The Bezos Centre for Sustainable Protein at the National University of Singapore (Bezos Centre @NUS) and Enterprise Singapore (EnterpriseSG) announced a new partnership to accelerate early-stage sustainable protein innovation through a startup competition. This is part of Bezos Centre @NUS’ US$3 million in grants over five years to support early and growth-stage startups in this space.

The inaugural Sustainable Protein Startup Competition will identify and support early-stage startups in sustainable protein solutions. Three winning startups will be selected through a competitive pitching process. Each winner will receive funding and in-kind support of up to S$175,000, while gaining access to Singapore's comprehensive ecosystem of resources. The competition will be launched on 1 August 2025 at the first International Summit on Sustainable Protein (ISSP). There are plans to hold the competition annually, supporting up to 15 early-stage global and local startups over a five-year period.

Professor Zhou Weibiao, Acting Director of the Bezos Centre for Sustainable Protein at NUS and Head of the NUS Department of Food Science and Technology, said, “One of our goals at the Bezos Centre for Sustainable Protein at NUS is to bridge world-class research in sustainable protein with real-world impact. This startup competition and the ISSP are natural extensions of that mission — paving the way for promising technologies to move from lab to market, and from local to global impact. Our partnership with EnterpriseSG will further support our aspiration by accelerating innovation and strengthening the sustainable protein ecosystem in Singapore and beyond.”  

Ms Jeannie Lim, Assistant Managing Director for Services and Growth Enterprises at EnterpriseSG, said, “Our partnership with the Bezos Centre @NUS comes at a crucial time to support promising foodtech startups in securing funding to develop their technologies and bring innovative solutions to market. This initiative reflects our commitment to advance promising sustainable food innovations while catalysing greater private sector participation and investments in Singapore's foodtech sector. We welcome foodtech startups to leverage Singapore’s resources, robust innovation ecosystem and progressive regulatory framework to accelerate their R&D efforts and scale their impact across the region.”

Bezos Centre @NUS x EnterpriseSG’s Sustainable Protein Startup Competition

The competition aims to support early-stage startups developing novel sustainable protein technologies that can address critical challenges in food security and climate resilience. This includes innovative solutions in fermentation, cultivated meat, plant-based proteins, and food safety & toxicology. Startups will be evaluated on their technology innovation and readiness, market potential and validation, business model scalability, and team capabilities.

From the entries, eight promising startups will advance to the final pitching round which will be held at the Singapore International Agri-Food Week in November 2025. Three winning startups will be selected, and each winner will receive seed funding and in-kind support of up to S$175,000. Bezos Centre @NUS will provide a S$75,000 cash grant and EnterpriseSG will match this with a S$75,000 Startup SG Founder (Competition) grant. Winners can utilise the seed funding for R&D, prototype development and to conduct pilot trials for sustainable protein products. Besides the funding, winners will receive up to S$25,000 worth of in-kind support from Bezos Centre @NUS. This includes mentorship, laboratory facilities, office space, startup incubation resources, and the opportunity to pitch at an annual startup event.

Interested startups can submit their applications from 1 August to 8 September 2025. More information on the Sustainable Protein Startup Competition is available here. 

Support for growth-stage sustainable protein startups

As part of its US$3 million funding to accelerate sustainable protein solutions, Bezos Centre @NUS has earmarked up to US$1.5 million to help growth-stage sustainable protein startups and high-potential projects scale up production, strengthen market presence, and drive international growth.

Bezos Centre @NUS plans to award grants of up to US$500,000 each to three promising startups or high-potential projects. EnterpriseSG will be partnering with Bezos Centre @NUS to further enhance this support through the Startup SG Tech Grant matching. More details will be announced in November this year.

ISSP: A collaborative platform to drive innovation and knowledge sharing in sustainable protein

The ISSP is co-organised by the Bezos Centre for Sustainable Protein at NUS, the Department of Food Science and Technology at the NUS Faculty of Science, the Singapore Food Agency, and Enterprise Singapore, with the Agency for Science, Technology and Research as a strategic partner. The two-day event will be held from 31 July to 1 August 2025 at Pan Pacific Hotel Singapore.

Talks and discussions held at ISSP addressed one of the most urgent global challenges: how to sustainably feed a growing and aging global population, projected to exceed 9 billion by 2050, amidst intensifying climate pressures and resource constraints. To cater to the rising demand for safe and high-quality protein, it is imperative to develop innovative solutions that guarantee food security and optimal nutrition while reducing environmental impacts. ISSP provides a unique platform for experts, stakeholders, and innovators from diverse fields to converge and collaborate on advancing sustainable proteins.

Core themes at ISSP include:

• Scientific Knowledge Exchange: Showcasing cutting-edge research on sustainable proteins by global and regional thought leaders.

• Technology Translation and Growth: Accelerating promising research and commercialisation efforts through competitions, industry feedback, and curated investor engagement.

• Ecosystem Building: Facilitating collaborations among academia, startups, government bodies and industry.

• Showcasing Capabilities: Highlighting ready-to-license innovations and technical expertise from research institutes, universities, and enterprise partners.

ISSP convened more than 200 participants from top research institutes, alternative protein hubs, industry, and policy circles. Notable speakers at the summit included Professor Francesco Branca, Invited Professor from the Institute of Global Health at the University of Geneva, and Dr Lynnette Marie Neufeld, Director of the Food and Nutrition Division at the Food and Agriculture Organisation of the United Nations (FAO).

More information on ISSP is available at: https://issp2025.sg/index.php

The Bezos Centre for Sustainable Protein at the National University of Singapore (Bezos Centre @NUS) and Enterprise Singapore (EnterpriseSG) announced a new partnership to accelerate early-stage sustainable protein innovation through a startup competition. This is part of Bezos Centre @NUS’ US$3 million in grants over five years to support early and growth-stage startups in this space.

The inaugural Sustainable Protein Startup Competition will identify and support early-stage startups in sustainable protein solutions. Three winning startups will be selected through a competitive pitching process. Each winner will receive funding and in-kind support of up to S$175,000, while gaining access to Singapore's comprehensive ecosystem of resources. The competition will be launched on 1 August 2025 at the first International Summit on Sustainable Protein (ISSP, more details on ISSP in Annexe). There are plans to hold the competition annually, supporting up to 15 early-stage global and local startups over a five-year period.

Professor Zhou Weibiao, Acting Director of the Bezos Centre for Sustainable Protein at NUS and Head of the NUS Department of Food Science and Technology, said, “One of our goals at the Bezos Centre for Sustainable Protein at NUS is to bridge world-class research in sustainable protein with real-world impact. This startup competition and the ISSP are natural extensions of that mission — paving the way for promising technologies to move from lab to market, and from local to global impact. Our partnership with EnterpriseSG will further support our aspiration by accelerating innovation and strengthening the sustainable protein ecosystem in Singapore and beyond.”  

Ms Jeannie Lim, Assistant Managing Director for Services and Growth Enterprises at EnterpriseSG, said, “Our partnership with the Bezos Centre @NUS comes at a crucial time to support promising foodtech startups in securing funding to develop their technologies and bring innovative solutions to market. This initiative reflects our commitment to advance promising sustainable food innovations while catalysing greater private sector participation and investments in Singapore's foodtech sector. We welcome foodtech startups to leverage Singapore’s resources, robust innovation ecosystem and progressive regulatory framework to accelerate their R&D efforts and scale their impact across the region.”

Bezos Centre @NUS x EnterpriseSG’s Sustainable Protein Startup Competition

The competition aims to support early-stage startups developing novel sustainable protein technologies that can address critical challenges in food security and climate resilience. This includes innovative solutions in fermentation, cultivated meat, plant-based proteins, and food safety & toxicology. Startups will be evaluated on their technology innovation and readiness, market potential and validation, business model scalability, and team capabilities.

From the entries, eight promising startups will advance to the final pitching round which will be held at the Singapore International Agri-Food Week in November 2025. Three winning startups will be selected, and each winner will receive seed funding and in-kind support of up to S$175,000. Bezos Centre @NUS will provide a S$75,000 cash grant and EnterpriseSG will match this with a S$75,000 Startup SG Founder (Competition) grant. Winners can utilise the seed funding for R&D, prototype development and to conduct pilot trials for sustainable protein products. Besides the funding, winners will receive up to S$25,000 worth of in-kind support from Bezos Centre @NUS. This includes mentorship, laboratory facilities, office space, startup incubation resources, and the opportunity to pitch at an annual startup event.

Interested startups can submit their applications from 1 August to 8 September 2025. More information on the Sustainable Protein Startup Competition is available at https://go.gov.sg/spsc-2025.  

Support for growth-stage sustainable protein startups

As part of its US$3 million funding to accelerate sustainable protein solutions, Bezos Centre @NUS has earmarked up to US$1.5 million to help growth-stage sustainable protein startups and high-potential projects scale up production, strengthen market presence, and drive international growth.

Bezos Centre @NUS plans to award grants of up to US$500,000 each to three promising startups or high-potential projects. EnterpriseSG will be partnering with Bezos Centre @NUS to further enhance this support through the Startup SG Tech Grant matching. More details will be announced in November this year.

All life is connected in a vast family tree. Every organism exists in relationship to its ancestors, descendants, and cousins, and the path between any two individuals can be traced. The same is true of cells within organisms — each of the trillions of cells in the human body is produced through successive divisions from a fertilized egg, and can all be related to one another through a cellular family tree. In simpler organisms, such as the worm C. elegans, this cellular family tree has been fully mapped, but the cellular family tree of a human is many times larger and more complex.

In the past, MIT professor and Whitehead Institute for Biomedical Research member Jonathan Weissman and other researchers developed lineage tracing methods to track and reconstruct the family trees of cell divisions in model organisms in order to understand more about the relationships between cells and how they assemble into tissues, organs, and — in some cases — tumors. These methods could help to answer many questions about how organisms develop and diseases like cancer are initiated and progress.

Now, Weissman and colleagues have developed an advanced lineage tracing tool that not only captures an accurate family tree of cell divisions, but also combines that with spatial information: identifying where each cell ends up within a tissue. The researchers used their tool, PEtracer, to observe the growth of metastatic tumors in mice. Combining lineage tracing and spatial data provided the researchers with a detailed view of how elements intrinsic to the cancer cells and from their environments influenced tumor growth, as Weissman and postdocs in his lab Luke Koblan, Kathryn Yost, and Pu Zheng, and graduate student William Colgan share in a paper published in the journal Science on July 24.

“Developing this tool required combining diverse skill sets through the sort of ambitious interdisciplinary collaboration that’s only possible at a place like Whitehead Institute,” says Weissman, who is also a Howard Hughes Medical Institute investigator. “Luke came in with an expertise in genetic engineering, Pu in imaging, Katie in cancer biology, and William in computation, but the real key to their success was their ability to work together to build PEtracer.”

“Understanding how cells move in time and space is an important way to look at biology, and here we were able to see both of those things in high resolution. The idea is that by understanding both a cell’s past and where it ends up, you can see how different factors throughout its life influenced its behaviors. In this study, we use these approaches to look at tumor growth, though in principle we can now begin to apply these tools to study other biology of interest, like embryonic development,” Koblan says.

Designing a tool to track cells in space and time

PEtracer tracks cells’ lineages by repeatedly adding short, predetermined codes to the DNA of cells over time. Each piece of code, called a lineage tracing mark, is made up of five bases, the building blocks of DNA. These marks are inserted using a gene editing technology called prime editing, which directly rewrites stretches of DNA with minimal undesired byproducts. Over time, each cell acquires more lineage tracing marks, while also maintaining the marks of its ancestors. The researchers can then compare cells’ combinations of marks to figure out relationships and reconstruct the family tree.

“We used computational modeling to design the tool from first principles, to make sure that it was highly accurate, and compatible with imaging technology. We ran many simulations to land on the optimal parameters for a new lineage tracing tool, and then engineered our system to fit those parameters,” Colgan says.

When the tissue — in this case, a tumor growing in the lung of a mouse — had sufficiently grown, the researchers collected these tissues and used advanced imaging approaches to look at each cell’s lineage relationship to other cells via the lineage tracing marks, along with its spatial position within the imaged tissue and its identity (as determined by the levels of different RNAs expressed in each cell). PEtracer is compatible with both imaging approaches and sequencing methods that capture genetic information from single cells.

“Making it possible to collect and analyze all of this data from the imaging was a large challenge,” Zheng says. “What’s particularly exciting to me is not just that we were able to collect terabytes of data, but that we designed the project to collect data that we knew we could use to answer important questions and drive biological discovery.”

Reconstructing the history of a tumor

Combining the lineage tracing, gene expression, and spatial data let the researchers understand how the tumor grew. They could tell how closely related neighboring cells are and compare their traits. Using this approach, the researchers found that the tumors they were analyzing were made up of four distinct modules, or neighborhoods, of cells.

The tumor cells closest to the lung, the most nutrient-dense region, were the most fit, meaning their lineage history indicated the highest rate of cell division over time. Fitness in cancer cells tends to correlate to how aggressively tumors will grow.

The cells at the “leading edge” of the tumor, the far side from the lung, were more diverse and not as fit. Below the leading edge was a low-oxygen neighborhood of cells that might once have been leading edge cells, now trapped in a less-desirable spot. Between these cells and the lung-adjacent cells was the tumor core, a region with both living and dead cells, as well as cellular debris.

The researchers found that cancer cells across the family tree were equally likely to end up in most of the regions, with the exception of the lung-adjacent region, where a few branches of the family tree dominated. This suggests that the cancer cells’ differing traits were heavily influenced by their environments, or the conditions in their local neighborhoods, rather than their family history. Further evidence of this point was that expression of certain fitness-related genes, such as Fgf1/Fgfbp1, correlated to a cell’s location, rather than its ancestry. However, lung-adjacent cells also had inherited traits that gave them an edge, including expression of the fitness-related gene Cldn4­ — showing that family history influenced outcomes as well.

These findings demonstrate how cancer growth is influenced both by factors intrinsic to certain lineages of cancer cells and by environmental factors that shape the behavior of cancer cells exposed to them.

“By looking at so many dimensions of the tumor in concert, we could gain insights that would not have been possible with a more limited view,” Yost says. “Being able to characterize different populations of cells within a tumor will enable researchers to develop therapies that target the most aggressive populations more effectively.”

“Now that we’ve done the hard work of designing the tool, we’re excited to apply it to look at all sorts of questions in health and disease, in embryonic development, and across other model species, with an eye toward understanding important problems in human health,” Koblan says. “The data we collect will also be useful for training AI models of cellular behavior. We’re excited to share this technology with other researchers and see what we all can discover.”

Salt creeping, a phenomenon that occurs in both natural and industrial processes, describes the collection and migration of salt crystals from evaporating solutions onto surfaces. Once they start collecting, the crystals climb, spreading away from the solution. This creeping behavior, according to researchers, can cause damage or be harnessed for good, depending on the context. New research published June 30 in the journal Langmuir is the first to show salt creeping at a single-crystal scale and beneath a liquid’s meniscus.

“The work not only explains how salt creeping begins, but why it begins and when it does,” says Joseph Phelim Mooney, a postdoc in the MIT Device Research Laboratory and one of the authors of the new study. “We hope this level of insight helps others, whether they’re tackling water scarcity, preserving ancient murals, or designing longer-lasting infrastructure.”

The work is the first to directly visualize how salt crystals grow and interact with surfaces underneath a liquid meniscus, something that’s been theorized for decades but never actually imaged or confirmed at this level, and it offers fundamental insights that could impact a wide range of fields — from mineral extraction and desalination to anti-fouling coatings, membrane design for separation science, and even art conservation, where salt damage is a major threat to heritage materials.

In civil engineering applications, for example, the research can help explain why and when salt crystals start growing across surfaces like concrete, stone, or building materials. “These crystals can exert pressure and cause cracking or flaking, reducing the long-term durability of structures,” says Mooney. “By pinpointing the moment when salt begins to creep, engineers can better design protective coatings or drainage systems to prevent this form of degradation.”

For a field like art conservation, where salt can be devastating to murals, frescoes, and ancient artifacts, often forming beneath the surface before visible damage appears, the work can help identify the exact conditions that cause salt to start moving and spreading, allowing conservators to act earlier and more precisely to protect heritage objects.

The work began during Mooney’s Marie Curie Fellowship at MIT. “I was focused on improving desalination systems and quickly ran into [salt buildup as] a major roadblock,” he says. “[Salt] was everywhere, coating surfaces, clogging flow paths, and undermining the efficiency of our designs. I realized we didn’t fully understand how or why salt starts creeping across surfaces in the first place.”

That experience led Mooney to team up with colleagues to dig into the fundamentals of salt crystallization at the air–liquid–solid interface. “We wanted to zoom in, to really see the moment salt begins to move, so we turned to in situ X-ray microscopy,” he says. “What we found gave us a whole new way to think about surface fouling, material degradation, and controlled crystallization.”

The new research may, in fact, allow better control of a crystallization processes required to remove salt from water in zero-liquid discharge systems. It can also be used to explain how and when scaling happens on equipment surfaces, and may support emerging climate technologies that depend on smart control of evaporation and crystallization.

The work also supports mineral and salt extraction applications, where salt creeping can be both a bottleneck and an opportunity. In these applications, Mooney says, “by understanding the precise physics of salt formation at surfaces, operators can optimize crystal growth, improving recovery rates and reducing material losses.”

Mooney’s co-authors on the paper include fellow MIT Device Lab researchers Omer Refet Caylan, Bachir El Fil (now an associate professor at Georgia Tech), and Lenan Zhang (now an associate professor at Cornell University); Jeff Punch and Vanessa Egan of the University of Limerick; and Jintong Gao of Cornell.

The research was conducted using in situ X-ray microscopy. Mooney says the team’s big realization moment occurred when they were able to observe a single salt crystal pinning itself to the surface, which kicked off a cascading chain reaction of growth.

“People had speculated about this, but we captured it on X-ray for the first time. It felt like watching the microscopic moment where everything tips, the ignition points of a self-propagating process,” says Mooney. “Even more surprising was what followed: The salt crystal didn’t just grow passively to fill the available space. It pierced through the liquid-air interface and reshaped the meniscus itself, setting up the perfect conditions for the next crystal. That subtle, recursive mechanism had never been visually documented before — and seeing it play out in real time completely changed how we thought about salt crystallization.”

The paper, “In Situ X-ray Microscopy Unraveling the Onset of Salt Creeping at a Single-Crystal Level,” is available now in the journal Langmuir. Research was conducted in MIT.nano. 

Salt creeping, a phenomenon that occurs in both natural and industrial processes, describes the collection and migration of salt crystals from evaporating solutions onto surfaces. Once they start collecting, the crystals climb, spreading away from the solution. This creeping behavior, according to researchers, can cause damage or be harnessed for good, depending on the context. New research published June 30 in the journal Langmuir is the first to show salt creeping at a single-crystal scale and beneath a liquid’s meniscus.

“The work not only explains how salt creeping begins, but why it begins and when it does,” says Joseph Phelim Mooney, a postdoc in the MIT Device Research Laboratory and one of the authors of the new study. “We hope this level of insight helps others, whether they’re tackling water scarcity, preserving ancient murals, or designing longer-lasting infrastructure.”

The work is the first to directly visualize how salt crystals grow and interact with surfaces underneath a liquid meniscus, something that’s been theorized for decades but never actually imaged or confirmed at this level, and it offers fundamental insights that could impact a wide range of fields — from mineral extraction and desalination to anti-fouling coatings, membrane design for separation science, and even art conservation, where salt damage is a major threat to heritage materials.

In civil engineering applications, for example, the research can help explain why and when salt crystals start growing across surfaces like concrete, stone, or building materials. “These crystals can exert pressure and cause cracking or flaking, reducing the long-term durability of structures,” says Mooney. “By pinpointing the moment when salt begins to creep, engineers can better design protective coatings or drainage systems to prevent this form of degradation.”

For a field like art conservation, where salt can be devastating to murals, frescoes, and ancient artifacts, often forming beneath the surface before visible damage appears, the work can help identify the exact conditions that cause salt to start moving and spreading, allowing conservators to act earlier and more precisely to protect heritage objects.

The work began during Mooney’s Marie Curie Fellowship at MIT. “I was focused on improving desalination systems and quickly ran into [salt buildup as] a major roadblock,” he says. “[Salt] was everywhere, coating surfaces, clogging flow paths, and undermining the efficiency of our designs. I realized we didn’t fully understand how or why salt starts creeping across surfaces in the first place.”

That experience led Mooney to team up with colleagues to dig into the fundamentals of salt crystallization at the air–liquid–solid interface. “We wanted to zoom in, to really see the moment salt begins to move, so we turned to in situ X-ray microscopy,” he says. “What we found gave us a whole new way to think about surface fouling, material degradation, and controlled crystallization.”

The new research may, in fact, allow better control of a crystallization processes required to remove salt from water in zero-liquid discharge systems. It can also be used to explain how and when scaling happens on equipment surfaces, and may support emerging climate technologies that depend on smart control of evaporation and crystallization.

The work also supports mineral and salt extraction applications, where salt creeping can be both a bottleneck and an opportunity. In these applications, Mooney says, “by understanding the precise physics of salt formation at surfaces, operators can optimize crystal growth, improving recovery rates and reducing material losses.”

Mooney’s co-authors on the paper include fellow MIT Device Lab researchers Omer Refet Caylan, Bachir El Fil (now an associate professor at Georgia Tech), and Lenan Zhang (now an associate professor at Cornell University); Jeff Punch and Vanessa Egan of the University of Limerick; and Jintong Gao of Cornell.

The research was conducted using in situ X-ray microscopy. Mooney says the team’s big realization moment occurred when they were able to observe a single salt crystal pinning itself to the surface, which kicked off a cascading chain reaction of growth.

“People had speculated about this, but we captured it on X-ray for the first time. It felt like watching the microscopic moment where everything tips, the ignition points of a self-propagating process,” says Mooney. “Even more surprising was what followed: The salt crystal didn’t just grow passively to fill the available space. It pierced through the liquid-air interface and reshaped the meniscus itself, setting up the perfect conditions for the next crystal. That subtle, recursive mechanism had never been visually documented before — and seeing it play out in real time completely changed how we thought about salt crystallization.”

The paper, “In Situ X-ray Microscopy Unraveling the Onset of Salt Creeping at a Single-Crystal Level,” is available now in the journal Langmuir. Research was conducted in MIT.nano. 

More than 400 students from over 20 pre-tertiary and tertiary institutions across Singapore and the Asia-Pacific region convened to debate critical global concerns at the 22nd Singapore Model United Nations (SMUN) conference that was organised by the NUS Political Science Society (NUS PSSOC) in June at NUS University Town (UTown).

Themed “Building a New Era of Diplomacy”, SMUN 2025 provided an incisive lens through which young participants navigated the intricate complexities of global geopolitics by role-playing as UN delegates and members of international organisations such as the International Monetary Fund and the World Trade Organisation to discuss current issues such as digital currencies and tariffs.

A key highlight of the event was the thought-provoking keynote by distinguished diplomat and academic Mr Kishore Mahbubani, Distinguished Fellow at the NUS Asia Research Institute. In his address, Mr Mahbubani explored the shifting paradigms of global power and offered compelling insights into the current international order. He highlighted the remarkable success of ASEAN as a testament to effective regional cooperation and presented it as a model for navigating complex global challenges.

Mr Mahbubani also critically examined the structure of the UN, particularly the influence of the permanent members’ veto powers. While acknowledging that the UN’s effectiveness is often shaped by the interests of the major powers, he emphasised that it remains in the interest of small states to strengthen the multilateral system by “build[ing] upon a rules-based order and cooperat[ing] in issues of global importance such as climate change and denuclearisation.” His remarks struck a chord with the young delegates, reinforcing their understanding of the importance of multilateralism and the pursuit of shared global interests to shape a more equitable world. 

Diverse agendas and dynamic debates: Inside the councils of SMUN 2025

This year’s conference featured delegates from 11 councils tackling a wide array of pertinent topics. In the United Nations Security Council, discussions centred on reforming peacekeeping operations – with delegates debating their effectiveness, future direction and the complexities of maintaining peace in conflicts such as the Syrian Civil War. The United Nations Economic Commission for Europe (UNECE) engaged in intricate negotiations on transboundary water treaties across the Pan-European region, a critical issue with far-reaching environmental and geopolitical implications. Meanwhile, the House of Commons (HOC) simulation offered a unique platform to examine the ongoing complexities and multifaceted consequences of Brexit, giving participants the opportunity to explore national political dynamics within an international context. 

Through extensive research, preparation of detailed position papers, and the development of innovative policy proposals, delegates engaged in intense debates, striving to pass resolutions through collaborative effort and majority vote.

As with previous iterations, SMUN 2025 was meticulously designed to spur active participation and meaningful dialogue. Participants who demonstrated significant contributions throughout the conference were duly recognised with prestigious awards such as Best Delegate, Outstanding Delegate, Honourable Mentions, and Best Position Paper, celebrating their dedication and diplomatic acumen.

Mr Arav Taneja from Temasek Secondary School received an honourable mention award for his outstanding contributions in the House of Representatives of Japan (HRJ). Recounting his experience, he shared, “When I first walked into the House of Representatives Japan SMUN conference room, I felt like a small fish in a big lake. However, as we progressed through council sessions, I found my voice. I engaged in constructive debate with other delegates and worked on resolutions like it was second nature. I gained invaluable knowledge not only about Japan but also about global dynamics. However, I think the biggest takeaway was the connections and friendships I made along the way.”

PSSOC President and Year 2 student from the NUS Faculty of Arts and Social Sciences Ms Irdina Duran, expressed her thanks to all involved for the success of SMUN 2025. “We are immensely grateful to Mr Mahbubani for his profound insights and to all the dedicated participants who made this year’s conference an undeniable success.”

SMUN 2025 Secretary-General and first-year student from the NUS Faculty of Law Mr Aditya Garladinne noted that this year's event has proven to be a vibrant crucible of intellect and diplomacy. Far more than a mere simulation, it is a testament to the boundless potential of our youth, he said.

“Witnessing these bright minds grappling with the world's most pressing challenges is not just inspiring; it is a profound reassurance that the future of global cooperation rests in exceptionally capable hands. This event truly exemplifies how experiential learning can cultivate not only future leaders but also compassionate and globally aware citizens." 

By the NUS Political Science Society at the NUS Faculty of Arts and Social Sciences

International Friendship Day, observed annually on 30 July, celebrates friendships between people of different countries and cultures to cultivate a shared sense of solidarity and promote dialogue to bridge differences.

