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Suzy M. Nelson, MIT vice chancellor for student life since 2016, will retire from the Institute this month. Nelson leaves MIT with a strong, coordinated Division of Student Life (DSL) built from years of close partnership with student leaders. One of her most noteworthy contributions is the elevation of student well-being, now integral to the Institute’s educational mission.

Under Nelson’s leadership, MIT created a culture of well-being, building out a strong support network, enhancing health promotion efforts, and expanding spaces dedicated to student wellness. These efforts emphasized the importance of students finding a sense of purpose, developing positive relations, and flourishing in mind and body.

Working with colleagues from across campus, DSL advanced new ways to coordinate student support services for undergraduate and graduate students experiencing significant challenges. MIT students now increasingly turn to programs such as Student Support Services (S3), which serves 82 percent of all undergraduates by the time they graduate, and 60 percent every year. A new CARE team coordinates resources that undergraduates, graduate students, and the MIT community rely on in a crisis, and has quickly become the thread that weaves together offices caring for students.

Nelson prioritized student residences as the cornerstone of the MIT campus experience. She led an effort to develop architectural principles for housing to guide construction and renewal planning; partnered with alumni from fraternities, sororities, and independent living groups to undertake a comprehensive review of chapters’ deferred maintenance and facility needs; expanded services to the residential communities; and developed close working relationships with the faculty heads of house.

“Suzy has been a wonderful leader and humane presence on our campus,” says David Mindell, head of house for Edgerton House. “She has partnered with the heads of house through trying times, as well as triumphs, and for that we are grateful. The students and their welfare have always been her first priority.”

Additionally, Nelson oversaw the construction of three new campus residences: Graduate Tower at Site 4 in 2020; New Vassar in 2021; and Graduate Junction in 2024, an innovative third-party partnership. The additional on-campus graduate housing increased capacity to accommodate nearly half of MIT’s graduate students and exceeded MIT’s commitment to the City of Cambridge. The division also worked on the renewal of four residences — New House (completed 2018), Burton Connor (completed 2022), East Campus (completed 2025), and now McCormick Hall (targeted for completion in 2028).

Beyond housing, Nelson advanced a major transformation of the core of the Stratton Student Center, leading to the opening of the Wellbeing Lab, new dance studios, improved lounge spaces, and refreshed offices for student organizations.

The Department of Athletics, Physical Education, and Recreation has played a critical role in MIT’s well-being efforts, incorporating wellness modules into physical education and wellness classes and offering recreation, intramural, and club sports opportunities to both undergraduates and graduate students. Additionally, the Institute’s athletics programs thrived during Nelson’s tenure, with MIT Engineer teams earning four national championships in 2024-25 and the number of All-Americans increasing significantly over the last five years. MIT remains the all-time leader in Academic All-Americans across all NCAA divisions. Nelson also encouraged expansion and renewal of athletic facilities, providing all students the opportunity to train using state-of-the-art equipment.

Nelson’s leadership shaped the Institute’s approach to community standards. She helped refine student conduct processes and streamline the Committee on Discipline process. She also championed new policies supporting free expression and launched a new Dialogue Collaborative focused on coordinating efforts that promote dialogue across the campus.

“Suzy is a remarkable leader and she has been a potent positive force within the MIT community. Her fusing of strategic vision, collaborative skill, and deep commitment to the students, DSL staff, and broader MIT community have been the hallmark of her extraordinary leadership,” says Erich Caulfield, chair of the Student Life Visiting Committee. “She has been a joy to work with, and her incomparable wisdom has been invaluable during this unprecedented period in student life at MIT and in higher education nationally."

Highly responsive to the changing needs of students, Nelson partnered closely with the Committee on Student Life and student leaders from the Undergraduate Association, Graduate Student Council, Dormcon, and the Graduate Housing Advisory Committee, as well as student representatives for individual projects. For example, Nelson created a working group to improve how student organizations gain recognition, manage membership, and apply for resources. She has worked closely with students on expanding access to nutritious food and supporting food security, and, this past year, she engaged students to help assess MIT’s dining services.

“Suzy brought her visionary leadership and humanity to DSL over this past decade to create profound, positive, and lasting change. All of us at the Institute, and especially our students, have benefited — and will continue to benefit — from the Nelson Era,” says Chancellor Melissa Nobles.

Throughout her career at MIT, colleagues and students came to know Nelson for her collaborative style. As she navigated crises over the past decade, most notably the Covid-19 pandemic, she always convened students, faculty, and staff. And she found new ways of elevating staff, creating the Staff Engagement Advisory Board, a group of 45-plus volunteers who support the division’s positive work environment and promote professional development opportunities. Her team in DSL is grateful for the many ways she strengthened and professionalized the division, and they will continue to abide by her unwavering belief that students should help shape the communities where they live and learn.

Nelson says of her retirement, “I am grateful to have served with so many exceptional faculty, staff, and students over these past 10 years. Because of their focused dedication to being here for students, I have every confidence that the transition to new leadership will be seamless.”

MIT will soon welcome Akirah Bradley-Armstrong as the next vice chancellor for student life. She most recently served as the vice chancellor for student affairs and success at the University of California at Santa Cruz, and has more than two decades of experience working in student affairs. She will start in the position on Oct. 19.

When corporate leaders discuss climate strategy, the message that still dominates is that, despite competing priorities and external pressures, sustainability matters.

According to the 2026 State of Supply Chain Sustainability report from the MIT Sustainable Supply Chain Lab, which surveyed 1,810 supply chain professionals across 91 countries, 83 percent rate sustainability as important or very important to long-term business success, the highest level in the report's seven-year history.

But commitment, the report reveals, is not the same as capacity. While 59 percent of respondents report becoming more committed to sustainability following recent U.S. policy shifts that deprioritize environmental concerns, the same organizations are being forced to compromise those commitments. Sixty-eight percent report that tariff uncertainty has compelled them to delay, scale back, or abandon sustainability strategies. Nearly one-third have seen tariff-related disruptions actually increase their operational emissions.

"Commitment is strong, measurement is expanding, and AI is accelerating," says Sreedevi Rajagopalan, director of the MIT Sustainable Supply Chain Lab and lead author of the report. "What comes next is harder: converting commitment into accountability, operational integration into strategic decision-making, and incremental decarbonization into genuine transition."

The result is a supply chain sustainability agenda stuck between conviction and constraint.

Beyond tariffs, more than eight in 10 respondents across North America, Europe, and Asia agree that ongoing geopolitical conflict is reducing the effectiveness of their organization’s climate action. Organizations built decarbonization plans on assumptions of stable trade and relative peace, yet neither assumption is holding.

The external disruption reveals an internal vulnerability. Among companies with publicly stated sustainability goals, only 51 percent have established a formal cross-functional sustainability team, which is the governance structure most strongly associated with embedding sustainability into daily decisions. In addition, only 33 percent place sustainability oversight at the board level. Public ambition, in other words, is vastly outrunning internal accountability infrastructure. Companies with formal cross-functional teams report sustainability is "frequently or always" built into decisions 57 percent of the time. Where responsibility sits in a single function, that drops to 13 percent — a 44-point gap illustrating the return on governance investment.

Sustainability is embedding most deeply where companies make daily operational choices: transportation and logistics (39 percent), procurement (35 percent), warehousing (34 percent), and product design (33 percent). However, it thins out dramatically in decisions that lock in long-term emissions: capital investment, network design, and supply chain risk planning. This reveals that companies are becoming better at operating more sustainably, but not at designing sustainable supply chains in the long term.

The artificial intelligence story compounds this tension. Seventy-eight percent of North American organizations report current use of AI in sustainability initiatives, and 87 percent of adopters report positive impact on sustainability goals. Eighty-six percent have reduced material waste, 81 percent have reduced energy use, and 63 percent use AI to estimate missing supplier emissions data. However, among mature agentic AI users, only 53 percent report a reduction in total greenhouse gas emissions. Twenty-nine percent report an increase, suggesting that operational efficiency does not automatically translate into lower net emissions.

This tension is particularly visible in warehousing. Among advanced and fully automated warehouses using AI, 78 percent report higher energy use since implementing AI, and 76 percent report higher waste, yet only 24 percent use AI-driven real-time monitoring to track environmental consequences. Many organizations may not realize their energy footprint is climbing until well after implementation.

Another key area of the report is freight transportation. Nearly every freight company is reducing emissions through route optimization, load consolidation, and driver-behavior programs. However, 43 percent of fleets still operate mostly or entirely on conventional diesel and only 8 percent have completed a full transition to low-emission energy. The divergence between regions reveals why: In Europe, 84 percent of freight companies report that recent policy changes have positively affected decarbonization strategy. In North America, only 44 percent do. When policy signals are unclear, capital-intensive technology transitions stall. For example, battery-electric vehicles are at scale in 60 percent of European companies, but only 36 percent in North America.

The report reveals that one bright spot has emerged in Scope 3, or indirect, emissions collaboration. Participation in joint supplier engagement and decarbonization programs surged from 31 to 58 percent in a single year. In addition, partnerships to manage shared suppliers' emissions more than doubled, from 23 to 55 percent. The share working entirely alone collapsed from 18 to 6 percent. The report, supported by the C.H. Robinson Foundation, Breakthrough, and CO₂ AI and Dassault Systèmes, reveals that companies are recognizing that Scope 3 cannot be solved in isolation.

Across all these areas, the consistent pattern the report reveals is that corporate commitment is not the prevailing constraint to ongoing sustainability efforts. Rather, governance, accountability structures, technology infrastructure, and measurement capabilities are the current obstacles. Organizations that close the external gap by building resilience against disruption and the internal gap by hard-wiring accountability into structure will be the ones whose commitments survive. Those that do not will find that ambition without accountability moves the needle very little.

As an embryo develops from a small cluster of stem cells, those once “blank slate” cells begin to take on more specialized roles like brain, liver, or muscle cells, and organize themselves into three-dimensional structures such as tissues and organs.

The fate of each cell — what type of specialized cell it will become — depends on which genes are turned on or off in the cell. These patterns of gene activity shape the cell’s structure and function, enabling it to take on a specific role in the body.

But this decision isn’t up to individual cells. They constantly send and receive chemical signals to and from neighboring cells, which help them understand where they are, what stage of development they are in, and what they should become. These messages spread through multi-step sequences called signaling pathways, which translate external signals into specific changes in gene activity in the cell.

For researchers, being able to retrace the sequence of instructions a cell has received would offer a powerful way to understand how tissues develop and how these processes go awry in disease.

But this has been difficult to achieve because scientists have long assumed that the effects of signaling pathways vary widely across cell types, meaning they would need to map each pathway separately in each cell type — an arduous and painstaking process.

Now, in a new study led by MIT assistant professor of biology and Whitehead Institute for Biomedical Research member Pulin Li and graduate student Nicholas Hutchins, researchers have discovered that each signaling pathway leaves behind a unique “fingerprint” — a distinctive pattern of gene activity that reflects the particular signals the cell has encountered.

Importantly, these fingerprints are consistent across different cell types for the same signaling pathway, which means that instead of mapping each cell type separately, scientists can reconstruct signaling histories across many cell types using these pathway-specific fingerprints.

This discovery was made possible through a machine learning model called IRIS. This model can detect fingerprints of different signaling pathways and pinpoint which signals a cell received at different stages of development inside an embryo, even for cell types it hasn’t encountered before.

This artificial intelligence-driven approach marks a major advance over traditional methods, which require researchers to experimentally test every pathway in every possible cell type, and opens up the possibility to comprehensively map the signaling histories of every cell inside a mouse or human embryo at an unprecedented scale.

“Think of voice recognition systems like Siri, which are trained mainly in English, but then use that training to help them recognize other languages,” says Li. “This is called transfer learning, and this is why IRIS can work across many different cell types.”

The researchers’ detailed open-access findings, published in the journal Nature Methods on Sept. 8, could accelerate stem cell engineering for regenerative medicine and improve the creation of organoids — miniature, 3D models that mimic real organs — for studying disease mechanisms and testing new drugs. This is because once researchers learn the pattern of signals that drives a stem cell to become a specific cell type, they can recreate those signals to control the fate of stem cells in a lab or medical setting.

IRIS is a neural network-based model, which, in essence, is an AI-driven system designed to recognize patterns in complex data, similar to how our brains spot patterns. It examines a cell’s overall gene activity and estimates which signaling pathways were likely “on” at specific times during development.

Li and Hutchins trained IRIS on a large experimental dataset that measured how thousands of human embryonic stem cells responded to dozens of combinations of six major signaling pathways at multiple stages of development. This created a comprehensive map, or atlas, of how signaling combinations influence cell behavior.

The team then tested IRIS using single cells from mouse embryos during gastrulation, a stage when cells are rapidly branching into different fates. IRIS could accurately predict when and where specific signaling pathways would activate in cells destined to become part of heart, gut, muscle, and spinal cord tissue. This variation in signaling molecules is what guides cells to form the right structures in the right places.

With this approach, scientists can not only begin to understand the fundamentals of cell-to-cell communication, but also gain a practical roadmap for guiding stem cells into specific, functional cell types in the lab.

When the researchers employed IRIS to identify signals needed to create a cell type critical for lung development, the model predicted that activating a specific signaling pathway would encourage lung-specific development. Experiments in mouse embryos confirmed the model’s prediction. By identifying the precise combinations of signals that drive lung cell development, they can more reliably generate accurate, lab-grown models of lung tissue.

“In these ways, IRIS is helping us decode the language cells use to talk to each other at a much faster rate than we could realistically achieve through experiments,” Hutchins says.

These improved models would allow researchers to study diseases like asthma, lung cancer, and pulmonary fibrosis, in which lung tissue becomes scarred, often without a known cause. They can then use these models to test potential therapies, and ultimately design regenerative treatments that can repair the damaged tissue.

With rising temperatures, the buttressing ice shelves at the edges the Antarctic ice sheet are melting and calving off at record rates, leaving behind sheer cliffs of solid ice. The stability of these exposed ice cliffs could influence how Antarctica further breaks apart and contributes to sea level rise around the world.

Scientists have assumed that an ice cliff’s stability depends on its height: Ice cliffs that are taller than 100 meters — about the height of the Statue of Liberty — should collapse under their own weight. And once a cliff sloughs off, it should expose even taller, inland cliffs that collapse in turn. At least, that’s been the idea.

But researchers at MIT and the Woods Hole Oceanographic Institution (WHOI) have found that the stability of an ice cliff depends on more than its height. And even when a cliff topples, it doesn’t necessarily set off a runaway collapse.

In a study appearing today in Science, the team presents an analysis of over 15,000 ice cliffs in Antarctica that were monitored by satellites over a four-year period. They observed how the ice cliffs and the larger ice sheet evolved over time, and found that those ice cliffs that collapsed did so at a much faster rate than scientists had predicted. But these collapsing cliffs also quickly stabilized, instead of continuing to erode the larger ice sheet.

“We found that when the cliffs were retreating, they were doing so faster than what has been modeled, which would mean more ice loss and higher contributions of sea level rise,” says study author Caroline Needell, a graduate student in the MIT-WHOI Joint Program. “But because we found that the retreat was short-lived, and all the glaciers eventually stabilized, that would suggest that things won’t be so bad. We don’t see this runaway effect.”

The study’s co-authors are Catherine Walker, assistant scientist at WHOI, and Jeremy Bassis, associate professor at the University of Michigan.

In glacial science, ice cliff collapse is described by a process known as marine ice cliff instability (MICI), which assumes that if an ice cliff exceeds a critical height, it will collapse. If then a taller cliff is exposed, it too should collapse, triggering a cascade of instabilities that shrink and erode the ice sheet over time.

In previous studies, scientists have characterized MICI mainly by modeling the physical properties of ice, scaling these relationships up to the size of an ice cliff and combining these predictions with a few observations of actual ice cliffs in Antarctica. Through these efforts, scientists had determined that ice cliffs become unstable and collapse when they are taller about 100 meters.

“Predictive ice-sheet models that have been used so far to represent ice cliff instability just take into account cliff height,” Needell notes. “That’s the only variable that matters. And once the cliff height reaches the critical threshold, usually 100 meters, it should initiate this runaway effect of ice sheet retreat.”

But she and her colleagues wondered: How tall do ice cliffs really get? And when they collapse, do they really set off a runaway retreat?

To answer these questions, they looked to data collected by NASA’s Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) — a satellite that passes over the Earth’s polar regions and uses a laser altimeter to measure the height of ice sheets, glaciers, sea ice, and the level of the ocean in and around Antarctica.

The team collected the satellite’s data from 2019 to 2023, which included measurements every three months of over 15,000 ice cliffs. From this dataset, they observed that the average ice cliff was about 30 meters tall — much shorter than the proposed critical height. But they did see a few rare ice cliffs that exceeded 100 meters.

They also looked for other features that might influence a cliff’s stability. Interestingly, they found that ice cliffs that more or less stayed in place over time also had thicker mélange — a term for the slushy mix of broken icebergs and sea ice that gathers at the front of an ice cliff.

“We measured mélange height and found taller cliffs have thicker mélange, which could be a built-in stabilizing effect,” Needell explains.

The satellite data also showed that the height of the surrounding water can stabilize a cliff: The deeper the water, the more it can buoy and push back against the cliff, keeping it from collapsing.

“After we did that global analysis, we thought, ok, let’s zoom in on the tallest cliffs and see how they’re behaving,” Needell says.

The team focused in on four regions around the Antarctic ice sheet where the satellite captured the tallest ice cliffs, each about 100 meters tall, with one that was 127 meters tall. They compiled measurements of these four cliffs and how they evolved every three months, over four years.

From these measurements, they saw that indeed, the tallest cliffs collapsed, and the ice sheet in those regions retreated, at a surprisingly faster rate than MICI models had predicted.

“It’s this rapid retreat, punctuated by periods of stabilization,” Needell says. “That was puzzling to us. Then, we thought: We need to look at all the things that affect cliff height.”

The first thing they considered was the topography of the land underneath the ice. If that topography is relatively flat, then the slope of the overlying ice sheet reflects that actual height of the ice itself. But if there’s a bump or rise in the land, the ice sitting directly over that bump is in fact shorter than it appears. The topography then could act as a literal speed bump, thinning the overlying ice, and making it less likely to collapse.

When the researchers checked topographic measurements of Antarctic, they found that indeed, ice cliffs stabilized around regions where the land happened to rise up.

They also found that the taller a cliff is, the faster they “thin out” — as they collapse, once tall cliffs settle into shorter, more stable cliffs that prevents further collapse.

“Now we know what to look for,” Needell says. “We really need to know the bed topography, and we need to know the ice thinning rate, in order to get ice cliff instability right, because this value really matters in the context of projecting sea level rise.”

This research was supported, in part, by NASA, the U.S. National Science Foundation, and the DOMINOS project, a component of the International Thwaites Glacier Collaboration.

Around the world, many people report distrusting their government. In some places, such as Latin America, North America, and Africa, trust in government has declined in recent decades. How can governments rebuild this trust?

New research from the MIT Governance Lab and the behavioral science research organization Busara examines how government partnerships with trusted private organizations can shape public trust in government. In Kenya, county governments have worked with a telecommunications company to develop an online public service portal. A survey of people in and around the capital city of Nairobi found that highlighting the role of the company increased people’s willingness to use the portal, but hurt their trust in government.

The results of this work, published in the September issue of the journal Government Information Quarterly, can help researchers understand how government digitization projects and relationships with technology companies affect public trust.

Studies have suggested that without public trust in government, people are less likely to follow rules and laws, and governments have a harder time solving problems that require public cooperation. Research has linked low levels of trust to people not paying taxes, lower voter turnout, lower Covid-19 vaccination rates, and higher Covid-19 infection rates.

In Kenya, a 2023 survey found that only 43 percent of people trusted the government. Corruption, poor service delivery, and flawed elections have likely hurt public trust. The Kenyan government is also a pioneer in digital technology for governance. Mobile money is widely used, and a centralized service delivery platform called eCitizen has made thousands of digitized services available to citizens. Such digital services can allow governments to serve people more efficiently and, if executed well, can build trust in government competency.

Recently, Kenya’s 47 county governments partnered with Safaricom, a highly trusted telecommunications company, to create an online portal called My County Hub. The portal aims to allow people to request government emergency services, pay for business permits and parking, receive information from the government, and more.

In the new study, researchers told the more than 11,000 online survey participants how they could access My County Hub and some of the services it would offer. Some people were told the county government and Safaricom were both responsible for the portal, while others were told the government alone or the company alone was responsible. The survey also asked people about their trust in the county government before and after they received this information. The researchers hypothesized that people who were told the partnership was a public-private collaboration would have more trust at the end of the survey, with people’s trust in Safaricom transferring to the government partner.

Overall, people expressed more trust in government after being told about My County Hub, suggesting that digitization itself could be tied to increasing trust. However, trust did not seem to transfer from Safaricom to the government: people who were told about the partnership did not come to trust the government more than those who were told that the government alone was responsible. Trust sometimes transfers in settings where people don’t have strong beliefs about the untrusted group. But people tend to have strong beliefs about government, which could make it harder to sway their opinions.

People who were told Safaricom alone was responsible for the portal expressed a greater willingness to use the portal and more interest in learning more about it. But relative to people who were told the government alone made the portal, the people in the Safaricom group had lower levels of trust in government afterwards. This could be because people viewed Safaricom as doing the work of the government, which could lower their faith in the government’s competency.

The results suggest that while emphasizing the role companies play in developing digital services could encourage people to use these services in the short term, it could also erode people’s confidence in the government in the long term. People with higher trust in government were also more willing to use My County Hub in the future, so long-term trust in government could be important for service provision.

The researchers also noticed that people told about the partnership did see their trust in government increase if they had high levels of trust in the government and Safaricom at the start of the survey. And the decreased trust in government in the Safaricom group was driven by people who trusted Safaricom but distrusted the government at the start.

These findings are in line with prior research showing that preexisting trust levels influence how trust changes during digitization efforts. In the future, research on people who distrust the government could look at other strategies for building trust through digital services.

The study’s authors include Lily L. Tsai, Ford Professor of Political Science and the director and founder of the MIT Governance Lab, and MIT Governance Lab Research Director Lula Chen.

As artificial intelligence use becomes more widespread, scholars are working to understand its effects on the people who use it. MIT Professor Eric So offers one of the most complete syntheses yet, in his new book, “The Collision: What AI Does to Us,” published this month by W.W. Norton. Recognizing that AI offers opportunities to complete work faster, and in some cases has been turned into useful domain-specific tools, So nonetheless looks at the costs of relying on it too broadly. He contends that heavy use of AI can lead to a kind of cognitive atrophy, in which people fail to develop and retain essential thinking skills. In general, he says, evidence shows that our ability to learn gets dramatically better when we wrestle with material, work through initial failures, keep iterating, and in the process acquire better thinking skills while retaining more knowledge.

These conclusions are drawn from recent research by So and others, in academia and industry, including analysis from AI firms themselves, such as an Anthropic study showing that about half of queries from college students were low-effort queries for simple answers. So also weaves personal reflections into his narrative — having long studied financial markets and behavior, he is familiar with analyzing how new tools and information affect our decisions. MIT News talked to So, who is the Sloan Distinguished Professor of Global Economics and Behavioral Science in the MIT Sloan School of Management, about the book.

A: I wrote this book in response to patterns I was seeing in myself and other individuals. I open the book with this story where I was instinctively reaching for AI to help me think through problems I normally would have handled myself. A colleague had reached out for assistance and I had essentially taken her question and written half a sentence, then said, “ChatGPT, help me finish this professionally.” I made a few light edits and sent it off. And I sat back and really felt incredibly concerned about how I had removed my own thinking from the process.

I have found students are outsourcing their thinking to AI, rather than thinking through things themselves, turning to it to answer homework that would normally help them build mastery in a subject. We need to start understanding what this technology is doing to us and how it interacts with human cognition and our psychology, and give people some practical insights.

Q: Part of that means understanding the incentives people have to use AI, or how the allure of AI takes hold, even when there are long-term costs, in your view.

A: There are converging factors drawing us to AI. For example we have a real biological instinct to conserve our mental energy, and socially, we face situations where AI is used by other people to race ahead, and it rachets up the pressure on everyone else to use AI. Combine that with the problems detecting AI use in a practical way, and all these factors create what I call “AI gravity,” this constant pull to use AI. It’s not simply a matter of telling people to think for themselves. At this moment we’re in, AI is colliding with society.

Q: So we need to look at these systemic factors, not just individual decisions, when it comes to AI use, like businesses urging employees to use it.

A: Many organizations are not just suggesting use of AI: It is now the basis of your evaluation, meaning if you want a bonus or promotion, it is incumbent upon you to use these tools. I think a subtle process is taking hold in academia, where people say, “My future employer will want me to use AI, so I’d better start using it now.” But the problem is that as you start to use AI now, we’re losing touch with the practices and capabilities we’re seeking to build, and in turn that makes us more dependent on using AI in the future, and it becomes a self-fulfilling prophecy.

A: AI is not just changing what we can do, it’s changing who we are as thinkers. AI removes a lot of the friction necessary to build foundational mastery. It is causing many people to lose their capacity for pushing through problems. AI offers instant relief, and we become accustomed to that. Many people are turning to it as an access point for information, so it’s affecting our ability to remember. Also, AI is changing our interpersonal relationships. A key point in the book is that these models are simultaneously sycophantic, meaning they want to make us happy because that’s how they drive engagement and get positive feedback, but they are also really persuasive. We lose the ability to connect with other people because we’re so accustomed to this frictionless environment where the models always agree with us.

Q: Your book emphasizes the value of “productive struggle.” You write, “Struggle doesn’t mean you’re suffering.” Instead, we get a handle on things by struggling to learn them.

A: We need to recast how we think about struggle, as something that is formative rather than a problem to be solved. I draw a lot of parallels in the book between some of the societal changes that occurred post-industrialization, when suddenly people no longer needed to be physically active to be economically productive. We saw more people suddenly develop physical exercise as something we do intentionally, that required reframing of what physical struggle meant. And I think we need that sort of shift about what we think about cognitive struggle.

Q: Given all this, what can we do to maintain some reasonable level of cognitive effort today?

A: At the personal level, I emphasize four principles for navigating this age of AI. One is embracing the struggle, as we’ve talked about. The second rule is: Value who you are without AI. I’ve seen so many presentations over the last couple of years where someone has put together this very polished-looking presentation with Claude, but when they present it, it’s clear they didn’t put in the work necessary to understand the material. The formative struggle creates what I refer to as an “inseparable task bundle,” since making the slides allows me to engage in substantive discussion. In the absence of valuing who you are without AI, we get the product, but not the ability to capitalize on it.

The third rule is about reinvesting your cognitive surplus. There are a lot of tasks for which AI can save you time — rote activities — but it matters what you do with those time savings. If you’re just funneling the same energy into the same tasks, you’re missing out on the potential transformational benefits of AI. The most innovative changes are not the ones that simply allow us to automate, but stretch the capabilities of humanity in a productive way and build expertise. Or, people can channel that freed-up time to focus on family, learn a new language, cultivate skills.

