can just hear the calls, without knowing anything about the number of lily pads, can it figure out the configuration? The answer is yes: Predictive models do well at reconstructing the “world” in such a simple case. But even with lattices, as you increase the number of dimensions, the systems no longer can make that leap.

“For example, in a two-state or three-state lattice, we showed that the model does have a pretty good inductive bias toward the actual state,” says Chang. “But as we increase the number of states, then it starts to have a divergence from real-world models.”

A more complex problem is a system that can play the board game Othello, which involves players alternately placing black or white disks on a grid. The AI models can accurately predict what moves are allowable at a given point, but it turns out they do badly at inferring what the overall arrangement of pieces on the board is, including ones that are currently blocked from play.

The team then looked at five different categories of predictive models actually in use, and again, the more complex the systems involved, the more poorly the predictive modes performed at matching the true underlying world model.

With this new metric of inductive bias, “our hope is to provide a kind of test bed where you can evaluate different models, different training approaches, on problems where we know what the true world model is,” Vafa says. If it performs well on these cases where we already know the underlying reality, then we can have greater faith that its predictions may be useful even in cases “where we don’t really know what the truth is,” he says.

People are already trying to use these kinds of predictive AI systems to aid in scientific discovery, including such things as properties of chemical compounds that have never actually been created, or of potential pharmaceutical compounds, or for predicting the folding behavior and properties of unknown protein molecules. “For the more realistic problems,” Vafa says, “even for something like basic mechanics, we found that there seems to be a long way to go.”

Chang says, “There’s been a lot of hype around foundation models, where people are trying to build domain-specific foundation models — biology-based foundation models, physics-based foundation models, robotics foundation models, foundation models for other types of domains where people have been collecting a ton of data” and training these models to make predictions, “and then hoping that it acquires some knowledge of the domain itself, to be used for other downstream tasks.”

This work shows there’s a long way to go, but it also helps to show a path forward. “Our paper suggests that we can apply our metrics to evaluate how much the representation is learning, so that we can come up with better ways of training foundation models, or at least evaluate the models that we’re training currently,” Chang says. “As an engineering field, once we have a metric for something, people are really, really good at optimizing that metric.”

© Image: iStock

Health

Mediterranean diet offsets genetic risk for dementia, study finds

Greatest benefit for those with highest predisposition to Alzheimer’s disease

Mass General Brigham Communications

August 25, 2025 4 min read

Mako, co-founded by assistant professor Mohamed Abdelfattah, sets out to tackle one of artificial intelligence’s most pressing infrastructure challenges: optimizing the computing efficiency of graphics processing units.

How Princeton summer programs help high school, public college, transfer, veteran and other students succeed in school and beyond.

Science & Tech

Seeding solutions for bipolar disorder

Kermit Pattison

Harvard Staff Writer

August 25, 2025 9 min read

Brain Science grants promote new approaches to treat the condition and discover underlying causes

A new system developed by Cornell Tech researchers helps users detect when their online accounts have been compromised — without exposing their personal devices to invasive tracking by web services.

A research team from ETH Zurich has taught the four-legged robot ANYmal to play badminton – including precise arm movements, quick reflexes and nimble footwork.

For both research and medical purposes, researchers have spent decades pushing the limits of microscopy to produce ever deeper and sharper images of brain activity, not only in the cortex but also in regions underneath, such as the hippocampus. In a new study, a team of MIT scientists and engineers demonstrates a new microscope system capable of peering exceptionally deep into brain tissues to detect the molecular activity of individual cells by using sound.

“The major advance here is to enable us to image deeper at single-cell resolution,” says neuroscientist Mriganka Sur, a corresponding author along with mechanical engineering professor Peter So and principal research scientist Brian Anthony. Sur is the Paul and Lilah Newton Professor in The Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences at MIT.

In the journal Light: Science and Applications, the team demonstrates that they could detect NAD(P)H, a molecule tightly associated with cell metabolism in general and electrical activity in neurons in particular, all the way through samples such as a 1.1-millimeter “cerebral organoid,” a 3D-mini brain-like tissue generated from human stem cells, and a 0.7-milimeter-thick slice of mouse brain tissue.

In fact, says co-lead author and mechanical engineering postdoc W. David Lee, who conceived the microscope’s innovative design, the system could have peered far deeper, but the test samples weren’t big enough to demonstrate that.

“That’s when we hit the glass on the other side,” he says. “I think we’re pretty confident about going deeper.”

Still, a depth of 1.1 milimeters is more than five times deeper than other microscope technologies can resolve NAD(P)H within dense brain tissue. The new system achieved the depth and sharpness by combining several advanced technologies to precisely and efficiently excite the molecule and then to detect the resulting energy, all without having to add any external labels, either via added chemicals or genetically engineered fluorescence.

Rather than focusing the required NAD(P)H excitation energy on a neuron with near ultraviolet light at its normal peak absorption, the scope accomplishes the excitation by focusing an intense, extremely short burst of light (a quadrillionth of a second long) at three times the normal absorption wavelength. Such “three-photon” excitation penetrates deep into tissue with less scattering by brain tissue because of the longer wavelength of the light (“like fog lamps,” Sur says). Meanwhile, although the excitation produces a weak fluorescent signal of light from NAD(P)H, most of the absorbed energy produces a localized (about 10 microns) thermal expansion within the cell, which produces sound waves that travel relatively easily through tissue compared to the fluorescence emission. A sensitive ultrasound microphone in the microscope detects those waves and, with enough sound data, software turns them into high-resolution images (much like a sonogram does). Imaging created in this way is “three-photon photoacoustic imaging.”

“We merged all these techniques — three-photon, label-free, photoacoustic detection,” says co-lead author Tatsuya Osaki, a research scientist in the Picower Institute in Sur’s lab. “We integrated all these cutting-edge techniques into one process to establish this ‘Multiphoton-In and Acoustic-Out’ platform.”