As a global university, NUS nurtures students into global citizens through overseas opportunities, collaborations with foreign universities, and immersion in a cosmopolitan community that has 123 nationalities represented among staff and students. This diversity is especially prominent at the Yong Siew Toh Conservatory of Music (YST), which boasts one of the most culturally diverse student bodies at NUS. There are more than 20 nationalities in its undergraduate student population of about 240, creating a vibrant environment for friendships to blossom across different nationalities and cultures.

In celebration of International Friendship Day, five Voice majors hailing from various parts of Southeast Asia, South Korea, and India share how their multinational group of friends has enriched and shaped their university experience both inside and outside the classroom.

Embracing curiosity and kindness

Like many university friend groups, Val Chong, Samiksha Argal, Park Minjun, and Leanne Reese first met and bonded in shared core classes in Year 1. Representing Singapore, India, South Korea, and the Philippines respectively, they became acquainted through casual chats between lessons and grabbing meals together after classes and performances.

Notably, the friend group also comprises their major’s entire Class of 2026, as YST only takes in an average of three to five Voice students per year. Due to the small size of the cohort, Indonesian Jason Suryaatmaja (Class of 2025) would often fill in for missing parts in their group performances and soon joined their circle of friends, adding another nationality to the already diverse squad.

Making friends with people of other nationalities feels natural in YST, said Minjun: “We’re lucky because our faculty is one of the smallest at NUS with people from so many different backgrounds. It makes it easier to get close to people from other countries.”

Navigating cultural differences is a major part of the YST experience, especially when classes are led by an international faculty and exchanging feedback is an important part of the learning experience. Cultural differences can often impact how tone and intention are conveyed and perceived, and nuance can sometimes be lost when a thought is translated between languages.

The five friends overcame this challenge by embracing an attitude of curiosity towards cultures other than their own, as exemplified in a quote that Leanne learnt from a professor while on exchange in Scotland: “Don’t be interesting; be interested.” This mindset led them to gain a better understanding of each other’s colloquialisms and mannerisms, which differ across cultures and languages. Over time, they have developed a shared trust and now count the ability to exchange candid feedback among themselves as one of the most valuable benefits of their friend group.

“Val is an amazing performer, so when we are rehearsing for productions, I will ask her, ‘Do you think this works?’ I ask Minjun for technical advice and we explore singing techniques together. It has really helped me on the career side,” Jason said, prompting some good-natured ribbing from his friends that they are only useful to him as career support.

The supportive environment they have nurtured in their group also extends to their interactions with students from other cohorts, such as during weekly Voice studio classes. Val shared that they make it a point to chat with their juniors during these shared classes and cheer them on even when they make mistakes.

Said Samiksha: “Through the years, we have created the kind of environment that we want for ourselves and others – a warm and nurturing one where everyone can grow and learn together.”

Growth lies outside the comfort zone

While cultural differences can pose challenges at times, they create positive opportunities for growth and connection too. As a Singaporean, Val takes on the responsibility of helping the others feel at home, introducing them to the Chinese New Year tradition of yusheng and taking them café-hopping to explore the country.

“As a local, it is easy to stick to our own cliques, but it is also important to make our fellow international students feel welcome and ‘at home’. We can invite them out for a meal, or even a simple greeting could make their day,” she said.

“YST is so diverse that we’re compelled to integrate, and honestly that pushed me out of my comfort zone as an introvert. Talking to people from different cultures, you’ll learn so much about them and the world in general,” said Samiksha, who recalls learning the entire history of Korea from Minjun, starting from the Stone Age to the present day.

Val noted that her friendships with people whose countries are undergoing crises make her more informed about what is happening overseas and prompt her to empathise.

She added that such interactions can also serve as an important first step in countering racial and cultural stereotypes. “Stereotypes often arise from preconceived notions about others. But when we take the time to talk to others and understand them, we start to realise that we need to correct our assumptions.”

Forming friendships with people of other cultures is crucial for foreign students who do not have family in Singapore, said Leanne, who has heard of students leaving due to loneliness. “It’s really important to have someone who can listen and be there for you,” she said. “There were tough times when school got hectic and stressful, but it’s the friends I’ve made that supported me through my dark times and helped me emerge stronger.”

Ultimately, international friendships are not so different from friendships among people of the same culture, the students said. Social media makes it easier than ever to find common topics like pop culture and memes that can serve as a starting point for conversations, and once a connection is established, deeper shared interests can be discovered to strengthen the friendship.

The group’s favourite stress-busting activity is cycling to West Coast Park, where they would sit by the sea and talk about life, and some of their best memories are from post-concert suppers at the row of eateries along Clementi Road known to NUS students as “Supper Stretch”. “We always eat at the same three places, and I don’t know why we’re not bored of the food yet, but it’s just a lot of fun,” said Samiksha.

Said Minjun: “Most of us are living and studying here alone, but having these friends to journey alongside us makes life a lot more fun and exciting.”

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

• Berita Minggu, 27 July 2025, p9

• Suria News Online, 25 July 2025
• Lianhe Zaobao, 26 July 2025, Singapore, p7

• Lianhe Zaobao, 26 July 2025, Business, p19

• Berita Harian, 26 July 2025, p7

• Money 89.3FM, 22 July 2025
• Hao 96.3FM, 22 July 2025
• The Straits Times, 23 July 2025, Business, pA17
• The Business Times, 23 July 2025, p3
• Lianhe Zaobao, 23 July 2025, Business, p22

• 8world Online, 22 July 2025
• 8world Online, 22 July 2025
• Vasantham News Online, 22 July 2025
• The Business Times, 23 July 2025, p2
• Lianhe Zaobao, 23 July 2025, Business, p22
• Berita Harian, 23 July 2025, p5
• Berita Harian, 23 July 2025, p5

If you rotate an image of a molecular structure, a human can tell the rotated image is still the same molecule, but a machine-learning model might think it is a new data point. In computer science parlance, the molecule is “symmetric,” meaning the fundamental structure of that molecule remains the same if it undergoes certain transformations, like rotation.

If a drug discovery model doesn’t understand symmetry, it could make inaccurate predictions about molecular properties. But despite some empirical successes, it’s been unclear whether there is a computationally efficient method to train a good model that is guaranteed to respect symmetry.

A new study by MIT researchers answers this question, and shows the first method for machine learning with symmetry that is provably efficient in terms of both the amount of computation and data needed.

These results clarify a foundational question, and they could aid researchers in the development of more powerful machine-learning models that are designed to handle symmetry. Such models would be useful in a variety of applications, from discovering new materials to identifying astronomical anomalies to unraveling complex climate patterns.

“These symmetries are important because they are some sort of information that nature is telling us about the data, and we should take it into account in our machine-learning models. We’ve now shown that it is possible to do machine-learning with symmetric data in an efficient way,” says Behrooz Tahmasebi, an MIT graduate student and co-lead author of this study.

He is joined on the paper by co-lead author and MIT graduate student Ashkan Soleymani; Stefanie Jegelka, an associate professor of electrical engineering and computer science (EECS) and a member of the Institute for Data, Systems, and Society (IDSS) and the Computer Science and Artificial Intelligence Laboratory (CSAIL); and senior author Patrick Jaillet, the Dugald C. Jackson Professor of Electrical Engineering and Computer Science and a principal investigator in the Laboratory for Information and Decision Systems (LIDS). The research was recently presented at the International Conference on Machine Learning.

Studying symmetry

Symmetric data appear in many domains, especially the natural sciences and physics. A model that recognizes symmetries is able to identify an object, like a car, no matter where that object is placed in an image, for example.

Unless a machine-learning model is designed to handle symmetry, it could be less accurate and prone to failure when faced with new symmetric data in real-world situations. On the flip side, models that take advantage of symmetry could be faster and require fewer data for training.

But training a model to process symmetric data is no easy task.

One common approach is called data augmentation, where researchers transform each symmetric data point into multiple data points to help the model generalize better to new data. For instance, one could rotate a molecular structure many times to produce new training data, but if researchers want the model to be guaranteed to respect symmetry, this can be computationally prohibitive.

An alternative approach is to encode symmetry into the model’s architecture. A well-known example of this is a graph neural network (GNN), which inherently handles symmetric data because of how it is designed.

“Graph neural networks are fast and efficient, and they take care of symmetry quite well, but nobody really knows what these models are learning or why they work. Understanding GNNs is a main motivation of our work, so we started with a theoretical evaluation of what happens when data are symmetric,” Tahmasebi says.

They explored the statistical-computational tradeoff in machine learning with symmetric data. This tradeoff means methods that require fewer data can be more computationally expensive, so researchers need to find the right balance.

Building on this theoretical evaluation, the researchers designed an efficient algorithm for machine learning with symmetric data.

Mathematical combinations

To do this, they borrowed ideas from algebra to shrink and simplify the problem. Then, they reformulated the problem using ideas from geometry that effectively capture symmetry.

Finally, they combined the algebra and the geometry into an optimization problem that can be solved efficiently, resulting in their new algorithm.

“Most of the theory and applications were focusing on either algebra or geometry. Here we just combined them,” Tahmasebi says.

The algorithm requires fewer data samples for training than classical approaches, which would improve a model’s accuracy and ability to adapt to new applications.

By proving that scientists can develop efficient algorithms for machine learning with symmetry, and demonstrating how it can be done, these results could lead to the development of new neural network architectures that could be more accurate and less resource-intensive than current models.

Scientists could also use this analysis as a starting point to examine the inner workings of GNNs, and how their operations differ from the algorithm the MIT researchers developed.

“Once we know that better, we can design more interpretable, more robust, and more efficient neural network architectures,” adds Soleymani.

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

Music technology took center stage at MIT during “FUTURE PHASES,” an evening of works for string orchestra and electronics, presented by the MIT Music Technology and Computation Graduate Program as part of the 2025 International Computer Music Conference (ICMC). 

The well-attended event was held last month in the Thomas Tull Concert Hall within the new Edward and Joyce Linde Music Building. Produced in collaboration with the MIT Media Lab’s Opera of the Future Group and Boston’s self-conducted chamber orchestra A Far Cry, “FUTURE PHASES” was the first event to be presented by the MIT Music Technology and Computation Graduate Program in MIT Music’s new space.

“FUTURE PHASES” offerings included two new works by MIT composers: the world premiere of “EV6,” by MIT Music’s Kenan Sahin Distinguished Professor Evan Ziporyn and professor of the practice Eran Egozy; and the U.S. premiere of “FLOW Symphony,” by the MIT Media Lab’s Muriel R. Cooper Professor of Music and Media Tod Machover. Three additional works were selected by a jury from an open call for works: “The Wind Will Carry Us Away,” by Ali Balighi; “A Blank Page,” by Celeste Betancur Gutiérrez and Luna Valentin; and “Coastal Portrait: Cycles and Thresholds,” by Peter Lane. Each work was performed by Boston’s own multi-Grammy-nominated string orchestra, A Far Cry.

“The ICMC is all about presenting the latest research, compositions, and performances in electronic music,” says Egozy, director of the new Music Technology and Computation Graduate Program at MIT. When approached to be a part of this year’s conference, “it seemed the perfect opportunity to showcase MIT’s commitment to music technology, and in particular the exciting new areas being developed right now: a new master’s program in music technology and computation, the new Edward and Joyce Linde Music Building with its enhanced music technology facilities, and new faculty arriving at MIT with joint appointments between MIT Music and Theater Arts (MTA) and the Department of Electrical Engineering and Computer Science (EECS).” These recently hired professors include Anna Huang, a keynote speaker for the conference and creator of the machine learning model Coconet that powered Google’s first AI Doodle, the Bach Doodle.

Egozy emphasizes the uniqueness of this occasion: “You have to understand that this is a very special situation. Having a full 18-member string orchestra [A Far Cry] perform new works that include electronics does not happen very often. In most cases, ICMC performances consist either entirely of electronics and computer-generated music, or perhaps a small ensemble of two-to-four musicians. So the opportunity we could present to the larger community of music technology was particularly exciting.”

To take advantage of this exciting opportunity, an open call was put out internationally to select the other pieces that would accompany Ziporyn and Egozy’s “EV6” and Machover’s “FLOW Symphony.” Three pieces were selected from a total of 46 entries to be a part of the evening’s program by a panel of judges that included Egozy, Machover, and other distinguished composers and technologists.

“We received a huge variety of works from this call,” says Egozy. “We saw all kinds of musical styles and ways that electronics would be used. No two pieces were very similar to each other, and I think because of that, our audience got a sense of how varied and interesting a concert can be for this format. A Far Cry was really the unifying presence. They played all pieces with great passion and nuance. They have a way of really drawing audiences into the music. And, of course, with the Thomas Tull Concert Hall being in the round, the audience felt even more connected to the music.”

Egozy continues, “we took advantage of the technology built into the Thomas Tull Concert Hall, which has 24 built-in speakers for surround sound allowing us to broadcast unique, amplified sound to every seat in the house. Chances are that every person might have experienced the sound slightly differently, but there was always some sense of a multidimensional evolution of sound and music as the pieces unfolded.”

The five works of the evening employed a range of technological components that included playing synthesized, prerecorded, or electronically manipulated sounds; attaching microphones to instruments for use in real-time signal processing algorithms; broadcasting custom-generated musical notation to the musicians; utilizing generative AI to process live sound and play it back in interesting and unpredictable ways; and audience participation, where spectators use their cellphones as musical instruments to become a part of the ensemble.

Ziporyn and Egozy’s piece, “EV6,” took particular advantage of this last innovation: “Evan and I had previously collaborated on a system called Tutti, which means ‘together’ in Italian. Tutti gives an audience the ability to use their smartphones as musical instruments so that we can all play together.” Egozy developed the technology, which was first used in the MIT Campaign for a Better World in 2017. The original application involved a three-minute piece for cellphones only. “But for this concert,” Egozy explains, “Evan had the idea that we could use the same technology to write a new piece — this time, for audience phones and a live string orchestra as well.”

To explain the piece’s title, Ziporyn says, “I drive an EV6; it’s my first electric car, and when I first got it, it felt like I was driving an iPhone. But of course it’s still just a car: it’s got wheels and an engine, and it gets me from one place to another. It seemed like a good metaphor for this piece, in which a lot of the sound is literally played on cellphones, but still has to work like any other piece of music. It’s also a bit of an homage to David Bowie’s song ‘TVC 15,’ which is about falling in love with a robot.”

Egozy adds, “We wanted audience members to feel what it is like to play together in an orchestra. Through this technology, each audience member becomes a part of an orchestral section (winds, brass, strings, etc.). As they play together, they can hear their whole section playing similar music while also hearing other sections in different parts of the hall play different music. This allows an audience to feel a responsibility to their section, hear how music can move between different sections of an orchestra, and experience the thrill of live performance. In ‘EV6,’ this experience was even more electrifying because everyone in the audience got to play with a live string orchestra — perhaps for the first time in recorded history.”

After the concert, guests were treated to six music technology demonstrations that showcased the research of undergraduate and graduate students from both the MIT Music program and the MIT Media Lab. These included a gamified interface for harnessing just intonation systems (Antonis Christou); insights from a human-AI co-created concert (Lancelot Blanchard and Perry Naseck); a system for analyzing piano playing data across campus (Ayyub Abdulrezak ’24, MEng ’25); capturing music features from audio using latent frequency-masked autoencoders (Mason Wang); a device that turns any surface into a drum machine (Matthew Caren ’25); and a play-along interface for learning traditional Senegalese rhythms (Mariano Salcedo ’25). This last example led to the creation of Senegroove, a drumming-based application specifically designed for an upcoming edX online course taught by ethnomusicologist and MIT associate professor in music Patricia Tang, and world-renowned Senegalese drummer and MIT lecturer in music Lamine Touré, who provided performance videos of the foundational rhythms used in the system.

Ultimately, Egozy muses, “'FUTURE PHASES' showed how having the right space — in this case, the new Edward and Joyce Linde Music Building — really can be a driving force for new ways of thinking, new projects, and new ways of collaborating. My hope is that everyone in the MIT community, the Boston area, and beyond soon discovers what a truly amazing place and space we have built, and are still building here, for music and music technology at MIT.”

Driven by a shared passion for finance and investing, second-year students Fong Zhi Heng from NUS Faculty of Arts and Social Sciences and Brandon Lim from NUS Business School teamed up for the 2025 UBS Finance Challenge. The pair beat 300 other teams to clinch the first prize at the competition.

The UBS Finance Challenge is a high-profile stock pitch competition that brings together some of the brightest and most innovative university students in finance from around the world to showcase their market research and equity analysis skills. Now in its fifth year, the competition has expanded to a dual-region format, allowing participants to compete in either Hong Kong or Mainland China, with top teams from both tracks advancing to the finals in Shanghai to vie for the coveted Greater China Championship title.

United by a shared passion for finance

Zhi Heng and Brandon first connected through the NUS Asset & Wealth Management Club and the Next Gen Investors Endowment Ltd, a Singapore-based non-profit organisation that promotes investor education to local and international students. They quickly became friends over a shared passion for applying their classroom learnings to solve real-world challenges through competitions.  

In April, the pair had already made waves by winning the Singapore & Southeast Asia rounds of the 2025 CFA Institute Research Challenge at NUS. Following this success, they were eager to take on a bigger stage. 

“For us, the UBS Finance Challenge presented yet another valuable learning opportunity,” said Brandon. “We wanted to put our finance skills into practice again and push ourselves, especially since it was our first time doing a long/short equity research pitch.”

Pitching with precision and perseverance

Teams in the UBS Finance Challenge were required to analyse stocks from one of six sectors – industrials, consumer, technology, internet, healthcare and energy – and to develop a long-short pairs trade strategy. This involved evaluating trade logic, industry outlook, company financials, stock valuations and presenting actionable trade recommendations.

Zhi Heng and Brandon centred their investment pitch on China’s restaurant sector, comparing two major players – Yum China, the country’s largest quick-service operator, and Jiumaojiu International Holdings, a full-service restaurant specialising in Chinese cuisine. They argued that Yum China would outperform Jiumaojiu; driven by the long-term growth trend toward takeaway and fast food convenience in China. Conversely, dine-in models, such as Jiumaojiu would suffer as they face headwinds from declining foot traffic. On this basis, they proposed a pair trade strategy of buying Yum China stock and shorting Jiumaojiu stock.

Having just wrapped up the CFA Research Institute Challenge in April, the pair had only two weeks to prepare for the UBS Finance Challenge. Balancing competing priorities made it even more intense.

Zhi Heng recalled, “It was a tough balancing act – I was interning, and Brandon was studying for his finals during the day, so we had to work on our presentation late into the night. Some nights we barely slept. But with sheer determination and a genuine passion for understanding the company we were pitching, we pushed through and made it work.”

The duo also made the strategic decision to personally fund their travel to Hong Kong for an in-person semi-final presentation – a move they believed gave them a competitive edge. “We felt that presenting in person gave us a better opportunity to connect with the judges and communicate our ideas more effectively,” shared Brandon.

Both students credited their earlier experience in the 2025 CFA Institute Research Challenge as instrumental to their success in the UBS challenge, noting how they benefitted immensely from the mentorship received, as the experience shaped their thought processes and helped refine their research and pitching abilities.

Zhi Heng also acknowledged the role of the NUS Investment Society, a student-led club promoting financial literacy, in sharpening his analytical capabilities. “Regular stock pitch sessions and feedback from seniors and alumni really honed my research skills,” he said. “Courses like Accounting and Economics gave me the technical foundation to understand financial statements and market dynamics, but it was the student groups and competitions that brought those concepts to life.”

More than a win: Valuable skills and future careers in finance

For their win, the pair received prizes worth 14,000 yuan (S$2,500) and secured the opportunity for fast-tracked interviews with UBS.

“We’re definitely elated,” Brandon said. “But more than the win, it was the invaluable skills gained and the personalised feedback from the judges that mattered most. They were able to provide meaningful and actionable insights that we could use in our future internships and careers in finance.”

In a milestone for campus operations and digital transformation, NUS has launched Singapore’s first university-based Integrated Operations Centre (IOC) – a state-of-the-art facility that is redefining how campus services are managed.

Fully operational since January 2025 and located at Prince George’s Park, the facility consolidates real-time data from multiple critical systems, including security, emergency services, and building management, onto a single, integrated digital platform. With this capability, NUS now has unprecedented oversight of its operations across its sprawling, multi-site campus, which serves a community of over 64,000 students, faculty and staff.

“The IOC is more than just a control room, it is a strategic enabler that helps us manage the complexity of campus operations with greater precision and foresight,” said Mr Koh Yan Leng, NUS Vice President (Campus Infrastructure). “This is a key pillar in our Smart Campus journey and a model of what’s possible when digital transformation is aligned with operational excellence.”

One centre, many systems

Conceptualised in 2022 as part of NUS’ Smart Campus Envisioning, the IOC was designed to address longstanding operational pain points such as fragmented legacy systems, manual incident reporting, and reactive maintenance practices.

The project was piloted at NUS University Town in May 2024 and became fully operational early this year. Today, the IOC provides centralised oversight across six key domains: security, emergency response, facilities, transport, housing, and sustainability.

At the heart of the IOC is a cutting-edge digital twin of the NUS campus, an interactive 3D model layered with live feeds from CCTV cameras, IoT sensors, and building maintenance and management systems. This allows operators to visualise the entire campus environment, conduct virtual patrols and respond to incidents in real time.

This is coupled with a dynamic, integrated dashboard that consolidates alerts from multiple systems, enabling operators to detect anomalies, coordinate responses and streamline workflows, thereby reducing downtime and enhancing service delivery.

“From emergency responses to campus-wide facilities monitoring, the IOC gives our operations teams a single source of truth,” said Mr Leonard Li, Head of IOC. “We can detect incidents ranging from an isolated fire alarm to a chiller plant fault within 20 seconds and automatically trigger a work order via the NUS iReport system. That speed is a game changer.”

Smarter operations, better outcomes

Beyond the control room, the benefits of the IOC are already being felt across campus. Students and staff enjoy faster fixes for faulty facilities and more reliable Internal Shuttle Bus services. Operations teams report smoother coordination, reduced downtime, and clearer performance metrics. Campus safety has been enhanced through AI-powered surveillance tools, including facial and licence plate recognition technology – all achieved without the need for expanded manpower.

“The IOC is built on scalable, secure infrastructure that supports continuous improvement,” said Mr Lim Yeow Khee, Section Head of Integrated Operations, “By integrating disparate systems and adding layers of automation, we’ve laid the groundwork for long-term innovation in campus infrastructure.”

In its first months of full operation, the IOC has already delivered tangible improvements. Fault response times have improved by 45 per cent for urgent cases and by 57 per cent for non-urgent ones, while emergency dispatch times have reduced by up to 50 per cent with better triaging capabilities. Operator productivity has also increased by 40 per cent, aided by the centralised dashboard and automated alerts.

Scaling and enhancing capabilities

While operational efficiency remains a core focus, the IOC is also paving the way for a more sustainable campus. For example, real-time alerts are now triggered when utility consumption exceeds baseline thresholds by 40 per cent, allowing for timely corrective action. Over time, these alerts will be linked to automated responses built into standard operating procedures, further improving efficiency.

“We’ve observed that most IOCs stop at security or facility management,” noted Mr Li. “What sets the NUS IOC apart is the vision to support evolving campus services in mobility, space optimisation, and even hospitality.”

With University Town now fully integrated, the IOC is expanding its capabilities under Phase 2 to the NUS Kent Ridge and Bukit Timah Campuses. Planned enhancements in this phase include using real-time mobility dashboards to optimise Internal Shuttle Bus operations, aligning NUS’ environmental targets with sustainability reporting, and adopting smart city technologies such as Wi-Fi-based crowd sensing and large vision model (LVM) AI analytics for deeper operational insights.

“The scale of operations needed to support a university of this size is often invisible,” said Mr Koh. “The IOC shines a light on these efforts while giving us the tools to do them better,” he added.

By University Campus Infrastructure

By Prof Kwan Ok Lee, Deputy Head of the Dept of Real Estate at NUS Business School

• CNA Online, 28 July 2025

Researchers from MIT and elsewhere have designed a novel transmitter chip that significantly improves the energy efficiency of wireless communications, which could boost the range and battery life of a connected device.

Their approach employs a unique modulation scheme to encode digital data into a wireless signal, which reduces the amount of error in the transmission and leads to more reliable communications.

The compact, flexible system could be incorporated into existing internet-of-things devices to provide immediate gains, while also meeting the more stringent efficiency requirements of future 6G technologies.

The versatility of the chip could make it well-suited for a range of applications that require careful management of energy for communications, such as industrial sensors that continuously monitor factory conditions and smart appliances that provide real-time notifications.

“By thinking outside the box, we created a more efficient, intelligent circuit for next-generation devices that is also even better than the state-of-the-art for legacy architectures. This is just one example of how adopting a modular approach to allow for adaptability can drive innovation at every level,” says Muriel Médard, the School of Science NEC Professor of Software Science and Engineering, a professor in the MIT Department of Electrical Engineering and Computer Science (EECS), and co-author of a paper on the new transmitter.

Médard’s co-authors include Timur Zirtiloglu, the lead author and a graduate student at Boston University; Arman Tan, a graduate student at BU; Basak Ozaydin, an MIT graduate student in EECS; Ken Duffy, a professor at Northeastern University; and Rabia Tugce Yazicigil, associate professor of electrical and computer engineering at BU. The research was recently presented at the IEEE Radio Frequency Circuits Symposium.

Optimizing transmissions

In wireless devices, a transmitter converts digital data into an electromagnetic signal that is sent over the airwaves to a receiver. The transmitter does this by mapping digital bits to symbols that represent the amplitude and phase of the electromagnetic signal, which is a process called modulation.

Traditional systems transmit signals that are evenly spaced by creating a uniform pattern of symbols, which helps avoid interference. But this uniform structure lacks adaptability and can be inefficient, since wireless channel conditions are dynamic and often change rapidly.

As an alternative, optimal modulation schemes follow a non-uniform pattern that can adapt to changing channel conditions, maximizing the amount of data transmitted while minimizing energy usage.

But while optimal modulation can be more energy efficient, it is also more susceptible to errors, especially in crowded wireless environments. When the signals aren’t uniform in length, it can be harder for the receiver to distinguish between symbols and noise that squeezed into the transmission.

To overcome this problem, the MIT transmitter adds a small amount of padding, in the form of extra bits between symbols, so that every transmission is the same length.

This helps the receiver identify the beginning and end of each transmission, preventing misinterpretation of the message. However, the device enjoys the energy efficiency gains of using a non-uniform, optimal modulation scheme.

This approach works because of a technique the researchers previously developed known as GRAND, which is a universal decoding algorithm that crack any code by guessing the noise that affected the transmission.

Here, they employ a GRAND-inspired algorithm to adjust the length of the received transmission by guessing the extra bits that have been added. In this way, the receiver can effectively reconstruct the original message.