The last rule is about using AI as your cognitive trainer. AI is this dual force, where it is very bad for thinking if you use it as an answer machine, but can be great for learning if you use it as a tutor. AI can be tailored to you. However, it requires that you seek out the friction of having a tutor, relative to having an answer machine. And that requires motivation.

This is a very challenging moment, and the problem is one we need to take seriously.

More than 100 people filled the MIT Chapel on Sept. 24 to pay tribute to Assistant Professor Thomas Rose, the MIT archaeometallurgist who died last month following a bicycle crash in Cambridge, Massachusetts.

Faculty, students, and staff affiliated with the Department of Materials Science and Engineering (DMSE) and the Center for Materials Research in Archaeology and Ethnology (CMRAE), along with family and friends, gathered for a memorial service where colleagues spoke about the 37-year-old, who joined the DMSE faculty in January and had just started building a research program at MIT. They remembered him as a brilliant researcher and generous colleague with an extraordinary breadth of expertise and eclectic passions ranging from rowing to archery.

A portrait of Rose sat on an easel next to the chapel’s flower-covered altar. A cellist played as guests entered and took their seats.

Chaplain to the Institute Thea Keith-Lucas opened the service by reflecting on the loss felt across MIT.

“We honor the unique gifts that he brought to this world. We honor the ways that he was growing, and the possibilities that were cut short. We honor the small, unseen moments of courage and connection that create a life,” she said.

To give a sense of what Rose meant to DMSE, department head Polina Anikeeva briefly explained archaeometallurgy — the study of how ancient societies produced and used metals — and its development at MIT, beginning in the 1960s. The field flourished after the establishment of CMRAE in the 1970s, but over the following decades it began to contract in the United States, and MIT had difficulty finding people with an increasingly rare set of skills.

“We were looking for a unicorn. We were looking for two decades,” Anikeeva said. When Anikeeva and other DMSE faculty reviewed Rose’s resume in 2025, they were impressed by his unique training in geoscience, metallurgy, and archaeology and met him soon after. “We saw a brilliant, creative colleague — unassuming, intellectually fearless, adaptable, resourceful, and eagerly looking into the future.”

Mike Tarkanian, senior lecturer in DMSE and associate director for CMRAE, described the Zoom interview with Rose that kicked off the faculty search. Rose spoke fluently about his work, interests, and research vision — without the slides or notes that candidates typically rely on.

“I hung on every word — but it was so unusual and so risky that I wasn’t sure how it would be received,” Tarkanian said. “As soon as the call ended, my phone rang and it was a colleague who was also on the Zoom and he said, ‘That’s our guy. That has to be our guy.’”

Anikeeva also spoke of the enthusiasm he brought with him every day at MIT, noting his “almost childlike curiosity and his enormous positivity.”

To honor Rose and carry forward the work he had begun at MIT, Anikeeva said DMSE would launch the Thomas Rose Fellowship for Excellence in Archaeometallurgy, dedicated to supporting the most promising young scholars at the PhD and postdoctoral levels, “so that we can continue this field for the world and for the U.S. and for the memory of Thomas.”

Speakers also remembered Rose’s kindness, generosity, and empathy toward everyone around him. Tarkanian stressed Rose’s commitment to community and helping others, noting his contributions to research tools used by archaeologists worldwide. He gave an example from Rose’s undergraduate years in his native Germany, when he gave tutorials for first-year archaeological science students who were struggling with chemistry and math. A former student told Tarkanian that an email Rose sent inviting students to the tutorial ended with, “I guess I’ll be more nervous than you folks tomorrow.”

“This was the Thomas we only started to get to know,” Tarkanian said, “someone who offered his expertise to others in need without question, while being thoughtful enough to worry out loud that he might be the nervous one and, by doing so, putting others at ease.”

DMSE Assistant Professor Tania Lopez-Silva, who started at MIT six months before Rose, recalled his patience and understanding. When she arranged a lunch with him, he said he would eat anything but mushrooms and eggplant.

“Somehow I misunderstood him, and I chose the place that probably has the most mushroom and eggplant options possible,” Lopez-Silva said. “He said that he still wanted to try the place, and he would find something to eat. That was Thomas — kind, patient, open-minded, and always willing to make the best of things.”

That quality came through in a story by another DMSE faculty member, Assistant Professor Thomas J. Wallin, who recalled a photo Rose had sent him of the apartment he had found overlooking the Charles River. Wallin was impressed, but Rose said with characteristic directness that it was quite small, but “very long and narrow” — which turned out to be a good thing.

“He explained that because it was narrow, he could stand at the edge of his balcony and shoot at his archery target through his hallway, past his kitchen, at his door, and it would be the proper distance,” Wallin said, drawing chuckles in the chapel. “I don’t think that’s a request many Boston realtors get, but that was Thomas.”

Rose’s connections also extended beyond DMSE. Katya Tiukhtikova, joined by fellow MIT Archery Club member Kiarra Ning, remembered how Rose treated others.

“He only focused on the good in people, and gave everyone a chance to show the best parts of themselves,” Tiukhtikova said.

She also recalled the contagious excitement Rose brought to archery practice, saying he “always brightened the days of everyone who was lucky enough to share the archery range with him.”

Tarkanian ended his remarks with a reflection on academic life and the importance of generosity and humility alongside brilliance and achievement.

“If part of Thomas’s legacy here at MIT is that we become a little more generous with our time, a little more humble about our accomplishments, and a little more deliberate about building communities in which other people can thrive and grow, that would be a legacy worthy of him.”

In 2021, Texas suffered the worst energy infrastructure failure in state history, following three severe winter storms in February. At least 246 people were killed as a result of the disruptions, which led to food, water, heat and electricity shortages.

If energy officials had had more time to prepare for the storms, for example by bringing in more trucks of natural gas to disrupted areas, damage from the event could have been mitigated. Unfortunately, weather forecasts beyond two weeks are notoriously unreliable, making for a tighter window for energy operators, farmers, and emergency response officials around the globe.

A new technique by a team including researchers at MIT significantly improves weather forecasting beyond two weeks. The researchers developed a machine-learning model that learns to correct errors in existing forecasting models to improve their output over the two-to-six-week time frame. When applied to a gold-standard forecasting model, it doubled forecasting accuracy over that time frame, placing first in a prominent international forecasting competition.

In a new paper in Nature, the researchers describe their approach and show how it enables forecasts that for the first time beat climatologic averages between two and six weeks.

“Not only does this do better on time averaged forecasts for this range, which is already very useful for farmers and members of the energy industry trying to match supply with demand, but it also improves predictions of extreme weather events, whether it’s flooding or cold spells or something else,” co-author and MIT Research Scientist Judah Cohen says. “It’s really important to predict both, but extreme events might be the most impactful part of this for society.”

Joining Cohen on the paper are lead authors Hannah Guan, a Harvard College undergraduate; Soukayna Mouatadid, a University of Toronto postdoc; Genevieve Flaspohler SM ’18, PhD ’22; Paulo Orenstein, an associate professor at Brazil’s National Institute for Pure and Applied Mathematics; Jeremy Berman of the data company DTN; Jakob Schloer and Joshua Talib, researchers with the European Center for Medium-Range Weather Forecasts; and Alex Lu, Haiyu Dong, Jonathan Weyn, Zekun Ni, and Lester Mackey, all researchers at Microsoft.

Longer forecasts

In the 1970s, longtime MIT Professor Edward Lorenz came up with the idea of chaos theory to describe how tiny changes in initial weather conditions lead to completely different outcomes.

“He came up with this theoretical two-week limit where beyond that, we just can’t predict weather because the noise in the system grows so much that it overwhelms any signal you start with,” Cohen says. “That had a strong influence on the field that may have held people back from exploring forecasting beyond the two-week range. I never really believed that completely. In theory the atmosphere can create an infinite number of patterns, but it likes to fall into these favorite patterns over and over again. Once one thing happens, I think you can predict the domino that will fall after that, and the domino that will fall after that.”

Today, the best weather forecasts come from either dynamical models, which numerically simulate how the atmosphere and oceans evolve over time, or artificial intelligence models, which learn to make predictions from historical weather data. AI models have shown improvements in predicting weather at short ranges, but they’re even worse than dynamical models at longer time ranges.

Cohen, who is based in the MIT Department of Civil and Environmental Engineering, focuses on forecasting weather two to six weeks in advance, which is known as subseasonal forecasting. “That range is referred to as the ‘predictability desert,’ because in the short range you can capitalize on the initial conditions in the climate system and the atmosphere, and on the longer term you have these slow, varying boundary forces, like the super El Niño everyone’s focused on this winter,” Cohen says. “The subseasonal range can’t take advantage of either of those. In most cases forecasting in the subseasonal ranges is no different than going to a casino and rolling the dice.”

Cohen credits his co-author Lester Mackey, whom he has been collaborating with for more than a decade, with the initial idea of using machine learning to reduce errors in forecasting models.

“We built this wrapper — Lester calls it postprocessing,” Cohen says. “We know AI models have a lot of errors, so we thought maybe we can quantify those errors and remove them.”

While other forecasting systems do more simplistic error correction, the research team’s postprocessing layer uses AI to learn from a forecasting system’s mistakes based on historic data and shifts its forecasts. In that time range, forecasters use so-called probabilistic forecasts, where the likelihood of a range of possibilities if given.

“As more forecasts and data become available, our model adaptively improves its correction,” Cohen says.

To build the model, the researchers used MIT Engaging, a supercomputing cluster hosted by MIT’s Office of Research Computing and Data, but the resulting model does not require a supercomputer to run.

To put their model to the test, last year the research team submitted three models that included their post-processing layer to the European Center for Medium-Range Weather Forecasts (ECMWF), which is considered the best forecasting center in the world. Each season ECMWF holds forecasting competitions between models.

The submission that won first place combined ECMWF’s forecasting model with another, and then applied the postprocessing correction on top. That approach ended up doubling the original model’s predictive power in the subseasonal range. For the first time, the model offered an improvement over relying on climatologic averages.

“Up until now, no one had been able to show a model predicts weather better than long term averages for these subseasonal forecasts,” Cohen says. “They’ve pretty much all been worse than that — you could literally flip a coin and do better. People have questioned if it’s even worth investing in models at all for this time range. We consistently beat climatology averages with our models.”

The model also showed a statistically significant improvement in the ability to predict extreme weather.

“That’s one of the more important findings, because it’s so difficult to accomplish,” Cohen says. “When the ECMWF developed their own, non-AI, error-correction to their model, it got worse at predicting extreme weather. I think that’s because traditional error, or bias, correction is always trying to get to the mean, whereas extreme weather is on the fringe of probabilities.”

In the Nature paper, the team showed their technique could be used to improve land forecasts in most regions across the globe. They also applied their bias correction technique to other models and showed it improved those as well, putting even lesser models on par with the ECMWF’s forecasts.

“What’s nice about our model is it’s an add-on wrapper,” Cohen explains. “We used it with the ECMWF’s dynamical model because that was considered the best, but it can be used with any model. It even improves models that are already bias corrected using another AI algorithm. So it shows a robust improvement. And it’s open source and low cost.”

Cohen says the meteorological office for the Republic of Kenya has already reached out about implementing the model to improve forecasting of the annual monsoon season to improve agricultural yields this winter. He hopes other groups will also use the model.

“This could help you decide when to fertilize around rain, when there’s a freeze coming to protect crops during winter harvests, or help you avoid droughts,” Cohen says. “It could also be useful for energy and emergency management. I’ve talked to energy officials who say, ‘If I’m told two days before that an ice storm’s coming, I don’t have enough time, but if I had two weeks, that could be enough to make adequate preparations.”

The work was supported, in part, by the U.S. National Science Foundation, the Carlos Chagas Filho Foundation for Research Support, and the European Union.

For over 30 years, Ishii has explored how the digital and physical worlds can merge more meaningfully. Working with students, MIT colleagues, and international collaborators through his Tangible Media group, Ishii has pursued research marked by intellectual tension and artistic vision. He and his collaborators have produced truly original concepts including Tangible User Interfaces, which are interactive systems where physical objects, rather than screens, mice, or keyboards, are used to control and interact with digital information; and Tangible Bits, introduced at CHI ‘97, which established how people engage with digital information through physical objects. His later concepts, Radical Atoms and TeleAbsence, inspire researchers and designers worldwide.

“Beyond his research, Ishii has made lasting contributions to the global design community through ACM CHI [the Association for Computing Machinery Conference on Human Factors in Computing Systems], TEI [conference on Tangible, Embedded and Embodied Interaction], and UIST [User Interface Software and Technology], and by mentoring generations of designers and researchers,” said Professor Leila Ensaniat in her Industrial Designers Society of America Awards committee chair introduction.

Ishii's approach shows how bringing the arts, science, engineering, and design together creates innovations with real-world applications. His models now influence product development across industries, shaping how millions of people interact with information and each other.

As artificial intelligence becomes more and more powerful, its ability to handle human-like tasks is undeniable. Artificial neural networks can learn to recognize images, use language, or write computer code. Like the brain’s circuits, they reorganize themselves as they learn, adjusting the strengths of their connections to improve their performance.

The question for many neuroscientists is: What can these machine systems teach them about how the brain learns? And what kinds of models are likely to be the most informative?

In a paper published Oct. 9 in the journal PNAS, scientists at MIT’s McGovern Institute for Brain Research and K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center argue that a crucial metric for the biological plausibility of an artificial neural network’s approach to learning is the network’s ability to organize itself, without the guiding hand of a developer. They show that it is possible for an artificial neural network to build its own learning machinery. With some simple instructions about how and when connections should change, a group of randomly connected units can reshape itself into an effective information-processing network, learning from the data it is trained on.

When it comes to training AI to do its job, the approach is powerful. But Liao and his colleagues caution that networks designed in this way may not offer much insight into biological learning. “The brain doesn't have an external designer or architect,” Liao says. “The brain has to organize itself. And we need to know how simple rules are going to make the brain organize.”

The rules that reshape neural connections as an animal learns may be different in different parts of the brain, or they may change as the brain becomes more mature. Neuroscientists are working to understand how those rules work and how they might be disrupted by aging or disease — a vital step toward developing new therapies and prevention strategies. Liao points to the development of brain-computer interfaces, which aim to restore communication when the brain’s language abilities are lost or let paralyzed patients control neuroprosthetic limbs, as one potential application.

“None of these can work without knowing the rules behind neuron-neuron interactions,” he says. “That’s not achievable by just designing a circuit that works very well.” An artificial neural network’s value to neuroscience should instead be judged by the extent to which it can organize itself — not just its connections, but also the machinery that adjusts those connections.

Working with Poggio and Harnett, Liao, Ziyin, and Gan set out to develop and validate a self-organizing computational network capable of learning. Liao explained that the network’s structure begins with multiple layers of randomly interconnected neuron-like units. Within each layer, every neuron-like component is subject to the same rules about how connections should be adjusted. “They just interact with each other using that rule, and they develop some type of behavior,” he says.

The team’s network implements the kind of simple rules thought to rewire brain circuits as humans and other animals learn from their experiences. In both the brain and the machine network, for example, “neurons that fire together wire together.” In other words, when two connected neurons are activated at the same time, the connection between them strengthens.

The team showed that a self-organizing network governed by rules like these can learn to recognize patterns and classify images. They explain that in their model, learning is enabled by self-assembling motifs that emerge according to simple rules, then serve as building blocks for functional circuits. As their network learned, these motifs reorganized themselves using an optimization strategy similar to the approach built into most modern machine learning systems, called gradient descent, in which connections are gradually adjusted in response to errors in order to improve performance.

Most modern AI implements gradient descent through some form of the back-propagation algorithm, which neuroscientists say is unlikely to be used by the brain. The team compared its self-organizing network to networks designed to learn in this way and found that it performed at a similar level.

Poggio says finding a biological parallel to back-propagation could trigger significant progress in both AI and neuroscience.

“If the synaptic motifs we propose are, in fact, used by the brain to do what backpropagation does for artificial neural networks, then we will have a strong connection at the level of fundamental mechanisms between artificial and natural networks — between machines and brains,” he says.

Liao says a self-organizing network does not necessarily learn the same way the brain does — but it’s a critical starting point for biological plausibility.

“Self-organization is the first high-level framework,” he says. “Once we are good with self-organization as a measure, then we can talk about next thing.”

While the group’s main objective is better understanding the brain, Liao says that knowledge could ultimately improve machine learning systems, too, enabling engineers to develop networks that perform better or in more human-like ways.

MIT Lincoln Laboratory hosted the National Security Commission on Emerging Biotechnology (NSCEB) on Sept. 3, for a stop on its national roadshow. The event convened representatives of state organizations, defense centers, universities, technology companies, and business executives to discuss how Massachusetts' biotechnology ecosystem can strengthen national biodefense capabilities.

"We have a lot to leverage in this region to advance biotechnology," said Christina Rudzinski, assistant head of the Biotechnology and Human Systems Division at Lincoln Laboratory. "Many people are already working together to push the bioindustrial sector beyond biopharmaceuticals and into a broader ecosystem across industries. This synergy is where NSCEB comes in — connecting policy to the real-world work already happening, including here at Lincoln Laboratory and across the state."

Biotechnology touches many economic and defense sectors, from disease-resistant crops and lifesaving medicines to biofuels and advanced manufacturing materials. In 2022, Congress established the NSCEB to study the national security implications of this rapidly advancing field and issue policy recommendations to ensure continued U.S. leadership.

In April 2025, the 11-member bipartisan and bicameral commission released its report, calling for a minimum of $15 billion in federal investment over five years. According to the report, China is investing aggressively to dominate the field, echoing a pattern the United States has seen before in sectors such as semiconductors.

"The coalition of stakeholders working in the Massachusetts biotech ecosystem illustrates how collaborative regional work will help keep the U.S. in the lead by producing exceptional innovation across all areas of biotechnology," said NSCEB Executive Director Caitlin Frazer.

Now in the final year of its three-year mandate, NSCEB is touring the country to see local biotechnology ecosystems in action. The commission will bring insights back to Washington to help members of Congress support their regions' strengths.

Massachusetts has identified biotechnology as a priority technology area under its Strategic Hub for Innovation, Exchange, and Leadership in Defense (SHIELD) initiative. Launched by Governor Maura Healey in October 2025 alongside $47 million of initial aligned investment, the initiative has moved with speed and at scale. This includes standing up the BioNexus program that leverages state matching funds to develop biotechnology and biomanufacturing solutions for the U.S. Army Combat Capabilities Development Command (DEVCOM). Further, Healey included a $100 million authorization for defense sector capital projects in the Mass Wins Act, an economic development bill filed in April. The bill is currently being considered by the state legislature.

During the roadshow event, Ben Linville-Engler, who co-chairs the SHIELD Working Group and serves as deputy director of the Massachusetts Technology Collaborative, led a panel discussion with representatives from the Massachusetts Life Sciences Center, Business Executives for National Security, Activate, DEVCOM Soldier Center, MassChallenge, Perimeter, Center for Advanced Manufacturing, and the Massachusetts AI Hub. They are among more than 40 partner organizations participating in SHIELD.

"There is incredible convergence between economic development and national security in all domains — land, air, sea, space, cyber, and bio," said Linville-Engler, citing the state's $48.6 billion in defense-sector economic contributions last year. "Massachusetts is committed to biotechnology and national security, and not just in Boston and Cambridge but across the entire state."

In July, the SHIELD Working Group submitted a report outlining steps to speed the transition of technology into deployable capabilities, strengthen the defense workforce pipeline, and reinforce the state's national security industrial base.

Lincoln Laboratory is well positioned to support these priorities. As a Department of War federally funded R&D center, the laboratory has deep knowledge of national security needs and sustained ties to academic, commercial, and U.S. government laboratories. "These attributes can help strengthen biotechnology collaborations and integrate and transition solutions across Massachusetts and New England," Rudzinski said.

One project advancing this vision is the Biotechnology Scale-up Hub for Innovation, Economic Development, Labor Training, and Defense (BioSHIELD). Planned for Worcester, Massachusetts, this 200,000-square-foot biomanufacturing facility is designed to mobilize the region's defense-biotechnology sector.

"BioSHIELD's mission is to accelerate breakthrough biomanufacturing solutions that strengthen national resilience, support commercial innovation, and grow the regional economy," said Edward Wack, project co-lead and principal staff in the laboratory's Biotechnology and Human Systems Division. "It will bring together industry, academia, and government to transform ideas into scalable biological products, supporting a secure and competitive bioindustrial future."

The project responds to NSCEB's call for a national network of biomanufacturing scale-up facilities, a capacity currently missing in New England, according to Wack. The facility will offer shared equipment, infrastructure, and secure laboratory space to help bridge the "valley of death" between early-stage discovery and commercial- or defense-scale production. BioSHIELD's ready-built support networks will be key to overcoming technological and regulatory hurdles, helping innovators rapidly move capabilities to market or military use.

A coalition supporting the project includes Massachusetts Biomedical Initiatives, Worcester Polytechnic Institute, Bio Base Europe Pilot Plant, and Lincoln Laboratory. It builds on a foundational effort in central Massachusetts funding the Massachusetts TechHubs program. At the event, Wack announced the coalition has signed a memorandum of understanding to formalize the partnership. Design workshops are scheduled to begin soon, ahead of funding and construction.

Other discussions centered on the intersection of biotechnology and AI, another critical area for national security. Panelists represented Boston University, the University of Massachusetts Boston, Massachusetts Life Sciences Center, Parker Institute for Cancer Immunotherapy, Wyss Institute, and Phicil-itate Change. They discussed challenges in collecting and disseminating high-quality, AI-ready datasets and opportunities for AI to improve biomanufacturing and therapeutics.

Closing the event, Wack called for continued collaboration as the nation moves at speed and scale to stay at the forefront of biotechnology.

"At Lincoln Laboratory, we've built a strong foundation in biotechnology for more than 25 years, and it's heartening to see this level of coordinated work taking shape. It resonates with so many of us who view biotechnology as both an economic and national security imperative," Wack said.

“This tomato is blowing my mind,” says Nermine Hassanin. The MIT graduate student in architecture is marveling at the fruit in her salad.

She and several MIT School of Architecture and Planning classmates had spent their morning working at Hannan Healthy Foods Farm in Lincoln, Massachusetts, and then harvested the contents of the salad they were sharing at a picnic table: lettuce, kale, cucumbers, green onions, and tomatoes.

“It doesn’t get much fresher unless you’re eating this in the field,” responds Zachary Rapaport, a classmate and co-founder of MIT Farm, a community-driven program at MIT that is radically changing what it means to eat farm-fresh food on campus and gain practical experience in urban farming.

In fact, the program resonated so quickly with so many that it was awarded the Karl Taylor Compton Prize for a group this spring — the highest award presented by the Institute to students in recognition of excellent achievements in citizenship and devotion to the welfare of MIT.

Seated with Hassanin and Rapaport were classmates Aisha Cheema and MIT Farm co-founder Aleks Banaś, undergraduate architecture major Amy Kim, Sanne Wright MCP ’24, and Mohammed Hannan, the owner of Hannan Healthy Foods Farm. The story of how they came to enjoy this meal on a brilliant summer Sunday is equal parts vision, hard work, and serendipity.

In their first year at MIT, Rapaport and Banaś were classmates in a design workshop 4.184 (ClimateCorps@MIT) that focused on climate action within communities. Bonding over their interest in food systems, the two shared a desire to continue the community service work that took them outside of the classroom.

“We started ideating on what it would mean to create relationships with students, faculty, and staff on campus with partners who were already growing food in the community,” says Banaś. “We saw many organizations in the community already doing inspiring work, so instead of starting from scratch we wanted to plug into this system.”

Their concept for MIT Farm explored a holistic view of community, food security, and supporting local farming in a time of climate resilience. It also capitalized on the desires of two budding architects to develop practices they see as essential for sustainable communities.

“After working in an architecture practice, I came to grad school to find a way to create an architecture practice grounded within real communities,” says Banaś. “For me, architecture is not just about creating a building, but creating an environment for other interactions. I think we have a certain level of responsibility to the people around us; we need to learn how to work together with them.”

“What I’ve learned through developing MIT Farm is that farming requires you to be accountable to seasonal change, to people, and to the environment in which we’re working,” adds Rapaport. “There are things you only begin to notice when you spend time with a place and really engage with it. In many ways, architecture is the same. We need to be very attentive to the world around us if we’re going to make good design.”

In 2025, Rapaport and Banaś actively sought out like-minded organizations on campus — including MIT Professor Miho Mazereeuw’s Urban Risk Lab, the MIT Office of Sustainability, and the Open Space Committee — to build relationships and germinate ideas. They found enthusiastic supporters for their ideas which kept them both engaged and excited. That spring, they entered their concept for MIT Farm into the iCampus competition, which recognizes innovative and creative applications of technology that improve living and learning at MIT; they were awarded one of the three cash prizes.

Between supporters on campus and burgeoning relationships with like-minded organizations off campus, Rapaport and Banaś began to execute their vision.

One idea — a farm cart — kick-started what Rapaport terms “the growing side” of MIT Farm. He, Banaś, and classmate Courage Dzidula Kpodo MArch ’25 designed and built the two-tiered cart. It is constructed of pine with polycarbonate panels and divided into sections that support active vegetable gardens. Repurposed pipette tip containers sourced from campus labs serve as seedling planters. Supported on four wheels, the cart can be pushed or pulled anywhere.

“By introducing mobility, we began growing food without having it tethered to a specific plot of land,” says Rapaport.

They discovered it was more than just a “moveable farm.” The cart became a DJ booth at school happy hours; was wheeled to nearby public schools as a learning tool; showcased at the “Day of Climate” event at the MIT Museum; and transformed into a tiny winter greenhouse at the East Campus dorms.

Rapaport and Banaś also located farm communities in Boston and beyond to develop volunteering and mentoring opportunities. Now, through MIT Farm, students can volunteer to work the two rooftop gardens at Boston Medical Center (BMC), a compact farm that generates 6,000 pounds of produce to serve patients and the BMC community.

A second option for students to work at a city farm is with Eastie Farm in East Boston. A nonprofit with a focus on food security and climate justice, the organization supports local farms that struggle to compete with industrial agriculture.

Last summer, Rapaport and Banaś connected with Mohammed Hannan, the Lincoln farm owner and a laboratory manager at MIT. MIT students had already found their way to his farm, about 15 miles from Cambridge. Four years earlier, Heather Paxson, an anthropology professor and associate dean for faculty at MIT School of Humanities and Social Sciences, brought her anthropology class to visit Hannan Farm to learn about small-scale certified organic operations.

Hannan was open to the idea of the MIT community working on the farm. He says it is “increasingly challenging” to find people who want to farm. He stresses the broader importance of community support for local agriculture, noting that food security and sustainable farming require collective effort from society — not just from farmers.