Lee and Osaki combined with research scientist Xiang Zhang and postdoc Rebecca Zubajlo to lead the study, in which the team demonstrated reliable detection of the sound signal through the samples. So far, the team has produced visual images from the sound at various depths as they refine their signal processing.

In the study, the team also shows simultaneous “third-harmonic generation” imaging, which comes from the three-photon stimulation and finely renders cellular structures, alongside their photoacoustic imaging, which detects NAD(P)H. They also note that their photoacoustic method could detect other molecules such as the genetically encoded calcium indicator GCaMP, that neuroscientists use to signal neural electrical activity.

With the concept of label-free, multiphoton, photoacoustic microscopy (LF-MP-PAM) established in the paper, the team is now looking ahead to neuroscience and clinical applications.

For instance, through the company Precision Healing, Inc., which he founded and sold, Lee has already established that NAD(P)H imaging can inform wound care. In the brain, levels of the molecule are known to vary in conditions such as Alzheimer’s disease, Rett syndrome, and seizures, making it a potentially valuable biomarker. Because the new system is label-free (i.e., no added chemicals or altered genes), it could be used in humans, for instance, during brain surgeries.

The next step for the team is to demonstrate it in a living animal, rather than just in in vitro and ex-vivo tissues. The technical challenge there is that the microphone can no longer be on the opposite side of the sample from the light source (as it was in the current study). It has to be on top, just like the light source.

Lee says he expects that full imaging at depths of 2 milimeters in live brains is entirely feasible, given the results in the new study.

“In principle, it should work,” he says.

Mercedes Balcells and Elazer Edelman are also authors of the paper. Funding for the research came from sources including the National Institutes of Health, the Simon Center for the Social Brain, the lab of Peter So, The Picower Institute for Learning and Memory, and the Freedom Together Foundation.

© Photo: Tatsuya Osaki

The MIT Sailing Pavilion hosted an altogether different marine vessel recently: a prototype of a solar electric boat developed by James Worden ’89, the founder of the MIT Solar Electric Vehicle Team (SEVT). Worden visited the pavilion on a sizzling, sunny day in late July to offer students from the SEVT, the MIT Edgerton Center, MIT Sea Grant, and the broader community an inside look at the Anita, named for his late wife.

Worden’s fascination with solar power began at age 10, when he picked up a solar chip at a “hippy-like” conference in his hometown of Arlington, Massachusetts. “My eyes just lit up,” he says. He built his first solar electric vehicle in high school, fashioned out of cardboard and wood (taking first place at the 1984 Massachusetts Science Fair), and continued his journey at MIT, founding SEVT in 1986. It was through SEVT that he met his wife and lifelong business partner, Anita Rajan Worden ’90. Together, they founded two companies in the solar electric and hybrid vehicles space, and in 2022 launched a solar electric boat company.

On the Charles River, Worden took visitors for short rides on Anita, including a group of current SEVT students who peppered him with questions. The 20-foot pontoon boat, just 12 feet wide and 7 feet tall, is made of carbon fiber composites, single crystalline solar photovoltaic cells, and lithium iron phosphate battery cells. Ultimately, Worden envisions the prototype could have applications as mini-ferry boats and water taxis.

With warmth and humor, he drew parallels between the boat’s components and mechanics and those of the solar cars the students are building. “It’s fun! If you think about all the stuff you guys are doing, it’s all the same stuff,” he told them, “optimizing all the different systems and making them work.” He also explained the design considerations unique to boating applications, like refining the hull shape for efficiency and maneuverability in variable water and wind conditions, and the critical importance of protecting wiring and controls from open water and condensate.

“Seeing Anita in all its glory was super cool,” says Nicole Lin, vice captain of SEVT. “When I first saw it, I could immediately map the different parts of the solar car to its marine counterparts, which was astonishing to see how far I’ve come as an engineer with SEVT. James also explained the boat using solar car terms, as he drew on his experience with solar cars for his solar boats. It blew my mind to see the engineering we learned with SEVT in action.”

Over the years, the Wordens have been avid supporters of SEVT and the Edgerton Center, so the visit was, in part, a way to pay it forward to MIT. “There’s a lot of connections,” he says. He’s still awed by the fact that Harold “Doc” Edgerton, upon learning about his interest in building solar cars, carved out a lab space for him to use in Building 20 — as a first-year student. And a few years ago, as Worden became interested in marine vessels, he tapped Sea Grant Education Administrator Drew Bennett for a 90-minute whiteboard lecture, “MIT fire-hose style,” on hydrodynamics. “It was awesome!” he says.

© Photo: Sarah Foote

For both research and medical purposes, researchers have spent decades pushing the limits of microscopy to produce ever deeper and sharper images of brain activity, not only in the cortex but also in regions underneath, such as the hippocampus. In a new study, a team of MIT scientists and engineers demonstrates a new microscope system capable of peering exceptionally deep into brain tissues to detect the molecular activity of individual cells by using sound.

“The major advance here is to enable us to image deeper at single-cell resolution,” says neuroscientist Mriganka Sur, a corresponding author along with mechanical engineering professor Peter So and principal research scientist Brian Anthony. Sur is the Paul and Lilah Newton Professor in The Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences at MIT.

In the journal Light: Science and Applications, the team demonstrates that they could detect NAD(P)H, a molecule tightly associated with cell metabolism in general and electrical activity in neurons in particular, all the way through samples such as a 1.1-millimeter “cerebral organoid,” a 3D-mini brain-like tissue generated from human stem cells, and a 0.7-milimeter-thick slice of mouse brain tissue.

In fact, says co-lead author and mechanical engineering postdoc W. David Lee, who conceived the microscope’s innovative design, the system could have peered far deeper, but the test samples weren’t big enough to demonstrate that.

“That’s when we hit the glass on the other side,” he says. “I think we’re pretty confident about going deeper.”

Still, a depth of 1.1 milimeters is more than five times deeper than other microscope technologies can resolve NAD(P)H within dense brain tissue. The new system achieved the depth and sharpness by combining several advanced technologies to precisely and efficiently excite the molecule and then to detect the resulting energy, all without having to add any external labels, either via added chemicals or genetically engineered fluorescence.