“Now, thanks to GRAND, we can have a transmitter that is capable of doing these more efficient transmissions with non-uniform constellations of data, and we can see the gains,” Médard says.

A flexible circuit

The new chip, which has a compact architecture that allows the researchers to integrate additional efficiency-boosting methods, enabled transmissions with only about one-quarter the amount of signal error of methods that use optimal modulation.

Surprisingly, the device also achieved significantly lower error rates than transmitters that use traditional modulation.

“The traditional approach has become so ingrained that it was challenging to not get lured back to the status quo, especially since we were changing things that we often take for granted and concepts we’ve been teaching for decades,” Médard says.

This innovative architecture could be used to improve the energy efficiency and reliability of current wireless communication devices, while also offering the flexibility to be incorporated into future devices that employ optimal modulation.

Next, the researchers want to adapt their approach to leverage additional techniques that could boost efficiency and reduce the error rates in wireless transmissions.

“This optimal modulation transmitter radio frequency integrated circuit is a game-changing innovation over the traditional RF signal modulation. It’s set to play a major role for the next generation of wireless connectivity such as 6G and Wi-Fi,” says Rocco Tam, NXP Fellow for Wireless Connectivity SoC Research and Development at NXP Semiconductors, who was not involved with this research.

This work is supported, in part, by the U.S. Defense Advanced Research Projects Agency (DARPA), the National Science Foundation (NSF), and the Texas Analog Center for Excellence. 

A lot of attention has been paid to how climate change can drive biodiversity loss. Now, MIT researchers have shown the reverse is also true: Reductions in biodiversity can jeopardize one of Earth’s most powerful levers for mitigating climate change.

In a paper published in PNAS, the researchers showed that following deforestation, naturally-regrowing tropical forests, with healthy populations of seed-dispersing animals, can absorb up to four times more carbon than similar forests with fewer seed-dispersing animals.

Because tropical forests are currently Earth’s largest land-based carbon sink, the findings improve our understanding of a potent tool to fight climate change.

“The results underscore the importance of animals in maintaining healthy, carbon-rich tropical forests,” says Evan Fricke, a research scientist in the MIT Department of Civil and Environmental Engineering and the lead author of the new study. “When seed-dispersing animals decline, we risk weakening the climate-mitigating power of tropical forests.”

Fricke’s co-authors on the paper include César Terrer, the Tianfu Career Development Associate Professor at MIT; Charles Harvey, an MIT professor of civil and environmental engineering; and Susan Cook-Patton of The Nature Conservancy.

The study combines a wide array of data on animal biodiversity, movement, and seed dispersal across thousands of animal species, along with carbon accumulation data from thousands of tropical forest sites.

The researchers say the results are the clearest evidence yet that seed-dispersing animals play an important role in forests’ ability to absorb carbon, and that the findings underscore the need to address biodiversity loss and climate change as connected parts of a delicate ecosystem rather as separate problems in isolation.

“It’s been clear that climate change threatens biodiversity, and now this study shows how biodiversity losses can exacerbate climate change,” Fricke says. “Understanding that two-way street helps us understand the connections between these challenges, and how we can address them. These are challenges we need to tackle in tandem, and the contribution of animals to tropical forest carbon shows that there are win-wins possible when supporting biodiversity and fighting climate change at the same time.”

Putting the pieces together

The next time you see a video of a monkey or bird enjoying a piece of fruit, consider that the animals are actually playing an important role in their ecosystems. Research has shown that by digesting the seeds and defecating somewhere else, animals can help with the germination, growth, and long-term survival of the plant.

Fricke has been studying animals that disperse seeds for nearly 15 years. His previous research has shown that without animal seed dispersal, trees have lower survival rates and a harder time keeping up with environmental changes.

“We’re now thinking more about the roles that animals might play in affecting the climate through seed dispersal,” Fricke says. “We know that in tropical forests, where more than three-quarters of trees rely on animals for seed dispersal, the decline of seed dispersal could affect not just the biodiversity of forests, but how they bounce back from deforestation. We also know that all around the world, animal populations are declining.”

Regrowing forests is an often-cited way to mitigate the effects of climate change, but the influence of biodiversity on forests’ ability to absorb carbon has not been fully quantified, especially at larger scales.

For their study, the researchers combined data from thousands of separate studies and used new tools for quantifying disparate but interconnected ecological processes. After analyzing data from more than 17,000 vegetation plots, the researchers decided to focus on tropical regions, looking at data on where seed-dispersing animals live, how many seeds each animal disperses, and how they affect germination.

The researchers then incorporated data showing how human activity impacts different seed-dispersing animals’ presence and movement. They found, for example, that animals move less when they consume seeds in areas with a bigger human footprint.

Combining all that data, the researchers created an index of seed-dispersal disruption that revealed a link between human activities and declines in animal seed dispersal. They then analyzed the relationship between that index and records of carbon accumulation in naturally regrowing tropical forests over time, controlling for factors like drought conditions, the prevalence of fires, and the presence of grazing livestock.

“It was a big task to bring data from thousands of field studies together into a map of the disruption of seed dispersal,” Fricke says. “But it lets us go beyond just asking what animals are there to actually quantifying the ecological roles those animals are playing and understanding how human pressures affect them.”

The researchers acknowledged that the quality of animal biodiversity data could be improved and introduces uncertainty into their findings. They also note that other processes, such as pollination, seed predation, and competition influence seed dispersal and can constrain forest regrowth. Still, the findings were in line with recent estimates.

“What’s particularly new about this study is we’re actually getting the numbers around these effects,” Fricke says. “Finding that seed dispersal disruption explains a fourfold difference in carbon absorption across the thousands of tropical regrowth sites included in the study points to seed dispersers as a major lever on tropical forest carbon.”

Quantifying lost carbon

In forests identified as potential regrowth sites, the researchers found seed-dispersal declines were linked to reductions in carbon absorption each year averaging 1.8 metric tons per hectare, equal to a reduction in regrowth of 57 percent.

The researchers say the results show natural regrowth projects will be more impactful in landscapes where seed-dispersing animals have been less disrupted, including areas that were recently deforested, are near high-integrity forests, or have higher tree cover.

“In the discussion around planting trees versus allowing trees to regrow naturally, regrowth is basically free, whereas planting trees costs money, and it also leads to less diverse forests,” Terrer says. “With these results, now we can understand where natural regrowth can happen effectively because there are animals planting the seeds for free, and we also can identify areas where, because animals are affected, natural regrowth is not going to happen, and therefore planting trees actively is necessary.”

To support seed-dispersing animals, the researchers encourage interventions that protect or improve their habitats and that reduce pressures on species, ranging from wildlife corridors to restrictions on wildlife trade. Restoring the ecological roles of seed dispersers is also possible by reintroducing seed-dispersing species where they’ve been lost or planting certain trees that attract those animals.

The findings could also make modeling the climate impact of naturally regrowing forests more accurate.

“Overlooking the impact of seed-dispersal disruption may overestimate natural regrowth potential in many areas and underestimate it in others,” the authors write.

The researchers believe the findings open up new avenues of inquiry for the field.

“Forests provide a huge climate subsidy by sequestering about a third of all human carbon emissions,” Terrer says. “Tropical forests are by far the most important carbon sink globally, but in the last few decades, their ability to sequester carbon has been declining. We will next explore how much of that decline is due to an increase in extreme droughts or fires versus declines in animal seed dispersal.”

Overall, the researchers hope the study helps improves our understanding of the planet’s complex ecological processes.

“When we lose our animals, we’re losing the ecological infrastructure that keeps our tropical forests healthy and resilient,” Fricke says.

The research was supported by the MIT Climate and Sustainability Consortium, the Government of Portugal, and the Bezos Earth Fund.

Scientists often seek new materials derived from polymers. Rather than starting a polymer search from scratch, they save time and money by blending existing polymers to achieve desired properties.

But identifying the best blend is a thorny problem. Not only is there a practically limitless number of potential combinations, but polymers interact in complex ways, so the properties of a new blend are challenging to predict.

To accelerate the discovery of new materials, MIT researchers developed a fully autonomous experimental platform that can efficiently identify optimal polymer blends.

The closed-loop workflow uses a powerful algorithm to explore a wide range of potential polymer blends, feeding a selection of combinations to a robotic system that mixes chemicals and tests each blend.

Based on the results, the algorithm decides which experiments to conduct next, continuing the process until the new polymer meets the user’s goals.

During experiments, the system autonomously identified hundreds of blends that outperformed their constituent polymers. Interestingly, the researchers found that the best-performing blends did not necessarily use the best individual components.

“I found that to be good confirmation of the value of using an optimization algorithm that considers the full design space at the same time,” says Connor Coley, the Class of 1957 Career Development Assistant Professor in the MIT departments of Chemical Engineering and Electrical Engineering and Computer Science, and senior author of a paper on this new approach. “If you consider the full formulation space, you can potentially find new or better properties. Using a different approach, you could easily overlook the underperforming components that happen to be the important parts of the best blend.”

This workflow could someday facilitate the discovery of polymer blend materials that lead to advancements like improved battery electrolytes, more cost-effective solar panels, or tailored nanoparticles for safer drug delivery.

Coley is joined on the paper by lead author Guangqi Wu, a former MIT postdoc who is now a Marie Skłodowska-Curie Postdoctoral Fellow at Oxford University; Tianyi Jin, an MIT graduate student; and Alfredo Alexander-Katz, the Michael and Sonja Koerner Professor in the MIT Department of Materials Science and Engineering. The work appears today in Matter.

Building better blends

When scientists design new polymer blends, they are faced with a nearly endless number of possible polymers to start with. Once they select a few to mix, they still must choose the composition of each polymer and the concentration of polymers in the blend.

“Having that large of a design space necessitates algorithmic solutions and higher-throughput workflows because you simply couldn’t test all the combinations using brute force,” Coley adds.

While researchers have studied autonomous workflows for single polymers, less work has focused on polymer blends because of the dramatically larger design space.

In this study, the MIT researchers sought new random heteropolymer blends, made by mixing two or more polymers with different structural features. These versatile polymers have shown particularly promising relevance to high-temperature enzymatic catalysis, a process that increases the rate of chemical reactions.

Their closed-loop workflow begins with an algorithm that, based on the user’s desired properties, autonomously identifies a handful of promising polymer blends.

The researchers originally tried a machine-learning model to predict the performance of new blends, but it was difficult to make accurate predictions across the astronomically large space of possibilities. Instead, they utilized a genetic algorithm, which uses biologically inspired operations like selection and mutation to find an optimal solution.

Their system encodes the composition of a polymer blend into what is effectively a digital chromosome, which the genetic algorithm iteratively improves to identify the most promising combinations.

“This algorithm is not new, but we had to modify the algorithm to fit into our system. For instance, we had to limit the number of polymers that could be in one material to make discovery more efficient,” Wu adds.

In addition, because the search space is so large, they tuned the algorithm to balance its choice of exploration (searching for random polymers) versus exploitation (optimizing the best polymers from the last experiment).

The algorithm sends 96 polymer blends at a time to the autonomous robotic platform, which mixes the chemicals and measures the properties of each.

The experiments were focused on improving the thermal stability of enzymes by optimizing the retained enzymatic activity (REA), a measure of how stable an enzyme is after mixing with the polymer blends and being exposed to high temperatures.

These results are sent back to the algorithm, which uses them to generate a new set of polymers until the system finds the optimal blend.

Accelerating discovery

Building the robotic system involved numerous challenges, such as developing a technique to evenly heat polymers and optimizing the speed at which the pipette tip moves up and down.

“In autonomous discovery platforms, we emphasize algorithmic innovations, but there are many detailed and subtle aspects of the procedure you have to validate before you can trust the information coming out of it,” Coley says.

When tested, the optimal blends their system identified often outperformed the polymers that formed them. The best overall blend performed 18 percent better than any of its individual components, achieving an REA of 73 percent.

“This indicates that, instead of developing new polymers, we could sometimes blend existing polymers to design new materials that perform even better than individual polymers do,” Wu says.

Moreover, their autonomous platform can generate and test 700 new polymer blends per day and only requires human intervention for refilling and replacing chemicals.

While this research focused on polymers for protein stabilization, their platform could be modified for other uses, like the development or new plastics or battery electrolytes.

In addition to exploring additional polymer properties, the researchers want to use experimental data to improve the efficiency of their algorithm and develop new algorithms to streamline the operations of the autonomous liquid handler.

“Technologically, there are urgent needs to enhance thermal stability of proteins and enzymes. The results demonstrated here are quite impressive. Being a platform technology and given the rapid advancement in machine learning and AI for material science, one can envision the possibility for this team to further enhance random heteropolymer performances or to optimize design based on end needs and usages,” says Ting Xu, a professor at the University of California at Berkeley, who was not involved with this work.

This work is funded, in part, by the U.S. Department of Energy, the National Science Foundation, and the Class of 1947 Career Development Chair.

MIT physicists have performed an idealized version of one of the most famous experiments in quantum physics. Their findings demonstrate, with atomic-level precision, the dual yet evasive nature of light. They also happen to confirm that Albert Einstein was wrong about this particular quantum scenario.

The experiment in question is the double-slit experiment, which was first performed in 1801 by the British scholar Thomas Young to show how light behaves as a wave. Today, with the formulation of quantum mechanics, the double-slit experiment is now known for its surprisingly simple demonstration of a head-scratching reality: that light exists as both a particle and a wave. Stranger still, this duality cannot be simultaneously observed. Seeing light in the form of particles instantly obscures its wave-like nature, and vice versa.

The original experiment involved shining a beam of light through two parallel slits in a screen and observing the pattern that formed on a second, faraway screen. One might expect to see two overlapping spots of light, which would imply that light exists as particles, a.k.a. photons, like paintballs that follow a direct path. But instead, the light produces alternating bright and dark stripes on the screen, in an interference pattern similar to what happens when two ripples in a pond meet. This suggests light behaves as a wave. Even weirder, when one tries to measure which slit the light is traveling through, the light suddenly behaves as particles and the interference pattern disappears.

The double-slit experiment is taught today in most high school physics classes as a simple way to illustrate the fundamental principle of quantum mechanics: that all physical objects, including light, are simultaneously particles and waves.

Nearly a century ago, the experiment was at the center of a friendly debate between physicists Albert Einstein and Niels Bohr. In 1927, Einstein argued that a photon particle should pass through just one of the two slits and in the process generate a slight force on that slit, like a bird rustling a leaf as it flies by. He proposed that one could detect such a force while also observing an interference pattern, thereby catching light’s particle and wave nature at the same time. In response, Bohr applied the quantum mechanical uncertainty principle and showed that the detection of the photon’s path would wash out the interference pattern.

Scientists have since carried out multiple versions of the double-slit experiment, and they have all, to various degrees, confirmed the validity of the quantum theory formulated by Bohr. Now, MIT physicists have performed the most “idealized” version of the double-slit experiment to date. Their version strips down the experiment to its quantum essentials. They used individual atoms as slits, and used weak beams of light so that each atom scattered at most one photon. By preparing the atoms in different quantum states, they were able to modify what information the atoms obtained about the path of the photons. The researchers thus confirmed the predictions of quantum theory: The more information was obtained about the path (i.e. the particle nature) of light, the lower the visibility of the interference pattern was. 

They demonstrated what Einstein got wrong. Whenever an atom is “rustled” by a passing photon, the wave interference is diminished.

“Einstein and Bohr would have never thought that this is possible, to perform such an experiment with single atoms and single photons,” says Wolfgang Ketterle, the John D. MacArthur Professor of Physics and leader of the MIT team. “What we have done is an idealized Gedanken experiment.”

Their results appear in the journal Physical Review Letters. Ketterle’s MIT co-authors include first author Vitaly Fedoseev, Hanzhen Lin, Yu-Kun Lu, Yoo Kyung Lee, and Jiahao Lyu, who all are affiliated with MIT’s Department of Physics, the Research Laboratory of Electronics, and the MIT-Harvard Center for Ultracold Atoms.

Cold confinement

Ketterle’s group at MIT experiments with atoms and molecules that they super-cool to temperatures just above absolute zero and arrange in configurations that they confine with laser light. Within these ultracold, carefully tuned clouds, exotic phenomena that only occur at the quantum, single-atom scale can emerge.

In a recent experiment, the team was investigating a seemingly unrelated question, studying how light scattering can reveal the properties of materials built from ultracold atoms.

“We realized we can quantify the degree to which this scattering process is like a particle or a wave, and we quickly realized we can apply this new method to realize this famous experiment in a very idealized way,” Fedoseev says.

In their new study, the team worked with more than 10,000 atoms, which they cooled to microkelvin temperatures. They used an array of laser beams to arrange the frozen atoms into an evenly spaced, crystal-like lattice configuration. In this arrangement, each atom is far enough away from any other atom that each can effectively be considered a single, isolated and identical atom. And 10,000 such atoms can produce a signal that is more easily detected, compared to a single atom or two.

The group reasoned that with this arrangement, they might shine a weak beam of light through the atoms and observe how a single photon scatters off two adjacent atoms, as a wave or a particle. This would be similar to how, in the original double-slit experiment, light passes through two slits.

“What we have done can be regarded as a new variant to the double-slit experiment,” Ketterle says. “These single atoms are like the smallest slits you could possibly build.”

Tuning fuzz

Working at the level of single photons required repeating the experiment many times and using an ultrasensitive detector to record the pattern of light scattered off the atoms. From the intensity of the detected light, the researchers could directly infer whether the light behaved as a particle or a wave.

They were particularly interested in the situation where half the photons they sent in behaved as waves, and half behaved as particles. They achieved this by using a method to tune the probability that a photon will appear as a wave versus a particle, by adjusting an atom’s “fuzziness,” or the certainty of its location. In their experiment, each of the 10,000 atoms is held in place by laser light that can be adjusted to tighten or loosen the light’s hold. The more loosely an atom is held, the fuzzier, or more “spatially extensive,” it appears. The fuzzier atom rustles more easily and records the path of the photon. Therefore, in tuning up an atom’s fuzziness, researchers can increase the probability that a photon will exhibit particle-like behavior. Their observations were in full agreement with the theoretical description.

Springs away

In their experiment, the group tested Einstein’s idea about how to detect the path of the photon. Conceptually, if each slit were cut into an extremely thin sheet of paper that was suspended in the air by a spring, a photon passing through one slit should shake the corresponding spring by a certain degree that would be a signal of the photon’s particle nature. In previous realizations of the double slit experiment, physicists have incorporated such a spring-like ingredient, and the spring played a major role in describing the photon’s dual nature.

But Ketterle and his colleagues were able to perform the experiment without the proverbial springs. The team’s cloud of atoms is initially held in place by laser light, similar to Einstein’s conception of a slit suspended by a spring. The researchers reasoned that if they were to do away with their “spring,” and observe exactly the same phenomenon, then it would show that the spring has no effect on a photon’s wave/particle duality.

This, too, was what they found. Over multiple runs, they turned off the spring-like laser holding the atoms in place and then quickly took a measurement in a millionth of a second,  before the atoms became more fuzzy and eventually fell down due to gravity. In this tiny amount of time, the atoms were effectively floating in free space. In this spring-free scenario, the team observed the same phenomenon: A photon’s wave and particle nature could not be observed simultaneously.

“In many descriptions, the springs play a major role. But we show, no, the springs do not matter here; what matters is only the fuzziness of the atoms,” Fedoseev says. “Therefore,  one has to use a more profound description, which uses quantum correlations between photons and atoms.”

The researchers note that the year 2025 has been declared by the United Nations as the International Year of Quantum Science and Technology, celebrating the formulation of quantum mechanics 100 years ago. The discussion between Bohr and Einstein about the double-slit experiment took place only two years later.

“It’s a wonderful coincidence that we could help clarify this historic controversy in the same year we celebrate quantum physics,” says co-author Lee.

This work was supported, in part, by the National Science Foundation, the U.S. Department of Defense, and the Gordon and Betty Moore Foundation.

Associate Professor Koh Ming Joo from NUS Department of Chemistry has received the Kyoto Rising-Star Lectureship Award (2025) in recognition of his impactful contributions to organic chemistry research.

The Kyoto Rising-Star Lectureship Award was first established in 2020 by the MSD Life Science Foundation and later expanded to become an international accolade in 2023.

Assoc Prof Koh has gained international acclaim for his research in sustainable catalysis and radical chemistry that addresses critical challenges in chemical synthesis through base metal catalysis and radical cross-coupling chemistry.

Assoc Prof Koh said, “It’s an incredible honour to win this award and represent Singapore and NUS in the international arena. It really motivates us to continue our work to develop sustainable solutions that address challenges in chemical synthesis.”

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By Dr Samer Elhajjar, Senior Lecturer from the Dept of Marketing at NUS Business School

• The Straits Times, 24 July 2025, Opinion, pB2

By Dr Clemens Chay, Research Fellow from the Middle East Institute at NUS

• CNA Online, 24 July 2025

Artificial Intelligence (AI) is speeding up scientific progress in diverse disciplines, from discovering new materials, to applying generative AI to detect dementia early, to improving climate modelling approaches by employing a combination of physical, hybrid AI and fully-data driven models.

These topics and more were explored at the inaugural AI4X 2025 conference held at NUS University Town. Taking place from 8 to 11 July 2025, the conference aims to spark cross-disciplinary collaboration and breakthroughs in AI innovation, accelerating scientific discovery and translational potential on all fronts.

AI4X 2025 is jointly organised by NUS Institute for Functional Intelligent Materials (I-FIM), together with the National Research Foundation, Singapore (NRF), Singapore’s Ministry of Education, Nanyang Technological University and the DSO National Laboratories. This strong alliance reflects the relevance and importance of the field in global research. I-FIM led the full planning and execution of the conference, which included conceptualisation, programme design, speaker selection and operations.

The world’s largest AI conference revolutionising the natural sciences, AI4X 2025 gathered close to 600 global researchers and industry leaders for talks and pre-conference workshops on AI innovation across sixteen different topics. It also comprised 27 keynote talks, 16 thematic sessions, and close to 100 poster presentations.

About 80 leading speakers from academia, start-ups, and global industry laboratories – including luminaries Gerbrand Ceder from the University of California, Berkeley, Boris Kozinsky from Harvard University, and Alex Aliper from Insilico – took part in the event.

“AI is reshaping how science is done,” said AI4X conference chair Professor Sir Konstantin Novoselov, Director of NUS I-FIM and winner of the 2010 Nobel Prize in Physics.

“Through AI4X, we aim to position Singapore and I-FIM as leaders in this global transition, by convening top minds to share, debate, and drive progress.”

Conceived in early 2024, the idea for AI4X was prompted by the increasing investment in the local “AI for Science” initiative, which focuses on the development and adoption of AI methods and tools that are transferable across multiple domains of science. In AI4X, the “X” represents all the scientific disciplines, including Climate, Environment, Medicine, Education, Finance, Mathematics and more.

Prof Novoselov noted that there were many things the researchers could learn from each other as experts from different disciplines. “We attempt to enhance this synergy through a mix of different topics in the talk sessions.”


Leveraging AI to enhance research

Conference speakers shared their views on how AI could further scientific progress, such as helping scientists work in more efficient ways.

The sheer volume of academic literature waiting to be read, combined with time-consuming experiments, often slows down scientific progress, said world-renowned mathematician Professor Weinan E in his talk “Building AI-Powered Infrastructure for Scientific Research”. 

“AI can transform the way we conduct scientific research,” he said.

Tools using AI can be used to streamline lab processes and automate experiments. “Instead of doing tedious work, you can spend more time thinking about new ideas,” said Prof E, who is the Inaugural Director of Beijing’s AI for Science Institute and a professor at Peking University’s Center for Machine Learning Research and School of Mathematical Sciences.

One key roadblock to scientific progress, he added, was the “curse of dimensionality”. As a problem’s dimensionality – the number of free parameters it has – grows, so did the amount of compute required using traditional methods. Deep learning, which powers most AI applications today, was the first methodology that could very accurately handle high-dimensional problems, said Prof E, whose team won the 2020 Association for Computing Machinery Gordon Bell Prize for their deep learning-based approach to simulating atomic movement.

Accelerating scientific discovery

Materials science is another frontier being reshaped by AI. The symmetrical atomic structure of a crystal is not merely aesthetic — it also governs its properties, from conductivity to transparency, said NUS I-FIM research fellow Dr Nikita Kazeev in a later presentation “Generation of Novel Stable Beautiful Materials”.

Designing crystals with ideal symmetrical properties would thus lead to the discovery of new materials for stronger batteries, solar cells, and more, explained Dr Kazeev in his talk.

He shared details of a new generative model he helped build, dubbed the “Wyckoff Transformer”, which can design uniquely symmetrical structures much faster than previous models. His team had introduced concepts such as “Wyckoff positions” (special positions that atoms tend to occupy) and “symmetry groups” to the model, which helped it learn about crystalline properties more easily.

What else does the future hold for AI in science? The talks and discussions at the AI4X 2025 Conference has shown that as technology advances, disciplinary boundaries will eventually disappear, with integrated databases and facilities in place.

Plans are underway to make AI4X an annual event, with expanded international participation and deeper industry engagement. The next edition, held in collaboration with the Acceleration Consortium at the University of Toronto, is slated for 2026.

Researchers at the National University of Singapore (NUS) have found new applications of magnetic field therapy with potential benefits for food sustainability and metabolic health.

One study, published in the International Journal of Molecular Sciences on 5 June 2025, demonstrated that short, weekly exposures to low-energy magnetic fields during early development enhanced muscle growth and quality in developing organisms, which has implications for improving meat yield and nutritional value in poultry production. A second study, published in the same journal on 6 June 2025, demonstrated that magnetic field therapy could alter the gut microbiome in beneficial ways that support better metabolism and bone health, with these effects transferable via microbiota-based therapies.

Led by Associate Professor Alfredo Franco-Obregón, Principal Investigator at the Institute for Health Innovation & Technology (iHealthtech) at NUS and faculty member at the Department of Surgery in NUS Yong Loo Lin School of Medicine, the research team showed that magnetic stimulation can induce long-lasting physiological changes without the need for drugs, genetic modification, or invasive procedures.

The team’s findings highlight how a non-invasive, low-energy magnetic approach may be used to improve food production outcomes, develop microbiome-based therapies, and strengthen preventive health strategies.

Improving meat quality and growth

Poultry is the most imported livestock commodity globally by volume, with demand projected to grow in years to come. Breeding strategies in the industry that focus solely on muscle growth have often come at the expense of meat quality. In particular, there has long been a trade-off between faster growth and better taste, texture and nutrition.

The team’s latest research explores a new way to resolve that challenge. They found that exposing eggs to short bursts of magnetic fields during incubation could improve embryonic survival, overall growth and the development of oxidative muscle, which is a muscle type rich in mitochondria and associated with improved flavour and nutritional profile.