“Many farmers around me are leaving farming to make their living elsewhere,” says Hannan. “It is a silent pandemic.”

When classes resumed last fall, Rapaport, Banaś, and classmates began volunteering on weekends at the farm. Working with the MIT Office of Sustainability and the Anthropology Section, the group started a pilot program selling Hannan Farm’s produce for a reduced price to students on campus on Mondays. The Fresh Food Bag program offered curated bags of produce that had been harvested a day earlier. The program was met with delight among the MIT community, with nary a vegetable left behind each week. Rapaport and Banaś say they will expand the program this fall to include offering bags of fresh produce in MIT dorms.

The work on the farm changes constantly, based on the season and business needs. One day students may be in the field replanting plants that didn’t establish properly; on another, they may be maintaining tools or working on infrastructure projects.

“There isn't really an ‘off season’ since the farm operates year-round, and we've worked to bring MIT students out in every season and semester,” says Rapaport. “During the winter, we repaired greenhouses, prepared seedling trays, and started getting ready for the next growing season.”

Hannan describes his collaboration with MIT students as “game changing,” highlighting how students gain practical, problem-solving experience while advising him on how to improve the farm’s infrastructure.

“It’s hard to visualize real-world problems when you are working in a lab [on campus],” says Hannan. “But the students are seeing the day-to-day challenges farmers are facing here. They are coming up with solutions that can alleviate these problems — and not just on my farm. We need more people from different backgrounds to see how we can locally grow more efficiently using the tools and technologies that we have. Sometimes we farmers don’t know where the technologies are — such as using solar irrigation. We’re not always tech-savvy, or we don’t have the expertise. Farming needs all of it.”

“MIT Farm feels deeply MIT: mind and hand, ethics and experimentation, systems thinking and dirt under the fingernails,” she says. “Aleks and Zach have created a student-led project with moral clarity, organizational intelligence, and genuine public purpose. It is modest in its means and expansive in its imagination and impact."

MIT engineers have developed a new way to program natural killer (NK) immune cells to target cancerous T cells or T cells that have become autoreactive, leading to diseases such as lymphoma and autoimmune hepatitis.

Using their new technique, the researchers can reprogram NK cells to target diseased T cells without ever removing the NK cells from the patient’s body. Instead, they use lipid nanoparticles to deliver messenger RNA directly to NK cells. These mRNA molecules encode proteins that allow the NK cells to seek and destroy the dysfunctional T cells.

Known as CAR NK cells, these cells could be deployed against a variety of diseases. The MIT team’s new approach could bypass one of the major obstacles to using these cells, which is the time and expense of manufacturing them outside the body.

“The manufacturing challenges with CAR NK cells are significant. The idea here is you can deliver a nanoparticle that will modify your own NK cells with a CAR construct and teach them to attack tumor cells,” says Daniel Anderson, a professor in MIT’s Department of Chemical Engineering and a member of MIT’s Koch Institute for Integrative Cancer Research and Institute for Medical Engineering and Science.

Anderson is the senior author of the study, which appears today in Nature Biotechnology. The paper’s lead authors are MIT postdoc Yuang Chen and MIT graduate students Julia Monkovic and Haseeb Mughal. Robert Langer, the David H. Koch Institute Professor at MIT, is also an author.

Over the past decade, the U.S. Food and Drug Administration has approved seven new cancer treatments based on engineered T cells called chimeric antigen receptor (CAR) T cells. Chimeric antigen receptors are proteins that can be embedded into T cells, allowing them to detect a target antigen, such as a cancer protein. When the target is detected, the T cell becomes activated and destroys the target cell.

This approach has had great success in treating blood cancers, primarily those that originate from B cells, including several types of leukemia, lymphoma, and multiple myeloma.

To make CAR T cells, doctors take a blood sample from the patient. The sample is sent to a lab where the T cells are isolated and then engineered to express the CAR protein. When enough CAR T cells have been generated, they are shipped back to the hospital and infused into the patient.

In recent years, scientists have begun using a similar strategy to create CAR NK cells. One potential advantage of CAR NK cells over CAR T cells is that they could be used to kill diseased T cells. Using CAR T cells to treat T cell diseases often doesn’t work well because T cells that are engineered to target T cell proteins may end up killing each other, in addition to the diseased T cells. That issue doesn’t arise when using NK cells to target T cells.

“NK cells can treat these T cell malignancies — not only T cell cancer, but also T cell autoimmune diseases,” Chen says.

However, manufacturing large quantities of CAR NK cells poses many of the same difficulties as generating CAR T cells. To simplify the process, the MIT team wanted to come up with a way to generate CAR NK cells “in vivo” — that is, in the patient’s own body, without having to remove any cells.

“If a patient can just get an infusion of nanoparticles, it would dramatically simplify the process and reduce costs,” Anderson says.

Currently, there are no FDA-approved CAR NK cell therapies, but many researchers are working on developing them to treat cancer or autoimmune disorders.

To generate these cells, the MIT team designed lipid nanoparticles (LNPs) that can deliver an mRNA payload. These strands of mRNA encode a CAR that can be targeted to specific cell types. For example, they could deliver a CAR that targets CD19, a protein found on B cells, or CD3, a protein found on autoreactive T cells.

The particles are coated with antibody fragments that bind to receptors found on the surface of NK cells, to ensure they reach the correct targets.

After these particles were infused into mice, the researchers found that they could successfully reprogram about 80 percent of the targeted NK cells. The researchers then tested the particles’ potential to treat cancer and autoimmune hepatitis, which is caused by autoreactive T cells.

In a mouse model of an aggressive form of melanoma, three of the five mice that received LNPs that reprogrammed their NK cells to target the cancer cells went into complete remission.

The researchers also saw significant tumor destruction when they used the LNPs to treat T cell lymphoma in a humanized mouse model, created when mice receive transfusions of human blood cells, allowing them to transiently generate a human-like immune system.

In these mice, tumors were eradicated after five injections of the reprogramming LNPs. This result was similar to what the researchers saw when they treated mice with CAR NK cells that were generated using the conventional approach — removing NK cells, reprogramming them, and returning them to the mice.

Lastly, the researchers demonstrated that their LNPs could generate CAR NK cells that target autoreactive T cells. For this application, they studied mice with autoimmune hepatitis, in which T cells attack hepatocytes in the liver. They found that in the treated mice, the autoreactive T cells were destroyed, and liver damage healed. These effects were seen after just one injection of the LNPs.

“This study is an excellent example of the power of nano delivery systems and their ability to potentially treat cancer and other diseases,” Langer says.

The researchers hope that these kinds of engineered NK cells could be used to treat a variety of autoimmune disorders caused by dysfunctional T cells, including multiple sclerosis and rheumatoid arthritis.

The research was funded by Sanofi, the Marble Center for Cancer Nanomedicine, and a Koch Institute Support (core) Grant from the National Cancer Institute.

When government officials craft policies, there is often a comment period for public feedback. Lawmakers have long had to sort through fake feedback that is mass-produced in an effort to sway policy, but they’ve generally gotten used to detecting it. With, artificial intelligence, however, people can submit realistic-seeming comments, which are harder to dismiss as being fabrications with no person behind them. That’s just one way AI can be used to generate text and statements, in civic conversations, for which no one can be found to be answerable.

This issue, among others, has caught the attention of Deb Roy, professor of media arts and sciences at MIT and director of the MIT Center for Constructive Communication (CCC). In a new essay published in the Bulletin of the Atomic Scientists, titled, “Who stands behind the words?,” Roy calls for a renewed emphasis on responsibility in communication, asserting that even when AI is used to craft text, there needs to be accountability and “answerability.”

Building better group dialogues to produce more public trust is a core part of the work of CCC. The group, along with Cortico, the closely affiliated nonprofit Roy co-founded, has helped facilitate group discussions on important issues in many situations — including civic meetings, schools, deliberative assemblies, and private organizations — and used tech tools, including AI, to help find themes among comments, and more. But the center also designs this process so that participants can check and override the tech-based output at each key step. Ultimately, a person is responsible for every public comment. MIT News spoke to Roy about the essay and the work of the center.

Q: You’ve identified this problem about the lack of accountability behind words that are generated by AI. When did you really first notice this?

A: It came into focus when Chat-GPT [which is powered by a large language model, or LLM] launched. As for my own history with language models, my PhD work explored what we might now call PLMs — Puny Language Models — so I’m quite familiar with language models and have tracked the field’s rapid evolution. But Chat-GPT turned a corner. Two things changed at the same time: the depth of the model and its knowledge, and the way you could interact with it.

I became obsessed with trying to find the limits of what it could do. And I felt that no matter how hard I tried to stretch my arms around it, I couldn’t. I built my tenure case at MIT on language grounding — the idea that it’s not enough for an AI system to just process lots of words and other symbols. I believed that to understand what words mean, you actually need a physical body and life experiences as part of the process. So I revisited the philosophy-of-AI literature I had once been steeped in, which asks what fundamentally distinguishes the kind of system that large language models exemplify, in extraordinarily advanced form, from the kind of processing we humans engage in. I gave several presentations to my own graduate students trying to make the case that, even though AI seems so intelligent, it’s actually different in kind — and I could not convince them.

And I thought, I know the difference, but I’m having trouble convincing even myself of what it is. And that led me to ask, “Wait a minute: Is there someone or something that stands behind the words, a human responsible for what is being said?” That’s the thing this extraordinary new language machine doesn’t have.

Whether these systems genuinely understand language is actively debated. But even if you grant them all the comprehension you like, they still lack standing. The buck can’t stop at a thing, a software system, with nothing to lose.

Q: The Bulletin piece has these interesting examples of civic feedback forums where it looks like there are real people saying things, and instead you have a barrage of comments from AI. How did you find some of these other examples in society where things go off the rails in this way?

A: After the release of ChatGPT, I started hearing a concern from people inside government who review public comments on proposed rules: a suspicion that not all of the comments were written by humans.

My reaction was: That’s going to delegitimize this channel for public feedback. That was several years ago, but everyone now knows this is happening. Public comment is a pillar of democracy. You don’t just put a new rule in place. People need to have a voice and feel like they’ve been heard before a rule is imposed on them. If you skip that step, people don’t accept rules as legitimate. That’s why that process is baked into how America works. And that pillar just cracked, and by now, in my mind, it’s fallen over. Has anyone noticed? My sense is the people on the receiving end of these comments have quietly adjusted the weight they give to public comments. It was never perfect, but boy, now much of its value is being swept away.

I open my essay with this example: In 2017, the Federal Communications Commission received more than 22 million public comments on its proposal to repeal net neutrality rules, and the New York attorney general later found that nearly 18 million of them were fake. As I write in the essay, “That fraud was caught because it left fingerprints: templated text, duplicated phrasing.” But large language models erase the fingerprints. Now every comment can be fluent and different, and reviewers can no longer tell which ones express views that real people formed and stand behind.

If you have enough of the stuff that’s fake — words that no one with a stake actually stands behind — it desensitizes you to what’s real. That filters back to the public, and people realize: If I take the time to express myself, they’re not even listening any more.

Q: Could you say a little bit more about your point that we need people with “standing,” who will take “accountability” for all pieces of text, even when those bits of text are generated by AI?

A: In the legal system, standing is a formal idea: sometimes a case isn’t heard because the people bringing it don’t have standing. But informally we use that term all the time. I’m not trying to formalize standing in everyday life. Instead I’m asking: What is the informal sense of the term, borrowed from the legal one, that we need to pay more attention to now? Here in New England, consider the open town meeting: The person who stands up to speak is generally expected to have standing, as someone from that town with a stake in what gets decided. And if it’s a stranger who drove from across the state, or from out of state, everyone’s like: Wait a minute, who is that? They’re not from around here. In general, there’s just enough friction and it’s costly enough to show up somewhere you don’t actually have standing, that it doesn’t happen much.

Now LLMs and the current digital networked environment collapse those boundaries. We always took for granted that if someone has a voice, it’s because they have standing. They have some basis to be heard. We need to pay attention to this, and really consider how to design systems so that the people with standing are the ones who get heard.

The other idea I tried to develop is “answerability.” It isn’t formalized in law, but it’s baked into the legal process. If you have standing to give testimony, there you are, ready for cross-examination. You have to be able to answer for what you’re saying. And if you’re making a specific accusation about someone, you have to be willing to back it with facts. Standing and answerability have to travel together: The right to be heard comes with the obligation to answer for what you say.

That doesn’t mean everyone has to attach their name to everything. I write in the Bulletin essay, “Privacy and answerability aren’t opposites. … An institution can verify a person and stand behind their words without exposing them.” To voice a view, you only need to show that a distinct, eligible person is speaking. An accusation asks more: Someone trusted, like a reporter, has to be able to know who is speaking. As I say: “Disclosure obligations should scale with power, and identity protections with vulnerability.”

None of this means AI can’t help. AI can do a lot of the work inside the chain of accountability — drafting, summarizing, finding patterns. If someone with a stake in a rule uses AI to draft a comment, adopts it as their own, and is ready to answer for it, that’s legitimate, in this context. What AI can’t do is anchor the chain. There has to be a person at the end who stands behind the words.

In the essay I lay out four design principles for building that in: “attributable authorship,” a “decomposed and inspectable process,” the use of “provenance as infrastructure,” and the need to “widen standing rather than concentrating it.” As I write, the “animating rule is that autonomy is permitted while unaccountability is not.” And the metric that matters most isn’t the efficiency gained by removing humans, but the number of people AI places into roles where they can answer for what they do.

Q: There is a phrase in the documents of the Center for Constructive Communication, that we need things to move “at the speed of trust,” not at the “speed of tech.” And that implies really thinking about building up these systems. Could you just say a little more about what that phrase means to you?

A: I learned that phrase when I was in Appleton, Wisconsin, with Katherine Cramer [of the University of Wisconsin], a longtime collaborator, talking with community organizers who were running “Dialogue across Divides” conversations there. They had hundreds of people involved. I thought I was developing technology tools with exactly that same purpose, and was so excited to see it. And I asked one of the people who ran that organization, “How fast did you scale this?” And he paused, and then he said, “At the speed of trust.”

And I took that to mean “slow.” Then I realized he was saying: For you, slow, because you haven’t yet earned our trust. And I’ve since learned that when you do have trust, things can move fast. So bringing in technology is really a process of engagement. It doesn’t work to say, “Trust us, we’re doing this because we think it’s for your own good.” People will turn to someone they already trust and ask, “Is this good for us?” And if that person says “Yes,” things can move fast.

Look at what’s happening at the intersection of AI and society: The speed of AI and the speed of democracy are running on different clocks. Talk to people who work in democratic practice and they say, “AI must slow down, because democracy can’t move that fast.” Talk to people in AI and they say, “AI cannot slow down, democracy needs to speed up.” If we can’t get these two time scales in sync, we’re headed for a breakway situation, where AI gets deployed faster than democratic processes can respond, and decisions get made without the public. That’s starting to happen right now.

Q: We need people actively doing things to build dialogue and figure out how tech tools can interact with that. At the Center for Constructive Communication, you’ve done a few iterations of the center’s Real Talk initiatives to build civic dialogues, as well as the Youth Voices Initiative in school settings, and currently you’re working on the New Social Forms project. Could you say a few words about them?

A: The Real Talk effort represents the longest-standing thread in this work. A huge emphasis of our work has been developing better ways to listen to one another. We create a context that is always small — small is beautiful here — where people are willing to share authentically. And we have the consent architecture and the technology to reveal the larger patterns, and then share them. We’ve done this with hundreds of communities around the country. And closer to home, we’ve brought the same approach to our campus here at MIT through the realtalk@MIT program, with support from the Office of the President and the Office of the Provost, to help our community build a culture around dialogue and listening.

The second thing I’d emphasize is that we’ve been doing more and more work on deliberative processes and citizen assemblies, and on how to make them more accessible. What if a group of high schoolers says, “We have a real problem with our cellphone policy?” Suppose you want to have an assembly that brings students, teachers, and administrators together, not just to listen to people’s concerns but to make policy recommendations. So we did that. The center worked with a high school in New York City, where technology helped people reveal their preferences and differences and listen to each other.

We’re also building answerability into the technology. A prototype called VoiceTrace, developed by PhD student Cassandra Overney in our group, links every output statement — a summary of what was heard in a listening process, or a recommendation coming out of a deliberation — to the specific voices behind it, so any claim can be traced back to the conversations it came from.

The New Social Forms project pertains to a very interesting pattern emerging in workplaces around the world, certainly the ones we’re connected to. AI is an extraordinary tool for individual empowerment, but in the words of one of our collaborators at the Google Experience Institute, people are tunneling. They go deep, using AI to develop refined ideas, but when they surface, their ideas have diverged from their teammates’. So groups and teams trying to work together with AI, there’s a human coordination problem.

We’re going to expand the initial work we’ve started with Google because there’s a lot of interest in collective communication, trust, and interaction among people trying to work together.

So is that a civic context? It’s a workplace, but workplaces are civic spaces, too. They govern people’s daily lives, they’re where many of us build civic skills and learn to cooperate across difference, and there’s a long tradition of civic life rooted in shared work. We’re all humans trying to interact with each other. These are the same issues. Justice Louis Brandeis once used the phrase “industrial democracy” to capture this. When workplaces lacked certain kinds of civic processes and civility — two interestingly related terms — labor disputes used to turn violent. Procedural justice, mediators, and people hearing one another are just as necessary in the workplace. In the end, it’s all about human beings. Hearing the humanity in one another is what democracy depends on, and it’s just as necessary for communities and organizations to function.

The Sierra Nevada mountain range in California is as stunning as it is diverse, with storms from the Pacific Ocean dumping rain and snow on the range’s western slopes while arid conditions mark much of its eastern face. David Des Marais, MIT’s Amgen Career Development Associate Professor, knows the mountains well. His childhood in San Francisco was filled with hiking and camping excursions throughout the range.

At some point during those trips, he started wondering about one of the few constants of the landscape: the presence of plants. How were plants able to thrive in all those conditions? He turned the question into a career.

Des Marais, who directs the Parsons Laboratory in the Department of Civil and Environmental Engineering, studies how plants interact with their environment. The research spans molecules and mountain ranges, exploring everything from how individual genes change a plant’s ability to grow to how climate change will impact populations of red oak trees across the eastern U.S. His work also considers how evolutionary forces act on plants to change populations over time.

Plants are central to his lab, but Des Marais sees them as a window into all living things. He’s even quick to credit the health of his houseplants and garden to his wife, an evolutionary biologist at Harvard University.

“Understanding why life is so diverse is fascinating to me,” Des Marais says. “Understanding how different organisms, from bacteria to dinosaurs, have figured out how to interact with their challenging environment and optimize their fitness is inherently interesting. Why plants in particular? As a geneticist and a physiologist, plants are compelling experimental systems. If I have a theory about gene X causing function Y, I can go into the lab and delete gene X from this species and put it into that species and test function directly. It’s a powerful approach.”

Des Marais does not spend all his days in a lab. In addition to directing the Parsons Laboratory, he teaches four classes at MIT and has two sons who are active in baseball and theater. He welcomes these responsibilities.

“The only reason MIT works is because there are people willing to sacrifice some of their research to keep the place running,” Des Marais says. “One of the things I like about academia is that there’s sort of an institutional memory, and you try to honor the legacy of where you’ve been while you shape the future.”

From California to MIT

Des Marais grew up near Stanford University as well as near facilities for NASA and the U.S. Geological Survey, which meant there were always plenty of scientists and engineers around. His mother was a teacher, and his father was a geochemist with a knack for pointing out that the landscape around them was the result of geological processes dating back millions of years — a habit Des Marais admits he didn’t like at the time but that, in hindsight, influenced his thinking.

“I’m pretty sure that got into my brain, especially the idea that what looks static often comes about because of processes, and we can study those processes,” he says.

Des Marais studied integrative biology as an undergraduate at the University of California at Berkeley. He completed his PhD at Duke University around plant genetics.

“There’s a whole series of outdoor events, from the Boy Scouts through college, that made me start to think about the challenges that plants face living in these stressful environments,” he says. “That drove me to plant science and environmental science.”

In a long postdoc at the University of Texas at Austin, Des Marais learned to conduct experiments teasing out the effect of specific genes as he studied how they changed a model plant’s relationship with water.

Des Marais came to Boston primarily for personal reasons: He and his wife, who was also a postdoc at UT Austin, entered the job market at the same time. After some awkward moments competing for the same jobs, his wife landed a tenure-track position at Harvard University. Shortly after, he joined Harvard as a staff scientist.

In 2017, Des Marais discovered MIT’s Department of Civil and Environmental Engineering was hiring a plant ecologist.

“I don’t even think of myself as a plant ecologist, but I applied on a whim and they were interested,” Des Marais says. “I do a lot around plant water relations, and I joke that there are all these hydrologists in the department who study how water moves through the soil and into the atmosphere through these magical pipes. The magical pipes are plants, which play an important role in the water cycle. I realized a ton of people at MIT were studying that phenomenon at regional or global scales, but no one really studied how or why plants move water in the first place.”

Today Des Marais’ lab work can be broken into two broad categories. The first is centered around how plants function, including their relationship with environmental factors like water, temperature, and nutrients. Des Marais says some of his most impactful studies link specific molecules to how a plant functions in a given ecosystem.

The second centers around fundamental questions in plant evolution, what Des Marais describes as how plants “make decisions” about when to grow, reproduce, germinate, and more.

One current research project looks at how climate change will impact red oak trees, an important species and carbon sink. He is also examining climate change’s impact on plants that are important for food systems.

“We know that climate change is impacting plant function, and it’s impacting plant nutrition, and that impacts human nutrition,” Des Marais says. “Climate change is going to affect where we can grow which plants efficiently and how we can get them where they need to be eaten. All that stuff is critically important for humans.”

Des Marais says it’s been fascinating working with experts in environmental chemistry and fluid mechanics as part of the Parsons Laboratory. He sees his role as director as being primarily about fostering community.

“The students are great,” Des Marais says. “It’s a funky old building, but we all work well together. We try to make sure everyone feels comfortable and welcome in the space.”

Even if he can’t take credit for his houseplants, Des Marais appreciates the vital role plants play for the planet.

“Plants are the foundation of the food chain, and there’s no way to get around that,” Des Marais says. “The efficiency of plant photosynthesis directly impacts our nutrition, and it impacts what we can feed the animals that we eat. It affects carbon cycling and water cycling — put simply, plant biology matters.”

Hall, a professor at Stanford University, and Woodford, a professor at Columbia University, were recognized for work that helped revitalize ideas about how government policy can spur growth to help end economic downturns. The Nobel citation describes their research as contributing to “new macroeconomic models that have significantly improved our ability to implement effective stabilisation policy.”

Both scholars helped integrate microeconomic analysis into macroeconomic models. In Hall’s case, the Nobel committee particularly cited research he conducted in the late 1970s shedding new light on economic expectations and household consumption. The committee also spotlighted Woodford’s construction of a comprehensive and influential macroeconomic model that was built on microeconomic foundations and introduced in his 2003 book, “Interest and Prices: Foundations of a Theory of Monetary Policy.”

The work of both scholars helped update models for intervention during economic crises, based on the ideas of John Maynard Keynes, which had worked many times in the 20th century but encountered newfound difficulties adapting to the conditions of the 1970s. So-called New Keynesian models used for economic stabilization today are a result of both economists’ research, the citation noted.

Hall is the Robert and Carole McNeil Joint Senior Fellow at the Hoover Institution and Professor in the Department of Economics at Stanford University. He received his BA in economics from the University of California at Berkeley before earning his PhD from MIT. He also served as an MIT faculty member in the 1970s.

Woodford is the the John Bates Clark Professor of Political Economy at Columbia University. He received a BA from the University of Chicago and a JD from Yale University Law School prior to receiving his PhD from MIT.

Remarkably, Hall becomes the fifth former doctoral student principally advised by MIT economist Robert M. Solow to earn the Nobel Prize in economics. The others are George Akerlof PhD ’66, Peter Diamond PhD ’63, William Nordhaus PhD ’67, and Joseph Stiglitz PhD ’67, all of whom had Solow as their principal advisor in MIT’s graduate program. Additionally, MIT professor of biology H. Robert Horvitz ’68, a Nobel laureate in 2002 in the category of physiology or medicine, wrote his undergraduate thesis in economics under the direction of Solow. In 1987, Solow received the Nobel Prize himself.

Woodford did his doctoral work under the primary supervision of Timothy Kehoe, now of the University of Minnesota — although Woodford does thank Solow in the first paragraph of his doctoral thesis, for helping him “distinguish important problems from exercises of merely technical interest.”

There are now 17 MIT alumni who have won the Nobel Prize in economics. Previously, 10 people have won the award while serving on the MIT faculty, and 10 former faculty have also been named recipients.

With artificial intelligence capabilities continuing to expand, universities now face complex, urgent questions about how to navigate increasing challenges, as well as new opportunities. Much attention is now directed toward this, including an MIT report on AI and education published this summer.

A new essay from MIT Statistics and Data Science Center Director Sasha Rakhlin, the Distinguished Professor in Data, Systems, and Society, IDSS and Brain and Cognitive Sciences, synthesizes recent discussions and readings about how AI is changing academia — particularly mathematics, statistics, machine learning, and engineering. Here, focusing primarily on graduate research and education, he describes key questions that departments and universities need to think about, and how they can best work with AI going forward.

Q: What is changing in research, and why is it happening so quickly?

A: Mathematics illustrates how quickly AI capabilities are advancing. Last year, a model reached gold-medal level at the International Mathematical Olympiad. Only a year later, models are producing new research results, including a recently proposed solution to one of the Millennium Prize Problems. Tasks that once demonstrated advanced mathematical ability can increasingly be performed by AI.

A central factor determining the pace of AI progress in a particular discipline is the speed and reliability of verification. Formalized proofs can be checked automatically; programs can be run and tested. Where evaluation is fast and reliable, systems can generate candidates, learn from outcomes, and improve. This also applies to AI research itself: improving models, training procedures, and their supporting tools. Better models can contribute to the next round of development, creating a compounding process that accelerates progress.

For experienced researchers, this creates opportunities to pursue questions whose technical demands previously put them out of reach. It also sharpens the distinction between obtaining a solution and understanding why it works, what generalizes, and what to ask next. However, we should not assume that abstraction, judgment, or problem formulation will remain beyond AI. Universities should prepare for a near future in which AI is smarter than all of us in many, perhaps most, aspects of our intellectual work.

Q: How should departments rethink academic credit and graduate training?

A: In a growing number of fields, a polished paper is becoming a weaker signal of individual expertise. Departments must begin now to reconsider what they reward, without simply defining valuable work as whatever AI cannot yet do. Asking good questions, replication, synthesis of ideas, informative negative results, and shared datasets may deserve greater recognition. More broadly, evaluation should establish what a researcher contributed and takes intellectual responsibility for, including when substantial parts of the work were performed by AI. These expectations should guide hiring, promotion, and funding — and be made explicit for current and incoming PhD students.