Rather than focusing the required NAD(P)H excitation energy on a neuron with near ultraviolet light at its normal peak absorption, the scope accomplishes the excitation by focusing an intense, extremely short burst of light (a quadrillionth of a second long) at three times the normal absorption wavelength. Such “three-photon” excitation penetrates deep into tissue with less scattering by brain tissue because of the longer wavelength of the light (“like fog lamps,” Sur says). Meanwhile, although the excitation produces a weak fluorescent signal of light from NAD(P)H, most of the absorbed energy produces a localized (about 10 microns) thermal expansion within the cell, which produces sound waves that travel relatively easily through tissue compared to the fluorescence emission. A sensitive ultrasound microphone in the microscope detects those waves and, with enough sound data, software turns them into high-resolution images (much like a sonogram does). Imaging created in this way is “three-photon photoacoustic imaging.”

“We merged all these techniques — three-photon, label-free, photoacoustic detection,” says co-lead author Tatsuya Osaki, a research scientist in the Picower Institute in Sur’s lab. “We integrated all these cutting-edge techniques into one process to establish this ‘Multiphoton-In and Acoustic-Out’ platform.”

Lee and Osaki combined with research scientist Xiang Zhang and postdoc Rebecca Zubajlo to lead the study, in which the team demonstrated reliable detection of the sound signal through the samples. So far, the team has produced visual images from the sound at various depths as they refine their signal processing.

In the study, the team also shows simultaneous “third-harmonic generation” imaging, which comes from the three-photon stimulation and finely renders cellular structures, alongside their photoacoustic imaging, which detects NAD(P)H. They also note that their photoacoustic method could detect other molecules such as the genetically encoded calcium indicator GCaMP, that neuroscientists use to signal neural electrical activity.

With the concept of label-free, multiphoton, photoacoustic microscopy (LF-MP-PAM) established in the paper, the team is now looking ahead to neuroscience and clinical applications.

For instance, through the company Precision Healing, Inc., which he founded and sold, Lee has already established that NAD(P)H imaging can inform wound care. In the brain, levels of the molecule are known to vary in conditions such as Alzheimer’s disease, Rett syndrome, and seizures, making it a potentially valuable biomarker. Because the new system is label-free (i.e., no added chemicals or altered genes), it could be used in humans, for instance, during brain surgeries.

The next step for the team is to demonstrate it in a living animal, rather than just in in vitro and ex-vivo tissues. The technical challenge there is that the microphone can no longer be on the opposite side of the sample from the light source (as it was in the current study). It has to be on top, just like the light source.

Lee says he expects that full imaging at depths of 2 milimeters in live brains is entirely feasible, given the results in the new study.

“In principle, it should work,” he says.

Mercedes Balcells and Elazer Edelman are also authors of the paper. Funding for the research came from sources including the National Institutes of Health, the Simon Center for the Social Brain, the lab of Peter So, The Picower Institute for Learning and Memory, and the Freedom Together Foundation.

© Photo: Tatsuya Osaki

• Berita Harian, 21 August 2025, p2
• Tamil Murasu, 21 August 2025, p2

By Dr Laurel Teo, Senior Research Fellow at the Institute of Policy Studies, Lee Kuan Yew School of Public Policy at NUS

• The Straits Times, 21 August 2025, Opinion, pB2

By Dr Jonathan Sim, Associate Fellow at the NUS Teaching Academy and Lecturer in the Dept of Philosophy, Faculty of Arts and Social Sciences at NUS

• CNA Online, 21 August 2025

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• The Straits Times, 21 August 2025, The Big Story, Front Page & pA2
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• Tabla!, 22 August 2025, p4
• Lianhe Zaobao, 25 August 2025, Opinion, p19

By Assoc Prof Lin Weiqiang, from the Dept of Geography, Faculty of Arts and Social Sciences at NUS

• The Straits Times, 20 August 2025, Opinion, pB2

• zbSunday, 17 August 2025, zbW, p3

A fast radio burst is an immense flash of radio emission that lasts for just a few milliseconds, during which it can momentarily outshine every other radio source in its galaxy. These flares can be so bright that their light can be seen from halfway across the universe, several billion light years away.

The sources of these brief and dazzling signals are unknown. But scientists now have a chance to study a fast radio burst (FRB) in unprecedented detail. An international team of scientists including physicists at MIT have detected a near and ultrabright fast radio burst some 130 million light-years from Earth in the constellation Ursa Major. It is one of the closest FRBs detected to date. It is also the brightest — so bright that the signal has garnered the informal moniker, RBFLOAT, for “radio brightest flash of all time.”

The burst’s brightness, paired with its proximity, is giving scientists the closest look yet at FRBs and the environments from which they emerge.

“Cosmically speaking, this fast radio burst is just in our neighborhood,” says Kiyoshi Masui, associate professor of physics and affiliate of MIT’s Kavli Institute for Astrophysics and Space Research. “This means we get this chance to study a pretty normal FRB in exquisite detail.”

Masui and his colleagues report their findings today in the Astrophysical Journal Letters.

Diverse bursts

The clarity of the new detection is thanks to a significant upgrade to The Canadian Hydrogen Intensity Mapping Experiment (CHIME), a large array of halfpipe-shaped antennae based in British Columbia. CHIME was originally designed to detect and map the distribution of hydrogen across the universe. The telescope is also sensitive to ultrafast and bright radio emissions. Since it started observations in 2018, CHIME has detected about 4,000 fast radio bursts, from all parts of the sky. But the telescope had not been able to precisely pinpoint the location of each fast radio burst, until now.

CHIME recently got a significant boost in precision, in the form of CHIME Outriggers — three miniature versions of CHIME, each sited in different parts of North America. Together, the telescopes work as one continent-sized system that can focus in on any bright flash that CHIME detects, to pin down its location in the sky with extreme precision.