The work builds on earlier research by the team, which showed that magnetic pulses could be used to stimulate animal cells to produce the nutrients needed for lab-grown meat, reducing the need for animal-derived growth supplements. The study pointed to a greener, safer, and more cost-effective method of cell-based meat production.

In the current pilot study, the team demonstrated that brief, twice-weekly magnetic exposure of eggs promoted muscle development, and did so without genetic modification or pharmaceutical intervention.

“By improving both meat growth and quality, and importantly, in a sustainable and drug-free manner, our research could potentially overcome a major bottleneck in poultry farming,” said Assoc Prof Franco-Obregón. “Our research also highlights how magnetic stimulation can influence tissue development, which has wide-reaching applications in various sectors from food to agriculture to biomedicine.”

Conditioning the gut microbiome to improve health

In the second study, the researchers looked into how pulsed electromagnetic field (PEMF) therapy affects the gut microbiome, and whether its benefits can be transferred to others.

Their study found that weekly magnetic therapy instigated changes in the gut microbiota that led to healthier fat metabolism, improved glucose regulation and stronger bones. In preclinical studies, when gut microbes from magnetically treated donors were transplanted into obese recipients, the latter exhibited similarly enhanced metabolic traits — without receiving direct magnetic treatment.

Interestingly, transferring gut bacteria from donors that received magnetic stimulation led to more consistent improvements in bone health and blood sugar control than transfers from donors that had exercised. This suggests that magnetic stimulation may shape the gut microbiome in uniquely helpful ways.

These preliminary findings extend a broader line of magnetic field research led by Assoc Prof Franco-Obregón and his collaborators. In earlier studies, they showed that magnetic muscle stimulation in humans could improve mobility and lean body mass, especially in older adults. Other studies demonstrated that pulsed magnetic fields could trigger muscle cells to release anticancer molecules, enhance chemotherapy drug uptake in tumours and disrupt the energy metabolism of cancer cells. Crucially, these were all possible without harming surrounding healthy tissue.

Among these diverse applications is a common thread: the use of low-energy, non-invasive pulsed magnetic fields to initiate biological responses that support regeneration, immune modulation or systemic metabolic balance.

“Our work continues to show that the body can respond to magnetic cues in powerful ways — sometimes more gently and effectively than drugs or physical interventions,” added Assoc Prof Franco-Obregón. “This latest research adds a feather to the cap, placing the gut microbiome on the list of systems that can be modulated in this way.”

The potential implications span multiple domains, from preventive health and metabolic disorders to therapeutic strategies for ageing populations. With metabolic disease and frailty on the rise in many parts of the world, magnetic conditioning of the microbiome could offer a scalable, non-invasive alternative to improve health resilience.

A joyous season celebrating the accomplishments of over 17,000 graduates from the Class of 2025, set against the backdrop of NUS’ 120^th anniversary, culminated in a festive Commencement Dinner held on 22 July 2025 at the dining hall of the College of Alice and Peter Tan and Residential College 4.

Gathering valedictorians and representatives from the Class of 2025, along with the newly minted honorary graduates – Ambassador Chan Heng Chee, Dr Noeleen Heyzer, and Dr Wong Ngit Liong – the dinner was attended by about 350 guests. They included NUS Pro-Chancellor Mr Po’ad Mattar, NUS Chairman Mr Hsieh Fu Hua, NUS Trustee Ms Chew Gek Khim, NUS President Professor Tan Eng Chye, as well as students, faculty, staff and alumni.

The celebratory night kicked off on a high note with an exuberant performance by students from various NUS Centre for the Arts dance groups. Specially choreographed for the occasion and blending influences from Indian, Chinese, Malay and Western artistic traditions, the piece showcased the multiculturalism and diversity of student life at NUS.

Leading the night’s proceedings were Class of 2025 graduates Ms Rachel Chin from the NUS Faculty of Arts and Social Sciences and Mr Clement Ling from the NUS Faculty of Science. Reminiscing about university life and the vibrant community they encountered along the way, they recounted the highlights of their NUS journeys.

“Looking back, I’m really grateful for the many experiences I’ve had at NUS — from taking eye-opening modules and electives, to going on an unforgettable exchange programme, and getting to contribute to signature events like Open House and Commencement,” Ms Chin said.

For Mr Ling, graduating as part of the Class of 2025 during NUS’ 120th anniversary made his experience especially memorable. “Over the past four years, I’ve had the chance to explore my interests in science and technology, meet inspiring peers and professors, and contribute to the NUS community through different ways. It’s truly been a journey I’ll never forget.”

Lead with the heart

In his speech at the event, NUS President Prof Tan Eng Chye commended the graduating class for their hard-won achievements, noting the determination and resilience that enabled them to rise above academic challenges, personal obstacles and periods of uncertainty.

He highlighted the strong foundation laid during their time at university – from the rigorous academic training to opportunities for personal growth and the lasting bonds they forged along the way. 

As they prepare to begin their next chapter, he encouraged the graduating class to stay grounded in the values that had shaped them and to lead with clarity and heart, drawing inspiration from NUS’ founding mission.

“Lead with your heart, with purpose lighting your way. This was the case in 1905, 120 years ago, when we started out as a small medical school for the community. Even as we have evolved and redefined our identity as an educational institution, we remain committed in our mission to serve our country and society,” he said.

Concluding his speech with a toast, he added, “Congratulations once again on this very important milestone and welcome to the NUS alumni family! You are the generation that will shape what comes next, leading us to NUS150… and beyond.”

Grit and resilience

Taking to the stage to address his fellow graduates, Mr Lee Yat Bun, Student Senior Advisor of the 46th NUS Students’ Union (NUSSU) Executive Committee, offered a heartfelt message to his peers.

“Four years at NUS didn’t just earn us a degree. It gave us something more valuable – character. It taught us how to stay grounded in uncertainty, how to persevere under pressure, and how to lead in times of change,” said Mr Lee, an NUS Computing graduate in Computer Science who served two terms as NUSSU President between AY21/22 and AY22/23.

“As we step into the future as the next generation of leaders, thinkers, and creators, we carry with us the grit, the resilience, and the adaptability that have been refined during our time here.”

The evening drew to a close with two musical performances by Elton and Friends, a band led by Mandopop singer-songwriter Elton Lee, a Class of 2025 graduate from the Yong Siew Toh Conservatory of Music (YST), who was accompanied by a string quartet comprising fellow YST graduates Reina Teo, Tan Xin Jie, Shannon Chan and Saran Charoennit.

Along with a stirring rendition of the classic ballad, “The Greatest Love of All”, the group also performed a poignant debut of “The Soundtrack of Youth” (谱写的青春), an original graduation song penned by Mr Lee, in a fitting close to the celebratory season.

This story concludes NUS News’ coverage of Commencement 2025, which celebrates the achievements of our graduates from the Class of 2025. For more on Commencement, read our stories and graduate profiles, check out the official Commencement website, or look up and tag #NUS2025 on our social media channels!

By Mr Shane Pereira, Research Associate at the IPS Social Lab, Lee Kuan Yew School of Public Policy at NUS, and Associate Professor Julien Cayla, from the Nanyang Business School at NTU

• The Straits Times, 22 July 2025, Opinion, pB2

By Dr Woo Jun Jie, Senior Lecturer at the Lee Kuan Yew School of Public Policy at NUS

• CNA Online, 22 July 2025

• Lianhe Zaobao, 21 July 2025, Singapore, p6

• Lianhe Zaobao, 21 July 2025, Singapore, p8

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

• Berita Minggu, 20 July 2025, p11

Gevorg Minasyan MAP ’23 first discovered the MITx MicroMasters Program in Data, Economics, and Design of Policy (DEDP) — jointly led by the Abdul Latif Jameel Poverty Action Lab (J-PAL) and MIT Open Learning — when he was looking to better understand the process of building effective, evidence-based policies while working at the Central Bank of Armenia. After completing the MicroMasters program, Minasyan was inspired to pursue MIT’s Master’s in Data, Economics, and Design of Policy program.

Today, Minasyan is the director of the Research and Training Center at the Central Bank of Armenia. He has not only been able to apply what he has learned at MIT to his work, but he has also sought to institutionalize a culture of evidence-based policymaking at the bank and more broadly in Armenia. He spoke with MIT Open Learning about his journey through the DEDP programs, key takeaways, and how what he learned at MIT continues to guide his work.

Q: What initially drew you to the DEDP MicroMasters, and what were some highlights of the program?

A: Working at the Central Bank of Armenia, I was constantly asking myself: Can we build a system in which public policy decisions are grounded in rigorous evidence? Too often, I observed public programs that were well-intentioned and seemed to address pressing challenges, but ultimately failed to bring tangible change. Sometimes it was due to flawed design; other times, the goals simply didn’t align with what the public actually needed or expected. These experiences left a deep impression on me and sparked a strong desire to better understand what works, what doesn’t, and why.

That search led me to the DEDP MicroMasters program, which turned out to be a pivotal step in my professional journey. From the very first course, I realized that this was not just another academic program — it was a completely new way of thinking about development policy. The courses combined rigorous training in economics, data analysis, and impact evaluation with a strong emphasis on practical application. We weren’t just learning formulas or running regressions — we were being trained to ask the right questions, to think critically about causality, and to understand the trade-offs of policy choices.

Another aspect that set the MicroMasters apart was its blended structure. I was able to pursue a globally top-tier education while continuing my full-time responsibilities at the Central Bank. This made the learning deeply relevant and immediately applicable. Even as I was studying, I found myself incorporating insights from class into my day-to-day policy work, whether it was refining how we evaluated financial inclusion programs or rethinking the way we analyzed administrative data.

At the same time, the global nature of the program created a vibrant, diverse community. I engaged with students and professionals from dozens of countries, each bringing different perspectives. These interactions enriched the coursework and helped me to realize that despite the differences in context, the challenges of effective policy design — and the power of evidence to improve lives — were remarkably universal. It was a rare combination: intellectually rigorous, practically grounded, globally connected, and personally transformative.

Q: Can you describe your experiences in the Master’s in Data, Economics, and Design of Policy residential program?

A: The MicroMasters experience inspired me to go further, and I decided to apply for the full-time, residential master’s at MIT. That year was nothing short of transformative. It not only sharpened my technical and analytical skills, but also fundamentally changed the way I think about policymaking.

One of the most influential courses I took during the master’s program was 14.760 (Firms, Markets, Trade, and Growth). The analytical tools it provided mapped directly onto the systemic challenges I saw among Armenian firms. Motivated by this connection, I developed a similar course, which I now teach at the American University of Armenia. Each year, I work with students to investigate the everyday constraints that hinder firm performance, with the ultimate goal of producing data-driven research that could inform business strategy in Armenia.

The residential master’s program taught me that evidence-based decision-making starts with a mindset shift. It’s not just about applying tools, it’s about being open to questioning assumptions, being transparent about uncertainty, and being humble enough to let data challenge intuition. I also came to appreciate that truly effective policy design isn’t about finding one-off solutions, but about creating dynamic feedback loops that allow us to continuously learn from implementation.

This is essential to refining programs in real time, adapting to new information, and avoiding the trap of static, one-size-fits-all approaches. Equally valuable was becoming part of the MIT and J-PAL’s global network. The relationships I built with researchers, practitioners, and fellow students from around the world gave me lasting insights into how institutions can systematically embed analysis in their core operations. This exposure helped me to see the possibilities not just for my own work, but for how public institutions like central banks can lead the way in advancing an evidence-based culture.

Q: How are you applying what you’ve learned in the DEDP programs to the Central Bank of Armenia?

A: As director of the Research and Training Center at the Central Bank of Armenia, I have taken on a new kind of responsibility: leading the effort to scale evidence-based decision-making not only within the Central Bank, but across a broader ecosystem of public institutions in Armenia. This means building internal capacity, rethinking how research informs policy, and fostering partnerships that promote a culture of data-driven decision-making.

Beyond the classroom, the skills I developed through the DEDP program have been critical to my role in shaping real-world policy in Armenia. A particularly timely example is our national push toward a cashless economy — one of the most prominent and complex reform agendas today. In recent years, the government has rolled out a suite of bold policies aimed at boosting the adoption of non-cash payments, all part of a larger vision to modernize the financial system, reduce the shadow economy, and increase transparency. Key initiatives include a cashback program designed to encourage pensioners to use digital payments and the mandatory installation of non-cash payment terminals across businesses nationwide. In my role on an inter-agency policy team, I rely heavily on the analytical tools from DEDP to evaluate these policies and propose regulatory adjustments to ensure the transition is not only effective, but also inclusive and sustainable.

The Central Bank of Armenia recently collaborated with J-PAL Europe to co-design and host a policy design and evaluation workshop. The workshop brought together policymakers, central bankers, and analysts from various sectors and focused on integrating evidence throughout the policy cycle, from defining the problem to designing interventions and conducting rigorous evaluations. It’s just the beginning, but it already reflects how the ideas, tools, and values I absorbed at MIT are now taking institutional form back home.

Our ultimate goal is to institutionalize the use of policy evaluation as a standard practice — not as an occasional activity, but as a core part of how we govern. We’re working to embed a stronger feedback culture in policymaking, one that prioritizes learning before scaling. More experimentation, piloting, and iteration are essential before committing to large-scale rollouts of public programs. This shift requires patience and persistence, but it is critical if we want policies that are not only well-designed, but also effective, inclusive, and responsive to people’s needs.

Looking ahead, I remain committed to advancing this transformation, by building the systems, skills, and partnerships that can sustain evidence-based policymaking in Armenia for the long term. 

In an office at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), a soft robotic hand carefully curls its fingers to grasp a small object. The intriguing part isn’t the mechanical design or embedded sensors — in fact, the hand contains none. Instead, the entire system relies on a single camera that watches the robot’s movements and uses that visual data to control it.

This capability comes from a new system CSAIL scientists developed, offering a different perspective on robotic control. Rather than using hand-designed models or complex sensor arrays, it allows robots to learn how their bodies respond to control commands, solely through vision. The approach, called Neural Jacobian Fields (NJF), gives robots a kind of bodily self-awareness. An open-access paper about the work was published in Nature on June 25.

“This work points to a shift from programming robots to teaching robots,” says Sizhe Lester Li, MIT PhD student in electrical engineering and computer science, CSAIL affiliate, and lead researcher on the work. “Today, many robotics tasks require extensive engineering and coding. In the future, we envision showing a robot what to do, and letting it learn how to achieve the goal autonomously.”

The motivation stems from a simple but powerful reframing: The main barrier to affordable, flexible robotics isn't hardware — it’s control of capability, which could be achieved in multiple ways. Traditional robots are built to be rigid and sensor-rich, making it easier to construct a digital twin, a precise mathematical replica used for control. But when a robot is soft, deformable, or irregularly shaped, those assumptions fall apart. Rather than forcing robots to match our models, NJF flips the script — giving robots the ability to learn their own internal model from observation.

Look and learn

This decoupling of modeling and hardware design could significantly expand the design space for robotics. In soft and bio-inspired robots, designers often embed sensors or reinforce parts of the structure just to make modeling feasible. NJF lifts that constraint. The system doesn’t need onboard sensors or design tweaks to make control possible. Designers are freer to explore unconventional, unconstrained morphologies without worrying about whether they’ll be able to model or control them later.

“Think about how you learn to control your fingers: you wiggle, you observe, you adapt,” says Li. “That’s what our system does. It experiments with random actions and figures out which controls move which parts of the robot.”

The system has proven robust across a range of robot types. The team tested NJF on a pneumatic soft robotic hand capable of pinching and grasping, a rigid Allegro hand, a 3D-printed robotic arm, and even a rotating platform with no embedded sensors. In every case, the system learned both the robot’s shape and how it responded to control signals, just from vision and random motion.

The researchers see potential far beyond the lab. Robots equipped with NJF could one day perform agricultural tasks with centimeter-level localization accuracy, operate on construction sites without elaborate sensor arrays, or navigate dynamic environments where traditional methods break down.

At the core of NJF is a neural network that captures two intertwined aspects of a robot’s embodiment: its three-dimensional geometry and its sensitivity to control inputs. The system builds on neural radiance fields (NeRF), a technique that reconstructs 3D scenes from images by mapping spatial coordinates to color and density values. NJF extends this approach by learning not only the robot’s shape, but also a Jacobian field, a function that predicts how any point on the robot’s body moves in response to motor commands.

To train the model, the robot performs random motions while multiple cameras record the outcomes. No human supervision or prior knowledge of the robot’s structure is required — the system simply infers the relationship between control signals and motion by watching.

Once training is complete, the robot only needs a single monocular camera for real-time closed-loop control, running at about 12 Hertz. This allows it to continuously observe itself, plan, and act responsively. That speed makes NJF more viable than many physics-based simulators for soft robots, which are often too computationally intensive for real-time use.

In early simulations, even simple 2D fingers and sliders were able to learn this mapping using just a few examples. By modeling how specific points deform or shift in response to action, NJF builds a dense map of controllability. That internal model allows it to generalize motion across the robot’s body, even when the data are noisy or incomplete.

“What’s really interesting is that the system figures out on its own which motors control which parts of the robot,” says Li. “This isn’t programmed — it emerges naturally through learning, much like a person discovering the buttons on a new device.”

The future is soft

For decades, robotics has favored rigid, easily modeled machines — like the industrial arms found in factories — because their properties simplify control. But the field has been moving toward soft, bio-inspired robots that can adapt to the real world more fluidly. The trade-off? These robots are harder to model.

“Robotics today often feels out of reach because of costly sensors and complex programming. Our goal with Neural Jacobian Fields is to lower the barrier, making robotics affordable, adaptable, and accessible to more people. Vision is a resilient, reliable sensor,” says senior author and MIT Assistant Professor Vincent Sitzmann, who leads the Scene Representation group. “It opens the door to robots that can operate in messy, unstructured environments, from farms to construction sites, without expensive infrastructure.”

“Vision alone can provide the cues needed for localization and control — eliminating the need for GPS, external tracking systems, or complex onboard sensors. This opens the door to robust, adaptive behavior in unstructured environments, from drones navigating indoors or underground without maps to mobile manipulators working in cluttered homes or warehouses, and even legged robots traversing uneven terrain,” says co-author Daniela Rus, MIT professor of electrical engineering and computer science and director of CSAIL. “By learning from visual feedback, these systems develop internal models of their own motion and dynamics, enabling flexible, self-supervised operation where traditional localization methods would fail.”

While training NJF currently requires multiple cameras and must be redone for each robot, the researchers are already imagining a more accessible version. In the future, hobbyists could record a robot’s random movements with their phone, much like you’d take a video of a rental car before driving off, and use that footage to create a control model, with no prior knowledge or special equipment required.

The system doesn’t yet generalize across different robots, and it lacks force or tactile sensing, limiting its effectiveness on contact-rich tasks. But the team is exploring new ways to address these limitations: improving generalization, handling occlusions, and extending the model’s ability to reason over longer spatial and temporal horizons.

“Just as humans develop an intuitive understanding of how their bodies move and respond to commands, NJF gives robots that kind of embodied self-awareness through vision alone,” says Li. “This understanding is a foundation for flexible manipulation and control in real-world environments. Our work, essentially, reflects a broader trend in robotics: moving away from manually programming detailed models toward teaching robots through observation and interaction.”

This paper brought together the computer vision and self-supervised learning work from the Sitzmann lab and the expertise in soft robots from the Rus lab. Li, Sitzmann, and Rus co-authored the paper with CSAIL affiliates Annan Zhang SM ’22, a PhD student in electrical engineering and computer science (EECS); Boyuan Chen, a PhD student in EECS; Hanna Matusik, an undergraduate researcher in mechanical engineering; and Chao Liu, a postdoc in the Senseable City Lab at MIT. 

The research was supported by the Solomon Buchsbaum Research Fund through MIT’s Research Support Committee, an MIT Presidential Fellowship, the National Science Foundation, and the Gwangju Institute of Science and Technology.

In an office at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), a soft robotic hand carefully curls its fingers to grasp a small object. The intriguing part isn’t the mechanical design or embedded sensors — in fact, the hand contains none. Instead, the entire system relies on a single camera that watches the robot’s movements and uses that visual data to control it.

This capability comes from a new system CSAIL scientists developed, offering a different perspective on robotic control. Rather than using hand-designed models or complex sensor arrays, it allows robots to learn how their bodies respond to control commands, solely through vision. The approach, called Neural Jacobian Fields (NJF), gives robots a kind of bodily self-awareness. An open-access paper about the work was published in Nature on June 25.

“This work points to a shift from programming robots to teaching robots,” says Sizhe Lester Li, MIT PhD student in electrical engineering and computer science, CSAIL affiliate, and lead researcher on the work. “Today, many robotics tasks require extensive engineering and coding. In the future, we envision showing a robot what to do, and letting it learn how to achieve the goal autonomously.”

The motivation stems from a simple but powerful reframing: The main barrier to affordable, flexible robotics isn't hardware — it’s control of capability, which could be achieved in multiple ways. Traditional robots are built to be rigid and sensor-rich, making it easier to construct a digital twin, a precise mathematical replica used for control. But when a robot is soft, deformable, or irregularly shaped, those assumptions fall apart. Rather than forcing robots to match our models, NJF flips the script — giving robots the ability to learn their own internal model from observation.

Look and learn

This decoupling of modeling and hardware design could significantly expand the design space for robotics. In soft and bio-inspired robots, designers often embed sensors or reinforce parts of the structure just to make modeling feasible. NJF lifts that constraint. The system doesn’t need onboard sensors or design tweaks to make control possible. Designers are freer to explore unconventional, unconstrained morphologies without worrying about whether they’ll be able to model or control them later.

“Think about how you learn to control your fingers: you wiggle, you observe, you adapt,” says Li. “That’s what our system does. It experiments with random actions and figures out which controls move which parts of the robot.”

The system has proven robust across a range of robot types. The team tested NJF on a pneumatic soft robotic hand capable of pinching and grasping, a rigid Allegro hand, a 3D-printed robotic arm, and even a rotating platform with no embedded sensors. In every case, the system learned both the robot’s shape and how it responded to control signals, just from vision and random motion.

The researchers see potential far beyond the lab. Robots equipped with NJF could one day perform agricultural tasks with centimeter-level localization accuracy, operate on construction sites without elaborate sensor arrays, or navigate dynamic environments where traditional methods break down.

At the core of NJF is a neural network that captures two intertwined aspects of a robot’s embodiment: its three-dimensional geometry and its sensitivity to control inputs. The system builds on neural radiance fields (NeRF), a technique that reconstructs 3D scenes from images by mapping spatial coordinates to color and density values. NJF extends this approach by learning not only the robot’s shape, but also a Jacobian field, a function that predicts how any point on the robot’s body moves in response to motor commands.

To train the model, the robot performs random motions while multiple cameras record the outcomes. No human supervision or prior knowledge of the robot’s structure is required — the system simply infers the relationship between control signals and motion by watching.

Once training is complete, the robot only needs a single monocular camera for real-time closed-loop control, running at about 12 Hertz. This allows it to continuously observe itself, plan, and act responsively. That speed makes NJF more viable than many physics-based simulators for soft robots, which are often too computationally intensive for real-time use.

In early simulations, even simple 2D fingers and sliders were able to learn this mapping using just a few examples. By modeling how specific points deform or shift in response to action, NJF builds a dense map of controllability. That internal model allows it to generalize motion across the robot’s body, even when the data are noisy or incomplete.

“What’s really interesting is that the system figures out on its own which motors control which parts of the robot,” says Li. “This isn’t programmed — it emerges naturally through learning, much like a person discovering the buttons on a new device.”

The future is soft

For decades, robotics has favored rigid, easily modeled machines — like the industrial arms found in factories — because their properties simplify control. But the field has been moving toward soft, bio-inspired robots that can adapt to the real world more fluidly. The trade-off? These robots are harder to model.

“Robotics today often feels out of reach because of costly sensors and complex programming. Our goal with Neural Jacobian Fields is to lower the barrier, making robotics affordable, adaptable, and accessible to more people. Vision is a resilient, reliable sensor,” says senior author and MIT Assistant Professor Vincent Sitzmann, who leads the Scene Representation group. “It opens the door to robots that can operate in messy, unstructured environments, from farms to construction sites, without expensive infrastructure.”

“Vision alone can provide the cues needed for localization and control — eliminating the need for GPS, external tracking systems, or complex onboard sensors. This opens the door to robust, adaptive behavior in unstructured environments, from drones navigating indoors or underground without maps to mobile manipulators working in cluttered homes or warehouses, and even legged robots traversing uneven terrain,” says co-author Daniela Rus, MIT professor of electrical engineering and computer science and director of CSAIL. “By learning from visual feedback, these systems develop internal models of their own motion and dynamics, enabling flexible, self-supervised operation where traditional localization methods would fail.”

While training NJF currently requires multiple cameras and must be redone for each robot, the researchers are already imagining a more accessible version. In the future, hobbyists could record a robot’s random movements with their phone, much like you’d take a video of a rental car before driving off, and use that footage to create a control model, with no prior knowledge or special equipment required.

The system doesn’t yet generalize across different robots, and it lacks force or tactile sensing, limiting its effectiveness on contact-rich tasks. But the team is exploring new ways to address these limitations: improving generalization, handling occlusions, and extending the model’s ability to reason over longer spatial and temporal horizons.

“Just as humans develop an intuitive understanding of how their bodies move and respond to commands, NJF gives robots that kind of embodied self-awareness through vision alone,” says Li. “This understanding is a foundation for flexible manipulation and control in real-world environments. Our work, essentially, reflects a broader trend in robotics: moving away from manually programming detailed models toward teaching robots through observation and interaction.”

This paper brought together the computer vision and self-supervised learning work from the Sitzmann lab and the expertise in soft robots from the Rus lab. Li, Sitzmann, and Rus co-authored the paper with CSAIL affiliates Annan Zhang SM ’22, a PhD student in electrical engineering and computer science (EECS); Boyuan Chen, a PhD student in EECS; Hanna Matusik, an undergraduate researcher in mechanical engineering; and Chao Liu, a postdoc in the Senseable City Lab at MIT. 

The research was supported by the Solomon Buchsbaum Research Fund through MIT’s Research Support Committee, an MIT Presidential Fellowship, the National Science Foundation, and the Gwangju Institute of Science and Technology.

City life is often described as “fast-paced.” A new study suggests that’s more true than ever.

The research, co-authored by MIT scholars, shows that the average walking speed of pedestrians in three northeastern U.S. cities increased 15 percent from 1980 to 2010. The number of people lingering in public spaces declined by 14 percent in that time as well.

The researchers used machine-learning tools to assess 1980s-era video footage captured by renowned urbanist William Whyte, in Boston, New York, and Philadelphia. They compared the old material with newer videos from the same locations.