Training poses a harder problem. Intellectual muscles develop through exercise. Routine calculations, coding, failed approaches, and small discoveries have long helped students acquire intuition and judgment. Delegating this work can remove formative experiences, even as AI allows students to attempt more ambitious projects. We need to distinguish unnecessary friction from work through which expertise develops. Students should learn to formulate problems, audit model outputs, reproduce results, and defend their choices. Fundamentals may become more valuable as the basis for using these tools well.

Q: What should MIT and other universities build now?

A: This is where I see a real opportunity for universities. Let's first be clear about the goal: Universities should be able to pursue questions over long horizons, share results openly, and evaluate claims independently. Partnerships with industry will be essential, but we should not assume that commercial priorities will cover the breadth of science or remain aligned with it over time. Some degree of technological independence will therefore be necessary.

Academia's most important strategic asset may be the accumulated knowledge and experience within its laboratories. Published papers present a selective record: Failed experiments, abandoned directions, and the reasons an approach did not work often remain unpublished. Scientists and engineers hold tacit knowledge, acquired through experience, about which interventions are likely to fail and why. This missing context may help explain why models currently struggle in some domains, especially the empirical sciences, to anticipate consequences that are clear to an expert. These limitations may be temporary, and capturing negative results and scientists' interpretations could make models much better at scientific exploration.

We can imagine a future in which MIT's laboratories function more like a single, living, scientific organism, connected through shared AI research infrastructure. For example, suppose a neuroscience laboratory needs better methods for segmenting neurons in microscopy images. An AI agent could recognize a relevant advance from a computer vision group, connect the researchers, propose benchmarks, and help them iterate. It could surface unresolved questions to students and faculty, and make lessons from one laboratory available to others. To make this possible, we should build workflows that capture hypotheses, interventions, outcomes, failures, and interpretations. Shared systems should connect these workflows, allowing agents to use tools and information across laboratories with appropriate permissions. This will require substantial public and institutional investment now in compute, secure data systems, and expertise in adapting and post-training AI models.

Tracing this process could also preserve the lineage of ideas and make contributions, including those of graduate students, easier to recognize. With agreed rules for consent and credit, researchers could collaborate more openly, with greater confidence that their work would be acknowledged. This could also help answer the earlier question of how to recognize and reward intellectual contributions.

AI systems can already synthesize and reason about information from more sources than any individual researcher could absorb, and their ability to make useful connections will continue to improve. We should use this capacity to bring us together around hard scientific and engineering problems, helping us build on one another's strengths and insights. To me, this is a promising path forward, but universities need to invest now to make it possible.

A new course in the Department of Electrical Science and Engineering (EECS) is not focused on machine learning or microprocessors, on cryptography or quantum computing. Instead, 6.C31 (Hack Yourself) draws student interest with an intriguing pitch: that by using computing tools to implement a personal toolkit of more than 60 positive habits, students can substantially improve their college experiences.

A joint venture with the Experimental Study Group (ESG), developed by EECS Senior Lecturer Ana Bell, ESG Visiting Instructor Carter Jernigan, and ESG Senior Lecturer Paola Rebusco, Hack Yourself offers students the opportunity to upgrade their lives. The course is offered under the Common Ground for computing and AI education, an initiative of the MIT Schwarzman College of Computing that brings multiple departments together to develop and teach new courses and launch new programs that blend computing with other disciplines.

“We wanted to build an MIT course that exposes students, in a structured way, to what we actually know about learning and thriving,” explain the course’s instructors. “That said, MIT students are skeptical unless they can see evidence. They want data. They want methods. So we built the class around research-backed ideas that students can explore themselves, and that’s where computational thinking comes in. The course treats well-being and learning almost like systems problems: How do you gather meaningful data, break a big question into manageable parts, test an intervention, and evaluate whether it worked?”

The course’s ambitious goals fall into two categories: personal interventions that students can deploy in their own lives, and analytical skills that they can use to test whether their new habits are working. “It’s an introduction to the data science pipeline and evidence-based thinking,” says Bell. “We start from the idea that good analysis begins with good data. Students spend time learning how to collect information thoughtfully: designing surveys, conducting interviews, thinking about bias and measurement. Those are human-centered skills that complement technical training and remain valuable no matter what technologies exist years from now.”

The data that students collect are as diverse as their goals. “A few patterns come up consistently — reducing smartphone use and getting more consistent sleep,” says Jernigan. “For juniors and seniors especially, a lot of interest centers on work habits, productivity, and the transition into the workplace, which naturally pulls them toward teamwork, leadership, and relationships. What’s interesting is that no single intervention emerged as universally ‘best.’”

Many of the course’s assignments center on teamwork, using research-backed interventions for more-effective group collaboration. “Some are small interventions students can use anywhere — for instance, replacing default small talk with a prompt like, ‘Tell me something good going on in your life,’” explains Jernigan. “We also teach practices like the pre-mortem, where a group imagines a project has already failed, lists everything that could have gone wrong, and works backward to prevent those problems.”

Even the class’s reading assignments are collaborative, using Perusall (a shared annotation app). “We polled students about which strategies were most personally impactful and which they’d recommend to others, and the answers varied a lot,” says Jernigan. “That’s part of the point. Different tools resonate with different people, so we help students build a broad toolkit.”

Part of that toolkit looks like a tiny photo album. The course’s “Intention cards” are professionally printed, playing-card-sized collectibles that the students accumulate as they move through the course. Each card distills a research-backed habit or strategy into a few words and a colorful AI-generated image as a tangible reminder of the skill. “Students earn a few each week and end up with more than 60 by the end of the semester,” says Jernigan.

As the students gain more cards, they store them in a booklet — but the instructors have noticed them carefully tending and tidying their collections.

“It’s been interesting to watch the collection dynamic work in ways we didn’t fully design for,” Rebusco reports. “Students reorganize the cards, revisit old ones when they get new ones, and carry the booklets around. Without explicitly thinking about it, they end up engaging in spaced repetition and recall — the same learning principles we discuss in class.”

Junior Sarah Hopp has amassed a large collection of the cards. The course 6-3 Computer Science and Engineering major, from Stevens Point, Wisconsin, has found that the mementos work as both a memory tool and a sentimental souvenir. “With each card, I also tend to remember the class we talked about that intervention, and any positive memories I have from that class experience.”

Hopp’s favorite class experience was an assignment, originally developed in 2001 by Laura King, in which the student is challenged to envision her “best possible future self” in close detail for 20 minutes, imagining completed life goals and successful outcomes, before writing that imagined future self a letter. “It gave me time to really think about what I want from my future and what I want it to look like,” says Hopp. “It also forced me to focus on the positives and the accomplishments I want to achieve, rather than thinking about the negatives and my fears for the future.”

“When I first signed up for Hack Yourself, I looked forward to having a psychologically insightful and reflective class environment that I figured would be a helpful break from my usual STEM-heavy course content,” says student Amitoj Singh, who is majoring in 6-3. “I wanted to build a habit of using strategies from class to improve my lifestyle in college.” One of the strategies Singh implemented stemmed from a class discussion on flow state (a state of full and joyous task absorption experienced when a person is immersed in challenging and creative work). After learning that even brief interruptions could significantly impact productivity and derail flow, Singh changed how he scheduled his time, creating longer and more coherent “blocks” for his desired goals. “I think one of the most powerful tools [the class uses] is the massive pool of research the lectures and content draw from,” says Singh, who is now working on “satisficing,” or learning when to deem an outcome satisfactory rather than chasing a perhaps-unattainable state of optimization.

The Hack Yourself curriculum is intentionally designed to steer students toward small experiments and goals that feel personal to them. “We’ve had students explore relationships, sports performance, music, motivation, and productivity,” says Jernigan. “They develop hypotheses, think about measurement and bias, design surveys or interviews, and create an analysis plan. They don’t run a large-scale study, but they learn how to think rigorously about their proposed questions and hypotheses, using the same kind of decomposition and iterative thinking we use in computing.” Part of that rigor focuses on the use of large language models — specifically, how to evaluate the quality of both the prompt and the response. “We ask students to engage critically with modern AI tools,” says Bell. “They can use generative AI to explore ideas or analyze data, but they’re required to reflect on the process and critique the quality of what comes back. The durable skill isn’t fluency with today’s AI — it’s knowing how to evaluate any tool that comes next.”

Bell, Jernigan, and Rebusco hope that their students will leave with the tools to ask better questions — a lifelong skill. As they note, “We intentionally avoided making this another machine learning course. Students already get plenty of exposure to advanced technical tools elsewhere at MIT. In this course, we focus on a fundamental element: the data science pipeline itself. How do you collect useful data? How do you ask good questions? How do you design a small experiment before trying to boil the ocean? That mindset scales well, even as technology evolves.”

MIT engineers have developed a flexible, shape-sensing sheet that digitally reconstructs its own form as it bends and twists.

The sensor is similar in principle to some motion-capture garments, which track a person’s physical movements to mimic them in a virtual avatar. Those designs use rigid sensors that are stitched into a suit at strategic locations.

In contrast, the team’s new design uses multiple soft optical fibers that zig-zag throughout a soft and stretchable sheet. When the sheet bends, the fibers bend in kind, changing the pattern of light that travels through them. The researchers developed an algorithm to interpret the bending light patterns on the fly, to create a digital image that moves the same way the sheet is moving. The team also showed that the design is resistant to damage: Even when a few fibers are cut or disconnected, the remaining fibers can still accurately reconstruct the sheet’s overall shape.

“Our setup is targeting fully soft and stretchable sensing surfaces so it’s potentially safer for interacting with a human or a soft environment,” says Qifan Yu, a mechanical engineering graduate student at MIT.

The team envisions that the new design could be fashioned into a soft and pliable garment that a user could comfortably wear to physically control a video game character or a tele-operated robot. The sheet could also be used in physical therapy settings, where it could wrap around a patient’s leg or arm to track and record their mobility and range of motion during each session.

“In physical therapy, you often need to gather patient data on how they are moving and stretching their arm during rehabilitation, for example,” Yu says. “Surface shape sensing could be very useful in that way.”

Yu and his colleagues present their design in a study appearing in the journal Advanced Intelligent Systems. The study’s co-authors at MIT are graduate student Nina Cao and assistant professor of mechanical engineering Kaitlyn Becker.

The team’s shape-sensing design is a tweak on the standard optical fiber. Also known as a waveguide, an optical fiber is designed to guide light efficiently through a fiber. Optical fibers consist of a core of transparent material, such as glass, wrapped in a dark, opaque cladding. When light is sent through one end of the fiber, the dark cladding traps the light within the glass core, which itself is extremely smooth, allowing the light to pass straight through the fiber with near-perfect efficiency. Waveguides are used in fiber optic cables for super fast, efficient data transmission and telecommunications.

Scientists have also experimented with waveguides as simple shape sensors: When the fibers bend, they affect how much light can make it all the way through. The more a fiber bends, the less light comes out the other end. The amount of light that shines out, then, can be a measure of the degree the fiber bends. In this way, optical fibers have been used to sense simple curvatures.

“People have used waveguides to sense the shape of a line, which they have applied to a robot arm to see how it curves,” Yu says. “But they haven’t been applied to surface shape sensing, to reconstruct the 3D shape of a surface. That’s what we’re trying to do here.”

In their new design, the researchers fabricated their own optical fibers, with some twists. Like conventional waveguides, they made their fibers from a transparent core surrounded by dark cladding. Rather than hard glass, they used a clear, flexible rubber core and a cladding made from the same rubbery material, dyed black.

And instead of keeping the core completely smooth, they intentionally roughed up one side. If one side of the fiber is rougher than the other, they reasoned, then when the fiber is bent one way, any light passing through would scatter off the rough surface and affect the total amount of light that makes it out the other end. This would be a different amount than would pass through if the fiber were bent at the same angle but toward the fiber’s smooth side. In this way, the half-roughened fiber should act as a bidirectional shape sensor.

The team fabricated multiple bidirectional optical fibers and looked to embed them into a soft sheet of silicone, arranged so that the fibers would reconstruct the sheet’s shape as it bends and twists. To do so, the researchers carried out simulations of sheets embedded with different optical fiber patterns, from a straightforward checkerboard to criss-crossed, zig-zag arrangements.

They simulated different ways to bend or twist the sheets, and measured the output of light from each sheet’s configuration of fibers. They converted these light measurements into estimates of how much each fiber must be bending, and combined these to construct an overall 3D shape of the sheet, which they compared to the original simulated sheet shape. From these simulations, they found that a particular spacing of zig-zagging fibers was closest to recreating the sheet’s original shape.

The researchers then fabricated a shape-sensing sheet with the same zig-zag pattern of optical fibers embedded into the sheet. They incorporated an LED at one end of each fiber and a light sensor at the other end, which they connected to an external circuit board to collect and amplify the light measurements. They also developed an algorithm to automatically convert the measurements from fibers into a reconstruction of the sheet’s 3D shape as a whole.

In experiments, they showed that the algorithm smoothly created a virtual reconstruction of the sheet, almost in real time, as the researchers twisted it into different forms. For instance, when they folded the sheet diagonally, and then again in the opposite direction, the virtual twin mimicked the changing shapes, almost in real time.

They also placed the sheet in different 3D-printed molds so they could precisely measure the difference between the digitally reconstructed sheet and the physical sheet laying over each mold. In these tests, they found the soft sensing sheet was more accurate than other designs.

“We use a metric that describes the distance between the actual surface and the reconstructed surface, and from that, we found our error was less than 0.4 centimeters,” Yu says. “Existing designs, which are based on rigid sensors, have errors of around 1 to 2 centimeters. So that’s respectable, and at least on par with existing technologies.”

The team will further optimize the sensing sheet, first by thinning it down. Currently, the optical fibers are 1 millimeter thick. Other fabrication processes could shave them down to tens of micrometers, which can be thinner than a strand of hair. Then, the researchers envision embedding many more fibers into a garment, to sense detailed changes in its shape and form, for instance to gauge a patient’s performance in physical therapy.

“We hope to build tools that augment what a physical therapist can do and help them track and quantify their patients’ progress over time,” says Becker. “These sensing sheets could help a physical therapist to recall and compare more precisely how an evaluation went today versus two months and hundreds of appointments ago.”

The global building boom of power-hungry data centers is straining electrical grids, causing greater reliance on energy from polluting fossil fuels.

Christina Delimitrou, a newly tenured associate professor at MIT, is fighting this environmental threat by rethinking how the computer servers and networking equipment inside those data centers operate.

She and her group apply machine learning to make large-scale data centers more efficient, secure, and reliable. They redesign outdated cloud computing systems, develop methods to manage shared hardware resources, and create streamlined server architectures.

These advances allow data center operators to coax more computational power out of existing hardware.

“If data centers are not utilized to the best of their capabilities, then they will burn much more power than they need to meet growing user demand,” says Delimitrou, the KDD Career Development Associate Professor in Communications and Technology in the Department of Electrical Engineering and Computer Science (EECS) and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). “There is a lot of bloating, especially on the software side of these systems. If we can remove that bloating in a way that doesn’t compromise performance, then we won’t need to build as many new data centers.”

She also harnesses AI to help programmers find and fix problems in cloud-based applications, like music-streaming services or video conferencing systems. This eliminates application downtime that hampers performance and drains computational resources.

“By managing resources more effectively in the cloud, the end user gets more predictable performance from the application running on their smartphone,” she adds.

Delimitrou grew up in a midsized town within the vast plains of northern Greece. Her early interest in math and science was sparked, in part, by the ancient history of her homeland, where Euclid and Pythagoras studied mathematical problems more than 2,000 years ago.

She also drew scientific inspiration from her parents. Her mother worked as a chemical engineer and her father as a pharmacist — and both encouraged their daughter’s innate curiosity.

Her early affinity for math led Delimitrou to study computer engineering at the National Technical University of Athens, even though she didn’t know much about the field. She quickly gravitated toward courses that focused on the applied science of engineering.

For her diploma thesis — a project all students complete during their fifth and final year of study — she studied resource management in a computer when multiple applications are running at once.

“A lot of the challenges I was looking at then would get much harder if, instead of a single system, you had 100,000 of these systems. That was a problem that piqued my interest,” she says.

Seeking to make a bigger impact as a researcher, Delimitrou pursued a graduate degree at Stanford University. She began tackling inefficiencies in cloud computing systems and large-scale data centers, which was a rapidly growing area of research.

Through that work, Delimitrou and her research mentor, Christos Kozyrakis, the Leonard Bosack and Sandy K. Lerner Professor of Engineering, realized many large computing systems were underutilized.

“You would expect, with all the demand for these systems, that they should be running close to 100 percent capacity. But we found that most were running at only about 15 percent capacity,” she says. “This is not a resource-efficient or sustainable way of scaling these systems.”

To push that utilization closer to 100 percent, she began investigating machine-learning solutions to streamline cumbersome computational processes. Machine learning could automate resource management operations in the cloud, identifying solutions that developers might miss on their own.

“Applying machine learning to solve a large-scale system problem was a novel approach at the time. It was a bit risky because people had not yet shown that these techniques would work,” Delimitrou says. “But empirical approaches require a lot of expertise, and the scale of the system is so large that it is difficult for users to manage. This is why machine learning is often the best solution.”

After earning her PhD, Delimitrou continued this line of work as an assistant professor at Cornell University.

One tool her group developed, Seer, uses deep learning to anticipate and prevent problems in web applications before they happen. This averts widespread slowdowns that may occur if a developer tries to fix a problem manually.

As she delved deeper into cloud computing, Delimitrou observed that cloud applications were changing. Developers were now splitting applications into smaller pieces to spread across multiple servers, which increases the speed of deployment.

“But the servers were not built for this new style of application design. So, I rethought some of my earlier work to build machine-learning systems for this new class of applications,” she says.

To tackle these new challenges, she found herself collaborating more often with faculty members who had different software and hardware expertise. Those collaborations opened exciting new research areas.

A few years later, she decided to join MIT because of the opportunity to collaborate with researchers at the top of their fields in hardware and software engineering. She became an assistant professor in EECS in 2022.

At MIT, Delimitrou also enjoys the teaching aspect of her role. One of her favorite courses to teach is 6.191 (Computation Structure), a popular undergraduate class with about 350 students each semester.

While it’s challenging to keep the course material fresh when the field constantly evolves, she strives to inspire creativity in her students.

“I want the students to learn how to think and learn on their own. Part of that involves shifting away from formulaic assignments and making classes more open-ended. I’d rather give the students something to make them think more deeply,” she says.

In the lab, a creative mindset helps Delimitrou and her team identify novel solutions to problems in cloud computing that others might overlook.

For instance, she extended prior work on debugging problems in cloud applications to encompass not just errors in the code, but also security issues that can make user data vulnerable to hackers.

She also uses AI to redesign software systems so they better fit the capabilities of existing hardware.

“One of the challenges when it comes to applying AI to these systems is that the AI is not interpretable,” she says. “A lot of the work we are doing now involves adding explainability into these AI tools so people can get useful feedback from the system.”

That not only helps developers ensure AI is giving the right answer, but also provides insights into how to design systems better in the future.

She finds that studying these large-scale cloud computing systems is becoming more challenging because tech companies that operate data centers use proprietary hardware, unlike the commodity equipment of early cloud computing days, as well as software systems that can’t be accessed by academic teams. A solution that works in the lab might not work in the real world.

To that end, Delimitrou and her group create clones of proprietary systems and applications. One tool they developed, called Ditto, mimics an application’s structure and performance characteristics, enabling a wide range of studies.

She expects her work to continue shifting as machine-learning models become more advanced, opening new possibilities to boost application performance and hardware efficiency.

“But you still have to use AI carefully. While it can greatly accelerate the application development side, we still need to audit it and be especially careful about how these models are applied so we don’t lose the ability to gain insights out of the solutions AI is giving,” she says.

Outside the lab, Delimitrou enjoys spending time with her husband and 1-year-old daughter.

While she doesn’t have as much time for hobbies these days, she also enjoys gardening and building an electrical toy train track for her daughter, as well as playing classical piano and painting nature scenes. She became interested in painting at Cornell, where she would often paint the many waterfalls near the campus.

Whether she is working in the garden or painting, Delimitrou says she finds spending time outdoors to be a relaxing escape from the technical nature of her work, but also an important reminder of the role her research plays in sustainability.

Margaret Hamilton, a profoundly influential computer scientist best known for leading the software engineering team at MIT’s Instrumentation Lab during NASA’s Apollo program, died on Sep. 30. She was 90.

A computing pioneer who authored over 130 publications, Hamilton helped to establish software engineering as a dedicated discipline. She worked at MIT from 1959 until the mid-1970s, after which she became a successful computing entrepreneur and CEO.

Her life’s work was recognized with many awards and honors, including the 2016 Presidential Medal of Freedom from President Barack Obama, whose citation noted: “Hamilton defined new forms of software engineering and helped launch an industry that would forever change human history. Her software architecture led to giant leaps for humankind, writing the code that helped America set foot on the moon.”

“To say Margaret Hamilton was a pioneer — to say she was ahead of her time — would be a dramatic understatement. She was a software engineer at a time when that field was in its infancy, and she not only developed advanced code herself but also led a team in using that nascent technology to develop one of the most complex systems humanity had ever achieved,” says Olivier de Weck, the Apollo Program Professor and interim head of the MIT Department Aeronautics and Astronautics.

“The Apollo program still stands as one of our greatest testaments to the power of collaboration, ingenuity, and engineering, and Margaret Hamilton was a fundamental contributor to that program’s success. And for her that was just the beginning! She went on to become an entrepreneur and remained on the cutting edge of systems software throughout an extraordinary career, while acting as an advocate, mentor, and inspiration to millions.”

Born in Paoli, Indiana, in 1936, Hamilton began studying mathematics at the University of Michigan in 1955 before transferring to Earlham College, where she earned a BA in mathematics with a minor in philosophy in 1958. She moved to Boston, Massachusetts, in 1959 with her husband while he pursued a law degree.

Hamilton soon found a temporary position in the meteorology department at MIT, working with professor of meteorology Edward N. Lorenz SM ’43, ScD ’48 on weather prediction software. This was her first entry point into computer programming, and her work would go on to inform Lorenz’s future publications on chaos theory.

From there, Hamilton took a role as a programmer at MIT Lincoln Laboratory in 1961, working on the Semi-Automatic Ground Environment (SAGE) project, the United States’ first air defense system. Hamilton wrote software for the prototype AN/FSQ-7 computer (XD-1), used by the U.S. Air Force to search for potentially unfriendly aircraft. During this time, Hamilton began to take an interest in software reliability — a new and largely unexplored concept at the time.

In 1965 Hamilton was preparing to pursue graduate studies when her husband saw an advertisement in the newspaper: The Instrumentation Lab at MIT was seeking people to develop software to “send man to the moon.” The lab had won the contract from NASA to build the onboard flight software for the Apollo program. Intrigued by the challenge, Hamilton applied, and was hired as the first programmer for the Apollo project at MIT, as well as the first female programmer in the project.

Hamilton worked first on the software for the uncrewed Apollo missions and then was promoted into leading the team developing the on-board flight software for the crewed missions. By 1968 she was assistant director in charge of the Command and Service Module team, and more than 400 people were working on Apollo’s software.

“From my own perspective, the software experience itself (designing it, developing it, evolving it, watching it perform and learning from it for future systems) was at least as exciting as the events surrounding the mission,” Hamilton told MIT News in 2009. “There was no second chance. We knew that. We took our work seriously, many of us beginning this journey while still in our 20s. Coming up with solutions and new ideas was an adventure. Dedication and commitment were a given. Mutual respect was across the board. Because software was a mystery, a black box, upper management gave us total freedom and trust. We had to find a way, and we did. Looking back, we were the luckiest people in the world; there was no choice but to be pioneers.”

“Defensive” programming and priority-driven software take humans to the moon

Hamilton discovered a talent for leadership, as well as a keen instinct for problem-solving and critical thinking that would prove to save Project Apollo several times over.

There was the incident that became known as “the Lauren error”: One day, her daughter Lauren, then four years old, was playing with the command module simulator at the Instrumentation Lab when she somehow activated a pre-launch program, called P01, while the simulator was in midflight — which crashed the simulator altogether. Hamilton created a program add-on in the technical documentation warning users not to launch P01 during flight.

She also proposed a software fix to prevent the error happening during a real mission, but she was overruled on the grounds that the highly trained astronauts were never going to make that same mistake. Yet during the Apollo 8 mission in 1968, that’s exactly what happened: Jim Lovell inadvertently launched P01 during the flight, causing the on-flight navigational data to vanish. Hamilton and her team were called in to solve the error, and after that her proposed changes were integrated into the program. This was an early example of “defensive” programming, the practice of building software that could anticipate or fix errors on its own.

Hamilton’s most famous contribution to the Apollo program came during the pivotal Apollo 11 mission to land on the moon in July of 1969. Moments before the Eagle module was set to land on the lunar surface, the onboard computer raised the alarm. It had detected a 1202 error: The computer was overloaded due to a fault in a hardware switch, and it was possible the system would not be able to handle the complex landing procedure.

But Hamilton and her team had engineered priority-driven software, able to shut down unnecessary background tasks in order to prioritize mission-critical tasks. Houston trusted Hamilton’s software and allowed the mission to proceed, and two men walked on the moon for the first time.

Hamilton continued to work at the Instrumentation Lab into the 1970s. As the Apollo program wound down, the Instrumentation Lab spun out of MIT to become the independent Draper Laboratory.

“Margaret left an indelible mark on Draper, and we will be forever grateful,” says Jerry M. Wohletz SM ’97, PhD ’00, president and CEO at Draper. “Through her leadership and contributions to the development of the onboard flight software for NASA’s Apollo Guidance Computer, she helped ensure that Apollo astronauts landed safely on the lunar surface and safely returned home. We will honor her legacy at Draper forever.”

Hamilton went on to create her first software company, Higher Order Software, in 1976. The firm was based on Hamilton’s software engineering approach of error prevention and fault tolerance.

A decade later, Hamilton founded another software company, Hamilton Technologies, “to provide products and services to modernize the planning, system engineering and software development process in order to maximize reliability, lower cost and accelerate time to market.”

Hamilton Technologies’ flagship product is the Universal Systems Language (USL), a systems modeling language and methodology for engineering complex software systems that prioritize error prevention and defensive programming.

Throughout her career, Hamilton worked to achieve recognition for software engineering as a dedicated discipline.

“I fought to bring the software legitimacy so that it — and those building it — would be given its due respect, and thus I began to use the term ‘software engineering’ to distinguish it from hardware and other kinds of engineering, yet treat each type of engineering as part of the overall systems engineering process,” Hamilton told El Pais in 2018. “When I first started using this phrase, it was considered to be quite amusing. It was an ongoing joke for a long time. They liked to kid me about my radical ideas. Software eventually and necessarily gained the same respect as any other discipline.”