“Imagine we are in New York and there’s a firefly in Florida that is bright for a thousandth of a second, which is usually how quick FRBs are,” says MIT Kavli graduate student Shion Andrew. “Localizing an FRB to a specific part of its host galaxy is analogous to figuring out not just what tree the firefly came from, but which branch it’s sitting on.”

The new fast radio burst is the first detection made using the combination of CHIME and the completed CHIME Outriggers. Together, the telescope array identified the FRB and determined not only the specific galaxy, but also the region of the galaxy from where the burst originated. It appears that the burst arose from the edge of the galaxy, just outside of a star-forming region. The precise localization of the FRB is allowing scientists to study the environment around the signal for clues to what brews up such bursts.

“As we’re getting these much more precise looks at FRBs, we’re better able to see the diversity of environments they’re coming from,” says MIT physics postdoc Adam Lanman.

Lanman, Andrew, and Masui are members of the CHIME Collaboration — which includes scientists from multiple institutions around the world — and are authors of the new paper detailing the discovery of the new FRB detection.

An older edge

Each of CHIME’s Outrigger stations continuously monitors the same swath of sky as the parent CHIME array. Both CHIME and the Outriggers “listen” for radio flashes, at incredibly short, millisecond timescales. Even over several minutes, such precision monitoring can amount to a huge amount of data. If CHIME detects no FRB signal, the Outriggers automatically delete the last 40 seconds of data to make room for the next span of measurements.

On March 16, 2025, CHIME detected an ultrabright flash of radio emissions, which automatically triggered the CHIME Outriggers to record the data. Initially, the flash was so bright that astronomers were unsure whether it was an FRB or simply a terrestrial event caused, for instance, by a burst of cellular communications.

That notion was put to rest as the CHIME Outrigger telescopes focused in on the flash and pinned down its location to NGC4141 — a spiral galaxy in the constellation Ursa Major about 130 million light years away, which happens to be surprisingly close to our own Milky Way. The detection is one of the closest and brightest fast radio bursts detected to date.

Follow-up observations in the same region revealed that the burst came from the very edge of an active region of star formation. While it’s still a mystery as to what source could produce FRBs, scientists’ leading hypothesis points to magnetars — young neutron stars with extremely powerful magnetic fields that can spin out high-energy flares across the electromagnetic spectrum, including in the radio band. Physicists suspect that magnetars are found in the center of star formation regions, where the youngest, most active stars are forged. The location of the new FRB, just outside a star-forming region in its galaxy, may suggest that the source of the burst is a slightly older magnetar.

“These are mostly hints,” Masui says. “But the precise localization of this burst is letting us dive into the details of how old an FRB source could be. If it were right in the middle, it would only be thousands of years old — very young for a star. This one, being on the edge, may have had a little more time to bake.”

No repeats

In addition to pinpointing where the new FRB was in the sky, the scientists also looked back through CHIME data to see whether any similar flares occurred in the same region in the past. Since the first FRB was discovered in 2007, astronomers have detected over 4,000 radio flares. Most of these bursts are one-offs. But a few percent have been observed to repeat, flashing every so often. And an even smaller fraction of these repeaters flash in a pattern, like a rhythmic heartbeat, before flaring out. A central question surrounding fast radio bursts is whether repeaters and nonrepeaters come from different origins.

The scientists looked through CHIME’s six years of data and came up empty: This new FRB appears to be a one-off, at least in the last six years. The findings are particularly exciting, given the burst’s proximity. Because it is so close and so bright, scientists can probe the environment in and around the burst for clues to what might produce a nonrepeating FRB.

“Right now we’re in the middle of this story of whether repeating and nonrepeating FRBs are different. These observations are putting together bits and pieces of the puzzle,” Masui says.

“There’s evidence to suggest that not all FRB progenitors are the same,” Andrew adds. “We’re on track to localize hundreds of FRBs every year. The hope is that a larger sample of FRBs localized to their host environments can help reveal the full diversity of these populations.”

The construction of the CHIME Outriggers was funded by the Gordon and Betty Moore Foundation and the U.S. National Science Foundation. The construction of CHIME was funded by the Canada Foundation for Innovation and provinces of Quebec, Ontario, and British Columbia.

© Credit: Danielle Futselaar

Health

Physicians embrace AI note-taking technology

Ryan Jaslow

Mass General Brigham Communications

August 21, 2025 5 min read

‘There is literally no other intervention in our field that impacts burnout to this extent’ 

Rising global temperatures affect human activity in many ways. Now, a new study illuminates an important dimension of the problem: Very hot days are associated with more negative moods, as shown by a large-scale look at social media postings.

Overall, the study examines 1.2 billion social media posts from 157 countries over the span of a year. The research finds that when the temperature rises above 95 degrees Fahrenheit, or 35 degrees Celsius, expressed sentiments become about 25 percent more negative in lower-income countries and about 8 percent more negative in better-off countries. Extreme heat affects people emotionally, not just physically.

“Our study reveals that rising temperatures don’t just threaten physical health or economic productivity — they also affect how people feel, every day, all over the world,” says Siqi Zheng, a professor in MIT’s Department of Urban Studies and Planning (DUSP) and Center for Real Estate (CRE), and co-author of a new paper detailing the results. “This work opens up a new frontier in understanding how climate stress is shaping human well-being at a planetary scale.”

The paper, “Unequal Impacts of Rising Temperatures on Global Human Sentiment,” is published today in the journal One Earth. The authors are Jianghao Wang, of the Chinese Academy of Sciences; Nicolas Guetta-Jeanrenaud SM ’22, a graduate of MIT’s Technology and Policy Program (TPP) and Institute for Data, Systems, and Society; Juan Palacios, a visiting assistant professor at MIT’s Sustainable Urbanization Lab (SUL) and an assistant professor Maastricht University; Yichun Fan, of SUL and Duke University; Devika Kakkar, of Harvard University; Nick Obradovich, of SUL and the Laureate Institute for Brain Research in Tulsa; and Zheng, who is the STL Champion Professor of Urban and Real Estate Sustainability at CRE and DUSP. Zheng is also the faculty director of CRE and founded the Sustainable Urbanization Lab in 2019.