“Something has changed over the past 40 years,” says MIT professor of the practice Carlo Ratti, a co-author of the new study. “How fast we walk, how people meet in public space — what we’re seeing here is that public spaces are working in somewhat different ways, more as a thoroughfare and less a space of encounter.”

The paper, “Exploring the social life of urban spaces through AI,” is published this week in the Proceedings of the National Academy of Sciences. The co-authors are Arianna Salazar-Miranda MCP ’16, PhD ’23, an assistant professor at Yale University’s School of the Environment; Zhuanguan Fan of the University of Hong Kong; Michael Baick; Keith N. Hampton, a professor at Michigan State University; Fabio Duarte, associate director of the Senseable City Lab; Becky P.Y. Loo of the University of Hong Kong; Edward Glaeser, the Fred and Eleanor Glimp Professor of Economics at Harvard University; and Ratti, who is also director of MIT’s Senseable City Lab.

The results could help inform urban planning, as designers seek to create new public areas or modify existing ones.

“Public space is such an important element of civic life, and today partly because it counteracts the polarization of digital space,” says Salazar-Miranda. “The more we can keep improving public space, the more we can make our cities suited for convening.”

Meet you at the Met

Whyte was a prominent social thinker whose famous 1956 book, “The Organization Man,” probing the apparent culture of corporate conformity in the U.S., became a touchstone of its decade.

However, Whyte spent the latter decades of his career focused on urbanism. The footage he filmed, from 1978 through 1980, was archived by a Brooklyn-based nonprofit organization called the Project for Public Spaces and later digitized by Hampton and his students.

Whyte chose to make his recording at four spots in the three cities combined: Boston’s Downtown Crossing area; New York City’s Bryant Park; the steps of the Metropolitan Museum of Art in New York, a famous gathering point and people-watching spot; and Philadelphia’s Chestnut Street.

In 2010, a group led by Hampton then shot new footage at those locations, at the same times of day Whyte had, to compare and contrast current-day dynamics with those of Whyte’s time. To conduct the study, the co-authors used computer vision and AI models to summarize and quantify the activity in the videos.

The researchers have found that some things have not changed greatly. The percentage of people walking alone barely moved, from 67 percent in 1980 to 68 percent in 2010. On the other hand, the percentage of individuals entering these public spaces who became part of a group declined a bit. In 1980, 5.5 percent of the people approaching these spots met up with a group; in 2010, that was down to 2 percent.

“Perhaps there’s a more transactional nature to public space today,” Ratti says.

Fewer outdoor groups: Anomie or Starbucks?

If people’s behavioral patterns have altered since 1980, it’s natural to ask why. Certainly some of the visible changes seem consistent with the pervasive use of cellphones; people organize their social lives by phone now, and perhaps zip around more quickly from place to place as a result.

“When you look at the footage from William Whyte, the people in public spaces were looking at each other more,” Ratti says. “It was a place you could start a conversation or run into a friend. You couldn’t do things online then. Today, behavior is more predicated on texting first, to meet in public space.”

As the scholars note, if groups of people hang out together slightly less often in public spaces, there could be still another reason for that: Starbucks and its competitors. As the paper states, outdoor group socializing may be less common due to “the proliferation of coffee shops and other indoor venues. Instead of lingering on sidewalks, people may have moved their social interactions into air-conditioned, more comfortable private spaces.”

Certainly coffeeshops were far less common in big cities in 1980, and the big chain coffeeshops did not exist.

On the other hand, public-space behavior might have been evolving all this time regardless of Starbucks and the like. The researchers say the new study offers a proof-of-concept for its method and has encouraged them to conduct additional work. Ratti, Duarte, and other researchers from MIT’s Senseable City Lab have turned their attention to an extensive survey of European public spaces in an attempt to shed more light on the interaction between people and the public forum.

“We are collecting footage from 40 squares in Europe,” Duarte says. “The question is: How can we learn at a larger scale? This is in part what we’re doing.” 

One of the shared, fundamental goals of most chemistry researchers is the need to predict a molecule’s properties, such as its boiling or melting point. Once researchers can pinpoint that prediction, they’re able to move forward with their work yielding discoveries that lead to medicines, materials, and more. Historically, however, the traditional methods of unveiling these predictions are associated with a significant cost — expending time and wear and tear on equipment, in addition to funds.

Enter a branch of artificial intelligence known as machine learning (ML). ML has lessened the burden of molecule property prediction to a degree, but the advanced tools that most effectively expedite the process — by learning from existing data to make rapid predictions for new molecules — require the user to have a significant level of programming expertise. This creates an accessibility barrier for many chemists, who may not have the significant computational proficiency required to navigate the prediction pipeline. 

To alleviate this challenge, researchers in the McGuire Research Group at MIT have created ChemXploreML, a user-friendly desktop app that helps chemists make these critical predictions without requiring advanced programming skills. Freely available, easy to download, and functional on mainstream platforms, this app is also built to operate entirely offline, which helps keep research data proprietary. The exciting new technology is outlined in an article published recently in the Journal of Chemical Information and Modeling.

One specific hurdle in chemical machine learning is translating molecular structures into a numerical language that computers can understand. ChemXploreML automates this complex process with powerful, built-in "molecular embedders" that transform chemical structures into informative numerical vectors. Next, the software implements state-of-the-art algorithms to identify patterns and accurately predict molecular properties like boiling and melting points, all through an intuitive, interactive graphical interface. 

"The goal of ChemXploreML is to democratize the use of machine learning in the chemical sciences,” says Aravindh Nivas Marimuthu, a postdoc in the McGuire Group and lead author of the article. “By creating an intuitive, powerful, and offline-capable desktop application, we are putting state-of-the-art predictive modeling directly into the hands of chemists, regardless of their programming background. This work not only accelerates the search for new drugs and materials by making the screening process faster and cheaper, but its flexible design also opens doors for future innovations.” 

ChemXploreML is designed to to evolve over time, so as future techniques and algorithms are developed, they can be seamlessly integrated into the app, ensuring that researchers are always able to access and implement the most up-to-date methods. The application was tested on five key molecular properties of organic compounds — melting point, boiling point, vapor pressure, critical temperature, and critical pressure — and achieved high accuracy scores of up to 93 percent for the critical temperature. The researchers also demonstrated that a new, more compact method of representing molecules (VICGAE) was nearly as accurate as standard methods, such as Mol2Vec, but was up to 10 times faster.

“We envision a future where any researcher can easily customize and apply machine learning to solve unique challenges, from developing sustainable materials to exploring the complex chemistry of interstellar space,” says Marimuthu. Joining him on the paper is senior author and Class of 1943 Career Development Assistant Professor of Chemistry Brett McGuire.

One of the shared, fundamental goals of most chemistry researchers is the need to predict a molecule’s properties, such as its boiling or melting point. Once researchers can pinpoint that prediction, they’re able to move forward with their work yielding discoveries that lead to medicines, materials, and more. Historically, however, the traditional methods of unveiling these predictions are associated with a significant cost — expending time and wear and tear on equipment, in addition to funds.

Enter a branch of artificial intelligence known as machine learning (ML). ML has lessened the burden of molecule property prediction to a degree, but the advanced tools that most effectively expedite the process — by learning from existing data to make rapid predictions for new molecules — require the user to have a significant level of programming expertise. This creates an accessibility barrier for many chemists, who may not have the significant computational proficiency required to navigate the prediction pipeline. 

To alleviate this challenge, researchers in the McGuire Research Group at MIT have created ChemXploreML, a user-friendly desktop app that helps chemists make these critical predictions without requiring advanced programming skills. Freely available, easy to download, and functional on mainstream platforms, this app is also built to operate entirely offline, which helps keep research data proprietary. The exciting new technology is outlined in an article published recently in the Journal of Chemical Information and Modeling.

One specific hurdle in chemical machine learning is translating molecular structures into a numerical language that computers can understand. ChemXploreML automates this complex process with powerful, built-in "molecular embedders" that transform chemical structures into informative numerical vectors. Next, the software implements state-of-the-art algorithms to identify patterns and accurately predict molecular properties like boiling and melting points, all through an intuitive, interactive graphical interface. 

"The goal of ChemXploreML is to democratize the use of machine learning in the chemical sciences,” says Aravindh Nivas Marimuthu, a postdoc in the McGuire Group and lead author of the article. “By creating an intuitive, powerful, and offline-capable desktop application, we are putting state-of-the-art predictive modeling directly into the hands of chemists, regardless of their programming background. This work not only accelerates the search for new drugs and materials by making the screening process faster and cheaper, but its flexible design also opens doors for future innovations.” 

ChemXploreML is designed to to evolve over time, so as future techniques and algorithms are developed, they can be seamlessly integrated into the app, ensuring that researchers are always able to access and implement the most up-to-date methods. The application was tested on five key molecular properties of organic compounds — melting point, boiling point, vapor pressure, critical temperature, and critical pressure — and achieved high accuracy scores of up to 93 percent for the critical temperature. The researchers also demonstrated that a new, more compact method of representing molecules (VICGAE) was nearly as accurate as standard methods, such as Mol2Vec, but was up to 10 times faster.

“We envision a future where any researcher can easily customize and apply machine learning to solve unique challenges, from developing sustainable materials to exploring the complex chemistry of interstellar space,” says Marimuthu. Joining him on the paper is senior author and Class of 1943 Career Development Assistant Professor of Chemistry Brett McGuire.

The following press release was issued today by the Broad Institute of MIT and Harvard.

Although outbreaks of Ebola virus are rare, the disease is severe and often fatal, with few treatment options. Rather than targeting the virus itself, one promising therapeutic approach would be to interrupt proteins in the human host cell that the virus relies upon. However, finding those regulators of viral infection using existing methods has been difficult and is especially challenging for the most dangerous viruses like Ebola that require stringent high-containment biosafety protocols.

Now, researchers at the Broad Institute and the National Emerging Infectious Diseases Laboratories (NEIDL) at Boston University have used an image-based screening method developed at the Broad to identify human genes that, when silenced, impair the Ebola virus’s ability to infect. The method, known as optical pooled screening (OPS), enabled the scientists to test, in about 40 million CRISPR-perturbed human cells, how silencing each gene in the human genome affects virus replication.

Using machine-learning-based analyses of images of perturbed cells, they identified multiple host proteins involved in various stages of Ebola infection that when suppressed crippled the ability of the virus to replicate. Those viral regulators could represent avenues to one day intervene therapeutically and reduce the severity of disease in people already infected with the virus. The approach could be used to explore the role of various proteins during infection with other pathogens, as a way to find new drugs for hard-to-treat infections.

The study appears in Nature Microbiology.

“This study demonstrates the power of OPS to probe the dependency of dangerous viruses like Ebola on host factors at all stages of the viral life cycle and explore new routes to improve human health,” said co-senior author Paul Blainey, a Broad core faculty member and professor in the Department of Biological Engineering at MIT.

Previously, members of the Blainey lab developed the optical pooled screening method as a way to combine the benefits of high-content imaging, which can show a range of detailed changes in large numbers of cells at once, with those of pooled perturbational screens, which show how genetic elements influence these changes. In this study, they partnered with the laboratory of Robert Davey at BU to apply optical pooled screening to Ebola virus.

The team used CRISPR to knock out each gene in the human genome, one at a time, in nearly 40 million human cells, and then infected each cell with Ebola virus. They next fixed those cells in place in laboratory dishes and inactivated them, so that the remaining processing could occur outside of the high-containment lab.

After taking images of the cells, they measured overall viral protein and RNA in each cell using the CellProfiler image analysis software, and to get even more information from the images, they turned to AI. With help from team members in the Eric and Wendy Schmidt Center at the Broad, led by study co-author and Broad core faculty member Caroline Uhler, they used a deep learning model to automatically determine the stage of Ebola infection for each single cell. The model was able to make subtle distinctions between stages of infection in a high-throughput way that wasn’t possible using prior methods.

“The work represents the deepest dive yet into how Ebola virus rewires the cell to cause disease, and the first real glimpse into the timing of that reprogramming,” said co-senior author Robert Davey, director of the National Emerging Infectious Diseases Laboratories at Boston University, and professor of microbiology at BU Chobanian and Avedisian School of Medicine. “AI gave us an unprecedented ability to do this at scale.”

By sequencing parts of the CRISPR guide RNA in all 40 million cells individually, the researchers determined which human gene had been silenced in each cell, indicating which host proteins (and potential viral regulators) were targeted. The analysis revealed hundreds of host proteins that, when silenced, altered overall infection level, including many required for viral entry into the cell.

Knocking out other genes enhanced the amount of virus within inclusion bodies, structures that form in the human cell to act as viral factories, and prevented the infection from progressing further. Some of these human genes, such as UQCRB, pointed to a previously unrecognized role for mitochondria in the Ebola virus infection process that could possibly be exploited therapeutically. Indeed, treating cells with a small molecule inhibitor of UQCRB reduced Ebola infection with no impact on the cell’s own health.

Other genes, when silenced, altered the balance between viral RNA and protein. For example, perturbing a gene called STRAP resulted in increased viral RNA relative to protein. The researchers are currently doing further studies in the lab to better understand the role of STRAP and other proteins in Ebola infection and whether they could be targeted therapeutically.

In a series of secondary screens, the scientists examined some of the highlighted genes’ roles in infection with related filoviruses. Silencing some of these genes interrupted replication of Sudan and Marburg viruses, which have high fatality rates and no approved treatments, so it’s possible a single treatment could be effective against multiple related viruses.

The study’s approach could also be used to examine other pathogens and emerging infectious diseases and look for new ways to treat them.

“With our method, we can measure many features at once and uncover new clues about the interplay between virus and host, in a way that’s not possible through other screening approaches,” said co-first author Rebecca Carlson, a former graduate researcher in the labs of Blainey and Nir Hacohen at the Broad and who co-led the work along with co-first author J.J. Patten at Boston University.

This work was funded in part by the Broad Institute, the National Human Genome Research Institute, the Burroughs Wellcome Fund, the Fannie and John Hertz Foundation, the National Science Foundation, the George F. Carrier Postdoctoral Fellowship, the Eric and Wendy Schmidt Center at the Broad Institute, the National Institutes of Health, and the Office of Naval Research.

Astronomers at MIT, Columbia University, and elsewhere have used NASA’s James Webb Space Telescope (JWST) to peer through the dust of nearby galaxies and into the aftermath of a black hole’s stellar feast.

In a study appearing today in Astrophysical Journal Letters, the researchers report that for the first time, JWST has observed several tidal disruption events — instances when a galaxy’s central black hole draws in a nearby star and whips up tidal forces that tear the star to shreds, giving off an enormous burst of energy in the process.

Scientists have observed about 100 tidal disruption events (TDEs) since the 1990s, mostly as X-ray or optical light that flashes across relatively dust-free galaxies. But as MIT researchers recently reported, there may be many more star-shredding events in the universe that are “hiding” in dustier, gas-veiled galaxies.

In their previous work, the team found that most of the X-ray and optical light that a TDE gives off can be obscured by a galaxy’s dust, and therefore can go unseen by traditional X-ray and optical telescopes. But that same burst of light can heat up the surrounding dust and generate a new signal, in the form of infrared light.

Now, the same researchers have used JWST — the world’s most powerful infrared detector — to study signals from four dusty galaxies where they suspect tidal disruption events have occurred. Within the dust, JWST detected clear fingerprints of black hole accretion, a process by which material, such as stellar debris, circles and eventually falls into a black hole. The telescope also detected patterns that are strikingly different from the dust that surrounds active galaxies, where the central black hole is constantly pulling in surrounding material.

Together, the observations confirm that a tidal disruption event did indeed occur in each of the four galaxies. What’s more, the researchers conclude that the four events were products of not active black holes but rather dormant ones, which experienced little to no activity until a star happened to pass by.

The new results highlight JWST’s potential to study in detail otherwise hidden tidal disruption events. They are also helping scientists to reveal key differences in the environments around active versus dormant black holes.

“These are the first JWST observations of tidal disruption events, and they look nothing like what we’ve ever seen before,” says lead author Megan Masterson, a graduate student in MIT’s Kavli Institute for Astrophysics and Space Research. “We’ve learned these are indeed powered by black hole accretion, and they don’t look like environments around normal active black holes. The fact that we’re now able to study what that dormant black hole environment actually looks like is an exciting aspect.”

The study’s MIT authors include Christos Panagiotou, Erin Kara, Anna-Christina Eilers, along with Kishalay De of Columbia University and collaborators from multiple other institutions.

Seeing the light

The new study expands on the team’s previous work using another infrared detector — NASA’s Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE) mission. Using an algorithm developed by co-author Kishalay De of Columbia University, the team searched through a decade’s worth of data from the telescope, looking for infrared “transients,” or short peaks of infrared activity from otherwise quiet galaxies that could be signals of a black hole briefly waking up and feasting on a passing star. That search unearthed about a dozen signals that the group determined were likely produced by a tidal disruption event.

“With that study, we found these 12 sources that look just like TDEs,” Masterson says. “We made a lot of arguments about how the signals were very energetic, and the galaxies didn’t look like they were active before, so the signals must have been from a sudden TDE. But except for these little pieces, there was no direct evidence.”

With the much more sensitive capabilities of JWST, the researchers hoped to discern key “spectral lines,” or infrared light at specific wavelengths, that would be clear fingerprints of conditions associated with a tidal disruption event.

“With NEOWISE, it’s as if our eyes could only see red light or blue light, whereas with JWST, we’re seeing the full rainbow,” Masterson says.

A Bonafide signal

In their new work, the group looked specifically for a peak in infrared, that could only be produced by black hole accretion — a process by which material is drawn toward a black hole in a circulating disk of gas. This disk produces an enormous amount of radiation that is so intense that it can kick out electrons from individual atoms. In particular, such accretion processes can blast several electrons out from atoms of neon, and the resulting ion can transition, releasing infrared radiation at a very specific wavelength that JWST can detect. 

“There’s nothing else in the universe that can excite this gas to these energies, except for black hole accretion,” Masterson says.

The researchers searched for this smoking-gun signal in four of the 12 TDE candidates they previously identified. The four signals include: the closest tidal disruption event detected to date, located in a galaxy some 130 million light years away; a TDE that also exhibits a burst of X-ray light; a signal that may have been produced by gas circulating at incredibly high speeds around a central black hole; and a signal that also included an optical flash, which scientists had previously suspected to be a supernova, or the collapse of a dying star, rather than tidal disruption event.

“These four signals were as close as we could get to a sure thing,” Masterson says. “But the JWST data helped us say definitively these are bonafide TDEs.”

When the team pointed JWST toward the galaxies of each of the four signals, in a program designed by De, they observed that the telltale spectral lines showed up in all four sources. These measurements confirmed that black hole accretion occurred in all four galaxies. But the question remained: Was this accretion a temporary feature, triggered by a tidal disruption and a black hole that briefly woke up to feast on a passing star? Or was this accretion a more permanent trait of “active” black holes that are always on? In the case of the latter, it would be less likely that a tidal disruption event had occurred.

To differentiate between the two possibilities, the team used the JWST data to detect another wavelength of infrared light, which indicates the presence of silicates, or dust in the galaxy. They then mapped this dust in each of the four galaxies and compared the patterns to those of active galaxies, which are known to harbor clumpy, donut-shaped dust clouds around the central black hole. Masterson observed that all four sources showed very different patterns compared to typical active galaxies, suggesting that the black hole at the center of each of the galaxies is not normally active, but dormant. If an accretion disk formed around such a black hole, the researchers conclude that it must have been a result of a tidal disruption event.

“Together, these observations say the only thing these flares could be are TDEs,” Masterson says.

She and her collaborators plan to uncover many more previously hidden tidal disruption events, with NEOWISE, JWST, and other infrared telescopes. With enough detections, they say TDEs can serve as effective probes of black hole properties. For instance, how much of a star is shredded, and how fast its debris is accreted and consumed, can reveal fundamental properties of a black hole, such as how massive it is and how fast it spins.

“The actual process of a black hole gobbling down all that stellar material takes a long time,” Masterson says. “It’s not an instantaneous process. And hopefully we can start to probe how long that process takes and what that environment looks like. No one knows because we just started discovering and studying these events.”

This research was supported, in part, by NASA.

A new theory-guided framework could help scientists probe the properties of new semiconductors for next-generation microelectronic devices, or discover materials that boost the performance of quantum computers.

Research to develop new or better materials typically involves investigating properties that can be reliably measured with existing lab equipment, but this represents just a fraction of the properties that scientists could potentially probe in principle. Some properties remain effectively “invisible” because they are too difficult to capture directly with existing methods.

Take electron-phonon interaction — this property plays a critical role in a material’s electrical, thermal, optical, and superconducting properties, but directly capturing it using existing techniques is notoriously challenging.

Now, MIT researchers have proposed a theoretically justified approach that could turn this challenge into an opportunity. Their method reinterprets neutron scattering, an often-overlooked interference effect as a potential direct probe of electron-phonon coupling strength.

The procedure creates two interaction effects in the material. The researchers show that, by deliberately designing their experiment to leverage the interference between the two interactions, they can capture the strength of a material’s electron-phonon interaction.

The researchers’ theory-informed methodology could be used to shape the design of future experiments, opening the door to measuring new quantities that were previously out of reach.

“Rather than discovering new spectroscopy techniques by pure accident, we can use theory to justify and inform the design of our experiments and our physical equipment,” says Mingda Li, the Class of 1947 Career Development Professor and an associate professor of nuclear science and engineering, and senior author of a paper on this experimental method.

Li is joined on the paper by co-lead authors Chuliang Fu, an MIT postdoc; Phum Siriviboon and Artittaya Boonkird, both MIT graduate students; as well as others at MIT, the National Institute of Standards and Technology, the University of California at Riverside, Michigan State University, and Oak Ridge National Laboratory. The research appears this week in Materials Today Physics.

Investigating interference

Neutron scattering is a powerful measurement technique that involves aiming a beam of neutrons at a material and studying how the neutrons are scattered after they strike it. The method is ideal for measuring a material’s atomic structure and magnetic properties.

When neutrons collide with the material sample, they interact with it through two different mechanisms, creating a nuclear interaction and a magnetic interaction. These interactions can interfere with each other.

“The scientific community has known about this interference effect for a long time, but researchers tend to view it as a complication that can obscure measurement signals. So it hasn’t received much focused attention,” Fu says.

The team and their collaborators took a conceptual “leap of faith” and decided to explore this oft-overlooked interference effect more deeply.

They flipped the traditional materials research approach on its head by starting with a multifaceted theoretical analysis. They explored what happens inside a material when the nuclear interaction and magnetic interaction interfere with each other.

Their analysis revealed that this interference pattern is directly proportional to the strength of the material’s electron-phonon interaction.

“This makes the interference effect a probe we can use to detect this interaction,” explains Siriviboon.

Electron-phonon interactions play a role in a wide range of material properties. They affect how heat flows through a material, impact a material’s ability to absorb and emit light, and can even lead to superconductivity.

But the complexity of these interactions makes them hard to directly measure using existing experimental techniques. Instead, researchers often rely on less precise, indirect methods to capture electron-phonon interactions.

However, leveraging this interference effect enables direct measurement of the electron-phonon interaction, a major advantage over other approaches.

“Being able to directly measure the electron-phonon interaction opens the door to many new possibilities,” says Boonkird.

Rethinking materials research

Based on their theoretical insights, the researchers designed an experimental setup to demonstrate their approach.

Since the available equipment wasn’t powerful enough for this type of neutron scattering experiment, they were only able to capture a weak electron-phonon interaction signal — but the results were clear enough to support their theory.

“These results justify the need for a new facility where the equipment might be 100 to 1,000 times more powerful, enabling scientists to clearly resolve the signal and measure the interaction,” adds Landry.

With improved neutron scattering facilities, like those proposed for the upcoming Second Target Station at Oak Ridge National Laboratory, this experimental method could be an effective technique for measuring many crucial material properties.

For instance, by helping scientists identify and harness better semiconductors, this approach could enable more energy-efficient appliances, faster wireless communication devices, and more reliable medical equipment like pacemakers and MRI scanners.   

Ultimately, the team sees this work as a broader message about the need to rethink the materials research process.

“Using theoretical insights to design experimental setups in advance can help us redefine the properties we can measure,” Fu says.

To that end, the team and their collaborators are currently exploring other types of interactions they could leverage to investigate additional material properties.

“This is a very interesting paper,” says Jon Taylor, director of the neutron scattering division at Oak Ridge National Laboratory, who was not involved with this research. “It would be interesting to have a neutron scattering method that is directly sensitive to charge lattice interactions or more generally electronic effects that were not just magnetic moments. It seems that such an effect is expectedly rather small, so facilities like STS could really help develop that fundamental understanding of the interaction and also leverage such effects routinely for research.”

This work is funded, in part, by the U.S. Department of Energy and the National Science Foundation.

MIT Professor Emeritus Keith H. Johnson, a quantum physicist who pioneered the use of theoretical methods in materials science and later applied his expertise to independent filmmaking, died in June in Cambridge, Massachusetts. He was 89.

A professor in MIT’s Department of Materials Science and Engineering (DMSE), Johnson used first principles to understand how electrons behave in materials — that is, he turned to fundamental laws of nature to calculate their behavior, rather than relying solely on experimental data. This approach gave scientists deeper insight into materials before they were made in a lab — helping lay the groundwork for today’s computer-driven methods of materials discovery.

DMSE Professor Harry Tuller, who collaborated with Johnson in the early 1980s, notes that while first-principles calculations are now commonplace, they were unusual at the time.

“Solid-state physicists were largely focused on modeling the electronic structure of materials like semiconductors and metals using extended wave functions,” Tuller says, referring to mathematical descriptions of electron behavior in crystals — a much quicker method. “Keith was among the minority that took a more localized chemical approach.”

That localized approach allowed Johnson to better examine materials with tiny imperfections called defects, such as in zinc oxide. His methods advanced the understanding of materials used in devices like gas sensors and water-splitting systems for hydrogen fuel. It also gave him deeper insight into complex systems such as superconductors — materials that conduct electricity without resistance — and molecular materials like “buckyballs.”

Johnson’s curiosity took creative form in 2001’s “Breaking Symmetry,” a sci-fi thriller he wrote, produced, and directed. Published on YouTube in 2020, it has been viewed more than 4 million times.

Trailblazing theorist at DMSE

Born in Reading, Pennsylvania, in 1936, Johnson showed an early interest in science. “After receiving a chemistry set as a child, he built a laboratory in his parents’ basement,” says his wife, Franziska Amacher-Johnson. “His early experiments were intense — once prompting an evacuation of the house due to chemical fumes.”