Among her many awards and honors, Hamilton was recognized with the NASA Exceptional Space Act Award for scientific and technical contributions in 2003; the Computer History Museum Fellow Award in 2017; the Intrepid Lifetime Achievement Award in 2019; and induction into the National Aviation Hall of Fame in 2022.

Later in life, Hamilton become an icon for women in science and technology, especially after a now-famous photo, showing her next to a printout of her MIT team’s Apollo code, began circulating online. In 2015, the Apollo software she helped to develop was added in its entirety to the code-sharing site GitHub. And in 2017, she became an official Lego Minifigure after a set originally designed by MIT science communicator Maia Weinstock, honoring her and several other women of NASA history, became available worldwide.

“Margaret Hamilton has been an inspiration to generations of computer scientists and engineers. Hers was a career dedicated to preventing errors and what she called ‘handling the unknown,’” says de Weck. “She personified leadership by example, and established a practice of software engineering based on problem-solving and systems engineering that we all benefit from.”

Hamilton is survived by her daughter, Lauren Hamilton; her son-in-law, Richard Selesnick; two grandsons; four great grandchildren; and her siblings John, David, and Kathryn. A memorial service will take place in the spring in Cambridge, Massachusetts.

Until recently, when a Massachusetts shellfish warden needed to know how much rain had fallen overnight before deciding whether to close a shellfishing area, the process might look something like this: call a local resident who happened to own a rain gauge, ask for the measurement, write it down, and use that number to make the call. Heavy rain may cause a closure due to stormwater runoff that can carry pollutants from land to water. If the area closed, the public found out the same low-tech way: a paper notice at the boat ramp, a phone call, or word passed along at the marina.

It’s a system built on trust between neighbors, but it doesn’t scale particularly well. According to the Massachusetts Division of Marine Fisheries’ 2024 Annual Report, Massachusetts shellfisheries yielded an ex-vessel value of over $441 million. This number includes shellfish spread across 738 classified growing areas along the coast that fall under a tangle of overlapping state and municipal jurisdictions. Add in thousands of recreational permit holders, and the old system starts to show its age.

With support from MIT’s Abdul Latif Jameel Water and Food Systems Lab (J-WAFS), MIT Sea Grant researchers have spent the past several years trying to close the gap between the data that exist and the data people can actually use. The effort was proposed to J-WAFS by Michael Triantafyllou, the Henry L. and Grace Doherty Professor in Ocean Science and Engineering and director of MIT Sea Grant, under the title “Cloud-Based Data Applications for Streamlining Natural Resource Management.”

The original aim of the proposal was to build a web-based system to help decision-making among managers and their stakeholders in order to support productivity of shellfisheries, safe human consumption, and vitality of coastal economies. The team wanted the platform to be scalable enough to serve the entire decision-making pipeline, from the state agency managing hundreds of growing areas down to an individual permit holder deciding whether it’s worth the drive to the water that day.

The result is a platform called Seashell. The team describes it as sitting at the intersection of big data, natural resource management, and what’s often called the blue economy — the sustainable use of ocean and coastal resources for economic growth — a phrase increasingly common in state and federal policy circles, and one Massachusetts has staked real ground on. Food security is a large part of the equation too. Many of these towns don’t just issue permits for sport; these areas are open so residents have shellfish to eat. When permit holders can trust a platform to tell them, in real time, whether an area is safe to harvest, that’s not just convenience. It’s local food supply staying reliable.

The project’s origins trace back to 2020, when MIT Sea Grant Research Scientist Carolina Bastidas and her colleagues built a shellfish restoration webpage and noticed that propagation data was scattered and often misrepresented. Propagation isn’t a minor detail; it’s how many coastal towns support food security for residents, a fact that came into sharper focus during the Covid-19 pandemic, when recreational shellfishing demand rose sharply enough that some towns limited permit sales just to protect their beds.

That early work led to an informal working group with the Massachusetts Division of Marine Fisheries (DMF) and towns of Falmouth, Mashpee, and Nantucket well before any formal funding existed. “We already had engagement,” says Ben Bray, geospatial applications developer at MIT. “We already had a very real starting point.”

By the time J-WAFS awarded the team a seed grant in 2022, letters of support from DMF and all three towns were already in hand, each describing the same underlying problem: a lack of an efficient, shared way to track and communicate shellfishing area status.

Bray’s path to this work goes back two decades. He joined MIT Sea Grant in 2005 to help build an internal grant management system, and the job pulled him toward an idea he’d recently encountered in Thomas Friedman’s book “The World Is Flat”: that data tend to sit stranded on disconnected “islands,” reachable only through the right application programming interface (API).

The underlying architecture reflects that same philosophy of connecting, rather than replacing, existing systems. Seashell runs on a PostgreSQL database and Amazon Web Services cloud infrastructure, designed from the outset to be exportable — towns or states that want to run it on their own servers can do so. Rather than asking DMF or municipalities to abandon the tools they already use, the system pulls in data through APIs from a long list of sources: National Oceanic and Atmospheric Administration tide stations, satellite feeds for sea surface temperature and chlorophyll-a, local weather networks, and DMF’s own geographic information system.

If Seashell has a defining trait, it’s that the towns using it had a hand in shaping nearly every piece of it. In 2023, the research team ran a regional assessment of the Massachusetts shellfishing community that drew responses from more than 312 people, 122 of whom volunteered for follow-up focus groups. In 2025, dedicated focus groups for both the public and administrative interfaces — described by Bastidas as involving anywhere from one to a handful of participants at a time — drove a final round of revisions before launch.

What the team learned sometimes cut against their own assumptions. “How little the end user wants to see and interact with” the data was the biggest surprise, Bray says. Focus group participants didn’t want a comprehensive dashboard of every growing area in the state; they wanted their own location, loaded automatically, with the fewest clicks possible. More simply, users wanted answers to questions such as: Is an area open? Is it high or low tide? What’s the water temperature? That preference reshaped the public interface around geolocation and minimal menu navigation, particularly for mobile use, allowing permit holders to check the system on their way out the door.

The towns themselves turned out to have sharply different needs, shaped largely by how many people they have on staff and who’s showing up at their docks. Many of these departments run on a skeleton crew with one or two staff, managing the entire permitting and enforcement operation. “All the organizations we work with are all understaffed,” Bray says. “I didn’t realize that going into this.”

That imbalance shaped something called the “Ways to Water” feature. Towns popular with seasonal and vacationing residents who buy a permit without knowing the local geography asked for a tool that could route users to the nearest shellfishing access points. They also wanted the tool to show parking availability, boat ramps, and travel time. This detail captures the blue economy dimension of the project well. Both tourism and traditional shellfishing for food security call for differing groups to share the same water and the same system, without necessarily sharing the same local knowledge.

Other requests were smaller, but no less telling. One shellfish officer asked simply whether the interface could be offered in different languages, a request the team is now addressing through built-in translation. It’s the kind of feature that doesn’t show up in a funding proposal’s list of objectives, but that turned out to matter a great deal to the people actually using the tool.

The relationship with DMF followed a different shape than the one with the towns. Where individual towns had fewer resources but could move quickly once they decided to adopt something, DMF moved more deliberately. “They had more ability than they had power,” Bray says of DMF staff, describing an agency with existing institutional constraints that limited the design approach the researchers could take. That distinction ultimately reshaped the project’s strategy: rather than building one system meant to unify DMF and every town under a single workflow, the team shifted toward treating towns and DMF as separate entities with distinct needs, while still syncing DMF’s official closure data into the platform automatically. Bray describes the process as investing effort where towns have the flexibility to adopt something new, rather than waiting on slower-moving state processes to catch up.

The system’s handling of jurisdiction is arguably its core technical achievement. DMF manages the baseline layer of the state’s 738 growing areas; individual towns manage sub-areas on top of that layer, closing sections for reasons ranging from water quality to family-designated harvesting zones reserved for local residents. Early on, the team assumed DMF would want to manage its polygons directly inside Seashell. DMF preferred to keep working in an existing ArcGIS system. So the MIT team adjusted, syncing Seashell to DMF’s existing records via API instead, and building in automatic alerts whenever DMF’s underlying data changed. It’s a small design decision with an outsized effect: DMF keeps full control of its own data while towns still benefit from updated, synced data.

The administrative interface goes a step further, pulling in real-time environmental data and using it to recommend closure decisions based on rules that towns configure themselves. It’s a direct stand-in for the old rain-gauge phone call, and it’s built to shrink the gap between when DMF issues a closure notice and when the public actually learns about it.

Seashell officially launched at the 2026 Northeast Aquaculture Conference and Exposition in Portland, Maine, this past January. The team is scheduled to present at the Massachusetts Shellfish Officers Association’s meeting this October, betting that broader town-level adoption will reduce the confusion that crops up when neighboring towns operate on entirely different systems. Some towns’ shellfish officers are using Seashell while the next town over might still be working on paper.

Bray sees something durable in the subject matter itself, apart from the technology. “Shellfishing is one of the few things, in terms of the ocean, in terms of cultural communities, that is bipartisan,” he says. Bastidas points to the layered value of the resource, not just its economic footprint, but its role in food security and in traditions, including Indigenous shellfishing practices, that predate Massachusetts’ current regulatory system by centuries.

Looking ahead, the team is exploring an extension into commercial fisheries and landing-data reporting, building on related, town-driven effort. More broadly, the researchers see little about the underlying framework that’s specific to shellfishing. The same architecture, they argue, could serve any natural resource management problem with a geospatial and jurisdictional dimension: wildlife management, water quality, coastal permitting.

For Bray, who led his first funded research project through this J-WAFS grant, the work has doubled as a lesson in institutional collaboration as much as software design. The project helped him understand how fast and how much a large state agency can move versus a small town, and where to put the effort to balance both sides. “You have to figure out what an organization is capable of providing, and work within that,” he says, “while still trying to push things forward.”

Uncrewed aerial vehicles (UAVs) could fly deep into the heart of a raging wildfire, avoiding sudden flare-ups and dodging falling tree branches to gather critical information for rescuers. But in this dangerous situation, the UAV could easily be damaged or destroyed by falling debris.

The UAV — and the rescuers who rely on it — would benefit from a new, autonomous navigation system that is guaranteed to avoid collisions, even in completely unfamiliar surroundings.

Developed by MIT researchers, this method plans a flight path for a UAV that eludes unknown obstacles that may move in unpredictable ways. It charts an efficient course through an unmapped environment that is mathematically proven to be safe from collisions.

Many popular navigation systems can only offer formal safety guarantees when the environment is static, or when the obstacles are known in advance.

By enabling the UAV to avoid moving obstacles while it is discovering its environment, this trajectory planner, which the researchers call “SANDO” (for “Safe AutoNomous trajectory planning for Dynamic unknOwn environments”) could be especially useful for applications like search and rescue missions into collapsed buildings, mine explorations through networks of hidden tunnels, or package delivery across a crowded neighborhood.

“In the hardest possible environment, where the UAV has no map of the area and there are unknown obstacles moving around, we established a mathematical guarantee of safety. The only thing the planner needs to know is the top speed the obstacles could reach. Given that, you could use it in any environment, without a map, and you know the UAV is not going to crash into anything,” says Kota Kondo SM ’23, PhD ’26, who recently earned his doctorate in aeronautics and astronautics at MIT and is lead author of a paper on this new system.

He is joined on the paper by Jesús Tordesillas PhD ’22, an assistant professor at Comillas Pontifical University in Madrid; MIT graduate students Juan Rached, Lili Sun, and Yixuan Jia; and senior author Jonathan P. How, a Ford Professor of Aeronautics and Astronautics and a principal investigator in the Laboratory for Information and Decision Systems (LIDS) and the Aerospace Controls Laboratory (ACL) at MIT. The research appears in the IEEE Transactions on Robotics.

Trajectory planners use images and data from a UAV’s onboard cameras and sensors to chart a flight path that reaches the vehicle’s goal.

Most existing planners are either designed for unknown static environments, where the obstacles don’t move, or they loosely avoid dynamic obstacles without providing a formal guarantee that the robot won’t crash.

Formal safety guarantees are important in high-stakes situations, such as if a UAV were delivering medical supplies to the site of a remote natural disaster. But trying to compute every possible crash in a dynamic environment would take too long for real-world deployments.

“In an unknown dynamic environment, you don’t have many assumptions to rely on. In those types of environments, researchers haven’t yet been able to mathematically guarantee that a trajectory is safe,” Kondo explains.

The MIT researchers used a rigorous mathematical approach to develop SANDO, their safe trajectory planner. They theoretically proved the algorithm always computes trajectories which are guaranteed to avoid collisions with moving obstacles in unknown environments.

SANDO starts by mapping out a safety corridor through the robot’s environment. This corridor is a series of connected regions of 3D space the robot can travel through, which are guaranteed not to contain any obstacles.

But unlike other systems, SANDO creates a time-sensitive safety corridor that considers the possible future movements of dynamic obstacles. It employs a special module that detects, groups, and monitors dynamic obstacles to estimate where they will move next.

While the system doesn’t know exactly where an obstacle will move in the future, it uses that obstacle’s maximum velocity to compute how far it could possibly go in a certain timespan. It puts a sphere around the obstacle that captures the farthest distance it could travel in all directions.

SANDO builds the safety corridor around these spheres to ensure the UAV will not collide with a moving object.

“In the real world, obstacles are going to move, so the safety corridor you create at one point won’t be useful as things move into the corridor. But because we consider this time component, we can now guarantee safety into the future,” Kondo says.

The system uses a heat-map based planner to identify “hot” regions of the environment with many obstacles and guides the UAV away from these dangerous areas. This helps the robot chart a more efficient course around danger zones.

Once it has established a collision-free safety corridor, SANDO optimizes the trajectory within that corridor to find the fastest path to reach the goal.

As the robot travels, SANDO adjusts the safety corridor and reformulates the trajectory to ensure the robot’s path remains collision-free until it reaches its goal.

The researchers employed a few tricks to make the optimization easier to solve so the UAV can rapidly recalculate trajectories using its onboard computer, quickly reacting to sudden changes.

“The most difficult part of developing SANDO was the math,” Kondo says. “When you try to guarantee safety, you need to be rigorous and ensure your theory covers every possible case, even edge cases. Once we had that mathematical guarantee, it was very easy to fly the UAVs.”

In simulations, SANDO reached the robot’s goal faster than several state-of-the-art systems while completely avoiding collisions in all environments.

SANDO also avoided all dynamic obstacles in 12 test flights with a real UAV, using the robot’s onboard computer and sensors to rapidly replan safe trajectories.

In the future, researchers could make SANDO more computationally efficient and combine the system with machine-learning models that allow the user to give instructions to a robot using plain language.

A central challenge in autonomous flight is that a path that is safe when it is planned may become unsafe as the environment changes. SANDO addresses this challenge with time-varying safe flight corridors and hard-constrained trajectory optimization that supports frequent onboard replanning. Its combination of spatiotemporal planning, formal safety analysis, and hardware validation provides a practical approach to autonomous flight in complex dynamic environments,” says Fei Gao, an associate professor at Zhejiang University in China, who was not involved with this research.

This research is funded, in part, by the Defense Science and Technology Agency of Singapore.

Chris Bourg, who has served as director of the MIT Libraries since 2015, has been named vice provost and Barbara K. Ostrom (1978) Director of the Libraries, MIT Provost Anantha Chandrakasan announced today.

“The title ‘vice provost’ signals the enduring importance of the MIT Libraries in the digital age,” said Chandrakasan. “While the ways in which the libraries meet the needs of our community might change as technology changes, their mission of protecting access to knowledge and information is as vital as ever.”

Thanks to a generous gift from Barbara K. Ostrom ’78, SM ’78, the position of the director of the libraries now has an endowment to help fund it in perpetuity. Bourg noted that the gift is especially meaningful for her, due to the sense of service she shares with Ostrom: they both completed Reserve Officers' Training Corps (ROTC) during their time as undergraduates — Bourg at Duke University and Ostrom at MIT — before going on to active military service.

“Barbara’s gift is an incredible acknowledgment of the progress the entire MIT Libraries staff has made toward our vision,” said Bourg, who noted that Ostrom had also supported an MIT Libraries initiative to highlight MIT’s women faculty by acquiring, preserving, and making accessible their personal archives. “Her generosity advances the libraries’ ability to support teaching, learning, and community-building at MIT well into the future.”

During her tenure, Bourg has emphasized digital access to content, a more open and equitable scholarly publishing landscape, and expanded support for data-intensive and computational research. She has helped both the libraries and the Institute adapt to AI-driven changes: she co-chaired the Working Group on Scholarly Content and Generative AI, charged with helping the MIT community navigate the legal, technical, and ethical issues involved when scholarly content is used to develop and train generative AI models. And she was a member of the Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training, which issued its final report in August.

Promoting open scholarship has been a longstanding focus of Bourg’s career. In 2018, the libraries launched the Center for Research on Equitable and Open Scholarship, with Bourg serving as founding director. She co-chaired the 2017 MIT Ad Hoc Task Force on Open Access to MIT’s Research; its recommendations led to the creation of the MIT Prize for Open Data, co-sponsored by the School of Science, and the development of the MIT Framework for Publisher Contracts. Guided by the framework, MIT became one of the first major U.S. institutions to cancel a journals contract with Elsevier in 2020, standing by its open scholarship principles while saving the Institute millions of dollars.

Bourg co-chaired the 2016 Ad Hoc Task Force on the Future of Libraries and has guided the MIT Libraries toward the vision laid out by the task force report. Under her leadership, the libraries completed a major renovation of Hayden Library and the Building 14 Courtyard and launched MIT Reads, an Institute-wide reading and discussion program designed to foster empathy and belonging within the MIT community.

Prior to joining MIT, Bourg worked for 12 years in the Stanford University Libraries. Before Stanford, she spent 10 years as an active-duty U.S. Army officer, including three years on the faculty at the United States Military Academy at West Point. She received a BA from Duke, an MA from the University of Maryland, and an MA and PhD in sociology from Stanford.

Bourg is a member of the board of the Center for Open Science, the external advisory board of the Stanford Data Science Institute’s Center for Open and Reproducible Science, and the board of Annual Reviews, a premier publisher of open review journals in 51 disciplines. She also currently serves on the National Academies of Science, Engineering, and Medicine consensus committee on Corrections and Retractions: Upgrading the Scientific Record.

MIT is launching a new strategic initiative today: MIT for America, which seeks to strengthen STEM education nationally, at many levels of learning.

Addressing a critical national need, the initiative ranges across STEM fields and will engage students from kindergarten through community college, preparing them to live and work in a world increasingly shaped by science and technology. MIT for America’s programs will develop new opportunities for student achievement in mathematics, hands-on design and fabrication, and constructive engagement with artificial intelligence.

President Sally Kornbluth made the announcement today in a letter to the Institute community.

“A natural extension of the Institute’s long record of national service, MIT for America is grounded in our deep belief in potential over pedigree, of the transformative power of learning by doing and in the open sharing of knowledge,” Kornbluth wrote.

The initiative represents an Institute-wide effort to address the national challenge of improving STEM education and helping it adapt to shifting societal needs. MIT for America will leverage the Institute’s longstanding strengths to develop new programs and expand existing projects, while amplifying MIT’s impact on national education.

MIT for America’s faculty leaders emphasize that the initiative will support programs that can be scaled up and that provide direct, face-to-face guidance for learners and educators.

“The goal is to provide programs that are both scalable and high-touch, and make an impact across the country,” says Professor Eric Klopfer, a faculty co-director of MIT for America and director of the Scheller Teacher Education Program and the Education Arcade at MIT. “All our programs take advantage of things we are well-positioned to do at MIT, and can be distributed around the country. That’s our guiding light.”

Cynthia Breazeal, faculty co-director of MIT for America, professor of media arts and sciences, and the Benesse Professor of Research in Education, says the initiative “is about bringing high-quality access to STEM and AI education, from kindergarten to community college, across the United States. We live in an innovation-driven world, and it’s important to have an education that gives you access to it, whether that’s so you can be a fully engaged citizen or have the opportunity for economic mobility.”

The initiative is addressing large-scale needs in STEM education. Only 29 percent of U.S. students in grades 9-12 build circuits, and only 6 percent of students enroll in computer science classes, even though about 60 percent of high schools offer such courses. MIT for America intends to substantially help communities across the country build the bandwidth needed to further develop STEM knowledge and workplace skills.

“This effort is about really rolling up sleeves and getting out there and engaging communities, and helping to build capacity across the United States,” Breazeal adds.

Mathematical thinking and problem solving: Work here builds on an existing program, the MIT for America Calculus Project, launched a year ago, in which MIT students and alumni volunteer to help provide calculus tutoring to students in school districts across the country.

Living, learning, and working with AI: This area addresses multiple issues revolving around AI, including teacher education that helps instructors integrate AI into classroom learning in productive ways, and programs that enhance AI skills and STEM education in technical-vocational schools and community colleges. The MIT RAISE program (Responsible AI for Social Empowerment and Education) is a leader in this domain.

Designing, making, and innovating: MIT for America plans to build out programs that encourage hands-on learning, design work, use of fab labs and makerspaces, and experiential learning, sometimes deploying tools such as national design challenges.

“Across the U.S., we want to unlock opportunities and experiences where students are,” says Claudia Urrea, head of MIT for America and head of the MIT pK-12 Initiative. “We want to find people who have not had the opportunity to discover their talent.”

In the first case, the MIT for America Calculus Project provides a ready model of outreach with growth potential. About half of U.S. school districts offer calculus, but many of those districts may be under-resourced. The MIT for America Calculus Project has been growing in partnership with an increasing number of school districts, while MIT undergraduates and alumni have enjoyed participating in the program.

“People have been really excited about the MIT for America Calculus Project, seeing both the impact that it has, and its model, which I think has resonated,” Klopfer says. “That’s really representative of what other programs could look like.”

In the second topic area, MIT for America can leverage burgeoning efforts such as MIT RAISE. The program helps educate teachers and engage students about implementing AI in ways that can complement the developing skills of students, enabling them to use AI in productive ways.

“RAISE is about providing materials and training for teachers that helps them have conversations and productive activities around AI,” Klopfer says. “It’s about understanding how AI works and discussing what the values are in your school. It has to be active, involving the students. If you try to force things, about AI one way or the other, that will fail.”

In an outgrowth of RAISE, MIT also launched a related program, Pathways for AI Training and Hiring (PATH), on Oct. 1. Starting with a partnership with Georgia State University, PATH aims to bring AI training to two-year and four-year colleges, to help students gain AI skills useful for workplaces.

“RAISE has had a lot of success with scaling already,” Breazeal says, noting that about 1 million students were involved with RAISE’s Day of AI event earlier this year. “At MIT, we want to empower youth voices to shape the future. It’s an excellent example of our ability to do that.”

And in the third topic area, MIT for America can help produce substantial growth in programs aimed at hands-on learning. That may include more projects such as MIT’s Regenerative Futures Challenge, a global program stemming from MIT’s pK-12 Initiative, which give students the opportunity to create climate and sustainability solutions. Additionally, the Fab Foundation, which grew out of MIT’s Center for Bits and Atoms Fab Lab program, offers tools and curriculum to learners at 300 fab labs across the U.S.

“It’s about coming together in places where there are people who can work together, mentor each other, and learn by example,” Klopfer says. “That’s the kind of thing we want to be creating at scale. We want to build on that.”

All told, MIT for America represents a vigorous all-campus effort to enhance education through sustained outreach and knowledge-sharing. At MIT, partners in creating the initiative include the MIT Media Lab; MIT RAISE; the MIT pK-12 Initiative; the Office of the Vice President for Resource Development; and the Office of Innovation and Strategy.

The leaders of MIT for America presented an overview of the program in September at MIT’s Alumni Leadership Conference, and they say alumni activity around the new initiative will be another key to its success.

“We really appreciate the support and engagement of our alumni,” Breazeal says. “We know they care about K-12 education and access to community colleges.” More broadly, she adds, “We do think this message will resonate with people all across the country.”

“I look forward to watching these programs flourish, evolve, and grow,” Kornbluth wrote to the community.

Like Earth, most planetary bodies circle their star in stable, detached orbits. These companionable systems can suddenly change when a planet comes too close to its star. In such a close encounter, a star can pull the planet in and swallow it whole.

Across the galaxy, astronomers have seen plenty of stable, detached planetary systems. They have also observed a handful of stars quickly engulfing their planet. Now, for the first time, scientists have spotted a system that is striking a curious balance between the two extremes. And it’s revealing a new way that stars can interact with planetary companions.

In a paper appearing today in Nature Astronomy, scientists at MIT and elsewhere have discovered a star leisurely snacking on a closely orbiting brown dwarf — a planet-like object that is more massive than a planet yet not quite as big as a star.

The new system, named ZTF J0440+2325, is within the Milky Way galaxy, roughly 300 light years from Earth, and represents the first observation of a low-mass object that is slowly and steadily consuming material from another low-mass object.

The rate at which the star is feeding from the brown dwarf suggests that this slow stellar cannibalism could carry on for hundreds of thousands, or even billions of years.

“When we think of stars interacting with planets or brown dwarfs, the picture is always that the star eventually swallows the other thing,” says Kevin Burdge, assistant professor of physics at MIT. “This is what will happen to the Earth when the sun becomes a red giant. But here, we’ve found an alternative: Instead of swallowing the thing up, the star can gradually eat it, for billions of years.”

The study’s MIT co-authors include Aaron Householder, Kaitlyn Shin, Saul Rappaport, Joheen Chakraborty, and Emma Chickles, along with collaborators from Caltech, the University of Hawaii, the Instituto de Astrofísica de Canarias and the Universidad de La Laguna in Spain, and the Harvard and Smithsonian Center for Astrophysics.

The new system was spotted initially by the Zwicky Transient Facility. The ZTF uses a camera as part of a telescope at the Palomar Observatory, in California, to scan the sky for rapid changes in brightness, which could signal the presence of a supernova, a gamma-ray burst, or colliding neutron stars.

Several years ago, Burdge was looking through ZTF data when he noticed a strange light curve, or pattern in brightness. Light curves for supernova resemble a bell curve, signaling the gradual brightening and then fading of a star as it bursts. But what Burdge picked out looked more like a triangle, that didn’t appear once, but again and again.

“I remember first looking at this and thinking: Stars don’t make triangular waveforms like this,” he recalls.

At the time, he and his colleagues were focused on a different signal, which they identified as a “black widow binary” — a system in which an extremely dense, spinning neutron star is slowly consuming a much smaller companion star, similar to how its arachnid namesake plays with its prey.

Burdge wondered whether the triangle signal might also be from a black widow. But the light from the signal was puzzling. In black widow binaries, the light appears to wobble, as a result of a very light, low-mass object, such as a small companion star, whipping around a much heavier object, such as a neutron star.

“We weren’t seeing that whipping back and forth here,” Burdge says. “It didn’t make any sense. We couldn’t explain what this was.”

But they had a hunch: Could the signal be coming not from a wobbly, David-and-Goliath system, but from a more balanced pair of objects, each with a similarly low mass?