Social media as a window

To conduct the study, the researchers evaluated 1.2 billion posts from the social media platforms Twitter and Weibo, all of which appeared in 2019. They used a natural language processing technique called Bidirectional Encoder Representations from Transformers (BERT), to analyze 65 languages across the 157 countries in the study.

Each social media post was given a sentiment rating from 0.0 (for very negative posts) to 1.0 (for very positive posts). The posts were then aggregated geographically to 2,988 locations and evaluated in correlation with area weather. From this method, the researchers could then deduce the connection between extreme temperatures and expressed sentiment.

“Social media data provides us with an unprecedented window into human emotions across cultures and continents,” Wang says. “This approach allows us to measure emotional impacts of climate change at a scale that traditional surveys simply cannot achieve, giving us real-time insights into how temperature affects human sentiment worldwide.”

To assess the effects of temperatures on sentiment in higher-income and middle-to-lower-income settings, the scholars also used a World Bank cutoff level of gross national income per-capita annual income of $13,845, finding that in places with incomes below that, the effects of heat on mood were triple those found in economically more robust settings.

“Thanks to the global coverage of our data, we find that people in low- and middle-income countries experience sentiment declines from extreme heat that are three times greater than those in high-income countries,” Fan says. “This underscores the importance of incorporating adaptation into future climate impact projections.”

In the long run

Using long-term global climate models, and expecting some adaptation to heat, the researchers also produced a long-range estimate of the effects of extreme temperatures on sentiment by the year 2100. Extending the current findings to that time frame, they project a 2.3 percent worsening of people’s emotional well-being based on high temperatures alone by then — although that is a far-range projection.

“It's clear now, with our present study adding to findings from prior studies, that weather alters sentiment on a global scale,” Obradovich says. “And as weather and climates change, helping individuals become more resilient to shocks to their emotional states will be an important component of overall societal adaptation.”

The researchers note that there are many nuances to the subject, and room for continued research in this area. For one thing, social media users are not likely to be a perfectly representative portion of the population, with young children and the elderly almost certainly using social media less than other people. However, as the researchers observe in the paper, the very young and elderly are probably particularly vulnerable to heat shocks, making the response to hot weather possible even larger than their study can capture.

The research is part of the Global Sentiment project led by the MIT Sustainable Urbanization Lab, and the study’s dataset is publicly available. Zheng and other co-authors have previously investigated these dynamics using social media, although never before at this scale.

“We hope this resource helps researchers, policymakers, and communities better prepare for a warming world,” Zheng says.

The research was supported, in part, by Zheng’s chaired professorship research fund, and grants Wang received from the National Natural Science Foundation of China and the Chinese Academy of Sciences. 

© Image: MIT News; iStock

The research introduces the first framework for analyzing how digital authentication tools can be exploited in contexts such as intimate partner violence, elder abuse and human trafficking.

• 8world Online, 18 August 2025

By Dr Woo Jun Jie, Senior Lecturer at the Lee Kuan Yew School of Public Policy at NUS

• CNA Online, 18 August 2025

A multiyear program at MIT Lincoln Laboratory to characterize how biological and chemical vapors and aerosols disperse through the New York City subway system is coming to a close. The program, part of the U.S. Department of Homeland Security (DHS) Science and Technology Directorate's Urban Area Security Initiative, builds on other efforts at Lincoln Laboratory to detect chemical and biological threats, validate air dispersion models, and improve emergency protocols in urban areas in case of an airborne attack. The results of this program will inform the New York Metropolitan Transportation Authority (MTA) on how best to install an efficient, cost-effective system for airborne threat detection and mitigation throughout the subway. On a broader scale, the study will help the national security community understand pragmatic chemical and biological defense options for mass transit, critical facilities, and special events.

Trina Vian from the laboratory's Counter–Weapons of Mass Destruction (WMD) Systems Group led this project, which she says had as much to do with air flow and sensors as it did with MTA protocols and NYC commuters. "There are real dangers associated with panic during an alarm. People can get hurt during mass evacuation, or lose trust in a system and the authorities that administer that system, if there are false alarms," she says. "A novel aspect of our project was to investigate effective low-regret response options, meaning those with little operational consequence to responding to a false alarm."

Currently, depending on the severity of the alarm, the MTA's response can include stopping service and evacuating passengers and employees.

A complex environment for testing

For the program, which started in 2019, Vian and her team collected data on how chemical and biological sensors performed in the subway, what factors affected sensor accuracy, and how different mitigation protocols fared in stopping an airborne threat from spreading and removing the threat from a contaminated location. For their tests, they released batches of a safe, custom-developed aerosol simulant within Grand Central Station that they could track with DNA barcodes. Each batch had a different barcode, which allowed the team to differentiate among them and quantitatively assess different combinations of mitigation strategies.

To control and isolate air flow, the team tested static air curtains as well as air filtration systems. They also tested a spray knockdown system developed by Sandia National Laboratories designed to reduce and isolate particulate hazards in large volume areas. The system sprays a fine water mist into the tunnels that attaches to threat particulates and uses gravity to rain out the threat material. The spray contains droplets of a particular size and concentration, and with an applied electrostatic field. The original idea for the system was adapted from the coal mining industry, which used liquid sprayers to reduce the amount of inhalable soot.

The tests were done in a busy environment, and the team was required to complete trainings on MTA protocols such as track safety and how to interact with the public.

"We had long and sometimes very dirty days," says Jason Han of the Counter–WMD Systems Group, who collected measurements in the tunnels and analyzed the data. "We all wore bright orange contractor safety vests, which made people think we were official employees of the MTA. We would often get approached by people asking for directions!"

At times, issues such as power outages or database errors could disrupt data capture.