He earned his undergraduate degree in physics at Princeton University and his doctorate from Temple University in 1965. He joined the MIT faculty in 1967, in what was then called the Department of Metallurgy and Materials Science, and worked there for nearly 30 years.

His early use of theory in materials science led to more trailblazing. To model the behavior of electrons in small clusters of atoms — such as material surfaces, boundaries between different materials called interfaces, and defects — Johnson used cluster molecular orbital calculations, a quantum mechanical technique that focuses on how electrons behave in tightly grouped atomic structures. These calculations offered insight into how defects and boundaries influence material performance.

“This coupled very nicely with our interests in understanding the roles of bulk defects, interface and surface energy states at grain boundaries and surfaces in metal oxides in impacting their performance in various devices,” Tuller says.

In one project, Johnson and Tuller co-advised a PhD student who conducted both experimental testing of zinc oxide devices and theoretical modeling using Johnson’s methods. At the time, such close collaboration between experimentalists and theorists was rare. Their work led to a “much clearer and advanced understanding of how the nature of defect states formed at interfaces impacted their performance, long before this type of collaboration between experimentalists and theorists became what is now the norm,” Tuller said.

Johnson’s primary computational tool was yet another innovation, called the scattered wave method (also known as Xα multiple scattering). Though the technique has roots in mid-20th century quantum chemistry and condensed matter physics, Johnson was a leading figure in adapting it to materials applications.

Brian Ahern PhD ’84, one of Johnson’s former students, recalls the power of his approach. In 1988, while evaluating whether certain superconducting materials could be used in a next-generation supercomputer for the Department of Defense, Ahern interviewed leading scientists across the country. Most shared optimistic assessments — except Johnson. Drawing on deep theoretical calculations, Johnson showed that the zero-resistance conditions required for such a machine were not realistically achievable with the available materials.

“I reported Johnson’s findings, and the Pentagon program was abandoned, saving millions of dollars,” Ahern says.

From superconductors to screenplays

Johnson remained captivated by superconductors. These materials can conduct electricity without energy loss, making them crucial to technologies such as MRI machines and quantum computers. But they typically operate at cryogenic temperatures, requiring costly equipment. When scientists discovered so-called high-temperature superconductors — materials that worked at comparatively warmer, but still very cold (-300 degrees Fahrenheit), temperatures — a global race kicked off to understand their behavior and look for superconductors that could function at room temperature.

Using the theoretical tools he had earlier developed, Johnson proposed that vibrations of small molecular units were responsible for superconductivity — a departure from conventional thinking about what caused superconductivity. In a 1992 paper, he showed that the model could apply to a range of materials, including ceramics and buckminsterfullerene, nicknamed buckyballs because its molecules resemble architect Buckminster Fuller’s geodesic domes. Johnson predicted that room-temperature superconductivity was unlikely, because the materials needed to support it would be too unstable to work reliably.

That didn’t stop him from imagining scientific breakthroughs in fiction. A consulting trip to Russia after the fall of the Soviet Union sparked Johnson’s interest in screenwriting. Among his screenplays was “Breaking Symmetry,” about a young astrophysicist at a fictionalized MIT who discovers secret research on a radical new energy technology. When a Hollywood production deal fell through, Johnson decided to fund and direct the film himself — and even created its special effects.

Even after his early retirement from MIT, in 1996, Johnson continued to pursue research. In 2021, he published a paper on water nanoclusters in space and their possible role in the origins of life, suggesting that their properties could help explain cosmic phenomena. He also used his analytical tools to propose visual, water-based models for dark matter and dark energy — what he called “quintessential water.” 

In his later years, Johnson became increasingly interested in presenting scientific ideas through images and intuition rather than dense equations, believing that nature should be understandable without complex mathematics, Amacher-Johnson says. He embraced multimedia and emerging digital tools — including artificial intelligence — to share his ideas. Several of his presentations can be found on his YouTube channel.

“He never confined himself to a single field,” Amacher-Johnson explains. “Physics, chemistry, biology, cosmology — all were part of his unified vision of understanding the universe.”

In addition to Amacher-Johnson, Johnson is survived by his daughter. 

Inspired by a hitchhiking fish that uses a specialized suction organ to latch onto sharks and other marine animals, researchers from MIT and other institutions have designed a mechanical adhesive device that can attach to soft surfaces underwater or in extreme conditions, and remain there for days or weeks.

This device, the researchers showed, can adhere to the lining of the GI tract, whose mucosal layer makes it very difficult to attach any kind of sensor or drug-delivery capsule. Using their new adhesive system, the researchers showed that they could achieve automatic self-adhesion, without motors, to deliver HIV antiviral drugs or RNA to the GI tract, and they could also deploy it as a sensor for gastroesophageal reflux disease (GERD). The device can also be attached to a swimming fish to monitor aquatic environments.

The design is based on the research team’s extensive studies of the remora’s sucker-like disc. These discs have several unique properties that allow them to adhere tightly to a variety of hosts, including sharks, marlins, and rays. However, how remoras maintain adhesion to soft, dynamically shifting surfaces remains largely unknown.

Understanding the fundamental physics and mechanics of how this part of the fish sticks to another organism helped us to establish the underpinnings of how to engineer a synthetic adhesive system,” says Giovanni Traverso, an associate professor of mechanical engineering at MIT, a gastroenterologist at Brigham and Women’s Hospital, an associate member of the Broad Institute of MIT and Harvard, and the senior author of the study.

MIT research scientist Ziliang (Troy) Kang is the lead author of the study, which appears today in Nature. The research team also includes authors from Brigham and Women’s Hospital, the Broad Institute, and Boston College.

Inspired by nature

Most protein and RNA drugs can’t be taken orally because they will be broken down before they can be absorbed into the GI tract. To overcome that, Traverso’s lab is working on ingestible devices that can be swallowed and then gradually release their payload over days, weeks, or even longer.

One major obstacle is that the digestive tract is lined with a slippery mucosal membrane that is constantly regenerating and is difficult for any device to stick to. Furthermore, any device that manages to attach to this lining is likely to be dislodged by food or liquids moving through the tract.

To find a solution to these challenges, the MIT team looked to the remora, also known as the sucker fish, which clings to its hosts for free transportation and access to food scraps. To explore how the remora attaches itself to dynamic, soft surfaces so strongly, Traverso’s teamed up with Christopher Kenaley, an associate professor of biology at Boston College who studies remoras and other fish.

Their studies revealed that the remora’s ability to stick to its host depends on a few different features. First, the large suction disc creates adhesion through pressure-based suction, just like a plunger. Additionally, each disc is divided into individual small adhesive compartments by rows of plates called lamellae wrapped in soft tissue. These compartments can independently create additional suction on nonhomogeneous soft surfaces.

There are nine species of remora, and in each one, these rows of lamellae are aligned a little bit differently — some are exclusively parallel, while others form patterns with rows tilted at different angles. These differences, the researchers found, could be the key to elucidating each species’ evolutionary adaptation to its host.

Remora albescens, a unique species that exhibits mucoadhesion in the oral cavity of rays, inspired the team to develop devices with enhanced adhesion to soft surfaces with its unparallel, highly tilted lamellae orientation. Other remora species, which attach to high-speed swimmers such as marlins and swordfish, tend to have highly parallel orientations, which help the hitchhikers slide without losing adhesion as they are rapidly dragged through the water. Still other species, which have a mix of parallel and angled rows, can attach to a variety of hosts.

Tiny spines that protrude from the lamellae help to achieve additional adhesion by interlocking with the host tissue. These spines, also called spinules, are several hundred microns long and grasp onto the tissue with minimal invasiveness.

“If the compartment suction is subjected to a shear force, the friction enabled by the mechanical interlocking of the spinules can help to maintain the suction,” Kang says.

Watery environments

By mimicking these anatomical features, the MIT team was able to create a device with similarly strong adhesion for a variety of applications in underwater environments.

The researchers used silicone rubber and temperature-responsive smart materials to create their adhesive device, which they call MUSAS (for “mechanical underwater soft adhesion system”). The fully passive, disc-shaped device contains rows of lamellae similar to those of the remora, and can self-adhere to the mucosal lining, leveraging GI contractions. The researchers found that for their purposes, a pattern of tilted rows was the most effective.

Within the lamellae are tiny microneedle-like structures that mimic the spinules seen in the remora. These tiny spines are made of a shape memory alloy that is activated when exposed to body temperatures, allowing the spines to interlock with each other and grasp onto the tissue surface.

The researchers showed that this device could attach to a variety of soft surfaces, even in wet or highly acidic conditions, including pig stomach tissue, nitrile gloves, and a tilapia swimming in a fish tank. Then, they tested the device for several different applications, including aquatic environmental monitoring. After adding a temperature sensor to the device, the researchers showed that they could attach the device to a fish and accurately measure water temperature as the fish swam at high speed.

To demonstrate medical applications, the researchers incorporated an impedance sensor into the device and showed that it could adhere to the esophagus in an animal model, which allowed them to monitor reflux of gastric fluid. This could offer an alternative to current sensors for GERD, which are delivered by a tube placed through the nose or mouth and pinned to the lower part of the esophagus.

They also showed that the device could be used for sustained release of two different types of therapeutics, in animal tests. First, they showed that they could integrate an HIV drug called cabotegravir into the materials that make up the device (polycaprolactone and silicone). Once adhered to the lining of the stomach, the drug gradually diffused out of the device, over a period of one week.

Cabotegravir is one of the drugs used for HIV PrEP — pre-exposure prophylaxis — as well as treatment of HIV. These treatments are usually given either as a daily pill or an injection administered every one to two months.

The researchers also created a version of the device that could be used for delivery of larger molecules such as RNA. For this kind of delivery, the researchers incorporated RNA into the microneedles of the lamellae, which could then inject them into the lining of the stomach. Using RNA encoding the gene for luciferase, a protein that emits light, the researchers showed that they could successfully deliver the gene to cells of the cheek or the esophagus.

The researchers now plan to adapt the device for delivering other types of drugs, as well as vaccines. Another possible application is using the devices for electrical stimulation, which Traverso’s lab has previously shown can activate hormones that regulate appetite.

The research was funded, in part, by the Gates Foundation, MIT’s Department of Mechanical Engineering, Brigham and Women’s Hospital, and the Advanced Research Projects Agency for Health.

Victor K. McElheny, the celebrated journalist and author who founded MIT’s Knight Science Journalism Program more than 40 years ago and served for 15 years as its director, died on July 14 in Lexington, Massachusetts, after a brief illness. He was 89.

Born in Boston and raised in Poughkeepsie, New York, McElheny’s storied journalism career spanned seven decades, during which he wrote for several of the nation’s leading newspapers and magazines, penned three critically acclaimed books, and produced groundbreaking coverage of national stories ranging from the Apollo moon landing to the sequencing of the human genome. He is remembered as a steadfast champion of science journalism who eloquently made the case for the profession’s importance in society and worked tirelessly to help the field — and its practitioners — thrive.

“Victor was a pioneering science journalist, at publications that included The Charlotte Observer, Science, and The New York Times, and an author of note, especially for his biographies of scientific luminaries from Edwin Land to James Watson,” says Deborah Blum, who now heads the MIT program McElheny founded. “Yet, he still found time in 1983 to create the Knight Science Journalism Program, to fight for it, find funding for it, and to build it into what it is today.”

A 1957 graduate of Harvard University, McElheny worked as a reporter for the school’s venerable newspaper, The Harvard Crimson, before eventually taking a job as a science reporter at The Charlotte Observer in North Carolina. In the decades that followed, he served as the European editor at Science magazine, science editor of the Boston Globe, and the technology specialist at The New York Times, among other prominent posts. McElheny’s 1970s reporting on emerging techniques in molecular biology earned the journalist a reputation as a leading reporter on the developing field of genetics — and helped lay the groundwork for his critically acclaimed 2003 biography, “Watson and DNA: Making a Scientific Revolution.” McElheny also authored a biography of Edwin Land, co-founder of the Polaroid Corp., and a well-received book about the groundbreaking effort to map the human genome.

The impact of McElheny’s own stalwart career is rivaled only by his indelible impact on the careers of legions of science journalists who have come behind him.

In 1983, after a stint as director of the Banbury Center at Cold Spring Harbor Laboratory, McElheny — along with then-MIT president Paul Gray and then-director of MIT’s Science, Technology, and Society Program, Carl Kaysen — helped launch a first-of-its-kind science journalism fellowship program, funded with support from the Alfred P. Sloan and Andrew W. Mellon foundations. “The notion took hold that it would be good for MIT to have a fellowship program for science journalists, on the model of the Nieman Fellowship at Harvard,” McElheny recalled in a 2013 MIT News story. (McElheny, himself, had been part of the Nieman’s 1962-63 fellowship class.) The goal, as he explained it, was to allow journalists to connect with researchers “to make acquaintances who will provide them not only with story tips, but with judgment.”

In 1987, McElheny secured a multimillion-dollar grant from the Knight Foundation, creating an endowment that continues to support the fellowship to this day. McElheny led the program — originally known as the Vannevar Bush Science Journalism Fellowship Program and later renamed the Knight Science Journalism Program — for 15 years before stepping down to make way for his successor, preeminent journalist and editor Boyce Rensberger.

“What motivated the man professionally was a deep desire that the public understand and appreciate science and technology,” Rensberger recalls of his predecessor. “And he knew the only way that could happen to people out of school was through science journalists and other science writers creating knowledgeable content for mass media.”

Over the Knight Science Journalism Program’s 42-year history, it has supported and helped advance the careers of more than 400 leading science journalists from around the world. Following his retirement, McElheny remained actively involved with the program, frequently visiting to drop in on seminars or share an inspiring word with incoming classes of fellows.

In 2018, McElheny and his wife, Ruth, teamed with Blum, who joined the program as director in 2015, to establish the Victor K. McElheny Award for local and regional science journalism. The award, which received early support from the Rita Allen Foundation, is now funded by a generous endowment created by the McElhenys. Now entering its seventh year, it has quickly built a reputation as a prestigious national competition honoring some of the country’s best local science journalism.

“Victor was a transformational figure for MIT,” says Agustín Rayo, dean of MIT’s School of Humanities, Arts, and Social Sciences, which houses the Knight Science Journalism Program. “He never ceased to impress me. He had an extraordinary understanding of the ways in which science and technology shape society, of the ways in which society has shaped MIT, and of the ways in which MIT can shape the world.”

“Victor touched so many lives in his long and storied career,” says Usha Lee McFarling, a former Knight Science Journalism Fellow who was recently named to succeed Blum as the program’s director. Even in recent weeks and months, she says, “Victor was bubbling over with ideas on how to keep the fellowship program he founded more than 40 years ago powerful and relevant.”

McElheny’s death was preceded by that of his wife, Ruth — also an accomplished science communicator — who died in April. He is survived by his brothers, Kenneth McElheny and Steven McElheny, and Steven’s wife Karen Sexton; his sister, Robin McElheny, and her husband Alex Griswold; his six nephews and nieces, Josiah and Tobias McElheny, Raphael Griswold, and Hanna, Molly, and Rosa McElheny; and Ruth’s nephew, Dennis Sullivan, and niece, Deirdre Sullivan.

Alumni of the Knight Science Journalism Program describe Victor McElheny’s passing as a huge loss for the entire field of science journalism — a loss of a visionary who generously shared both his remarkable knowledge of the history of the field and his inspiring vision of the possibilities for the future.

“Whether we’re talking about the stars, the Earth, the oceans, the atmosphere, or other planets, our level of understanding is increasing all the time,” McElheny mused to science writer Brittany Flaherty in a 2019 profile. “There’s always more — a lot more — for science journalists to do.”

For those who wish to honor McElheny’s memory, his family invites memorial gifts to the Victor K. McElheny Award Fund.

Seven faculty in the MIT School of Architecture and Planning (SA+P) have been honored for their contributions through promotions, effective July 1. Three faculty promotions are in the Department of Architecture; three are in the Department of Urban Studies and Planning; and one is in the Program in Media Arts and Sciences.

“Whether architects, urbanists, computer scientists, or nanotechnologists, they represent our school at its best, in its breadth of inquiry and mission to improve the relationship between human beings and their environments,” says SA+P Dean Hashim Sarkis.

Department of Architecture

Marcelo Coelho has been promoted to associate professor of the practice. Coelho is the director of the Design Intelligence Lab, which explores the intersection of human and machine intelligence across design, AI, and fabrication. His work ranges from light-based installations to physical computing. Recognition for his work includes two Prix Ars Electronica awards and Fast Company’s Innovation by Design Award. Coelho’s experimental approach redefines creative processes, transforming how we imagine and interact with intelligent systems. Coelho teaches courses that bring together industrial design, user experience, and artificial intelligence.

Holly Samuelson has been promoted to associate professor without tenure. Samuelson has co-authored over 40 peer-reviewed papers, winning a Best Paper award from the journal Energy and Building. As a recognized expert in architectural technology, she has been featured in media outlets such as The Washington Post, The Boston Globe, the BBC, and The Wall Street Journal.

Rafi Segal has been promoted to full professor. An award-winning designer, Segal works across architectural and urban scales, with projects ranging from Villa 003 in the ORDOS 100 series to the Kitgum Peace Museum in Uganda, the Ashdod Museum of Art in Israel, and the winning design proposal for the National Library of Israel in Jerusalem. His current work includes planning a new communal neighborhood for an Israeli kibbutz and curating the first exhibition on Alfred Neumann’s 1960s architecture.

Department of Urban Studies and Planning (DUSP)

Carlo Ratti has been reappointed as professor of the practice. Ratti is the director of the Senseable City Lab and a founding partner of the international design office Carlo Ratti Associati. He has co-authored over 500 publications and holds several patents. His work has been exhibited globally, including at the Venice Biennale, the Museum of Modern Art in New York City, and the Design Museum in Barcelona. Two of his projects, the Digital Water Pavilion and the Copenhagen Wheel, were named among TIME Magazine’s “Best Inventions of the Year.” He is the curator of the 2025 Venice Biennale’s 19th International Architecture Exhibition.

Albert Saiz has been promoted to full professor. Saiz serves as the director of MIT’s Urban Economics Lab, which conducts research on real estate economics, urban economics, housing markets, local public finance, zoning regulations, global real estate, and demographic trends affecting urban and real estate development worldwide. He also contributes to the broader research community as a visiting scholar at the Federal Reserve Bank of Philadelphia, a research fellow at the Institute for the Analysis of Labor, and editor for the Journal of Housing Economics.

Delia Wendel has been promoted to associate professor without tenure. Wendel’s research engages three main areas: forms of community repair after conflict and disaster, African urbanism, and spatial politics. Her interdisciplinary work draws together urban studies, critical peace studies, architectural history, cultural geography, and anthropology. At MIT DUSP, she leads the Planning for Peace critical collective and oversees the Mellon Foundation and the MIT Center for Art, Science and Technology-funded research and exhibition project, Memory Atlas for Repair. She also serves as the managing editor of Projections, the department’s annual peer-reviewed journal on critical issues in urban studies and planning.

Program in Media Arts and Sciences

Deblina Sarkar has been promoted to associate professor without tenure. As the director of the Nano-Cybernetic Biotrek Lab at the MIT Media Lab, she merges nanoelectronics, physics, and biology to create groundbreaking technologies, from ultra-thin quantum transistors to the first antenna that operates inside living cells. Her interdisciplinary work has earned her major honors, including the National Institutes of Health Director’s New Innovator Award and the IEEE Early Career Award in Nanotechnology.

It isn’t every day that you get to see the material in your textbooks come to life. For Bernard Liang, this year’s valedictorian from the NUS Alice Lee Centre for Nursing Studies (NUS Nursing), it was an experience he would never forget.

The undergraduate, then 27, was about to sit his second-year mid-term exams on Women and Children’s Health. At 3am a few days prior, he had to rush his pregnant wife to the hospital as her contractions intensified. By afternoon, their daughter was born; by evening, he was back to his lecture notes, preparing for his exams. On the day his wife was discharged from the hospital, he sped to school for his exams after sending his family home — and aced them.

While his university life was not always so dramatic, it was an intense balancing act between family and studies during his time at NUS. Over four years, the Singapore Armed Forces (SAF) Medical Corps regular welcomed two children, renovated his home, stayed combat fit — and, to top it off, graduated as valedictorian.

“I am deeply grateful to my wife, my family, my mentors, and everyone else who helped me in this journey. This is a validation of the sacrifices that not just me, but everyone supporting me, had to make,” said Bernard, 30, who received his Bachelor of Science (Nursing) (Honours) degree on 10 July 2025.

The army medic’s journey to NUS Nursing began six years into his time in the medical corps. Bernard, who had a diploma in veterinary technology, decided he wanted to “close the gap” between himself and his peers who were nursing-trained.

The NUS programme offered a good mix of theoretical knowledge and hands-on clinical work, noted Bernard, who received the SAF Military Domain Expert Scheme Study Award.
 

A hands-on approach to patient care

Transitioning from the rigours of military life to university took some getting used to at the beginning. “That time away from academia made it slightly challenging to readjust to the mindset of a student,” admitted Bernard.

But he soon hit his stride — armed with intellectual maturity, a steely resolve to soak up new knowledge, and existing skills in areas such as emergency resuscitation and triage.

He also benefitted from the guidance of NUS Nursing mentors such as Associate Professor Shefaly Shorey, who advised him on his thesis; as well as Assistant Professor Catherine Dong, whom he assisted with data collection, sample processing and more in-depth research studies such as the Heart-Brain Connection Translational Research Programme.

His clinical attachments left a deep impact. “We were taught theoretical frameworks — anatomy, physiology, how to assist doctors in providing better care. But an unexpected takeaway (was) how our patients taught us as well, because they have that lived experience of their own conditions,” he added.

He recalled encounters during home visits to patients as a student research assistant in Year 3, when he asked elderly stroke patients if they knew whether their condition had been caused by a blocked blood vessel or bleeding in the brain.

“When we asked them in (these) layman terms, they replied to us using medical terms, such as ‘ischaemic’ or ‘haemorrhagic’ stroke,” he said.

This experience taught him to view the patient-nurse dynamic in a different light.

“Instead of trying to micromanage them or (simply) educate them on their condition, we can shift to having a partnership with them,” he noted. “We should try to respect their knowledge and empower them to better manage their conditions.”
 

It takes a village

Beyond his studies, Bernard found time to captain the NUS Shooting Team — building on his leadership experience in the SAF, where he had led COVID-19 vaccinations at Selarang Camp and coordinated emergency preparedness for the National Day Parade.

He earned the NUS Achievement Award (Merit) for excellence in academics and sport, and also appeared on the Dean’s List.

Bernard credits his success to a robust support system at home, from his wife — the main caregiver of their children — to his parents and in-laws who stepped in regularly to help.

While an undergraduate, he carved out pockets of time to focus on schoolwork. After getting home in the evening, he would spend time with his daughter, before hitting the books from 10pm till as late as 2am after she had gone to bed. His NUS peers accommodated his schedule, agreeing to late-night Zoom meetings when they had to discuss groupwork.

Now back in the SAF Medical Corps and currently a Military Expert 4 Apprentice, Bernard believes his nursing training has given him a fuller, more patient-centric perspective of care.

“I hope I can be a leader who truly inspires trust and cultivates resilience in my men, and be someone who can champion continuous learning so we do not lose sight of our medical knowledge,” he added.

“My goal is to contribute towards advancing the standard of military medicine and integrate evidence-based care into our daily SAF operations.”

To help bridge a critical funding gap for early-stage tech ventures in Asia, NUS is launching a S$150 million Venture Capital (VC) Programme through its entrepreneurial arm, NUS Enterprise.

In Asia, venture capital funding has nosedived to a decade low of S$85 billion, with early-stage investments declining five per cent from 2023 to S$38 billion in 2024^[1]. In this challenging climate, tech start-ups, particularly those born from intensive research and development cycles, face some of the steepest hurdles.

“Research-based start-ups face distinct challenges, including R&D cycles that are much longer than traditional start-ups, making them especially vulnerable in today’s cautious investment environment,” said Professor Tan Eng Chye, NUS President. “The new NUS Venture Capital Programme brings together capital, strategic partnerships, and specialised venture expertise to create a more resilient path from lab to market.”

A first-of-its-kind initiative by a university in Asia

The NUS VC Programme is designed to enhance support for early-stage tech innovation by focusing on high-potential ventures within the NUS ecosystem, including start-ups from the National Graduate Research Innovation Programme (National GRIP).

National GRIP empowers innovators to transform lab-based research discoveries into globally competitive, market-ready ventures. While GRIP currently provides up to S$250,000 in seed funding per start-up, many start-ups require continued support to move beyond proof of concept and into commercial markets.

The programme addresses this need by focusing on post-seed growth, combining capital and structured, hands-on venture-building support to help these start-ups scale effectively.

Backing tech start-ups with capital and expertise

The NUS VC Programme comprises two key components.

First, NUS will commit S$50 million over the next three years in selected venture capital firms with strong track records in early-stage deep tech investments. These firms will provide structured, hands-on support to start-ups, including time, expertise, and access to their networks to help them scale effectively.

The first two VC partners are Granite Asia, a leading multi-asset investment platform with a 25-year track record of backing breakthrough technology ventures globally, and 4BIO Capital, a specialist life sciences investor focused on advanced therapeutics.

Second, NUS will commit S$100 million to an autonomous investment fund focused on NUS-affiliated start-ups, with the flexibility to invest alongside selected VC partners.

“NUS has built one of the most ambitious deep tech pipelines in Asia — a launchpad for breakthrough ideas with global relevance. This partnership sets a new model for how deep tech can scale: pairing world-class research with long-term capital, deep operational support, and Granite Asia’s global network. Together, we aim to accelerate the next generation of transformative technologies from Singapore to the world,” said Ms Jenny Lee and Mr Jixun Foo, Senior Managing Partners, Granite Asia.

“The NUS VC Programme marks a significant step forward for early-stage tech innovation in Asia. By pairing investment with expert-led support, we are accelerating the growth of promising start-ups and strengthening the region’s innovation ecosystem. At 4BIO Capital, we look forward to partnering with NUS on this exciting initiative and learning from each other as we build and grow the companies developing the treatments of tomorrow,” added Dr Dima Kuzmin, Managing Partner, 4BIO Capital.

Enabling a complete pathway from lab to market

According to Dr Tan Sian Wee, NUS Senior Vice President (Innovation & Enterprise), the programme is designed to plug a critical gap in the innovation ecosystem. “National GRIP is an important first step in helping deep tech start-ups take root,” he said. “The VC Programme builds on this by pairing promising ventures with globally connected investors, enabling a more complete pathway to scale and commercial success.”

What sets this programme apart is its direct engagement with leading VC firms selected not only for their track records, but also for their market access in global innovation hubs. Beyond funding, these partners provide structured support ranging from mentorship, investor feedback, market entry, fundraising networks to operational guidance.