“If you have less mass in the system overall, things can gently orbit each other without whipping back and forth,” Burdge says. “That was the idea. But we never had any proof. And this weird triangle just sat for years.”

Recently, Burdge and Householder, a graduate student in MIT’s Department of Earth, Atmospheric and Planetary Sciences, decided to revisit the triangle mystery. From the original ZTF signal, they determined the location of its source to be within the Milky Way galaxy, around 300 light years from Earth. They focused multiple telescopes on the source, named ZTF J0440+2325. From these observations, they measured various properties of the source, including its wobble. Compared to black widows and other similar binaries, the wobbling from ZTF J0440+2325 was much smaller — but not insignificant.

“That was the real clincher for this system,” Householder says. “When we measured that wobble, we found we were not seeing a black widow. This was a low-mass star that’s orbited by a brown dwarf. The wobble was too small in amplitude to be anything else.”

They determined that the star and the brown dwarf are extremely close, with the brown dwarf circling the star every 87 minutes, in an orbit that could fit within the diameter of the sun. Both objects are small by stellar standards. The star is around 85 times as massive as Jupiter, while the brown dwarf is around 25 times as massive.

With two low-mass objects circling at such close range, the scientists wondered if one object could be pulling material from the other. Such a process, known as accretion, is most often seen around objects that are extremely massive, though small in actual size, such as black holes and neutron stars. When a black hole accretes, or draws material from a much smaller nearby object, it pulls the matter around it in a disk.

“The difference here is: The thing absorbing matter is not a tiny black hole but a star, which is relatively big in size,” Burdge explains. “So matter just pummels directly onto the surface, at very high speeds, like an asteroid hitting the moon.”

The team carried out simulations of possible accretion in ZTF J0440+2325. Taking into account the properties of the star and the brown dwarf, they simulated particles of matter on the brown dwarf, and how these particles should behave within the system over time, according to the laws of physics and equations of motion.

“When we track those test particles, we see they indeed fall right onto the surface of the star,” Householder says. “This is the first time we’ve caught a low-mass star actively accreting from another low-mass object.”

What’s more, the team calculated that the brown dwarf must be feeding material to its star at a rate of about 1/100,000 of an Earth’s mass each year. That’s about 40 million dump trucks’ worth of material, or roughly 1.3 trillion one-pound burritos every second. While that may seem like a lot of matter to be losing, it is in fact a very small fraction of the brown dwarf. This rate, the researchers estimate, is actually quite slow and steady. Given the size of the system, they say the star could continue leisurely snacking on the brown dwarf, for billions of years.

This slow accretion, they say, would resemble a steady stream from the brown dwarf, onto the star. The researchers realized that if they were to view the system from afar, the brightness from the system would chart as a triangle, as the brown dwarf and its stream of matter circles its star.

“It’s like you’ve got this continuous fireball onto one of the objects, and as one orbits the other, that hotspot comes in and out of view, and the peak of the triangle signal is when you’re looking right at the fireball,” Burdge explains.

With the mystery of the triangle light curve solved, the team hopes to spot similar slow-feeding systems nearby.

“It’s inspiring a lot of new searches on our part,” Householder says. “I think we’re going to learn a lot about a different kind of way that planets and brown dwarfs interact with their host stars.”

MIT formally dedicated its newly transformed Met Warehouse (Building W41) on Wednesday, in an energetic evening ceremony heralding the start of a new era for the practice of design on campus.

The ceremony highlighted “the essential vision of the Met: connecting people, disciplines, and ideas, and inviting everyone to see where those connections might be,” said Mark Gorenberg ’76, chair of the MIT Corporation, during introductory remarks.

MIT President Sally Kornbluth called the new building “a testament to the transformative power of design, which has been central to MIT from the very beginning.” She also heralded the building as a cornerstone of MIT’s “magnetic new West Campus district for art and design,” which includes the Edward and Joyce Linde Music Building (W18) and the MIT Theater Arts building (W97).

Hashim Sarkis, dean of MIT’s School of Architecture and Planning, delivered keynote remarks at the event, linking together many themes of the evening and recounting the project’s development in the late 2010s.

In envisioning moving into the Met Warehouse, Sarkis said, the “building spoke to us. It offered us possibilities that we could clearly envision both outside and inside the thick brick walls. … It helped us imagine how we can live and work together as a campus and as a school. Over the months of discussions among the faculty and students across the school, it became clear that it was an idea whose time had come.”

Many members of the MIT Corporation were in attendance for the event, held in the Met Warehouse’s Sidara Auditorium. The ceremony was also an occasion for giving thanks to those who made the new Met Warehouse possible: campus leaders, donors, faculty, architects and designers, contractors and specialized builders, and many others who worked on the remarkable structure in different ways.

“The list, I promise you, runs longer than the credits at the end of “The Odyssey,” and what an odyssey this has been,” Sarkis quipped.

The Met Warehouse was originally constructed from 1884 to 1923, as a massive private storage facility with 2-foot thick brick walls, a corner turret, slit-like windows, and other features making it look like a fortress. About 500 feet long, with five stories, the building was long an imposing local curiosity.

MIT acquired the Met Warehouse in 1962 and in the 2010s began exploring possible new uses for it. By 2018, the idea of making it the new home of MIT’s School of Architecture and Planning had gained enough traction to move forward.

Project architects Diller Scofidio + Renfro created a variety of solutions to bring natural light into the warehouse and revamp its interior. With permission from the Cambridge Historical Commission, the designers replaced four segments of brick wall on the building’s north side with top-to-bottom glass sheets, which along with skylights bring in abundant light.

At the same time, the architects — led by Elizabeth Diller and Benjamin Gilmartin, who were at the dedication last night — overhauled the building’s interior, while working within many of its structural features. The refurbished Met Warehouse now features double-height design studios, an auditorium, a large entrance lobby, a ground-floor café, offices, and many flexible, reconfigurable classroom spaces designed to help faculty and students collaborate on an immense array of projects.

The Met Warehouse also features building-long open corridors on all five floors and a central staircase connecting all of them, as elements designed to enhance circulation and connectivity within the building.

In her remarks, Kornbluth heralded the Met Warehouse’s educational potential, noting the challenges artificial intelligence presents for education, as highlighted in an MIT-wide report released in August.

That report, she outlined, emphasized that education comes from “helping students to value the process of learning as a ‘productive struggle’ and from engaging them in ‘human settings where they … learn how to work with others, communicate their ideas, receive criticism constructively, build confidence, develop judgment, and act as members of a community.’”

With that in mind, Kornbluth said, “that sounds exactly like the kind of hands-on, in-person, collaborative problem-solving the new Met was made for. … This community is not only ready to withstand the educational challenges of AI … it’s also primed to help the rest of MIT meet the moment. And that is very good news for us all!”

Diller also addressed the audience, highlighting some of the key design challenges involved in the project and thanking many of those who worked on it, including Leers Weinzapfel Associates, the project’s associate architects, and Shawmut Design and Construction. The Met Warehouse, she emphasized, is meant to be used in many ways in the future, and was designed with enough flexibility so that it can continue to evolve.

“This building should remain a work in progress,” Diller said, adding that she would continue to regard it as “definitively unfinished.”

Professors John Ochsendorf, Caroline Jones, and Lawrence Vale — MIT faculty who are all associate deans in the School of Architecture and Planning — also spoke at the ceremony, outlining the implications of the building for the school’s many forms of research and collaborative work.

“It’s working,” Ochsendorf said, now that the building is inhabited on an everyday basis by students, faculty, and staff.

As with almost any large, long-term project, credit can be spread in many directions, and the speakers at the dedication ceremony gave ample thanks to those involved — and to the important supporters of the project.

“Hashim Sarkis has been its greatest champion,” Kornbluth said. “His leadership and imagination shaped not only this building, but the ambitious future it makes possible for the school.” She also thanked MIT President Emeritus L. Rafael Reif, a project supporter during his tenure, “for your foresight, your perseverance, and your insistence that the music, theater, and design communities at MIT deserve facilities worthy of the quality of their world-class work.”

For his part, Sarkis also gave credit to former MIT Corporation Chair Robert Millard ’73, saying, “Without Bob and his wife Bethany, this building would not be here today.” Gorenberg, in his remarks, also made a point of thanking the City of Cambridge for its extensive cooperation with MIT on the project.

Gorenberg also expressed his deep “gratitude to the Morningside Foundation,” the philanthropic arm of the T.H. Chan family. He cited the “extraordinary generosity” of the founding gift, from family members Gerald and Beryl Chan and Ronnie and Barbara Chan, establishing the Morningside Academy for Design (MAD), a major interdisciplinary center at MIT located in the Met Warehouse.

Speaking of Gerald L. Chan, Gorenberg added, “We cherish his wisdom and belief in MIT as a leading institution that can do good for the world.”

In a statement sent to MIT News, Chan said: “In this day and age when disciplinary boundaries are ever dissolving, it is important to have initiatives that tie all departments of the Institute together so that students can be facilitated to have broad exposures. Design provides such a possibility, and MAD is the venue.”

Sidara (formerly the Dar Group), a global collaborative of specialist design, engineering, and consulting firms owned by Maha and Talal Shair, supported the establishment of two central public spaces in the building, the Sidara Auditorium, and the Sidara Gallery, on the ground floor.

“At Sidara, we share MIT’s commitment to improving lives, solving critical challenges, and making room for cultures to shine,” Talal Shair said in a statement to MIT News. “We feel a profound resonance with SA+P across all three dimensions — so it was only fitting that we would support the transformation of the MET into a hub for education, research, and innovation. We trust the Sidara Auditorium and the Sidara Gallery will serve as spaces for idea exchange, inspiring future generations to think broadly and act boldly.”

The LUMA Foundation, a Zurich-based nonprofit founded by Maja Hoffmann in 2004 to support artistic production and the organization behind LUMA Arles, an interdisciplinary creative campus in southern France, also gave an establishing gift for the MIT LUMA Lab, based in the Met Warehouse, for projects combining art, science, technology, conservation, and design.

“LUMA Foundation has always been grounded in the belief that meaningful change begins by creating the field and conditions for people, disciplines, and forms of knowledge to encounter one another freely, critically, and with mutual respect,” Hoffmann said in a statement for MIT News. “The MET gives this principle a remarkable new context. To see faculty, students, researchers, artists, designers, scientists, and technologists connected through the MIT-LUMA Lab working alongside one another is a powerful expression of what such a place can enable. I am excited and proud to be contributing to this journey and be part of the MIT community and its future.”

Referencing MIT’s motto, “mens et manus,” which is Latin for “mind and hand,” Gorenberg wrapped up the dedication ceremony last night with an additional thought: “This is ‘mens et manus’ at its finest.”

The Massachusetts Institute of Technology Investment Management Company (MITIMCo) announced today that MIT’s unitized pool of endowment and other MIT funds generated an investment return of 10.3 percent during the fiscal year ending June 30, 2026, as measured using valuations received within one month of fiscal year end. At the end of the fiscal year, MIT’s endowment funds totaled $29.2 billion, excluding pledges. Over the 10 years ending June 30, 2026, MIT generated an annualized return of 11.7 percent.

The endowment is the bedrock of MIT’s finances, made possible by gifts from alumni and friends for more than a century. The use of the endowment is governed by a state law that requires MIT to maintain each endowed gift as a permanent fund, preserve its purchasing power, and spend it as directed by its original donor. Most of the endowment’s funds are restricted and must be used for a specific purpose. MIT uses the bulk of the income these endowed gifts generate to support financial aid, research, and education.

The endowment supports about half of undergraduate tuition, helping to enable the Institute’s need-blind and full-need undergraduate admissions policy, which ensures that an MIT education is accessible to the most talented students in the nation and the world regardless of their financial resources.

In fiscal 2026, MIT enhanced undergraduate financial aid, ensuring that all students from families with incomes below $200,000 and typical assets have tuition fully covered by scholarships, and that families with incomes below $100,000 and typical assets owe nothing toward their students’ MIT education. Eighty-eight percent of the Class of 2026 graduated with no debt. With our investments in financial aid, parents of MIT undergraduates receiving financial aid now pay on average 10 percent less on a real basis than at the end of the Great Recession.

MIT Student Financial Services works closely with all families of undergraduates who need financial aid to make MIT affordable for them. In 2025-26, the average need-based MIT undergraduate scholarship was $66,155. Fifty-eight percent of MIT undergraduates received need-based financial aid, and 44 percent of MIT undergraduate students received scholarship funding from MIT and other sources sufficient to cover the total cost of tuition.

MIT’s endowment enables it to do more cutting-edge research. Fueled by funding from the endowment, the Institute more than matches the amount of campus-based research funded by the U.S. government and other sponsors — expanding its beneficial impact without asking more from taxpayers.

MITIMCo is a unit of MIT, created to manage and oversee the investment of the Institute’s endowment, retirement, and operating funds.

MIT’s Report of the Treasurer for fiscal year 2026, which details the Institute’s annual financial performance, was made available publicly today.

The book “Disciplined Entrepreneurship” by Bill Aulet, managing director of the Martin Trust Center for MIT Entrepreneurship and the Ethernet Inventors Professor of the Practice at the MIT Sloan School of Management, walks readers through the 24 steps of starting a venture. With more than half a million copies sold, the approach has proven remarkably effective: MIT students who use the framework in the delta v startup accelerator program have a 61 percent survival/acquisition rate and have collectively raised over $3 billion dollars.

But while the framework is taught at hundreds of colleges around the world, it is not designed for younger students who want to learn about entrepreneurship. To fill that gap, the Trust Center created a free, AI-powered youth entrepreneurship platform called Dear Dreamer, made possible through a gift from the Frank & Eileen Foundation.

Dear Dreamer is open to all students in middle and high school. It adapts the disciplined entrepreneurship framework into a digital, self-paced learning experience featuring short educational videos, interactive exercises, and personalized feedback on the user’s idea. Aulet says the goal is to empower 50,000 young entrepreneurs by 2030.

MIT News spoke with Aulet about the mission of the project and how it came together.

A: We’ve had a lot of success teaching the disciplined entrepreneurship framework at MIT. Then we made it a course on the online learning platform edX, and we got hundreds of thousands of people taking the class. I used to get emails from people saying, “For the first time in my life, I see myself as an entrepreneur,” or “I see economic security.” At MIT, we often say, “MIT in the world, for the world.” At the Trust Center, we see our mission as, first, train people here at MIT, but also to create more entrepreneurs outside of MIT.

The platform was inspired by the vision of Audrey McLoghlin, the founder and CEO of apparel brand Frank & Eileen and president of the Frank & Eileen Foundation. In my first meeting with Audrey, we were already asking ourselves, “How do we make more entrepreneurs in the world?” I showed her Jetpack, MIT’s generative AI tool trained on disciplined entrepreneurship, which walks MIT students through the entrepreneurial process, and her eyes lit up. She said, “This is how we create more entrepreneurs: We make the work that you guys are doing here accessible to young people.” She wished she had a tool like this when she first started. She also said her daughter wants to be an entrepreneur, but students don’t get much guidance on entrepreneurship at school. MIT has been a great place to keep making this more accessible.

A: It takes the core disciplined entrepreneurship curriculum that we know works from our data, and makes it more interactive and engaging for young people. We spent a lot of time working with younger students to figure out how to make it more digestible to a 10-year old, 12-year old, or 14-year old. We put in different case studies and examples for each step of the framework, and we’ve designed the user interface to make it more like Instagram or YouTube, with videos featuring people that students can relate to. But ultimately the content follows the same path we know works; it’s just presented differently from what you would present to an MIT MBA or PhD.

A: Entrepreneurship is a mindset, a skillset, and a way of operating. It allows you to deal with change, and the world’s rate of change is going faster and faster. It’s really not just about founding companies. Founding companies is a great way to learn the mentality that, ‘We can be different. We can build something. We can achieve a lot.’ Someone once said, ‘If you give a person a job, you give them dignity.’ But if you make someone an entrepreneur, it’s like giving them super dignity. They go from a job seeker to a job creator, and they can focus on the things that they’re most interested in, working with the people they want, in the culture they want. We teach students the entrepreneurial mindset and we train them to systematically take an idea and then come up with a solution in an ambiguous, uncertain environment, and iterate on that.

Everyone might not start a company, but even if they go to work at bigger companies, there are a lot of benefits to the entrepreneurial mindset. I’m an entrepreneur now and I’m at MIT, which is not a startup. In a world that’s moving faster and faster, everyone has to deal with change and ambiguity. Starting companies is just a great way to learn. It’s kind of like learning to paint from a blank canvas.

In 2017, a man named Michael walked onto stage in front of a packed Kresge Auditorium at MIT and attempted to draw a spiral, a common test doctors use to diagnose Parkinson’s disease. His tremors, caused by the disease, made the exercise difficult.

Then, Michael put on a wristband device made by a team of MIT students as part of 2.009 (Product Engineering Processes), who were presenting their prototype that evening.

Michael pressed a button and the device produced a subtle vibration. The vibration sent signals up his wrist and into his brain. His tremors dramatically decreased, and within seconds he was able to draw the spiral with much more precision, to a roaring ovation from the audience.

“When this device is turned on, I feel like I used to feel when I didn’t have Parkinson’s disease,” Michael told the crowd. “It’s an amazing, amazing feeling.”

The performance was so impressive that the student team received requests from classmates and others asking where they could buy the device for family and friends living with tremors. Unfortunately, the students had to explain there was only one — for the time being.

The event set off a near decade-long journey that began by leveraging MIT entrepreneurial resources like MIT Sandbox, MIT FUSE, and the MIT Venture Mentoring Service. In 2020, the student team turned into an official company, Encora Therapeutics. But there were still dozens of hardware iterations ahead. Then there were clinical trials. In one trial, 78 percent of patients reported benefits after 90 days of home use.

This February, nine years after Michael’s brave demonstration, all that work finally paid off: The FDA cleared Encora’s device to help adults with essential tremor, a condition similar to Parkinson’s that causes shaking, often in the hands.

“It’s been a long and difficult — very difficult — journey, but also very rewarding, especially when we get feedback from patients,” says Daniel Carballo ’18, SM ’20, an Encora co-founder and vice president of strategy. “We hear stories from patients about how they’ve struggled with their condition and how much they benefit from this. It reminds us why we keep going.”

The three founders of Encora who are still with the company are Carballo, Allison Davanzo ’18, and Kyle Pina ’18. They were each seniors in 2017 when they enrolled in 2.009, MIT’s popular product-design class.

The semester began with a brainstorming session in which groups of about 17 students were asked to come up with dozens of potential product ideas. Carballo proposed a wearable device that used mechanical vibration to send signals to the brain to reduce tremors. The initial idea was to help patients with Parkinson’s disease.

“It was one of hundreds of throwaway ideas,” Carballo recalls. “The initial concept was inspired by classes I had taken in robotics around neural control of movement. I had a preliminary understanding of how an electromechanical device might interact with the body’s control systems and feedback loops that control movement to relieve pathological control of movement.”

The team eventually whittled their long list of ideas down to a few. Carballo’s idea was finally selected by a vote of 16-1 — with Carballo the only dissenting vote.

“I tried to explain to the team that this was so far-fetched that there was no way, in one semester, we would be able to make anything,” Carballo recalls. “Thankfully, I got outvoted.”

Through most of the semester, Carballo’s pessimism looked justified. At every class milestone, the team lagged behind other teams. Then, the week before final presentations, they met Michael, whose severe hand tremors were the result of early-onset Parkinson’s.

“He was a home renovator, so he worked with his hands, but his tremors had progressed to the point that he struggled to turn a screwdriver, use a drill, or even fill out paperwork,” Carballo recalls. “He had become reliant on his wife and daughter not only to run his business, but to help him with everyday activities.”

By this point, the 2.009 team had a prototype that would vibrate to send mechanical feedback to the brain. Carballo described it as “a foamcore box with a Raspberry Pi chip and some wires coming out.” When Michael put on the device and turned it on, his tremors dramatically decreased.

“It was like a light switch turned on and his tremors stopped,” Carballo says. “His wife and daughter started crying. A week later, he was gracious enough to repeat the process on stage for the final presentations.”

The presentation — and the outpouring of interest from people who wanted it for loved ones with Parkinson’s — made the team determined.

“It showed us there were a lot of people with this problem that could really benefit from this,” Carballo says. “A subset of the team became possessed. It would have been such a shame to know this could exist and to have it never leave the classroom.”

Some team members began using MIT’s entrepreneurial resources to commercialize the technology, initially focusing on Parkinson’s. They ran their first clinical trial with 20 Parkinson’s patients in 2022 in collaboration with MassGeneral Brigham. But they soon learned more patients are living with essential tremor.

“It was a greater unmet need,” Carballo says. “There are a lot of drugs being developed for Parkinson’s disease, but essential tremor hasn’t experienced that same innovation.”

Today scientists think tremor is caused by malfunctioning signals in the regions of the brain responsible for interpreting sensory input and coordinating movement.

“In these diseases, neurotransmitter deficiencies result in this pulsed signaling in the brain that manifests as tremors that are basically pulsed motor outputs,” Carballo says.

One current approach to target those signals is surgery that involves drilling holes in the skull to insert electrodes that drive new electrical patterns in the brain. Encora’s watch-like product targets the same brain regions with mechanical vibrations at the wrist.

“We’re applying mechanical stimulus, basically vibration, to stretch receptors in the wrist, which tell your body where it is in space,” Carballo explains. “The stimulation causes the receptors to activate and send a patterned signal to peripheral nerves of the wrist, that then carry the signal to the peripheral nervous system and into the central nervous system, to the same regions of the brain targeted by surgery.”

In 2024, Encora ran a randomized control trial with 47 patients living with essential tremor. Last year, the team ran a 59-person trial where patients used the devices at home. In both trials, more than 70 percent of patients experienced meaningful improvement.

The results were promising enough to gain what’s known as 510(k) clearance from the FDA for use as a medical device, in February of this year.

Most patients see rapid benefit when using Encora’s device. Patients have described the device as life-changing. Some say it allows them to do tasks they haven’t been able to do in years.

Today, Encora is focused on building a national sales force and working to secure coverage from insurers. As the company ramps up production, the product will finally become available for patients who qualify.

Further down the line, Encora hopes to fulfill its original mission of helping mitigate tremors in patients with Parkinson’s. Carballo believes the approach also holds promise for many other patients.

“There is a surprisingly long list of diseases that this could work for,” Carballo says. “The most obvious are neurological movement disorders, but the bigger picture of wearable neuromodulation is a rapidly growing field that has seen therapeutic benefit across a broad range of diseases. We see this as a platform technology.”

MIT Future Fest launched on Wednesday afternoon, featuring a visit from Massachusetts Governor Maura Healey before an opening panel at Kresge Auditorium.

Both Healey and MIT President Sally Kornbluth emphasized the power of curiosity and innovation during introductory remarks, while formally kicking off MIT Future Fest as a new kind of event that opens MIT’s doors to the world.

Spanning five days and featuring talks from leading MIT scholars, panel discussions, open labs, exhibitions, performances, screenings, and much more, MIT Future Fest adds up to a demonstration of campus research, teaching, thinking, and discovery.

“MIT and Massachusetts are known around the world as leaders in innovation,” Healey said. “Future Fest is a chance to celebrate that. But it’s also a chance to connect with each other. To learn from each other, and to imagine what comes next. In life sciences and biotech, robotics, advanced manufacturing, in energy and climate technology and quantum, and in the people who make it all happen.”

In remarks preceding Healey’s talk, Kornbluth observed that “in a world as complex and interconnected as ours, we aim to invent the future. We need all the help we can get, and that’s why it’s so important to have all of you join us over the next five days in exploration, conversation, and curiosity.”

Future Fest also underscores that at MIT, as Kornbluth put it, “We feel a drive to explore, to understand, to solve, to invent, to design, all in service of society.”

Since its founding, “MIT has long stood at the forefront of innovation and invention,” Healey noted. “‘The future starts here’ isn’t just a fitting theme for the next few days. It’s something we’ve been doing for the better part of 250 years in Massachusetts.”

Healey later appeared at an event about ocean sciences at the MIT Museum, whose staff have helped develop and organize MIT Future Fest.

All told, MIT Future Fest features over 100 speakers at more than 50 events, among many other activities. The MIT Museum is free to the public during Future Fest; MIT’s newly opened Met Warehouse is hosting its first public events on Saturday; and Sunday features the Cambridge Science Carnival, a series of family activities.

The remarks by Healey and Kornbluth were followed by a panel discussion, “The Future Begins Here,” featuring participants Noubar Afeyan PhD ’87, founder of Flagship Pioneering and co-founder of Moderna; Sangeeta Bhatia SM ’93, PhD ’97, the John J. and Dorothy Wilson Professor of Health Sciences and Technology and of Electrical Engineering and Computer Science at MIT, and director of MIT’s Marble Center for Cancer Nanomedicine; and Bob Mumgaard PhD ’15, CEO and co-founder of Commonwealth Fusion Systems.

Massachusetts Economic Development Secretary Eric Paley moderated the wide-ranging discussion, which touched on the cutting-edge Massachusetts innovation ecosystem, the implications of artificial intelligence for research and industry, and more.

The panelists noted that a confluence of ingredients — universities, other research institutions such as hospitals, venture capital, and more — has helped make Massachusetts the world leader in biotechnology and other forms of innovation.

“You can’t be here and not feel like things that the rest of the world feel are not possible are possibly possible,” Afeyan said. “It’s really interesting how the environment makes you feel like you should also do something special.” He added that the various ecosystem elements have created a “critical mass” needed to sustain broad-ranging innovation.

“This critical mass accumulated between the academic science, the hospital research, the entrepreneurial companies, large companies moved in, the government stepped in, state level, to try to help this community,” Afeyan observed. “It’s an inherent advantage of any ecosystem, it’s the co-location, it’s the connections.”

Mumgaard, whose company is building the first fusion power plant in the U.S., concurred that the combination of elements available in the state ecosystem is crucial for advanced innovation.

“In many ways the challenge is simply scale,” Mumgaard said. “How do you get enough capital, how do you get enough smart people, how do you get enough people that are hard workers, how do you get the alignment from the institutions that can give you the permissions and tailwinds to be able to go and do that? And we did find that in Massachusetts.”

He added: “You could not have really have built this company outside of Massachusetts.”

For her part, Bhatia, a leader in developing nanoscale technologies that have made inroads in battling numerous forms of cancer, observed that part of the inspiration for her career came from visiting the MIT campus as a high school-age student.

“It was just that idea that you could engineer things, you could create instruments to improve human health, that started me on my journey,” Bhatia said.

As a faculty member, she has helped found eight spinoff companies and helped direct an MIT study about how the Institute — which has already helped generate about 30,000 new companies — can do even more to produce startups and technology transfer. Ultimately, she has concluded, teaching researchers about the tools and skills they need to found companies can help generate even more productive networks of entrepreneurial activity.

“We can be more intentional. We can create communities. … There are all kinds of ways to do more,” Bhatia said.