"We learned fairly early on that we had to capture daily data backups and keep a daily evolving master list of unique sensor identifiers and locations," says fellow team member Cassie Smith. "We developed workflows and wrote scripts to help automate the process, which ensured successful sensor data capture and attribution."

The team also worked closely with the MTA to make sure their tests and data capture ran smoothly. "The MTA was great at helping us maintain the test bed, doing as much as they could in our physical absence," Vian says.

Calling on industry

Another crucial aspect of the program was to connect with the greater chemical and biological industrial community to solicit their sensors for testing. These partnerships reduced the cost for DHS to bring new sensing technologies into the project, and, in return, participants gained a testing and data collection opportunity within the challenging NYC subway environment.

The team ultimately fielded 16 different sensors, each with varying degrees of maturity, that operated through a range of methods, such as ultraviolet laser–induced fluorescence, polymerase chain reaction, and long-wave infrared spectrometry.

"The partners appreciated the unique data they got and the opportunity to work with the MTA and experience an environment and customer base that they may not have anticipated before," Vian says.

The team finished testing in 2024 and has delivered the final report to the DHS. The MTA will use the report to help expand their PROTECT chemical detection system (originally developed by Argonne National Laboratory) from Grand Central Station into adjacent stations. They expect to complete this work in 2026.

"The value of this program cannot be overstated. This partnership with DHS and MIT Lincoln Laboratory has led to the identification of the best-suited systems for the MTA’s unique operating environment," says Michael Gemelli, director of chemical, biological, radiological, and nuclear/WMD detection and mitigation at the New York MTA.

"Other transit authorities can leverage these results to start building effective chemical and biological defense systems for their own specific spaces and threat priorities," adds Benjamin Ervin, leader of Lincoln Laboratory's Counter–WMD Systems Group. "Specific test and evaluation within the operational environment of interest, however, is always recommended to ensure defense system objectives are met."

Building these types of decision-making reports for airborne chemical and biological sensing has been a part of Lincoln Laboratory's mission since the mid-1990s. The laboratory also helped to define priorities in the field when DHS was forming in the early 2000s.

Beyond this study, the Lincoln Laboratory is leading several other projects focused on forecasting the impact of novel chemical and biological threats within multiple domains — military, space, agriculture, health, etc. — and on prototyping rapid, autonomous, high-confidence biological identification capabilities for the homeland to provide actionable evidence of hazardous environments.

© Photo: Glen Cooper

A multiyear program at MIT Lincoln Laboratory to characterize how biological and chemical vapors and aerosols disperse through the New York City subway system is coming to a close. The program, part of the U.S. Department of Homeland Security (DHS) Science and Technology Directorate's Urban Area Security Initiative, builds on other efforts at Lincoln Laboratory to detect chemical and biological threats, validate air dispersion models, and improve emergency protocols in urban areas in case of an airborne attack. The results of this program will inform the New York Metropolitan Transportation Authority (MTA) on how best to install an efficient, cost-effective system for airborne threat detection and mitigation throughout the subway. On a broader scale, the study will help the national security community understand pragmatic chemical and biological defense options for mass transit, critical facilities, and special events.

Trina Vian from the laboratory's Counter–Weapons of Mass Destruction (WMD) Systems Group led this project, which she says had as much to do with air flow and sensors as it did with MTA protocols and NYC commuters. "There are real dangers associated with panic during an alarm. People can get hurt during mass evacuation, or lose trust in a system and the authorities that administer that system, if there are false alarms," she says. "A novel aspect of our project was to investigate effective low-regret response options, meaning those with little operational consequence to responding to a false alarm."

Currently, depending on the severity of the alarm, the MTA's response can include stopping service and evacuating passengers and employees.

A complex environment for testing

For the program, which started in 2019, Vian and her team collected data on how chemical and biological sensors performed in the subway, what factors affected sensor accuracy, and how different mitigation protocols fared in stopping an airborne threat from spreading and removing the threat from a contaminated location. For their tests, they released batches of a safe, custom-developed aerosol simulant within Grand Central Station that they could track with DNA barcodes. Each batch had a different barcode, which allowed the team to differentiate among them and quantitatively assess different combinations of mitigation strategies.

To control and isolate air flow, the team tested static air curtains as well as air filtration systems. They also tested a spray knockdown system developed by Sandia National Laboratories designed to reduce and isolate particulate hazards in large volume areas. The system sprays a fine water mist into the tunnels that attaches to threat particulates and uses gravity to rain out the threat material. The spray contains droplets of a particular size and concentration, and with an applied electrostatic field. The original idea for the system was adapted from the coal mining industry, which used liquid sprayers to reduce the amount of inhalable soot.

The tests were done in a busy environment, and the team was required to complete trainings on MTA protocols such as track safety and how to interact with the public.

"We had long and sometimes very dirty days," says Jason Han of the Counter–WMD Systems Group, who collected measurements in the tunnels and analyzed the data. "We all wore bright orange contractor safety vests, which made people think we were official employees of the MTA. We would often get approached by people asking for directions!"

At times, issues such as power outages or database errors could disrupt data capture.

"We learned fairly early on that we had to capture daily data backups and keep a daily evolving master list of unique sensor identifiers and locations," says fellow team member Cassie Smith. "We developed workflows and wrote scripts to help automate the process, which ensured successful sensor data capture and attribution."

The team also worked closely with the MTA to make sure their tests and data capture ran smoothly. "The MTA was great at helping us maintain the test bed, doing as much as they could in our physical absence," Vian says.

Calling on industry

Another crucial aspect of the program was to connect with the greater chemical and biological industrial community to solicit their sensors for testing. These partnerships reduced the cost for DHS to bring new sensing technologies into the project, and, in return, participants gained a testing and data collection opportunity within the challenging NYC subway environment.

The team ultimately fielded 16 different sensors, each with varying degrees of maturity, that operated through a range of methods, such as ultraviolet laser–induced fluorescence, polymerase chain reaction, and long-wave infrared spectrometry.