By strengthening the bridge between research and the market, NUS aims to boost the resilience and scalability of its tech start-ups and keep the innovation engine running despite a tough investment climate.

By NUS Enterprise

^[1] https://news.crunchbase.com/venture/asia-startup-funding-tanks-eoy-2024/

AI image generation — which relies on neural networks to create new images from a variety of inputs, including text prompts — is projected to become a billion-dollar industry by the end of this decade. Even with today’s technology, if you wanted to make a fanciful picture of, say, a friend planting a flag on Mars or heedlessly flying into a black hole, it could take less than a second. However, before they can perform tasks like that, image generators are commonly trained on massive datasets containing millions of images that are often paired with associated text. Training these generative models can be an arduous chore that takes weeks or months, consuming vast computational resources in the process.

But what if it were possible to generate images through AI methods without using a generator at all? That real possibility, along with other intriguing ideas, was described in a research paper presented at the International Conference on Machine Learning (ICML 2025), which was held in Vancouver, British Columbia, earlier this summer. The paper, describing novel techniques for manipulating and generating images, was written by Lukas Lao Beyer, a graduate student researcher in MIT’s Laboratory for Information and Decision Systems (LIDS); Tianhong Li, a postdoc at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL); Xinlei Chen of Facebook AI Research; Sertac Karaman, an MIT professor of aeronautics and astronautics and the director of LIDS; and Kaiming He, an MIT associate professor of electrical engineering and computer science.

This group effort had its origins in a class project for a graduate seminar on deep generative models that Lao Beyer took last fall. In conversations during the semester, it became apparent to both Lao Beyer and He, who taught the seminar, that this research had real potential, which went far beyond the confines of a typical homework assignment. Other collaborators were soon brought into the endeavor.

The starting point for Lao Beyer’s inquiry was a June 2024 paper, written by researchers from the Technical University of Munich and the Chinese company ByteDance, which introduced a new way of representing visual information called a one-dimensional tokenizer. With this device, which is also a kind of neural network, a 256x256-pixel image can be translated into a sequence of just 32 numbers, called tokens. “I wanted to understand how such a high level of compression could be achieved, and what the tokens themselves actually represented,” says Lao Beyer.

The previous generation of tokenizers would typically break up the same image into an array of 16x16 tokens — with each token encapsulating information, in highly condensed form, that corresponds to a specific portion of the original image. The new 1D tokenizers can encode an image more efficiently, using far fewer tokens overall, and these tokens are able to capture information about the entire image, not just a single quadrant. Each of these tokens, moreover, is a 12-digit number consisting of 1s and 0s, allowing for 2¹² (or about 4,000) possibilities altogether. “It’s like a vocabulary of 4,000 words that makes up an abstract, hidden language spoken by the computer,” He explains. “It’s not like a human language, but we can still try to find out what it means.”

That’s exactly what Lao Beyer had initially set out to explore — work that provided the seed for the ICML 2025 paper. The approach he took was pretty straightforward. If you want to find out what a particular token does, Lao Beyer says, “you can just take it out, swap in some random value, and see if there is a recognizable change in the output.” Replacing one token, he found, changes the image quality, turning a low-resolution image into a high-resolution image or vice versa. Another token affected the blurriness in the background, while another still influenced the brightness. He also found a token that’s related to the “pose,” meaning that, in the image of a robin, for instance, the bird’s head might shift from right to left.

“This was a never-before-seen result, as no one had observed visually identifiable changes from manipulating tokens,” Lao Beyer says. The finding raised the possibility of a new approach to editing images. And the MIT group has shown, in fact, how this process can be streamlined and automated, so that tokens don’t have to be modified by hand, one at a time.

He and his colleagues achieved an even more consequential result involving image generation. A system capable of generating images normally requires a tokenizer, which compresses and encodes visual data, along with a generator that can combine and arrange these compact representations in order to create novel images. The MIT researchers found a way to create images without using a generator at all. Their new approach makes use of a 1D tokenizer and a so-called detokenizer (also known as a decoder), which can reconstruct an image from a string of tokens. However, with guidance provided by an off-the-shelf neural network called CLIP — which cannot generate images on its own, but can measure how well a given image matches a certain text prompt — the team was able to convert an image of a red panda, for example, into a tiger. In addition, they could create images of a tiger, or any other desired form, starting completely from scratch — from a situation in which all the tokens are initially assigned random values (and then iteratively tweaked so that the reconstructed image increasingly matches the desired text prompt).

The group demonstrated that with this same setup — relying on a tokenizer and detokenizer, but no generator — they could also do “inpainting,” which means filling in parts of images that had somehow been blotted out. Avoiding the use of a generator for certain tasks could lead to a significant reduction in computational costs because generators, as mentioned, normally require extensive training.

What might seem odd about this team’s contributions, He explains, “is that we didn’t invent anything new. We didn’t invent a 1D tokenizer, and we didn’t invent the CLIP model, either. But we did discover that new capabilities can arise when you put all these pieces together.”

“This work redefines the role of tokenizers,” comments Saining Xie, a computer scientist at New York University. “It shows that image tokenizers — tools usually used just to compress images — can actually do a lot more. The fact that a simple (but highly compressed) 1D tokenizer can handle tasks like inpainting or text-guided editing, without needing to train a full-blown generative model, is pretty surprising.”

Zhuang Liu of Princeton University agrees, saying that the work of the MIT group “shows that we can generate and manipulate the images in a way that is much easier than we previously thought. Basically, it demonstrates that image generation can be a byproduct of a very effective image compressor, potentially reducing the cost of generating images several-fold.”

There could be many applications outside the field of computer vision, Karaman suggests. “For instance, we could consider tokenizing the actions of robots or self-driving cars in the same way, which may rapidly broaden the impact of this work.”

Lao Beyer is thinking along similar lines, noting that the extreme amount of compression afforded by 1D tokenizers allows you to do “some amazing things,” which could be applied to other fields. For example, in the area of self-driving cars, which is one of his research interests, the tokens could represent, instead of images, the different routes that a vehicle might take.

Xie is also intrigued by the applications that may come from these innovative ideas. “There are some really cool use cases this could unlock,” he says. 

AI image generation — which relies on neural networks to create new images from a variety of inputs, including text prompts — is projected to become a billion-dollar industry by the end of this decade. Even with today’s technology, if you wanted to make a fanciful picture of, say, a friend planting a flag on Mars or heedlessly flying into a black hole, it could take less than a second. However, before they can perform tasks like that, image generators are commonly trained on massive datasets containing millions of images that are often paired with associated text. Training these generative models can be an arduous chore that takes weeks or months, consuming vast computational resources in the process.

But what if it were possible to generate images through AI methods without using a generator at all? That real possibility, along with other intriguing ideas, was described in a research paper presented at the International Conference on Machine Learning (ICML 2025), which was held in Vancouver, British Columbia, earlier this summer. The paper, describing novel techniques for manipulating and generating images, was written by Lukas Lao Beyer, a graduate student researcher in MIT’s Laboratory for Information and Decision Systems (LIDS); Tianhong Li, a postdoc at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL); Xinlei Chen of Facebook AI Research; Sertac Karaman, an MIT professor of aeronautics and astronautics and the director of LIDS; and Kaiming He, an MIT associate professor of electrical engineering and computer science.

This group effort had its origins in a class project for a graduate seminar on deep generative models that Lao Beyer took last fall. In conversations during the semester, it became apparent to both Lao Beyer and He, who taught the seminar, that this research had real potential, which went far beyond the confines of a typical homework assignment. Other collaborators were soon brought into the endeavor.

The starting point for Lao Beyer’s inquiry was a June 2024 paper, written by researchers from the Technical University of Munich and the Chinese company ByteDance, which introduced a new way of representing visual information called a one-dimensional tokenizer. With this device, which is also a kind of neural network, a 256x256-pixel image can be translated into a sequence of just 32 numbers, called tokens. “I wanted to understand how such a high level of compression could be achieved, and what the tokens themselves actually represented,” says Lao Beyer.

The previous generation of tokenizers would typically break up the same image into an array of 16x16 tokens — with each token encapsulating information, in highly condensed form, that corresponds to a specific portion of the original image. The new 1D tokenizers can encode an image more efficiently, using far fewer tokens overall, and these tokens are able to capture information about the entire image, not just a single quadrant. Each of these tokens, moreover, is a 12-digit number consisting of 1s and 0s, allowing for 2¹² (or about 4,000) possibilities altogether. “It’s like a vocabulary of 4,000 words that makes up an abstract, hidden language spoken by the computer,” He explains. “It’s not like a human language, but we can still try to find out what it means.”

That’s exactly what Lao Beyer had initially set out to explore — work that provided the seed for the ICML 2025 paper. The approach he took was pretty straightforward. If you want to find out what a particular token does, Lao Beyer says, “you can just take it out, swap in some random value, and see if there is a recognizable change in the output.” Replacing one token, he found, changes the image quality, turning a low-resolution image into a high-resolution image or vice versa. Another token affected the blurriness in the background, while another still influenced the brightness. He also found a token that’s related to the “pose,” meaning that, in the image of a robin, for instance, the bird’s head might shift from right to left.

“This was a never-before-seen result, as no one had observed visually identifiable changes from manipulating tokens,” Lao Beyer says. The finding raised the possibility of a new approach to editing images. And the MIT group has shown, in fact, how this process can be streamlined and automated, so that tokens don’t have to be modified by hand, one at a time.

He and his colleagues achieved an even more consequential result involving image generation. A system capable of generating images normally requires a tokenizer, which compresses and encodes visual data, along with a generator that can combine and arrange these compact representations in order to create novel images. The MIT researchers found a way to create images without using a generator at all. Their new approach makes use of a 1D tokenizer and a so-called detokenizer (also known as a decoder), which can reconstruct an image from a string of tokens. However, with guidance provided by an off-the-shelf neural network called CLIP — which cannot generate images on its own, but can measure how well a given image matches a certain text prompt — the team was able to convert an image of a red panda, for example, into a tiger. In addition, they could create images of a tiger, or any other desired form, starting completely from scratch — from a situation in which all the tokens are initially assigned random values (and then iteratively tweaked so that the reconstructed image increasingly matches the desired text prompt).

The group demonstrated that with this same setup — relying on a tokenizer and detokenizer, but no generator — they could also do “inpainting,” which means filling in parts of images that had somehow been blotted out. Avoiding the use of a generator for certain tasks could lead to a significant reduction in computational costs because generators, as mentioned, normally require extensive training.

What might seem odd about this team’s contributions, He explains, “is that we didn’t invent anything new. We didn’t invent a 1D tokenizer, and we didn’t invent the CLIP model, either. But we did discover that new capabilities can arise when you put all these pieces together.”

“This work redefines the role of tokenizers,” comments Saining Xie, a computer scientist at New York University. “It shows that image tokenizers — tools usually used just to compress images — can actually do a lot more. The fact that a simple (but highly compressed) 1D tokenizer can handle tasks like inpainting or text-guided editing, without needing to train a full-blown generative model, is pretty surprising.”

Zhuang Liu of Princeton University agrees, saying that the work of the MIT group “shows that we can generate and manipulate the images in a way that is much easier than we previously thought. Basically, it demonstrates that image generation can be a byproduct of a very effective image compressor, potentially reducing the cost of generating images several-fold.”

There could be many applications outside the field of computer vision, Karaman suggests. “For instance, we could consider tokenizing the actions of robots or self-driving cars in the same way, which may rapidly broaden the impact of this work.”

Lao Beyer is thinking along similar lines, noting that the extreme amount of compression afforded by 1D tokenizers allows you to do “some amazing things,” which could be applied to other fields. For example, in the area of self-driving cars, which is one of his research interests, the tokens could represent, instead of images, the different routes that a vehicle might take.

Xie is also intrigued by the applications that may come from these innovative ideas. “There are some really cool use cases this could unlock,” he says. 

In 2001, MIT became the first higher education institution to provide educational resources for free to anyone in the world. Fast forward 24 years: The Institute has now launched a dynamic AI-enabled website for its non-degree learning opportunities, making it easier for learners around the world to discover the courses and resources available on MIT’s various learning platforms.

MIT Learn enables learners to access more than 12,700 educational resources — including introductory and advanced courses, courseware, videos, podcasts, and more — from departments across the Institute. MIT Learn is designed to seamlessly connect the existing Institute’s learning platforms in one place.

“With MIT Learn, we’re opening access to MIT’s digital learning opportunities for millions around the world,” says Dimitris Bertsimas, vice provost for open learning. “MIT Learn elevates learning with personalized recommendations powered by AI, guiding each learner toward deeper understanding. It is a stepping stone toward a broader vision of making these opportunities even more accessible to global learners through one unified learning platform.”

The goal for MIT Learn is twofold: to allow learners to find what they want to fulfill their curiosity, and to enable learners to develop a long-term relationship with MIT as a source of educational experiences. 

“By fostering long-term connections between learners and MIT, we not only provide a pathway to continued learning, but also advance MIT’s mission to disseminate knowledge globally,” says Ferdi Alimadhi, chief technology officer for MIT Open Learning and the lead of the MIT Learn project. “With this initial launch of MIT Learn, we’re introducing AI-powered features that leverage emerging technologies to help learners discover the right content, engage with it more deeply, and stay supported as they shape their own educational journeys.” 

With its sophisticated search, browse, and discovery capability, MIT Learn allows learners to explore topics without having to understand MIT’s organizational structure or know the names of departments and programs. An AI-powered recommendation feature called “Ask Tim” complements the site’s traditional search and browsing tools, helping learners quickly find courses and resources aligned with their personal and professional goals. Learners can also prompt “Ask Tim” for a summary of a course’s structure, topics, and expectations, leading to more-informed decisions before enrolling.

In select offerings, such as Molecular Biology: DNA Replication and Repair, Genetics: The Fundamentals, and Cell Biology: Transport and Signaling, learners can interact with an AI assistant by asking questions about a lecture, requesting flashcards of key concepts, and obtaining instant summaries. These select offerings also feature an AI tutor to support learners as they work through problem sets, guiding them toward the next step without giving away the answers. These features, Alimadhi says, are being introduced in a limited set of courses and modules to allow the MIT Open Learning team to gather insights and improve the learning experience before expanding more broadly.

“MIT Learn is a whole new front door to the Institute,” says Christopher Capozzola, senior associate dean for open learning, who worked with faculty across the Institute on the project. “Just as the Kendall Square renovations transformed the way that people interact with our physical campus, MIT Learn transforms how people engage with what we offer digitally.”

Learners who choose to create an account on MIT Learn receive personalized course recommendations and can create and curate lists of educational resources, follow their specific areas of interest, and receive notifications when new MIT content is available. They can also personalize their learning experience based on their specific interests and choose the format that is best suited to them.

"From anywhere and for anyone, MIT Learn makes lifelong learning more accessible and personalized, building on the Institute’s decades of global leadership in open learning,” says MIT Provost Anantha Chandrakasan.

MIT Learn was designed to account for a learner’s evolving needs throughout their learning journey. It highlights supplemental study materials for middle schoolers, high schoolers, and college students, upskilling opportunities for early-career professionals, reskilling programs for those considering a career shift, and resources for educators.

“MIT has an amazing collection of learning opportunities, covering a wide range of formats,” says Eric Grimson, chancellor for academic advancement, who oversaw the initial development of MIT Learn during his time as interim vice president for open learning. “The sheer size of that collection can be daunting, so creating a platform that brings all of those offerings together, in an easily searchable framework, greatly enhances our ability to serve learners.”

According to Peter Hirst, senior associate dean for executive education at MIT Sloan School of Management, one of the Institute's incredible strengths is its sheer volume and diversity of expertise, research, and learning opportunities. But it can be challenging to discover and follow all those opportunities — even for people who are immersed in the on-campus experience. MIT Learn, he says, is a solution to this problem.

“MIT Learn gathers all the knowledge and learning resources offered across all of MIT into a learner-friendly, curatable repository that enables anyone and everyone, whatever their interests or learning needs, to explore and engage in the wide range of learning resources and public certificate programs that MIT has to offer and that can help them achieve their goals,” Hirst says.

MIT Learn was spearheaded by MIT Open Learning, which aims to transform teaching and learning on and off the Institute’s campus. MIT Learn was developed with the direction of former provost Cynthia Barnhart, and in cooperation with MIT Sloan Executive Education and MIT Professional Education. During the design phase, OpenCourseWare Faculty Advisory Committee Chair Michael Short and MITx Faculty Advisory Committee Chair Caspar Hare contributed key insights, along with other numerous faculty involved with Open Learning’s product offerings, including OpenCourseWare, MITx, and MicroMasters programs. MIT Learn is also informed by the insights of the Ad Hoc Committee on MITx and MITx Online.

“For over 20 years, MIT staff and faculty have been creating a wealth of online resources, from lecture videos to practice problems, and from single online courses to entire credential-earning programs,” says Sara Fisher Ellison, a member of the Ad Hoc Committee on MITx and MITx Online and the faculty lead for the online MITx MicroMasters Program in Data, Economics, and Design of Policy. “Making these resources findable, searchable, and broadly available is a natural extension of MIT’s core educational mission. MIT Learn is a big, important step in that direction. We are excited for the world to see what we have to offer.”

Looking ahead, MIT Learn will also feature selected content from the MIT Press. As MIT Learn continues to grow, Open Learning is exploring collaborations with departments across the Institute with the goal of offering the fullest possible range of educational materials from MIT to learners around the world.

“MIT Learn is the latest step in a long tradition of the Institute providing innovative ways for learners to access knowledge,” Barnhart says. “This AI-enabled platform delivers on the Institute’s commitment to help people launch into learning journeys that can unlock life-changing opportunities.”

Let’s say you’re reading a story, or playing a game of chess. You may not have noticed, but each step of the way, your mind kept track of how the situation (or “state of the world”) was changing. You can imagine this as a sort of sequence of events list, which we use to update our prediction of what will happen next.

Language models like ChatGPT also track changes inside their own “mind” when finishing off a block of code or anticipating what you’ll write next. They typically make educated guesses using transformers — internal architectures that help the models understand sequential data — but the systems are sometimes incorrect because of flawed thinking patterns. Identifying and tweaking these underlying mechanisms helps language models become more reliable prognosticators, especially with more dynamic tasks like forecasting weather and financial markets.

But do these AI systems process developing situations like we do? A new paper from researchers in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Department of Electrical Engineering and Computer Science shows that the models instead use clever mathematical shortcuts between each progressive step in a sequence, eventually making reasonable predictions. The team made this observation by going under the hood of language models, evaluating how closely they could keep track of objects that change position rapidly. Their findings show that engineers can control when language models use particular workarounds as a way to improve the systems’ predictive capabilities.

Shell games

The researchers analyzed the inner workings of these models using a clever experiment reminiscent of a classic concentration game. Ever had to guess the final location of an object after it’s placed under a cup and shuffled with identical containers? The team used a similar test, where the model guessed the final arrangement of particular digits (also called a permutation). The models were given a starting sequence, such as “42135,” and instructions about when and where to move each digit, like moving the “4” to the third position and onward, without knowing the final result.

In these experiments, transformer-based models gradually learned to predict the correct final arrangements. Instead of shuffling the digits based on the instructions they were given, though, the systems aggregated information between successive states (or individual steps within the sequence) and calculated the final permutation.

One go-to pattern the team observed, called the “Associative Algorithm,” essentially organizes nearby steps into groups and then calculates a final guess. You can think of this process as being structured like a tree, where the initial numerical arrangement is the “root.” As you move up the tree, adjacent steps are grouped into different branches and multiplied together. At the top of the tree is the final combination of numbers, computed by multiplying each resulting sequence on the branches together.

The other way language models guessed the final permutation was through a crafty mechanism called the “Parity-Associative Algorithm,” which essentially whittles down options before grouping them. It determines whether the final arrangement is the result of an even or odd number of rearrangements of individual digits. Then, the mechanism groups adjacent sequences from different steps before multiplying them, just like the Associative Algorithm.

“These behaviors tell us that transformers perform simulation by associative scan. Instead of following state changes step-by-step, the models organize them into hierarchies,” says MIT PhD student and CSAIL affiliate Belinda Li SM ’23, a lead author on the paper. “How do we encourage transformers to learn better state tracking? Instead of imposing that these systems form inferences about data in a human-like, sequential way, perhaps we should cater to the approaches they naturally use when tracking state changes.”

“One avenue of research has been to expand test-time computing along the depth dimension, rather than the token dimension — by increasing the number of transformer layers rather than the number of chain-of-thought tokens during test-time reasoning,” adds Li. “Our work suggests that this approach would allow transformers to build deeper reasoning trees.”

Through the looking glass

Li and her co-authors observed how the Associative and Parity-Associative algorithms worked using tools that allowed them to peer inside the “mind” of language models. 

They first used a method called “probing,” which shows what information flows through an AI system. Imagine you could look into a model’s brain to see its thoughts at a specific moment — in a similar way, the technique maps out the system’s mid-experiment predictions about the final arrangement of digits.

A tool called “activation patching” was then used to show where the language model processes changes to a situation. It involves meddling with some of the system’s “ideas,” injecting incorrect information into certain parts of the network while keeping other parts constant, and seeing how the system will adjust its predictions.

These tools revealed when the algorithms would make errors and when the systems “figured out” how to correctly guess the final permutations. They observed that the Associative Algorithm learned faster than the Parity-Associative Algorithm, while also performing better on longer sequences. Li attributes the latter’s difficulties with more elaborate instructions to an over-reliance on heuristics (or rules that allow us to compute a reasonable solution fast) to predict permutations.

“We’ve found that when language models use a heuristic early on in training, they’ll start to build these tricks into their mechanisms,” says Li. “However, those models tend to generalize worse than ones that don’t rely on heuristics. We found that certain pre-training objectives can deter or encourage these patterns, so in the future, we may look to design techniques that discourage models from picking up bad habits.”

The researchers note that their experiments were done on small-scale language models fine-tuned on synthetic data, but found the model size had little effect on the results. This suggests that fine-tuning larger language models, like GPT 4.1, would likely yield similar results. The team plans to examine their hypotheses more closely by testing language models of different sizes that haven’t been fine-tuned, evaluating their performance on dynamic real-world tasks such as tracking code and following how stories evolve.

Harvard University postdoc Keyon Vafa, who was not involved in the paper, says that the researchers’ findings could create opportunities to advance language models. “Many uses of large language models rely on tracking state: anything from providing recipes to writing code to keeping track of details in a conversation,” he says. “This paper makes significant progress in understanding how language models perform these tasks. This progress provides us with interesting insights into what language models are doing and offers promising new strategies for improving them.”

Li wrote the paper with MIT undergraduate student Zifan “Carl” Guo and senior author Jacob Andreas, who is an MIT associate professor of electrical engineering and computer science and CSAIL principal investigator. Their research was supported, in part, by Open Philanthropy, the MIT Quest for Intelligence, the National Science Foundation, the Clare Boothe Luce Program for Women in STEM, and a Sloan Research Fellowship.

The researchers presented their research at the International Conference on Machine Learning (ICML) this week.

Let’s say you’re reading a story, or playing a game of chess. You may not have noticed, but each step of the way, your mind kept track of how the situation (or “state of the world”) was changing. You can imagine this as a sort of sequence of events list, which we use to update our prediction of what will happen next.

Language models like ChatGPT also track changes inside their own “mind” when finishing off a block of code or anticipating what you’ll write next. They typically make educated guesses using transformers — internal architectures that help the models understand sequential data — but the systems are sometimes incorrect because of flawed thinking patterns. Identifying and tweaking these underlying mechanisms helps language models become more reliable prognosticators, especially with more dynamic tasks like forecasting weather and financial markets.

But do these AI systems process developing situations like we do? A new paper from researchers in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and Department of Electrical Engineering and Computer Science shows that the models instead use clever mathematical shortcuts between each progressive step in a sequence, eventually making reasonable predictions. The team made this observation by going under the hood of language models, evaluating how closely they could keep track of objects that change position rapidly. Their findings show that engineers can control when language models use particular workarounds as a way to improve the systems’ predictive capabilities.

Shell games

The researchers analyzed the inner workings of these models using a clever experiment reminiscent of a classic concentration game. Ever had to guess the final location of an object after it’s placed under a cup and shuffled with identical containers? The team used a similar test, where the model guessed the final arrangement of particular digits (also called a permutation). The models were given a starting sequence, such as “42135,” and instructions about when and where to move each digit, like moving the “4” to the third position and onward, without knowing the final result.

In these experiments, transformer-based models gradually learned to predict the correct final arrangements. Instead of shuffling the digits based on the instructions they were given, though, the systems aggregated information between successive states (or individual steps within the sequence) and calculated the final permutation.

One go-to pattern the team observed, called the “Associative Algorithm,” essentially organizes nearby steps into groups and then calculates a final guess. You can think of this process as being structured like a tree, where the initial numerical arrangement is the “root.” As you move up the tree, adjacent steps are grouped into different branches and multiplied together. At the top of the tree is the final combination of numbers, computed by multiplying each resulting sequence on the branches together.

The other way language models guessed the final permutation was through a crafty mechanism called the “Parity-Associative Algorithm,” which essentially whittles down options before grouping them. It determines whether the final arrangement is the result of an even or odd number of rearrangements of individual digits. Then, the mechanism groups adjacent sequences from different steps before multiplying them, just like the Associative Algorithm.

“These behaviors tell us that transformers perform simulation by associative scan. Instead of following state changes step-by-step, the models organize them into hierarchies,” says MIT PhD student and CSAIL affiliate Belinda Li SM ’23, a lead author on the paper. “How do we encourage transformers to learn better state tracking? Instead of imposing that these systems form inferences about data in a human-like, sequential way, perhaps we should cater to the approaches they naturally use when tracking state changes.”

“One avenue of research has been to expand test-time computing along the depth dimension, rather than the token dimension — by increasing the number of transformer layers rather than the number of chain-of-thought tokens during test-time reasoning,” adds Li. “Our work suggests that this approach would allow transformers to build deeper reasoning trees.”

Through the looking glass

Li and her co-authors observed how the Associative and Parity-Associative algorithms worked using tools that allowed them to peer inside the “mind” of language models. 

They first used a method called “probing,” which shows what information flows through an AI system. Imagine you could look into a model’s brain to see its thoughts at a specific moment — in a similar way, the technique maps out the system’s mid-experiment predictions about the final arrangement of digits.

A tool called “activation patching” was then used to show where the language model processes changes to a situation. It involves meddling with some of the system’s “ideas,” injecting incorrect information into certain parts of the network while keeping other parts constant, and seeing how the system will adjust its predictions.