The panel discussion included reflections on AI and forecasts about the future of technology 20 years out, while ending with a reminder from Afeyan that, while the future is hard to forecast, it is at least partially shaped by people working consistently hard in pursuit of their goals.

“We can envision the future a lot better than we can predict it,” Afeyan said, when asked to forecast the effects of technology in a couple of decades, near the end of the discussion.

Afeyan concluded: “My parting thought on this would be to say … I think you can either live in the present, do the things you do, and get the future you deserve, or you can envision the future you want, and do everything in service of that.”

A new AI system that excels at challenging games with hidden information could someday help human decision-makers select ideal strategies to outfox opponents in complicated situations like military maneuvers.

Using advances in machine-learning, researchers from MIT, Carnegie Mellon University, New York University, and Stanford University developed an AI that defeated top-ranked human players of the board wargame Stratego by a large margin — something no AI system had been able to achieve.

Stratego, a two-player game of imperfect information, in which the opponent’s piece identities remain hidden, is often used as a benchmark to test the strategic thinking abilities of powerful AI models.

To build their model, the researchers combined efficient training algorithms with new techniques tailored for calculated decision-making in hidden information settings.

The AI system achieved greater performance at Stratego than the next best models, while being far cheaper and less computationally demanding to train. The system also outperformed top human players in other strategic games with different rules and designs, demonstrating how it can be generalized for a variety of use-cases.

The AI system could be adapted to help humans tackle many real-world problems with hidden information, such as business negotiations or cybersecurity.

“In the kind of imperfect information tasks you would face in reality, you often don’t have the luxury of enumerating through all the possibilities. There are just too many. Having AI algorithms that are general purpose and can provably perform this challenging task so well is a big step forward,” says Gabriele Farina, an assistant professor in the Department of Electrical Engineering and Computer Science (EECS), principal investigator at the Laboratory for Information and Decision Systems (LIDS), and senior author of a paper on this AI system.

He is joined on the paper by lead author Samuel Sokota, a graduate student at Carnegie Mellon; Eugene Vinitsky, an assistant professor at NYU; Zico Kolter, a professor at Carnegie Mellon; Hengyuan Hu, a graduate student at Stanford; and Zhiyuan Fan, an EECS graduate student at MIT. The research appears today in Nature.

In these interactions, some parties possess information others do not. For instance, traders in financial markets may not know the rationale behind the trades of others, while military forces likely don’t have full knowledge of enemy positions.

With hidden information, the decisions parties make, as well as the decisions they choose not to make, are intertwined in such a way that it is extremely difficult to determine the best steps to take next.

“The more you bluff, the more your opponent expects it, and the less each bluff is worth. It’s not obvious how to reason about that,” Sokota explains. “It’s very different from a setting like chess, where the best move is still the best move no matter how often you’ve played it.”

Stratego is often used to model imperfect information situations. In this board wargame, which resembles military chess, players arrange 40 pieces on their side of a board and then move pieces across the board to capture their opponent’s flag.

But the identity of all pieces remains secret until they collide, and then the lower-ranking piece is eliminated.

The possible piece configurations number more than 10 to the 66th power — an exponentially greater number than in chess — making Stratego extremely difficult for an AI system to play well.

Past efforts, such as Google’s DeepMind, relied on sophisticated operations that were computationally demanding and costly. But even with millions of dollars in training costs, these models were still not strong enough to beat top human Stratego players.

“With Stratego, there is an explosion of possible universes you might have to deal with. AI techniques that were developed for games like poker definitely could not scale in this setting,” Farina says.

The MIT researchers set out to develop a full AI system that could achieve superhuman performance for less cost, which they called Ataraxos (a Greek word used to describe one who is unbothered or free from anxiety).

To build Ataraxos, the researchers trained the model using a technique called self-play reinforcement learning. The model plays against itself many times to learn a strong “blueprint strategy” of how to excel at Stratego.

They designed especially efficient algorithms, which enabled Ataraxos to learn much faster than prior methods while ensuring it didn’t get stuck trying to predict every possible move. This reduces training costs and boosts performance.

“Our system reaches strictly higher playing strength than DeepNash (DeepMind’s system) while using less than one hundredth of the training examples and less than one thirtieth of the self-play games, indicating a massive improvement in efficiency,” says Farina.

During a game, Ataraxos uses the blueprint strategy as a starting point to set up the board and begin thinking about its next moves at each round of play.

But before acting, it refines its choices on the fly using a technique called decision-time planning. The system employs a generative model that uses probabilities to estimate the likely identities of the opponent’s hidden pieces, then evaluates future choices before selecting the next move.

“Rather than just guessing blindly, we use decision-time planning to find the most plausible state of the board. Using this generative model allows us to really zoom in on the specific board and opponent we are facing,” Farina says.

The innovative use of this generative model for decision-time planning was the missing piece that enabled Ataraxos to achieve superhuman performance.

Ataraxos beat the strongest Stratego player in the world by a record margin of 15-1-4 and achieved a 39-2 record against top human players at the Stratego world championship. “Ataraxos is good at calculating risk in a way that humans are not. A human might start freaking out if their most valuable piece is exposed, but the bot can be surprisingly composed. It doesn’t overcorrect and give away its secrets,” Farina says.

The researchers also adapted Ataraxos for other imperfect information games, including Barrage Stratego (a faster-paced variant with fewer pieces), Hanabi (a cooperative card game with many players), and Dou dizhu (a game in which two players cooperate against a third).

The system achieved superhuman performance in each instance, demonstrating the generality of this method.

In the future, the researchers want to build interpretability measures into Ataraxos so the system can explain its decision-making in a way that a human could understand.

“Humans must have the final say in whether a recommendation is followed, so before adoption can happen, we need a way to audit the model’s decisions. We still have a long way to go, but I hope these algorithms can be the foundation for a lot more work to come,” Farina says.

This research is funded, in part, by the Office of Naval Research, the New York University Department of Civil and Urban Engineering, the C2SMART Center, the National Science Foundation, and a Schmidt Sciences AI2050 Early Career Fellowship.

About 25 percent of lung adenocarcinomas have mutations of the gene KRAS, which drives uncontrolled cell growth. In recent years, the FDA has approved two KRAS inhibitors to treat patients with KRAS mutations. While these drugs can work well initially, tumors almost always develop resistance to them.

Usually, resistance emerges because cells reactivate KRAS activity, through mutations that prevent drug binding or by increasing KRAS expression that overpowers the effects of the inhibitor. However, in a new study, MIT researchers have modeled an alternative mechanism that cancer cells can use to become resistant to KRAS inhibition.

The researchers found that in some cases, lung tumors undergo transformation from adenocarcinoma to squamous cell carcinoma. Both of these tumor types are commonly found in the lungs, but they are thought to arise from different cells and have different genetic profiles.

When this transition occurs, tumor cells no longer require KRAS, and appear to turn on alternative signaling pathways that help them continue to grow. Ongoing work to identify those pathways may reveal targets for new drugs that could help prevent resistance to KRAS inhibitors.

“The main takeaway is that there seem to be different routes of resistance to KRAS inhibitors, and so we need to be thinking about how we can address this,” says Carrie Rodriguez, an MIT graduate student and one of the lead authors of the paper.

Nicolas Mathey-Andrews PhD ’25 is also a lead author of the study, which appears today in the journal Nature Genetics. The paper’s senior author is Tyler Jacks, the David H. Koch Professor of Biology and a member of MIT’s Koch Institute for Integrative Cancer Research.

The two FDA-approved KRAS inhibitors both target a mutation called KRAS-G12C. These drugs are approved only for use in patients whose tumors have failed to respond to other drugs, and these patients usually have cancer that has spread beyond the lungs.

KRAS inhibitors are effective in about 35 percent of the patients who receive them. However, in those cases, the tumors almost always end up becoming resistant by generating additional copies of the KRAS gene or finding other ways to turn on the MAP kinase signaling pathway, which is usually triggered by KRAS and stimulates cell growth.

“Resistance to targeted therapies is a very serious problem,” Rodriguez says. “Sometimes these KRAS inhibitors can hold cancers at bay, but most cases do end up relapsing.”

A 2021 study from researchers at Dana-Farber Cancer Institute, which analyzed tumors from 17 non-small cell lung cancer patients treated with KRAS-G12C inhibition, identified secondary resistance mutations in a majority of patients. In two of these patients, however, the researchers found that tumors transformed from adenocarcinomas to squamous cell carcinomas, but they did not harbor obvious resistance mutations.

Both adenocarcinomas and squamous cell carcinomas are classified as non-small cell lung cancers (NSCLCs), which are the most common type of primary lung cancer. Adenocarcinomas, the most common type of NSCLCs, often originate from the surfactant-producing cells that line the lungs, while squamous cell carcinomas originate in the cells that line the central airways of the lungs.

Mutations of KRAS are found much more frequently in adenocarcinomas than in squamous cell carcinomas

In this study, the researchers set out to model the factors that might drive the transition from adenocarcinomas to squamous cell carcinomas. To do that, they engineered a mouse lung cancer model to express the mutation that is targeted by the FDA-approved KRAS inhibitors.

Following treatment with a KRAS-G12C inhibitor, tumors with genetic loss of Nkx2-1, which normally helps maintain alveolar epithelial identity, were able to undergo adeno-to-squamous transition. Turning on a transcription factor called DeltaNp63, which is overactive in many squamous cell carcinomas, also made this transition more likely. Another transcription factor known as SOX2 also helped stimulate the transition, but this gene could not initiate the transition on its own.

Tumors that underwent these tissue transformations did not acquire the mutations that typically boost KRAS expression in adenocarcinomas. Instead, KRAS signaling was shut off. The researchers hypothesize that these cells may turn on another signaling pathway that helps them to continue growing.

“There seem to be several different routes where you can get to squamous transformation, either through loss of lung-lineage-defining transcription factors, or overexpression of these squamous master regulators, SOX2 or DeltaNp63. Those resistant squamous tumors no longer respond to KRAS inhibition because they shut off the signaling or at least dampen it significantly,” Rodriguez says.

The researchers are now further exploring what happens to tumor cells as they transition to a squamous state, in hopes of identifying vulnerabilities that could be targeted with new drugs.

“Fundamentally this is a transition that’s poorly understood, and we were happy to see that we were able to model it,” Mathey-Andrews says. “Future directions that have an eye toward translation will utilize those models to understand the process and conditions by which histologic transformation occurs, and then also nominate potential targets downstream.”

The research was funded, in part, by the Koch Institute Support (core) Grant from the National Cancer Institute, a Ruth Kirschstein National Service Research Award, the National Institute of General Medical Sciences, and the Ludwig Center at MIT.

J-PAL North America, a regional office of MIT’s Abdul Latif Jameel Poverty Action Lab (J-PAL), convened the second cohort of the Climate Action Learning Lab this spring with 17 climate leaders from four U.S. government agencies and nonprofit organizations. Participants then engaged in four months of programming designed to strengthen their skills in generating and using evidence, applying research insights to their own programs, and identifying promising policy-relevant interventions for evaluation.

The Climate Action Learning Lab first emerged in 2025 as a response to an urgent need for rigorous research on programs that seek to improve resilience to climate-related hazards and support the transition to a low-carbon economy. By helping participants identify interventions and develop evaluation plans, the Learning Lab aims to generate evidence about which approaches are most effective, and for whom.

“We have a unique opportunity to embed rigorous evaluation into promising programs as they are implemented in order to accurately measure their impacts on emissions reductions,” says Peter Christensen, scientific advisor of the J-PAL North America Environment, Energy, and Climate Change Sector. “By developing rigorous evaluations, Learning Lab participants can better understand the behavioral mechanisms that drive program impacts and cost-effectiveness, and build an evidence base to inform more effective policymaking.”

Following the success of last year’s Climate Action Learning Lab, which led to multiple research collaborations and the launch of two randomized evaluations, J-PAL North America recruited a second cohort of leaders representing four organizations: the City of Boston’s Sustainability Office, the Hawaii Climate Change Mitigation and Adaptation Commission, the Oregon Department of Environmental Quality, and the Electrification Coalition.

From May through August, the cohort engaged in a set of offerings including training on impact evaluation, learning how to formulate research questions, and assessing the generalizability of the existing evidence to their own contexts. Participants had the opportunity to explore J-PAL resources, including the recently released Climate Action Evidence Review, which synthesizes existing evidence and highlights important gaps where more research is critical to inform effective, equitable climate action.

“It was exciting to learn where the gaps in research are and where we have an opportunity to lead. The experience reinforced that we are addressing issues that have not been widely studied, and having access to researchers’ expertise was incredibly valuable,” says Ana Paola De La Vega, from the City of Boston’s Environment Department. Throughout the Learning Lab, the city explored a potential evaluation of its Boston Energy Saver program to better understand its impacts on energy cost savings and energy efficiency incentive uptake among small businesses.

Members of the Climate Action Learning Lab put theory into practice through personalized strategy sessions, where they examined potential programs for evaluation. Researchers from the J-PAL network joined select sessions to advise organizations on which programs to prioritize, considering factors such as research feasibility and existing evidence gaps. Each participating organization selected a target program and explored potential randomization approaches.

“The Climate Action Learning Lab provided our team with a valuable opportunity to catalog our projects and better understand what makes a program ready for evaluation. It also helped us identify which initiatives are the strongest candidates for rigorous evaluation and how to prioritize them,” says Leah Laramee, Hawaii climate change mitigation and adaptation coordinator. During the Learning Lab, the team assessed three potential programs for evaluation and selected a rebate finder that connects residents with climate-related programs, with a focus on understanding its impact on program uptake, particularly among vulnerable communities.

In August, J-PAL North America hosted a virtual summit to celebrate Learning Lab participants as emerging champions for evidence in the climate space. During the event, cohort members presented their priority research questions and strategic evaluation plans and received feedback from researchers and peers. These presentations highlighted the progress made throughout the engagement and provided an opportunity to discuss next steps for advancing organizations’ evaluation plans beyond the Learning Lab.

“Overall, the Learning Lab has provided our team with a strong foundation to think critically about our own programming when applying for grants, setting up new projects, and determining how to assess impact from the beginning. This knowledge could ultimately help us determine what approaches the Electrification Coalition can take in future policies and programs,” says Ashley Blackwell, deputy director at the Electrification Coalition.

Although formal Learning Lab programming has concluded, J-PAL North America will continue supporting organizations interested in launching a randomized evaluation through partnership development with researchers and potential funding opportunities. This Learning Lab cohort will join J-PAL North America’s Climate Action Community of Practice, alongside longtime partners and participants from the inaugural Learning Lab cohort. Together, Community of Practice members will continue to exchange ideas, build connections, and explore evidence-informed approaches to mitigation and adaptation strategies.

To learn more about J-PAL North America’s work in the energy, environment, and climate change sector, including our full range of activities, resources, and partnership opportunities, visit the Evidence for Climate Action Project webpage.

Swimming can take a lot of muscle. But as MIT engineers have found, even a single layer of muscle cells can power through water if designed right.

In a paper appearing today in the journal Advanced Functional Materials, the team presents a design for a thin, muscle-powered swimming robot. The “skeleton” of the aquabot is made from a film of gel that is about the length and width of a stick of gum. The two halves of the gel form the “fins” of the bot. Each fin is covered with a layer of live muscle cells that is much thinner than a single strand of hair. The cells are genetically engineered to twitch in response to light.

When the researchers shine light on one fin, the muscles on its surface twitch in response, causing the whole fin to flap with enough force to pull the robot through water. By flashing light on one fin or the other, at various intervals, they can control the swimming robot’s direction and speed.

The engineers showed that the paper-thin bot could swim and swivel through a simple watery maze. At its fastest, the robot can swim a distance of about four times its body length in one minute. That’s a snail’s pace compared to Olympic swimmers, who can cover up to 65 body lengths per minute. But the bot could hold its own against more leisurely swimmers like the cow shark, which explores the ocean at about the same rate.

“It takes a lot of force to move through water versus air,” says study author Ritu Raman, associate professor of mechanical engineering at MIT. “The robot’s quite strong, given its size.”

The new robot is the first example of a very thin, two-dimensional, muscle-powered robot capable of locomotion.

“Currently, biohybrid robots from our group and others’ are built from bulky, 3D chunks of lab-grown skeletal muscle that require millions of cells to fabricate,” says Raman, who notes that thinner, less bulky designs such as the team’s new bot could be cheaper to build and could move more efficiently. “We believe that biohybrid robots powered by living muscle could one day perform delicate jobs like exploring environments too fragile or unpredictable for conventional hardware, because living tissue is soft, responsive to its surroundings, and can heal itself.”

The study’s MIT co-authors are first author Maheera Bawa, Arielle Berman, Laura Schwendeman, Ferdows Afghah, and Seanbiron Johnson.

Last year, Raman’s group developed an iris-inspired disk of artificial muscle tissue. They stamped a disk of gel with a pattern of concentric and radial grooves, and deposited live muscle cells onto the gel’s surface. The cells formed a thin layer that grew along the grooves, and when stimulated with light, the cells moved in patterns that stretched and squeezed the disk, similar to how a human iris dilates and constricts the eye’s pupil.

That work was the first to demonstrate that muscle cells could be grown in a very thin layer, and in complex patterns that when stimulated could move in multiple, controllable directions.

“People hadn’t seen this muscle architecture engineered from scratch before,” Raman says. “And the cells were moving in multiple directions. But they only moved about 100 microns. From a robotics perspective, their movements were tiny.”

In their new work, the team aimed to maximize muscle movements to produce more force — enough, say, to power a swimming robot. The key, they found, was to optimize the skeleton on which the cells grow.

In their previous iris-inspired design, they grew muscle cells on fibrin, which is a type of ultrasoft gel that the team realized can quickly shrivel in response to the forces generated by the muscles. To better support cells and maximize their force, the researchers focused on tuning the underlying gel by changing three properties: the gel’s composition, its stiffness, and the size and shape of the grooves that are stamped into it.

“For engineering any type of tissue, it’s known that these are knobs you can tune,” Raman says. “And we wanted to optimize all these parameters to support live muscle cells.”

To find an optimal “skeleton” on which to grow muscle cells, the team experimented with multiple formulations of gel, of different stiffnesses, and stamped with grooves of different geometries. For instance, one groove type resembled a skinny square trough, where another was more of a long curved valley. They found that when they deposited muscles onto each type of grooved gel, cells settled into alignment in grooves that were more square than curved. More aligned cells tend to fuse into fibers that then form a stronger, more coordinated muscle tissue. Square grooves, they found, were the way to go.

Instead of using fibrin, they tried gelatin methacrylate (GelMA), a material that is often used in tissue engineering. They made different recipes of GelMA to create skeletons of different stiffnesses and observed how muscle cells grew when deposited on the gel’s surface. They found that cells grew in better alignment, and produced the most force, on stiffer gels.

The team also varied the gel thickness and found that a half-millimeter-thin film of GelMa offered good support for a single layer of muscle cells. The film was light enough that the cells were able to stick to the gel when they contracted, rather than peeling away.

Finally, the team “exercised” the muscles, using a training routine of flashing lights to strengthen the muscles.

With the pumped-up cells and the optimized gel, the team designed a thin, two-finned robot, comprising the gel, stamped on both sides with square-bottomed grooves and lined with muscle cells. The cells fused into fibers, eventually forming a strong, aligned muscle tissue.

“You can think of the robot as having two independent muscles,” Raman says. “If we shine a light on just one, only that muscle moves. If shining on both, they both flap.”

The researchers submerged the robot in a large petri dish of water and manually maneuvered a light source over the bot. The robot followed the light, flapping its fins in response to navigate through a maze that the team placed in the dish.

The current design is relatively basic as far as its form. The researchers intended first to show that the bot could produce enough force to swim.

“Our next goal is to optimize the body design to enable faster swimming,” Raman says. “But even at slow swim speeds, one could imagine a muscle-powered swimmer being used for purposes like environmental monitoring in aquatic environments.”

AI tools are increasingly replacing human judgements in some settings. For instance, resume screening algorithms are often used in hiring, where they may improve efficiency and consistency in decision-making.

But some scholars have raised concerns that the adoption of automated systems could eventually result in one algorithm being used to make all decisions in a particular industry. They worry so-called algorithmic monoculture could have negative consequences.

For example, in hiring, the thinking goes that algorithmic monoculture might result in systematic exclusion — a situation in which a job candidate rejected by one firm’s algorithm would likely also be rejected by every other firm’s algorithm.

However, MIT researchers now argue that algorithmic monoculture may not always be as bad as some scientists have suggested.

They systematically evaluated major objections to algorithmic monoculture, including systematic exclusion, and concluded this and many other arguments either fail or aren’t decisive against all forms of monoculture.

Instead, they mathematically prove that monoculture tends to create informational echo chambers that can hinder exploration. In hiring, this could make it less likely that the best candidates would get jobs — however, bundling various hiring algorithms into a single “ensemble” can overcome this limitation, the researchers show. This could sometimes enable monoculture to perform as well as, if not better than, a polyculture where different firms use different algorithms.

“A trend toward algorithmic monoculture is a realistic scenario, and a really important issue that is being brought about by the use of AI, but it is hard to say in the abstract whether monoculture would be a bad thing. It depends on the details, like the domain we are talking about and the accuracy of the algorithm itself,” says study co-author Brian Hedden, a professor in the Department of Linguistics and Philosophy, who holds an MIT Schwarzman College of Computing shared position with the Department of Electrical Engineering and Computer Science (EECS) and is also a principal investigator in the Laboratory for Information and Decision Systems (LIDS).

Hedden is joined on the paper by co-author Manish Raghavan, the Drew Houston (2005) Career Development Professor at the MIT Sloan School of Management, who also holds a college shared position in EECS, and is a LIDS principal investigator. The research appears in Philosophical Perspectives.

Algorithmic monoculture, in which all decisions across a certain domain are made using the same algorithm, is not a new phenomenon.

For instance, lending decisions were once made by independent bankers at individual banks, but now all bankers use the same information based on a borrower’s standardized credit scores, which are derived from the Fair Isaac Corporation (FICO) algorithm.

Similarly, a handful of resume screening algorithms are commonly used by many Fortune 500 companies.

“The worry is that, as more people use AI and algorithms to get information and make decisions, there is more of a vehicle for this kind of correlation to occur,” Raghavan says.

To better understand this issue, he and Hedden joined forces to systematically assess the promises and pitfalls of algorithmic monoculture. They focused on hiring, but their approach could apply to other domains, such as lending. (They note, however, that other domains, like generative AI content creation or AI-guided scientific research, may work differently, and that monoculture in some of these domains may be more problematic.)

The researchers began by exploring one common objection to algorithmic monoculture: that reliance on the same algorithm will systematically exclude certain people from opportunities.

This could occur in hiring because, if one firm screens out an individual’s resume, that applicant will likely face the same bad luck at each firm.

But after systematically evaluating this argument using a series of models that capture multiple situations, the researchers argue it isn’t compelling since the overall number of people hired is not affected by the fact that firms use the same algorithm.

Rather, algorithmic monoculture could improve bargaining power of job candidates.

“All the jobs get filled and the same number of people have jobs, but the firms are fighting over the same pool of candidates, which actually drives up wages,” Raghavan says.

They also explored objections related to agency. For instance, if a job candidate applies for a job and their resume is forwarded to every firm using the hiring algorithm, the candidate never gets a chance to adjust their resume to improve their chances.

“This seems like a good objection to bad forms of monoculture. But if you have a monoculture where you get to revise your resume and resubmit your materials, then this doesn’t hold up,” Hedden says.

On the flip side, monoculture could enable individuals to game the system. For instance, if having one’s resume in a certain format leads to a better outcome, job candidates could simply reformat their resumes to improve their chances.

“But it is not obvious that having one algorithm would incentivize this kind of gaming more than having a bunch of different algorithms used by different firms,” Hedden says. “In the latter scenario, you might just target a couple of firms’ algorithms and try to game them, giving yourself a bit of advantage with a few employers.”

They also considered a less-explored objection: that monoculture can increase homogenization of information.

Based on the “wisdom of crowds,” a theory from social psychology, a diverse group of independent decision makers can outperform a single person, Hedden explains.

In hiring, this means that having firms with diverse hiring algorithms can lead to a higher-quality pool of new hires.

Algorithmic monoculture could also cause candidates with the same characteristics and credentials to be hired every time by every firm. This may prevent firms from discovering candidates who may be better alternatives.

“Monoculture might inhibit the amount of discovery that happens overall. It is not clear if that is a bad thing, but it is definitely a worry when we think about designing AI for applications like science, art, or writing,” Raghavan says.

This problem could be mitigated by building randomness into a monocultural platform, which could induce a higher level of exploration, he adds.

In addition, the performance of monoculture depends on the algorithm. If a single algorithm is much more accurate than the many algorithms used by different firms, monoculture may be better system.

One way to boost performance may be to package multiple firms’ hiring algorithms into one “ensemble algorithm” that could assign each job candidate a score based on the average.

By conducting a series of simulations of different hiring situations, the researchers confirmed that such an “ensemble algorithm” could sometimes outperform the use of multiple algorithms.

However, it remains to be explored how feasible this kind of algorithmic “ensembling” would be in practice, Hedden says.

“A lot of the answers around the promises and pitfalls of algorithmic monoculture are going to be contextual. Even from a research perspective, there is still a lot of work to be done to figure out how we can approach these concerns from an empirical perspective,” Raghavan says.

Ultimately, the researchers hope this work inspires additional research about the long-term consequences of algorithmic monoculture, as well as studies that focus on the real-world complexities involved in a complex system like a job market.

Brian, a middle-aged financial consultant, spends his day sitting in front of three screens. Two of them involve his job. The third features a chatbot, which he has given a woman’s name and frequently uses. Brian spent years on the road for work earlier in his career, has never married, and recently broke up with a woman who said he was emotionally unavailable. What did Brian do, in response? He asked the chatbot if the woman had a point.

“It’s an extraordinary moment,” writes MIT Professor Sherry Turkle, who interviewed Brian (not his real name) while conducting her research for her new book about chatbots. “Brian asks an object with no emotions to tell him if he is emotionally withholding: ‘Speak to me about what you cannot experience.’ As soon as he asks the program to talk about intimacy, he’s asking it to punch above its weight.”

“People think the empathy of a machine is what empathy is, then turn away from the people in their lives because they’re not empathetic enough,” Turkle says. “We’re getting ourselves in a position where human beings are too much work, right at the moment when we have never needed other people more.”

After all, what is a chatbot? It’s a computer program predicting the most plausible next string of text in a conversation, based on massive amounts of data fed into it.

“What a chatbot does is offer pretend empathy,” Turkle says. “After it tells you how much it loves you and how much it understands you, it doesn’t care if you kill yourself or cook some pasta.”

Sadly, chatbots have in fact been associated with high-profile cases of teen suicide as well, sometimes after teens get drawn into extensive dialogues with chat tools.