"The partners appreciated the unique data they got and the opportunity to work with the MTA and experience an environment and customer base that they may not have anticipated before," Vian says.

The team finished testing in 2024 and has delivered the final report to the DHS. The MTA will use the report to help expand their PROTECT chemical detection system (originally developed by Argonne National Laboratory) from Grand Central Station into adjacent stations. They expect to complete this work in 2026.

"The value of this program cannot be overstated. This partnership with DHS and MIT Lincoln Laboratory has led to the identification of the best-suited systems for the MTA’s unique operating environment," says Michael Gemelli, director of chemical, biological, radiological, and nuclear/WMD detection and mitigation at the New York MTA.

"Other transit authorities can leverage these results to start building effective chemical and biological defense systems for their own specific spaces and threat priorities," adds Benjamin Ervin, leader of Lincoln Laboratory's Counter–WMD Systems Group. "Specific test and evaluation within the operational environment of interest, however, is always recommended to ensure defense system objectives are met."

Building these types of decision-making reports for airborne chemical and biological sensing has been a part of Lincoln Laboratory's mission since the mid-1990s. The laboratory also helped to define priorities in the field when DHS was forming in the early 2000s.

Beyond this study, the Lincoln Laboratory is leading several other projects focused on forecasting the impact of novel chemical and biological threats within multiple domains — military, space, agriculture, health, etc. — and on prototyping rapid, autonomous, high-confidence biological identification capabilities for the homeland to provide actionable evidence of hazardous environments.

© Photo: Glen Cooper

By Mr Ben Chester Cheong, Associate Academic Fellow at the Asia-Pacific Centre for Environmental Law, Faculty of Law at NUS

• The Straits Times, 19 August 2025, Opinion, pB2

• 8world Online, 18 August 2025
• Lianhe Zaobao, 19 August 2025, Singapore, p8

From the wars in Gaza and Ukraine to protracted crises in Africa, geopolitical tensions abound in today’s turbulent world. But there is a bright spot closer to home.

Guided by the basic principles of non-interference, non-aggression, decision-making through consensus, and quiet diplomacy, the Association of Southeast Asian Nations (ASEAN) has remained a shining example of hope in the region, said ASEAN Secretary-General Dr Kao Kim Hourn.

“History has taught us that peace is not a natural state of affairs – it is sustained by restraint, dialogue, diplomacy, and a shared commitment to order,” noted Dr Kao at a lecture organised by the NUS Centre for International Law (CIL) on 4 August 2025 that was attended by about 130 participants, including policymakers, diplomats, government officials as well as members of academia and the private sector from Singapore and the region.

“ASEAN’s journey stands as a profound testament to the tenacity required to make regionalism and multilateralism work.”

ASEAN is an area of focus for CIL, particularly in its role as a pillar of international law, said CIL Director, Dr Nilufer Oral. In her welcome remarks at the annual CIL-NUS ASEAN Distinguished Lecture, she hailed ASEAN as “an important building block in the international legal system”.

Echoing this, Dr Kao added: “Even in times of difficulty, we do not abandon our faith in the power of diplomacy, a culture of dialogue, and a sense of shared purpose. That is the ‘ASEAN Way’, and it remains a source of light in an increasingly uncertain and polarised world.”

His lecture, titled “ASEAN: A Bright Spot in a Darkening World”, focused on the continued relevance of the bloc, which was founded in 1967. But the brightness can dim occasionally.

“The recent flare-up along the Cambodia-Thailand border should serve as a sobering wake-up call…we can neither afford complacency, nor take peace for granted,” stressed the Cambodian diplomat, who has been ASEAN’s Secretary-General since 2023.

Clashes between the two countries in July de-escalated after an intervention by neighbouring leader Mr Anwar Ibrahim, Prime Minister of Malaysia, who is the current ASEAN Chair.

Unity amid uncertainty

Dr Kao emphasised ASEAN’s commitment to future-proofing the region, as set out in the “ASEAN 2045” agenda adopted at the 46th ASEAN Summit in May 2025.

“For the first time in ASEAN’s history, we have articulated a 20-year outlook that anticipates the global megatrends already reshaping the international system,” he said, highlighting the challenges of climate change, demographic shifts, and technological disruptions.

“‘ASEAN 2045’ seeks not just to respond to them, but also to harness them in shaping a dynamic, inclusive, and future-ready region.”

Other challenges threatening the rules-based world order include the rising tides of unilateralism, fragmentation, and protectionism, with Dr Kao calling for concerted efforts and vigilance to sustain this relationship of trust.

Such efforts are especially crucial given the outsized impact of external forces on the region.

For example, Dr Kao acknowledged that US President Donald Trump’s support was crucial to securing peace in the Indo-Pacific. But he said ASEAN had to make known to the US that its recent tariffs had caused “a lot of uncertainty” in the region, especially in the private sector.

“ASEAN has been trying to respond collectively as a region to the US,” said Dr Kao in his reply to a question on the rules-based order, adding that the bloc is also working to boost trade within the region and with partners like China, Korea, and India.

A beacon in a darkening world

Amid geopolitical tensions, ASEAN’s growing appeal is evident as more non-Southeast Asian countries join ASEAN-led partnerships. It has not only championed peace, but also “reinforced its position as a cornerstone of regional economic success”, said Dr Kao.

Currently the world’s fifth-largest economy, ASEAN is projected to become the fourth-largest by 2030. In 2023, it secured a record US$230 billion in foreign direct investment.

Asked if ASEAN was in need of reform, Dr Kao conceded the bloc had its “shortcomings” such as not delivering results quickly enough, and was working to address them. On a separate note, he highlighted that ASEAN was making inroads in AI-related issues ranging from governance to ethics, and was gaining “a lot of momentum” in cybersecurity.

Weighing in, Mr Ong Keng Yong, Singapore’s Ambassador-at-Large and current CIL Governing Board Member, who moderated the dialogue, added that countries needed more political will to enforce cybersecurity-related laws.