These tools revealed when the algorithms would make errors and when the systems “figured out” how to correctly guess the final permutations. They observed that the Associative Algorithm learned faster than the Parity-Associative Algorithm, while also performing better on longer sequences. Li attributes the latter’s difficulties with more elaborate instructions to an over-reliance on heuristics (or rules that allow us to compute a reasonable solution fast) to predict permutations.

“We’ve found that when language models use a heuristic early on in training, they’ll start to build these tricks into their mechanisms,” says Li. “However, those models tend to generalize worse than ones that don’t rely on heuristics. We found that certain pre-training objectives can deter or encourage these patterns, so in the future, we may look to design techniques that discourage models from picking up bad habits.”

The researchers note that their experiments were done on small-scale language models fine-tuned on synthetic data, but found the model size had little effect on the results. This suggests that fine-tuning larger language models, like GPT 4.1, would likely yield similar results. The team plans to examine their hypotheses more closely by testing language models of different sizes that haven’t been fine-tuned, evaluating their performance on dynamic real-world tasks such as tracking code and following how stories evolve.

Harvard University postdoc Keyon Vafa, who was not involved in the paper, says that the researchers’ findings could create opportunities to advance language models. “Many uses of large language models rely on tracking state: anything from providing recipes to writing code to keeping track of details in a conversation,” he says. “This paper makes significant progress in understanding how language models perform these tasks. This progress provides us with interesting insights into what language models are doing and offers promising new strategies for improving them.”

Li wrote the paper with MIT undergraduate student Zifan “Carl” Guo and senior author Jacob Andreas, who is an MIT associate professor of electrical engineering and computer science and CSAIL principal investigator. Their research was supported, in part, by Open Philanthropy, the MIT Quest for Intelligence, the National Science Foundation, the Clare Boothe Luce Program for Women in STEM, and a Sloan Research Fellowship.

The researchers presented their research at the International Conference on Machine Learning (ICML) this week.

Imagine an older adult who lives alone laughing and chatting while on a video call – with a jovial 3D avatar. On platforms like Replika, artificial intelligence (AI) companions can hold conversations that not only feel strikingly lifelike, but also generate a sense of connection akin to human relationships.

Such AI companions could even be used to improve the well-being of older adults who live alone by offering them opportunities to assist AI in tasks instead of the other way around, said Assistant Professor Yi-Chieh Lee, from the NUS Department of Computer Science. “We found that if people contribute to and ‘help’ the AI, they will increase their self-esteem and decrease their feelings of loneliness,” he said at an expert meeting held on 27 June 2025 at NUS. It was organised by NUS’ Centre for Family and Population Research (CFPR) in collaboration with Sweden’s Lund University.

Titled “AI-Powered Care Interventions and Support Systems for Older Persons with Limited Kin Availability”, the event drew a diverse slate of academics from Australia, Hong Kong, South Korea, and beyond, who discussed how AI may help fill the caregiving gap for older persons, particularly those ageing with limited family support. While AI has potential to support more personalised and integrated care, panellists emphasised not to overlook ethical concerns.

In Asst Prof Lee’s research, study participants were asked to help an AI agent named “Chatti” develop a messaging app by providing insights. Subsequently, they were asked to respond to emotive statements measuring their self-esteem and loneliness, such as “I am satisfied with myself” or “I feel that I lack companionship”. The award-winning study found that humans helping AI contributed to a greater sense of autonomy. In this way, AI can potentially help isolated older adults reconnect with society, he said.

Tech that lends a hand

Such AI solutions will play an increasingly crucial role as family networks shrink at an alarming rate. Research projects that by 2050, an average 65-year-old woman in China will have about 30 living relatives — down from 60 in 2000. Similar declines in kin availability are expected globally.

This means that older persons in the near future may have fewer family-based resources to draw on in old age, noted NUS Associate Professor of Sociology and Anthropology Bussarawan “Puk” Teerawichitchainan, who is Co-Director of CFPR.  

This issue is equally pressing in Singapore, where the total fertility rate has been persistently low, plateauing at 0.97 since 2023. “We must ask, how can emerging technologies, particularly AI-powered technology, complement family and community-based support systems?” said Assoc Prof Bussarawan, who co-organised the expert meeting with Dr Wenqian Xu, a postdoctoral fellow at Lund University.

AI can help to ease labour shortages in healthcare while making care more personalised. Associate Professor Moon Choi from the KAIST Graduate School of Science and Technology Policy in South Korea noted how AI-powered phone services like CLOVA CareCall are already conducting weekly check-ins with older adults in South Korea, which is currently experiencing a shortage of nurses.

AI is also increasingly viewed as a key enabler of future living environments designed to support ageing in place for all older adults, particularly those with limited kin ties. Dr Kentaro Watanabe from Japan’s National Institute of Advanced Industrial Science and Technology (AIST) emphasised that smart home environments must integrate both technological and social resources to adapt effectively to the evolving needs of older adults.

Looking ahead, AI could enable hyper-personalised support and care through the use of digital twins — virtual models that mirror an individual’s physical and biological traits. Professor Vivian Lou from the University of Hong Kong is working with her team to develop a digital twin based on her own skeletal and muscle data. Such projects could pave the way for support and care that is ultra-tailored to an individual’s healthcare needs.

But Professor Pei-Lee Teh of Monash University Malaysia offered a word of caution. “AI should complement, not replace a human. It may monitor our health, but it cannot hold our hand the way that our children, family, or friends can,” she said.

AI’s hidden biases

Like humans, AI is susceptible to bias. For example, it can be ageist, said Associate Professor Xin Zhang of Peking University.

Specifically, AI scores high in “benevolent ageism” – a well-meaning yet patronising attitude that assumes an older person needs help. Dr Kathryn Lynn Muyskens, a research fellow at NUS’ Centre for Biomedical Ethics, referred to it as “a form of misrecognition and a failed connection”, adding that it can contribute to the widespread issue of loneliness among older adults, rather than help to solve it.

“It’s good to have a robot that seeks engagement and offers some sort of amusement to the older person. But if we’re talking about older adults who are lonely, this isn’t an antidote,” she said. “I’ve never felt lonelier personally than when I’m in a crowded room, for example. It’s the misrecognition in society that can make you feel lonelier.”

Care infrastructure for older adults therefore needs to be centred on older adults themselves, not what others, including AI, think they need, said Assoc Prof Zhang.

Even older adults with cognitive conditions, such as dementia, can be included in the conversation. “The idea that people living with dementia have no agency is something we need to question… in my experience, you can still engage them in co-designing technology and solutions,” said Dr Barbara Barbosa Neves from the University of Sydney.

For some attendees, the meeting sparked new awareness and hope. “(It) inspired me to talk to older adults to understand what they’re really looking out for,” said Harry Wu, a Year 2 Business undergraduate from NUS who attended the event.

“We need to hear from older adults about what they think they need,” added the 21-year-old, who is also minoring in AI.

At the end of the day, technology is a double-edged sword. “With one hand it gives, and with the other, it takes away,” said Dr Muyskens. The question is, as she put it: “Will (AI) make us more or less human?”

“This event marks just the beginning of much-needed interdisciplinary dialogues on how AI can be thoughtfully integrated into care systems,” said the expert meeting’s organiser Assoc Prof Teerawichitchainan. “Singapore and several Asian societies are facing profound family and demographic transitions, and we must explore how technology can support, not replace, human care.”

“Co-designing technologies, research and policies with older adults, caregivers, and other key stakeholders is essential,” added co-organiser Dr Xu. “That’s how we create care systems that are both innovative and inclusive.”

The Faculty of Law at the National University of Singapore (NUS Law) is proud to announce a world first in legal publishing: a podcast series based on articles from the Singapore Journal of Legal Studies (SJLS). Produced using Google’s AI-powered tool, NotebookLM, this marks the first time a legal journal has used generative AI to turn peer-reviewed legal research into accessible audio content.

The initiative reflects NUS Law’s commitment to building on its academic legacy while embracing new ways to share legal knowledge. By converting academic writing into podcast episodes designed for a general audience, NUS Law aims to make complex legal ideas easier to understand, without compromising on intellectual depth.

“This innovation underscores NUS Law’s commitment to thought leadership, academic excellence, and public engagement,” said Professor Andrew Simester, Dean of NUS Law. “We’re proud to be leading the way globally by using technology to extend the reach and relevance of legal research. It is a key part of our vision to be that bridge between legal academia and society.”

Established in 1959, SJLS is one of the oldest law journals in the British Commonwealth and a cornerstone of legal commentary in Singapore and the region. It is managed by NUS Law and guided by an international Advisory Board. Its articles cover a wide spectrum of legal issues, from domestic to international developments, with particular focus on Singapore, Asia, and the broader common-law world.

While the journal’s readership traditionally includes lawyers, academics, and legal observers, the podcast series would benefit the wider legal community, business leaders and policy makers. The full articles remain available under open access on the SJLS website, while the podcasts present concise, approachable summaries to spark interest beyond the legal field.

Professor Julien Chaisse from the City University of Hong Kong, whose work is featured in the series said: “This format is engaging and I am genuinely impressed by the way it brings legal academic writing into a more accessible format without losing its substance.”

Professor Emilios Avgouleas, Chair of International Banking Law and Finance at the University of Edinburgh, added: “This is an impressive initiative that uses modern technology to disseminate legal and interdisciplinary scholarship more widely. It is truly the future for the distribution of law journal content.”

The podcast is available for free on Spotify, with new episodes released regularly.

Listen to the Singapore Journal of Legal Studies Podcast on Spotify.

Doing your taxes can feel like a very complicated task. Even so, it might be less intricate than trying to make sense of what people think about taxes.

Several years ago, MIT political scientist Andrea Campbell undertook an expansive research project to understand public opinion about taxation. Her efforts have now reached fruition, in a new book uncovering many complexities about attitudes toward taxes. Those complexities include a central tension: In the U.S., most people say they support the principle of progressive taxation — in which higher earners pay higher shares of their income. Yet people also say they prefer specific forms of taxes that are regressive, hitting lower- and middle-income earners relatively harder.

For instance, state sales taxes are considered regressive, since people who make less money spend a larger percentage of their incomes, meaning sales taxes eat up a larger proportion of their earnings. But a substantial portion of the public still finds them to be fair, partly because the wealthy cannot wriggle out of them.

“At an abstract or conceptual level, people say they like progressive tax systems more than flat or regressive tax systems,” Campbell says. “But when you look at public attitudes toward specific taxes, people’s views flip upside down. People say federal and state income taxes are unfair, but they say sales taxes, which are very regressive, are fair. Their attitudes on individual taxes are the opposite of what their overall commitments are.”

Now Campbell analyzes these issues in detail in her book, “Taxation and Resentment,” just published by Princeton University Press. Campbell is the Arthur and Ruth Sloan Professor of Political Science at MIT and a former head of MIT’s  Department of Political Science.

Filling out the record

Campbell originally planned “Taxation and Resentment” as a strictly historically-oriented look at the subject. But the absence of any one book compiling public-opinion data in this area was striking. So, she assembled data going back to the end of World War II, and even designed and ran a couple of her own public research surveys, which help undergird the book’s numbers.

“Political scientists write a lot about public attitudes toward spending in the United States, but not so much about attitudes toward taxes,” Campbell says. “The public-opinion record is very thin.”

The complexities of U.S. public opinion on taxes are plainly linked to the presence of numerous forms of taxes, including federal and state income taxes, sales taxes, payroll taxes, estate taxes, and capital gains taxes. The best-known, of course, is the federal income tax, whose quirks and loopholes seem to irk citizens.

“That really seizes people’s imaginations,” Campbell says. “Keeping the focus on federal income tax has been a clever strategy among those who want to cut it. People think it’s unfair because they look at all the tax breaks the rich get and think, ‘I don’t have access to those.’ Those breaks increase complexity, undermine people’s knowledge, heighten their anger, and of course are in there because they help rich people pay less. So, there ends up being a cycle.”

That same sense of unfairness does not translate to all other forms of taxation, however. Large majorities of people have supported lowering the estate tax, for example, even though the threshold at which the federal estate tax kicks in — $13.5 million — applies to very few families.

Then too, the public seems to perceive sales taxes as being fair because of the simplicity and lack of loopholes — an understandable view, but one that ignores the way that state sales taxes, as opposed to state income taxes, place a bigger burden on middle-class and lower-income workers.

“A regressive tax like a sales tax is more difficult to comprehend,” Campbell says. “We all pay the same rate, so it seems like a flat tax, but as your income goes up, the bite of that tax goes down. And that’s just very difficult for people to understand.”

Overall, as Campbell details, income levels do not have huge predictive value when it comes to tax attitudes. Party affiliation also has less impact than many people might suspect — Democrats and Republicans differ on taxes, though not as much, in some ways, as political independents, who often have the most anti-tax views of all.

Meanwhile, Campbell finds, white Americans with heightened concerns about redistribution of public goods among varying demographic groups are more opposed to taxes than those who do not share those redistribution concerns. And Black and Hispanic Americans, who may wind up on the short end of regressive policies, also express significantly anti-tax perspectives, albeit while expressing more support for the state functions funded by taxation.

“There are so many factors and components of public opinion around taxes,” Campbell says. “Many political and demographic groups have their own reasons for disliking the status quo.”

How much does public opinion matter?

The research in “Taxation and Resentment” will be of high value to many kinds of scholars. However, as Campbell notes, political scientists do not have consensus about how much public opinion influences policy. Some experts contend that donors and lobbyists essentially determine policy while the larger public is ignored. But Campbell does not agree that public sentiment amounts to nothing. Consider, she says, the vigorous and successful public campaign to lower the estate tax in the first decade of the 2000s.

“If public opinion doesn’t matter, then why were there these PR campaigns to try to convince people the estate tax was bad for small businesses, farmers, and other groups?” Campbell asks. “Clearly it’s because public opinion does matter. It’s far easier to get these policies implemented if the public is on your side than if the public is in opposition. Public opinion is not the only factor in policymaking, but it’s a contributing factor.”

To be sure, even in the formation of public opinion, there are complexities and nuance, as Campbell notes in the book. A system of progressive taxation means the people taxed at the highest rate are the most motivated to oppose the system — and may heavily influence public opinion, in a top-down manner.

Scholars in the field have praised “Taxation and Resentment.” Martin Gilens, chair of the Department of Public Policy at the University of California at Los Angeles, has called it an “important and very welcome addition to the literature on public attitudes about public policies … with rich and often unexpected findings.” Vanessa Williamson, a senior fellow at the Brookings Institution, has said the book is “essential reading for anyone who wants to understand what Americans actually think about taxes. The scope of the data Campbell brings to bear on this question is unparalleled, and the depth of her analysis of public opinion across time and demography is a monumental achievement.”

For her part, Campbell says she hopes people in a variety of groups will read the book — including policymakers, scholars in multiple fields, and students. Certainly, she thinks, after studying the issue, more people could stand to know more about taxes.

“The tax system is complex,” Campbell says, “and people don’t always understand their own stakes. There is often a fog surrounding taxes.”

The MIT Health and Life Sciences Collaborative (MIT HEALS) has announced the establishment of the Fairbairn Menstruation Science Fund, supporting a bold, high-impact initiative designed to revolutionize women’s health research.

Established through a gift from Emily and Malcolm Fairbairn, the fund will advance groundbreaking research on the function of the human uterus and its impact on sex-based differences in human immunology that contribute to gynecological disorders such as endometriosis, as well as other chronic systemic inflammatory diseases that disproportionately affect women, such as Lyme disease and lupus. The Fairbairns, based in the San Francisco Bay Area, have committed $10 million, with a call to action for an additional $10 million in matching funds.

“I’m deeply grateful to Emily and Malcolm Fairbairn for their visionary support of menstruation science at MIT. For too long, this area of research has lacked broad scientific investment and visibility, despite its profound impact on the health and lives of over half the population,” says Anantha P. Chandrakasan, MIT provost who was chief innovation and strategy officer and dean of engineering at the time of the gift, and Vannevar Bush Professor of Electrical Engineering and Computer Science.

Chandrakasan adds: “Thanks to groundbreaking work from researchers like Professor Linda Griffith and her team at the MIT Center for Gynepathology Research (CGR), we have an opportunity to advance our understanding and address critical challenges in menstruation science.”

Griffith, the School of Engineering Professor of Teaching Innovation in the departments of Biological Engineering and Mechanical Engineering, and director of CGR, says the Fairbairn Fund will permit the illumination of “the enormous sex-based differences in human immunity” and advance next-generation drug-discovery technologies.

One main thrust of the new initiative will further the development of “organs on chips,” living models of patients. Using living cells or tissues, such devices allow researchers to replicate and experiment with interactions that can occur in the body. Griffith and an interdisciplinary team of researchers have engineered a powerful microfluidic platform that supports chips that foster growth of tissues complete with blood vessels and circulating immune cells. The technology was developed for building endometriosis lesions from individual patients with known clinical characteristics. The chip allows the researchers to do preclinical testing of drugs on the human patient-derived endometriosis model rather than on laboratory animals, which often do not menstruate naturally and whose immune systems function differently than that of humans.

The Fairbairn Fund will build the infrastructure for a “living patient avatar” facility to develop such physiomimetic models for all kinds of health conditions.

“We acknowledge that there are some big-picture phenomenological questions that one can study in animals, but human immunology is so very different,” Griffith says. “Pharma and biotech realize that we need living models of patients and the computational models of carefully curated patient data if we are to move into greater success in clinical trials.”

The computational models of patient data that Griffith refers to are a key element in choosing how to design the patient avatars and determine which therapeutics to test on them. For instance, by using systems biology analysis of inflammation in patient abdominal fluid, Griffith and her collaborators identified an intracellular enzyme called jun kinase (JNK). They are now working with a biotech company to test specific inhibitors of JNK in their model. Griffith has also collaborated with Michal “Mikki” Tal, a principal scientist in MIT’s Department of Biological Engineering, on investigating a possible link between prior infection, such as by the Lyme-causing bacterium Borrelia, and a number of chronic inflammatory diseases in women. Automating assays of patient samples for higher throughput could systematically speed the generation of hypotheses guiding the development of patient model experimentation.

“This fund is catalytic,” Griffith says. “Industry and government, along with other foundations, will invest if the foundational infrastructure exists. They want to employ the technologies, but it is hard to get them developed to the point they are proven to be useful. This gets us through that difficult part of the journey.”

The fund will also support public engagement efforts to reduce stigma around menstruation and neglect of such conditions as abnormal uterine bleeding and debilitating anemia, endometriosis, and polycystic ovary syndrome — and in general bring greater attention to women’s health research. Endometriosis, for instance, in which tissue that resembles the uterine lining starts growing outside the uterus and causes painful inflammation, affects one in 10 women. It often goes undiagnosed for years, and can require repeated surgeries to remove its lesions. Meanwhile, little is known about what causes it, how to prevent it, or what could effectively stop it.

Women’s health research could further advance in many areas of medicine beyond conditions that disproportionately affect females. Griffith points out that the uterus, which sheds and regenerates its lining every month, demonstrates “scarless healing” that could warrant investigation. Also, deepened study of the uterus could shed light on immune tolerance for transplants, given that in a successful pregnancy an implanted fetus is not rejected, despite containing foreign material from the biological father.

For Emily Fairbairn, the fund is a critical step toward major advances in an often-overlooked area of medicine.

“My mission is to support intellectually honest, open-minded scientists who embrace risk, treat failure as feedback, and remain committed to discovery over dogma. This fund is a direct extension of that philosophy. It’s designed to fuel research into the biological realities of diseases that remain poorly understood, frequently dismissed, or disproportionately misdiagnosed in women,” Fairbairn says. “I’ve chosen to make this gift to MIT because Linda Griffith exemplifies the rare combination of scientific integrity and bold innovation — qualities essential for tackling the most neglected challenges in medicine.”

Fairbairn also refers to Griffith collaborator Michal Tal as being “deeply inspiring.”

“Her work embodies what’s possible when scientific excellence meets institutional courage. It is this spirit — bold, rigorous, and fearless — that inspired this gift and fuels our hope for the future of women’s health,” she says.

Fairbairn, who has suffered from both Lyme disease and endometriosis that required multiple surgeries, originally directed her philanthropy, including previous gifts to MIT, toward the study of Lyme disease and associated infections.

“My own experience with both Lyme and endometriosis deepened my conviction that science must better account for how female physiology, genetics, and psychology differ from men’s,” she says. “MIT stands out for treating women’s health not as a niche, but as a frontier. The Institute’s willingness to bridge immunology, neurobiology, bioengineering, and data science — alongside its development of cutting-edge platforms like human chips — offers a rare and necessary seriousness of purpose.”

For her part, Griffith refers to Fairbairn as “a citizen scientist who inspires us daily.”

“Her tireless advocacy for patients, especially women, who are dismissed and gas-lit, is priceless,” Griffith adds. “Emily has made me a better scientist, in service of humanity.”

The MIT Health and Life Sciences Collaborative (MIT HEALS) has announced the establishment of the Fairbairn Menstruation Science Fund, supporting a bold, high-impact initiative designed to revolutionize women’s health research.

Established through a gift from Emily and Malcolm Fairbairn, the fund will advance groundbreaking research on the function of the human uterus and its impact on sex-based differences in human immunology that contribute to gynecological disorders such as endometriosis, as well as other chronic systemic inflammatory diseases that disproportionately affect women, such as Lyme disease and lupus. The Fairbairns, based in the San Francisco Bay Area, have committed $10 million, with a call to action for an additional $10 million in matching funds.

“I’m deeply grateful to Emily and Malcolm Fairbairn for their visionary support of menstruation science at MIT. For too long, this area of research has lacked broad scientific investment and visibility, despite its profound impact on the health and lives of over half the population,” says Anantha P. Chandrakasan, MIT provost who was chief innovation and strategy officer and dean of engineering at the time of the gift, and Vannevar Bush Professor of Electrical Engineering and Computer Science.

Chandrakasan adds: “Thanks to groundbreaking work from researchers like Professor Linda Griffith and her team at the MIT Center for Gynepathology Research (CGR), we have an opportunity to advance our understanding and address critical challenges in menstruation science.”

Griffith, the School of Engineering Professor of Teaching Innovation in the departments of Biological Engineering and Mechanical Engineering, and director of CGR, says the Fairbairn Fund will permit the illumination of “the enormous sex-based differences in human immunity” and advance next-generation drug-discovery technologies.

One main thrust of the new initiative will further the development of “organs on chips,” living models of patients. Using living cells or tissues, such devices allow researchers to replicate and experiment with interactions that can occur in the body. Griffith and an interdisciplinary team of researchers have engineered a powerful microfluidic platform that supports chips that foster growth of tissues complete with blood vessels and circulating immune cells. The technology was developed for building endometriosis lesions from individual patients with known clinical characteristics. The chip allows the researchers to do preclinical testing of drugs on the human patient-derived endometriosis model rather than on laboratory animals, which often do not menstruate naturally and whose immune systems function differently than that of humans.

The Fairbairn Fund will build the infrastructure for a “living patient avatar” facility to develop such physiomimetic models for all kinds of health conditions.

“We acknowledge that there are some big-picture phenomenological questions that one can study in animals, but human immunology is so very different,” Griffith says. “Pharma and biotech realize that we need living models of patients and the computational models of carefully curated patient data if we are to move into greater success in clinical trials.”

The computational models of patient data that Griffith refers to are a key element in choosing how to design the patient avatars and determine which therapeutics to test on them. For instance, by using systems biology analysis of inflammation in patient abdominal fluid, Griffith and her collaborators identified an intracellular enzyme called jun kinase (JNK). They are now working with a biotech company to test specific inhibitors of JNK in their model. Griffith has also collaborated with Michal “Mikki” Tal, a principal scientist in MIT’s Department of Biological Engineering, on investigating a possible link between prior infection, such as by the Lyme-causing bacterium Borrelia, and a number of chronic inflammatory diseases in women. Automating assays of patient samples for higher throughput could systematically speed the generation of hypotheses guiding the development of patient model experimentation.

“This fund is catalytic,” Griffith says. “Industry and government, along with other foundations, will invest if the foundational infrastructure exists. They want to employ the technologies, but it is hard to get them developed to the point they are proven to be useful. This gets us through that difficult part of the journey.”

The fund will also support public engagement efforts to reduce stigma around menstruation and neglect of such conditions as abnormal uterine bleeding and debilitating anemia, endometriosis, and polycystic ovary syndrome — and in general bring greater attention to women’s health research. Endometriosis, for instance, in which tissue that resembles the uterine lining starts growing outside the uterus and causes painful inflammation, affects one in 10 women. It often goes undiagnosed for years, and can require repeated surgeries to remove its lesions. Meanwhile, little is known about what causes it, how to prevent it, or what could effectively stop it.

Women’s health research could further advance in many areas of medicine beyond conditions that disproportionately affect females. Griffith points out that the uterus, which sheds and regenerates its lining every month, demonstrates “scarless healing” that could warrant investigation. Also, deepened study of the uterus could shed light on immune tolerance for transplants, given that in a successful pregnancy an implanted fetus is not rejected, despite containing foreign material from the biological father.

For Emily Fairbairn, the fund is a critical step toward major advances in an often-overlooked area of medicine.

“My mission is to support intellectually honest, open-minded scientists who embrace risk, treat failure as feedback, and remain committed to discovery over dogma. This fund is a direct extension of that philosophy. It’s designed to fuel research into the biological realities of diseases that remain poorly understood, frequently dismissed, or disproportionately misdiagnosed in women,” Fairbairn says. “I’ve chosen to make this gift to MIT because Linda Griffith exemplifies the rare combination of scientific integrity and bold innovation — qualities essential for tackling the most neglected challenges in medicine.”

Fairbairn also refers to Griffith collaborator Michal Tal as being “deeply inspiring.”

“Her work embodies what’s possible when scientific excellence meets institutional courage. It is this spirit — bold, rigorous, and fearless — that inspired this gift and fuels our hope for the future of women’s health,” she says.

Fairbairn, who has suffered from both Lyme disease and endometriosis that required multiple surgeries, originally directed her philanthropy, including previous gifts to MIT, toward the study of Lyme disease and associated infections.

“My own experience with both Lyme and endometriosis deepened my conviction that science must better account for how female physiology, genetics, and psychology differ from men’s,” she says. “MIT stands out for treating women’s health not as a niche, but as a frontier. The Institute’s willingness to bridge immunology, neurobiology, bioengineering, and data science — alongside its development of cutting-edge platforms like human chips — offers a rare and necessary seriousness of purpose.”

For her part, Griffith refers to Fairbairn as “a citizen scientist who inspires us daily.”

“Her tireless advocacy for patients, especially women, who are dismissed and gas-lit, is priceless,” Griffith adds. “Emily has made me a better scientist, in service of humanity.”