Participants believed the topics discussed were timely. “The lecture was an accurate reflection of the processes and the recent priorities of ASEAN,” said Ms Diane Shayne D. Lipana, Acting Director of ASEAN Affairs at the Philippines’ Department of Foreign Affairs.

In these uncertain times, ASEAN’s role is starker than ever. “We will redouble our efforts to ensure that ASEAN remains – steadily and resolutely – a bright beacon in a darkening world,” Dr Kao added.

From toddlers’ timeouts to criminals’ prison sentences, punishment reinforces social norms, making it known that an offender has done something unacceptable. At least, that is usually the intent — but the strategy can backfire. When a punishment is perceived as too harsh, observers can be left with the impression that an authority figure is motivated by something other than justice.

It can be hard to predict what people will take away from a particular punishment, because everyone makes their own inferences not just about the acceptability of the act that led to the punishment, but also the legitimacy of the authority who imposed it. A new computational model developed by scientists at MIT’s McGovern Institute for Brain Research makes sense of these complicated cognitive processes, recreating the ways people learn from punishment and revealing how their reasoning is shaped by their prior beliefs.

Their work, reported Aug. 4 in the journal PNAS, explains how a single punishment can send different messages to different people, and even strengthen the opposing viewpoints of groups who hold different opinions about authorities or social norms.

“The key intuition in this model is the fact that you have to be evaluating simultaneously both the norm to be learned and the authority who’s punishing,” says McGovern investigator and John W. Jarve Professor of Brain and Cognitive Sciences Rebecca Saxe, who led the research. “One really important consequence of that is even where nobody disagrees about the facts — everybody knows what action happened, who punished it, and what they did to punish it — different observers of the same situation could come to different conclusions.”

For example, she says, a child who is sent to timeout after biting a sibling might interpret the event differently than the parent. One might see the punishment as proportional and important, teaching the child not to bite. But if the biting, to the toddler, seemed a reasonable tactic in the midst of a squabble, the punishment might be seen as unfair, and the lesson will be lost.

People draw on their own knowledge and opinions when they evaluate these situations — but to study how the brain interprets punishment, Saxe and graduate student Setayesh Radkani wanted to take those personal ideas out of the equation. They needed a clear understanding of the beliefs that people held when they observed a punishment, so they could learn how different kinds of information altered their perceptions. So Radkani set up scenarios in imaginary villages where authorities punished individuals for actions that had no obvious analog in the real world.

Participants observed these scenarios in a series of experiments, with different information offered in each one. In some cases, for example, participants were told that the person being punished was either an ally or a competitor of the authority, whereas in other cases, the authority’s possible bias was left ambiguous.

“That gives us a really controlled setup to vary prior beliefs,” Radkani explains. “We could ask what people learn from observing punitive decisions with different severities, in response to acts that vary in their level of wrongness, by authorities that vary in their level of different motives.”

For each scenario, participants were asked to evaluate four factors: how much the authority figure cared about justice; the selfishness of the authority; the authority’s bias for or against the individual being punished; and the wrongness of the punished act. The research team asked these questions when participants were first introduced to the hypothetical society, then tracked how their responses changed after they observed the punishment. Across the scenarios, participants’ initial beliefs about the authority and the wrongness of the act shaped the extent to which those beliefs shifted after they observed the punishment.

Radkani was able to replicate these nuanced interpretations using a cognitive model framed around an idea that Saxe’s team has long used to think about how people interpret the actions of others. That is, to make inferences about others’ intentions and beliefs, we assume that people choose actions that they expect will help them achieve their goals.

To apply that concept to the punishment scenarios, Radkani developed a model that evaluates the meaning of a punishment (an action aimed at achieving a goal of the authority) by considering the harm associated with that punishment; its costs or benefits to the authority; and its proportionality to the violation. By assessing these factors, along with prior beliefs about the authority and the punished act, the model was able to predict people’s responses to the hypothetical punishment scenarios, supporting the idea that people use a similar mental model. “You need to have them consider those things, or you can’t make sense of how people understand punishment when they observe it,” Saxe says.

Even though the team designed their experiments to preclude preconceived ideas about the people and actions in their imaginary villages, not everyone drew the same conclusions from the punishments they observed. Saxe’s group found that participants’ general attitudes toward authority influenced their interpretation of events. Those with more authoritarian attitudes — assessed through a standard survey — tended to judge punished acts as more wrong and authorities as more motivated by justice than other observers.

“If we differ from other people, there’s a knee-jerk tendency to say, ‘either they have different evidence from us, or they’re crazy,’” Saxe says. Instead, she says, “It’s part of the way humans think about each other’s actions.”

“When a group of people who start out with different prior beliefs get shared evidence, they will not end up necessarily with shared beliefs. That’s true even if everybody is behaving rationally,” says Saxe.

This way of thinking also means that the same action can simultaneously strengthen opposing viewpoints. The Saxe lab’s modeling and experiments showed that when those viewpoints shape individuals’ interpretations of future punishments, the groups’ opinions will continue to diverge. For instance, a punishment that seems too harsh to a group who suspects an authority is biased can make that group even more skeptical of the authority’s future actions. Meanwhile, people who see the same punishment as fair and the authority as just will be more likely to conclude that the authority figure’s future actions are also just. 

“You will get a vicious cycle of polarization, staying and actually spreading to new things,” says Radkani.

The researchers say their findings point toward strategies for communicating social norms through punishment. “It is exactly sensible in our model to do everything you can to make your action look like it’s coming out of a place of care for the long-term outcome of this individual, and that it’s proportional to the norm violation they did,” Saxe says. “That is your best shot at getting a punishment interpreted pedagogically, rather than as evidence that you’re a bully.”

Nevertheless, she says that won’t always be enough. “If the beliefs are strong the other way, it’s very hard to punish and still sustain a belief that you were motivated by justice.”

Joining Saxe and Radkani on the paper is Joshua Tenenbaum, MIT professor of brain and cognitive sciences. The study was funded, in part, by the Patrick J McGovern Foundation.

© Photo: iStock