Turkle explores this terrain in a new book, “Artificial Intimacy: Who We Become When We Talk to Machines,” published today by Little, Brown and Company. After surveying evidence and conducting new research, she emphatically concludes that chatbot use, while it may often feel like a short-term salve, is broadly detrimental in terms of human development and social connectivity.

“We’re doing ourselves a tremendous disservice at every moment in the life cycle,” says Turkle, the Abby Rockefeller Mauzé Professor of the Social Studies of Science and Technology at MIT.

A sociologist and clinical psychologist by training, Turkle is a longtime faculty member in MIT’s Program in Science, Technology, and Society. In books such as “The Second Self” (1984), “Life on the Screen” (1997), and “Alone Together” (2011), she has evaluated the interplay of technology, psychology, and personal identity. In “Reclaiming Conversation” (2015), she documented the costs of texting and using social media.

“I feel the story I’m telling is the inner history of technology,” Turkle says. “Not just what it does, but what it does to people.”

Turkle structures “Artificial Intimacy” around the stages of a human life and explores the implications of chatbots for each one. In her interviews with children, Turkle finds a persistent blurring of the line between chatbots, people, and other devices.

One 8-year-old uses the same phone to talk to their grandparents and to ChatGPT, regarding them all as “things you reach on your phone.” A weary graduate-student mother who uses a chatbot for bedtime stories says her daughter thinks the chatbot is “a person in the phone, absolutely.”

In this sense, chatbots may be interfering with even the most basic childhood processes of distinguishing people from inanimate objects. Turkle finds many additional problems with the use of chatbots as ever-present entertainment for children, noting that a certain amount of time alone helps the development of imagination and inner resources.

“Children can’t learn trust from a device that not only lies but doesn’t know when it lies,” Turkle writes. “They can’t develop the capacity for solitude that enables both a sense of self and the capacity for mutuality.”

All told, in affecting the ability of children to develop, the dangers of chatbots are “existential,” Turkle believes.

Adults don’t fare much better when they use chatbots heavily, Turkle says. “Artificial Intimacy” explores case after case of grown-ups who become dependent on chatbots as well: people going through divorces or estrangements who want dialogue, students looking for advice about applying to college or graduate school, workers who enlist ChatGPT to do assignments, and more. They start using chatbots, keep using chatbots, and before long seem less interested in human interaction, and less capable of it.

“Once people are involved with a connection with a robot, they start to think that it’s alive, that it cares about them, that it loves them,” Turkle says.

That means people can lose or fail to build their own capacities for new thought, and opt out of dealing with the real world in all its maddening-but-affirming complexity.

“We’re de-skilling ourselves as relational beings,” Turkle says. “We’re also de-skilling ourselves in work. We’re de-skilling ourselves in personal relationships. It’s across the board.” And speaking of a practice multiple people in the book have attempted, she says, “When we build chatbot avatars of dead relatives to keep them alive, we can lose our ability to mourn.”

We are not, in her estimation, doing the hard work of figuring other people out, seeing things from different points of view, and putting in the effort to build on those things and make society better.

“We’re starting to define being human as not doing the work,” Turkle says.

For all of that, a considerable amount of “Artificial Intimacy” involves understanding why people engage with chatbots. After all, as Turkle writes in the book, “Chatbots always agree with us and affirm us. They always offer us their full, undivided attention.” And once that happens, things seem to just go from there.

People, meanwhile, can be prickly, demanding, and moody. As Turkle says: “People say they prefer the chatbot because their husband or wife says, ‘Dear, you took out the garbage, but also do the dishes, and clean up the kitchen while you’re at it.’ And a chatbot just says, ‘Oh, you’re so wonderful.’”

She adds: “This technology is offering something people really want, which is to feel less vulnerable. And it offers it over and over again. You don’t want to have to ask somebody out? Don’t want to offer condolences? At every opportunity, technology says, ‘Why don’t you do this thing that’s less hard.’”

And as Turkle acknowledges in the book, there is a shortage of services such as mental health care providers in the U.S., where only about half of people have access to one, according to a federal government study she cites. Chatbots are helping to fill this void, for better or worse.

Turkle also notes that the age of social media has likely led people to have fewer in-person friendships and interactions, a problem that chatbots are now aiming to address.

“You take away people’s capacities, then you offer technology as the cure for the problems technology caused in the first place,” Turkle says.

In light of all this, given Turkle’s dim view of chatbots amid the spread of AI, what is actually to be done to restore human interaction? For starters, Turkle thinks, we need to face up to the idea that life is not frictionless — and that’s okay.

“Everything in the life cycle is about facing up to friction and fears and stress and tension, and understanding that friction is not a bad thing,” Turkle emphasizes. Developing the capacity to overcome difficulties is an essential part of life. In trying to sidestep the hard work of being social beings, she thinks, personal technology is enfeebling us.

Beyond that, Turkle envisions pushback against chatbots similar to the movement against, say, having phones in schools or letting young people use social media.

“I hope my book is part of a larger and larger movement,” Turkle says. “I don’t want to be alone. I want to be part of a movement pushing back.”

Other writers in this domain have praised “Artificial Intimacy.” Journalist Nicholas Carr, author of “The Shallows,” has stated it “will help you avoid the profound but often hidden threats AI poses to you and your relationships.” Psychologist Jonathan Haidt of New York University, author of “The Anxious Generation,” has stated that Turkle’s “groundbreaking research and beautiful writing make her the most qualified member of Team Humanity to call us back to our senses, and to each other.”

For her part, Turkle says, “I wanted to write a book that college students would read, that high school seniors, juniors would be able to read, that parents would pick up and not be intimidated by, that teachers would read.”

“If not a richer life in the real,” Turkle writes, “what’s our endgame?” The purpose of life, she underlines, is not to avoid it, but to tackle it head on.

“It’s one thing if you say, my teen is texting too much,” Turkle says. “It’s very different if your 2-year-old is thinking a plushy toy with a chatbot inside is their best friend. Because you are getting into the intimate infrastructure of what makes a person develop. Social media came for our attention. Chatbots come for our capacity for attachment. That’s toxic on another level. What is the endgame? That the baby will prefer chatbots to people who are much more complicated and hard? Is that who we want to be?”

The world needs more wind energy. But anyone designing new wind turbines or trying to squeeze more power out of existing ones faces a stiff challenge testing new approaches. That’s because the atmosphere is a tough place for a controlled experiment.

Some researchers use wind tunnels to conduct tests, but scaled-down wind turbines in traditional wind tunnels can differ widely from conditions in the field. (Wind turbines are the largest rotating machines ever made.) The problem hampers not only development of better wind turbines, but also our understanding of basic questions like how much power to expect from a turbine when winds change direction.

In a new open access paper published in PNAS Nexus, researchers closed the gap between experiments in the field and the lab by using a wind tunnel that features high pressurization to simulate the flow physics in the atmosphere. With this approach, the researchers determined how the alignment of the turbine and its tip speed relative to the wind influence the power it generates, offering new insights into how to get more power from existing wind farms.

They also used the approach to validate a computationally lightweight model that engineers can use to test different turbine designs and wind farm control strategies.

Together, the researchers estimate that optimizing the turbine alignment relative to wind, the blade pitch angles, which control the airfoil’s angle of attack, and the tip speed relative to the wind could potentially result in tens of thousands of dollars per turbine every year in additional revenue.

“The immediate impact of this study is that we’ve now both improved and validated models that go into wind turbine control protocols for existing farms,” says Michael Howland, MIT’s Jeffrey Cheah Career Development Professor. “The bigger, medium-term impact, with a much larger upside, is this new experimental paradigm to rapidly prototype, validate simulation models, and test hypotheses about better designs and control strategies much faster than has been possible before.”

Joining Howland on the paper are first author John Kurelek, an assistant professor at Queen’s University; MIT PhD candidates Ilan Upfal and Kirby Heck; Queen’s University postdoc Supun Pieris; Penn State University researcher Alexander Piqué; and Princeton University Professor Marcus Hultmark.

Howland has spent years developing models to simulate wind farm performance and developing new techniques to increase their power output. In 2022, he showed that accounting for the wake of individual turbines when controlling the entire wind farm could significantly increase power output.

But that work required his research team to first conduct a lengthy field experiment that temporarily resulted in lowering a real wind farm’s power output by intentionally misaligning turbines from the wind for months to better understand their performance in misalignment.

“Wind energy is a uniquely challenging problem to study experimentally,” Howland says. “We want to test the effect of a certain change in isolation, but wind farms operate in chaotic, turbulent environments where the weather is constantly evolving. Wind turbines have to react to weather conditions that we have no control over, and that introduces complexities in identifying the impact of the imposed change we are studying. The field sits at this unique intersection between environmental flow, mechanics, aerodynamics, and meteorology.”

The difficulty of running experiments at real wind farms has left researchers and engineers unsure of how changes in the alignment between the wind and turbine or factors like the turbine’s tip speed relative to the wind change power output.

In fact, the researchers say many predictive models people use are built on the assumption that turbines are always perfectly perpendicular to the wind. That’s rarely the case in the real world, even with modern turbines that gradually adjust their angle in response to the wind’s rapid directional changes.

“People have been debating which models are best for understanding the output from these wind farms, but if you have nothing to compare them against, it’s very difficult to advance the field,” Hultmark says. “This paper tries to do both of those things.”

Hultmark’s research lab at Princeton has pioneered the study of scaled-down wind turbines in pressurized wind tunnels, which, as previous studies have shown, better reproduce large-scale turbines in the atmosphere because pressure makes air more dense, resulting in more inertia within the scaled laboratory environment. For the new study, the researchers used a turbine measuring 15 centimeters in diameter at varying pressures of up to 240 atmospheres.

“By pressurizing the chamber, we’re testing a turbine that is, all else being equal, 15 to 20 meters in diameter, with the ability to go up to 35 meters in diameter,” lead-author Kurelek explains. “That’s because we’re increasing the density by a factor of 100 to 220 times,”

Kurelek sent the dimensions of the wind tunnel and wind turbine setup to Howland, who used them to calculate the aerodynamics, forces, and power production using a newly developed unified wind turbine model, which builds on previous work that developed a more general aerodynamic theory for wind turbines. The new model enables the researchers to simulate wind turbine performance across operating conditions without relying on empirical corrections that have historically been used in wind power models.

The researchers then ran a series of experiments in the tunnel over the course of several weeks, testing the turbine’s performance at different wind alignments and with different control strategies, to isolate how each factor affects performance.

They found power output could be significantly increased by adjusting the turbine’s tip speed based on its misalignment angle with the wind — a control strategy that is rarely employed in wind farms today but could offer a way to boost performance with minimal added costs.

“The big output of the experiments was clearly showing that new power maximums can be achieved when the turbine becomes misaligned with the wind through only changes to the tip speed,” Kurelek says.

Scaling the approach

The study served as validation for Howland’s model, which is fast enough to be run by engineers designing and operating wind turbines around the world using regular laptop computers.

“What we really want to know is if the turbines are always operating in some degree of misalignment with the wind, how should we control the turbine to get the maximum achievable power production?” Howland explains. “Our unified momentum model was able to make predictions of how to do this control a few years ago, and this is the first time we’e able to experimentally validate that model.”

Howland says validating models is only one part of the paper’s potential impact.

“This study also shows the huge opportunity to perform these high-throughput, controlled experiments in the pressurized facilities that Marcus and John work with, enabling us to achieve the right physics but in a time efficient and low-cost manner,” Howland says. “Right now, there’s a massive gap between idealized theoretical and simulation models and full-scale testing in extremely complicated field environments. Nothing is filling that gap except for these pressurized experiments. I hope this can be an enabler to investigate a huge range of unanswered wind energy questions in controlled environments.”

The work was supported in part by the Natural Sciences and Engineering Research Council of Canada; the National Science Foundation; and the MIT-GE Vernova Alliance.

RNA vaccines, which have been proven effective against Covid-19, are now being developed for many other diseases, including cancer. One of the drawbacks to these vaccines is that they require ultracold storage, but researchers from MIT have found a promising way to overcome that limitation.

With help from an AI algorithm, the researchers tweaked the formulation surrounding the lipid nanoparticles that are typically used to deliver mRNA vaccines, making the vaccines more heat-resistant. Using this approach, they formulated vaccines that could remain stable even when stored at room temperature for up to a year, or at nearly 100 degrees Fahrenheit for two months.

When Covid-19 vaccines carried by these particles were administered to mice, they generated just as strong an immune response as an RNA Covid-19 vaccine similar to one developed by Moderna. By using the AI algorithm to predict the optimal formulations for the particles, the researchers were able to cut down the number of experiments they needed to do, which rapidly sped up the development process.

“The real beauty of this algorithm is that we can use it with small data sets,” says Ana Jaklenec, a principal investigator in MIT’s Koch Institute for Integrative Cancer Research. “It’s really hard to run thousands of experiments, so this algorithm allows us to more easily achieve formulations with features that we want — in this case, stability.”

Jaklenec and Robert Langer, the David H. Koch Institute Professor, are the senior authors of the paper, which appears today in Nature Biotechnology. Graduate student Jinbi Tian and postdoc Khanh Tran are the lead authors of the paper.

RNA is a highly fragile molecule, so researchers stabilize it with lipid nanoparticles (LNPs) that protect the RNA from degradation and help it get into cells. However, these RNA-LNP vaccines still need to be kept cold (-20 to -80 degrees Celsius), which makes it difficult to ship them to regions that don’t have cold-storage facilities available.

Making these vaccines more heat-tolerant would not only enable them to be distributed more widely, but could also help researchers develop new vaccines that could be administered through novel methods such as microneedle patches. These patches contain hundreds of vaccine-filled microneedles, which dissolve when the patch is applied to the skin, releasing the vaccine.

To create more stable RNA vaccines, researchers have experimented with adding a variety of excipients — sugars, salts, or polymers — to the LNPs. Jaklenec and Langer recently developed polymer-stabilized LNPs that can withstand higher temperatures, but those LNPs were slightly different from the FDA-approved formulations that were used for the Moderna and Pfizer Covid-19 vaccines.

In their new paper, the researchers wanted to see if they could find a way to make those FDA-approved formulations more stable at high temperatures.

They began by reusing some of the excipients that had worked in their earlier efforts, but they were “really getting stuck,” Jaklenec says. “We were trying to use and screen excipients that we’ve previously used successfully to stabilize LNPs, but it just wasn’t working. It was really frustrating for the team.”

To speed up their progress, the researchers decided to try a machine-learning approach. Working with researchers at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL), they developed an algorithm that can make predictions based on very small datasets.

“We’d used our algorithms for various automated experimental design applications before, but never on a biological problem like vaccine stability,” says Mina Konaković Luković, an assistant professor of electrical engineering and computer science in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), who is also an author of the paper. “It was surprising to see how quickly the algorithm converged on a stable formulation — getting there in just a handful of iterations, rather than the exhaustive search that would normally be required.”

The researchers used this algorithm to analyze nearly 50 FDA-approved excipients. For each excipient, the researchers first measured how well it stabilized RNA when incorporated into an LNP. They used these particles to deliver mRNA encoding a protein called firefly luciferase, which produces bioluminescence, into cells. By measuring how much light was emitted by the cells, the researchers could determine how effectively each excipient protected the mRNA.

The researchers chose five of the most promising excipients and used their AI algorithm to predict ratios of those excipients that would best stabilize LNPs similar to those used by Moderna. Based on those predictions, the researchers tested two formulations at a time in cells, fed those results back into the algorithm, and generated more predictions. After several rounds, they chose one formulation that appeared promising enough to test in animal studies.

This process took only a few weeks, much less than it would have taken without guidance from the AI algorithm.

“Before we implemented the AI algorithm, we spent several months testing different combinations and also doing the prescreening of all the excipients that we could find, but nothing would get us to 100 percent stability,” Tran says.

To test the heat resistance of their new LNP formulation, the researchers used the particles to package Covid-19 mRNA antigens, then dehydrated them using a process called vacuum drying. These particles were then stored at 37 degrees Celsius (98 degrees Fahrenheit) for two months, or at room temperature for one year. Mice that were vaccinated with these particles, even after long-term storage, showed equivalent immune responses to mice that received vaccines carried by LNPs similar to the original Moderna formulation.

The researchers also used their new heat-resistant formulation to create solid microneedle patches that could deliver a SARS-CoV-2 antigen. These patches generated an immune response similar to that produced by the injectable RNA vaccines.

“Our approach broadens the application of not only mRNA vaccines, but also therapeutics or advanced drug-delivery platforms like controlled-release particles or microneedle patches, which requires the formulation to either be in solid state or to be stable at higher temperature,” Tian says.

The researchers also showed that they could use their algorithm to stabilize other LNP formulations, including one similar to those used by Pfizer to deliver its Covid-19 vaccine. This formulation uses the same excipients as the one the MIT team developed for the Moderna LNP, but in a different ratio. For each LNP, once a heat-resistant formulation has been developed, it could be adapted to deliver any type of mRNA payload, the researchers say.

RNA vaccines and other nucleic acid therapeutics are typically packaged within fatty molecules known as lipid nanoparticles (LNPs). MIT researchers have now come up with a way to produce these particles much more quickly, and with more precise control over their size and shape.

This new process, which can be run automatically without any human intervention, could greatly speed up the development of new RNA and DNA therapeutics, the researchers say.

“This method can help you determine what are the parameters that will generate specific size and shape attributes, before you take those particles and see which one will perform best,” says Cedric Devos, an MIT postdoc and one of the lead authors of the study. “It could be a quite powerful development tool.”

Current methods for designing new lipid nanoparticles often require time-consuming trial-and-error experiments, with limited investigation of desired particle size and shape. Tuning the size and shape of LNPs can open the possibility of targeting different organs and tissues.

“The size and shape of LNPs could not be reliably controlled by any previous production method. The problem may appear simple at first glance, but in reality it requires a deep understanding of lipid nanoparticle assembly,” says Allan Myerson, a professor of the practice in MIT’s Department of Chemical Engineering and the senior author of the new study.

MIT postdocs Aniket Udepurkar and Peter Sagmeister are also lead authors of the paper, which appears today in ACS Nano.

Vaccines based on mRNA work by delivering instructions to cells to produce a harmless version of a viral protein, which prompts the immune system to generate a response. However, if mRNA is injected on its own, it will be quickly broken down in the body.

“These are really a revolutionary type of therapeutics, but they need some kind of delivery vehicle to bring them to the right cells in the body,” Devos says.

For the mRNA Covid-19 vaccines and other mRNA therapeutics, scientists have used lipid nanoparticles for that packaging. LNPs usually consist of four components — an ionizable lipid, a phospholipid, cholesterol, and a lipid attached to a molecule of polyethylene glycol (PEG), which helps to stabilize the LNP.

To make the particles, two streams of fluid are mixed together at high speed. One consists of lipid molecules suspended in ethanol, and the other contains mRNA dissolved in an acidic buffer.

Those solutions aren’t mixed at equal flow rates, however. Instead, there is about three times more of the mRNA solution than the lipid solution. This unequal ratio helps promote the formation of lipid nanoparticles containing mRNA, but it doesn’t offer precise control over the size or shapes of the particles.

In a study published last year in ACS Nano, the MIT team showed that they could enable much better control of particle size by breaking the mixing process down into two steps.

In the first step, mRNA and lipids are mixed together at equal flow rates. Then, after a short delay, more of the buffer is added, which halts the growth of the particles. Longer delays produce larger particles.

“This gives you the ability to play around with the residence time, which is the time it takes between the first mixer and the second mixer. If you keep that residence time really long, it means your particles will grow a lot. If you keep it really short, you can keep them really small,” Devos says. “It gives you a lever over lipid nanoparticle manufacturing that wasn't available before.”

In that study, the researchers also showed that they could alter the shape of the particles. By changing the concentration of the buffer that they add in the second step, they were able to transform the particles from spheres to elongated particles that resemble avocados.

Both of these interventions — changing the delay residence time and changing the buffer composition — allow the researchers to control particle size and shape without otherwise altering the composition of the LNPs.

In the new ACS Nano paper, the researchers developed a way to automate the two-step mixing process. They also incorporated a commercially available dynamic light scattering device that can measure the sizes of the particles as they are formed.

The project also highlights the impact of MIT’s Undergraduate Research Opportunities Program (UROP). “Through this program, applied mathematics and computer science undergraduates Joy Ren, Sofiya Chubich, and Dylan Nguyen gained hands-on experience, learning how advanced software engineering can be integrated with chemical engineering to develop an automated platform for LNP process development,” Sagmeister says.

With this automated system, the researchers can specify a particle size, and the system will generate that size. It will also measure the resulting particles to make sure they’re the right size, and if not, adjust the delay time and other factors to steer them to the right size.

“The first paper really unlocked the new methodology to make lipid nanoparticles, to truly engineer them by size and shape,” Sagmeister says. “With the second study, we automate the whole process.”

The system can also produce particles of different shapes, but measuring those shapes has to be done outside of the automated system.

Using this approach, the researchers were able to investigate how changing the inputs to the system affects the size and shapes of the particles, on a much faster timescale than currently possible. The researchers then used the data they gathered from these experiments to train a machine-learning model that can predict the combination of factors that will generate a particular size or shape.

With this method, it could be much easier for developers of RNA therapeutics to generate different sizes of particles to test them for a particular application. Controlling the size of a lipid nanoparticle is critical because the particle’s size determines where in the body it is most likely to end up.

“If you make an LNP-based therapeutic with a target size of 150 nanometers, and one that is 70 nanometers, and everything else is the same, they will behave very differently,” Devos says.

The researchers have filed for a patent on their technology and are now working to commercialize it through a new company called BIZON Labs. Since receiving initial support through the Martin Trust Center for MIT Entrepreneurship’s Researcher 2 Entrepreneur (R2E) program, the team has also been accepted into MIT’s flagship accelerator program, delta v. The research was funded by the U.S. Food and Drug Administration and the Koch Institute Support (core) Grant from the National Cancer Institute. The work was carried out, in part, through the use of MIT.nano’s facilities.

Many people who are successful in STEM fields were lucky to have someone who turned them on to a topic and encouraged their interest. For Faith Reyes, that person was her high school physics teacher, Ms. Bolster.

Whereas Reyes had earlier studied math and science without seeing how those subjects could be applied outside the classroom, her teacher helped her connect those dots and experience the excitement of investigating the physical world. With Ms. Bolster’s help, Reyes started a physics club where students would gather before school and conduct experiments.

“I just sort of fell in love with physics then,” Reyes says. “And luckily, as I began to study it more and more, I found I landed exactly where I wanted to be.”

Reyes has carried that enthusiasm for physics into not only her research but her role as a personal tutor and teaching assistant at MIT, where she works to foster the same love of the subject in first- and second-year undergraduate students.

As an experimental particle physicist and sixth-year PhD student in the Formaggio Group in the Laboratory for Nuclear Science, Reyes studies neutrinos, elementary particles that have vanishingly little mass and rarely interact with other matter. Neutrinos are produced during radioactive decay, including the processes taking place inside nuclear reactors. Because they interact so infrequently, detecting them can be difficult. (We can’t feel them, but neutrinos from the sun are streaming through our bodies every minute.) Their unusual behavior makes them valuable to physicists seeking to understand what lies beyond the Standard Model, the framework that describes many of the fundamental particles and forces in nature.

“The Standard Model is extremely accurate and describes most of everything that we see,” Reyes says. “But it’s not complete.”

Reyes is a member of the Ricochet neutrino experiment, an international collaboration studying neutrinos produced by a nuclear reactor at the Institut Laue-Langevin in Grenoble, France. The experiment seeks to observe coherent elastic neutrino-nucleus scattering, a low-energy interaction in which a neutrino scatters off an atomic nucleus.

Through Ricochet, scientists aim to investigate some properties of these elusive particles, and contribute to, as Reyes puts it, “just fundamentally understanding the world in which we live.”

When looking back at her time in graduate school, Reyes’ path has not always followed the plan she initially envisioned.

When she joined Ricochet, she expected to work on a particular project located at MIT. But a few years into her PhD, it was clear that the project would not be ready within her timeline. Reyes instead shifted her focus to work taking place in France, where she began learning the technical details of the experiment’s detectors.

Her first visit lasted three months. At the time, Ricochet had two detectors, and Reyes spent much of her time performing the routine work required to understand how they operated.

As the experiment expanded to nine detectors and eventually 18, the amount of work required to manage the system grew substantially. Reyes and a colleague recognized that many of the repetitive tasks could be automated.

Together, they developed a software framework that could perform much of the low-level analysis and detector monitoring that Reyes had initially carried out manually.

The project became an important part of her development as a physicist. By working closely with the detectors and helping build tools to manage them, Reyes gained a detailed understanding of the experiment’s operations.

“It’s sort of like you’re building your own stuff to replace yourself,” she says. “Which is nice in a way because you can save yourself a lot of time.”

The opportunity was both validating and humbling. As a graduate student, she had moved from learning the basics of the experiment to helping guide the work of other scientists.

“It felt like my collaborators trusted me, and I had something of value to give to the collaboration,” Reyes says.

The people she has met through MIT and the Ricochet collaboration have been among the most rewarding parts of her graduate experience. Students, mentors, and collaborators have helped her think critically and become a better physicist, she says.

Her increasing leadership responsibilities have also changed how she approaches research.

Earlier in her academic career, Reyes says, she was more comfortable being told what to do than proposing her own scientific ideas. Over time, leading projects and coordinating groups pushed her to become more confident in her judgment.

“I think I was sometimes not really standing up for myself,” she says. “But now I feel more confident in my position and my prowess as a physicist.”

After completing her doctorate, Reyes hopes to continue conducting research. She is considering a postdoctoral position, which would allow her to continue working in physics at another institution.

Her time in France has also influenced her vision of the future. Reyes spent nine months there through the Chateaubriand Fellowship, following two earlier three-month visits. While the latest trip was primarily focused on research, working in the same office as her collaborators made it easier to coordinate across time zones and strengthened her connection to the experiment. She also grew fond of the country’s culture and work-life balance and would even consider living there.

“I fell in love with France and the people,” she says. “And also, the work culture.”

Outside the lab, Reyes enjoys playing video games and crocheting, a hobby she picked up during her time in France. She often crochets while watching movies or television, appreciating the opportunity to work with her hands while thinking about other things.

For a physicist whose work involves investigating some of the universe’s smallest and most elusive particles, the hobby offers a different kind of satisfaction: creating something tangible.

As Reyes moves toward the next stage of her career, she hopes to continue pursuing the questions that first drew her to physics. Her research may help reveal what lies beyond the Standard Model, but her experience at MIT has also shown her how much science depends on collaboration, adaptability, and the confidence to lead.

“I’ve learned a lot of lessons,” Reyes says. “Especially about wrangling people.”

Most colon cancer deaths are caused by the spread of tumor cells beyond the colon, usually to the liver. In a new study, MIT biologists identified a cellular pathway necessary for colorectal cancer metastasis.

The pathway they identified, controlled by a protein known as YAP1, is normally involved in tissue repair. When activated in cancer cells, it promotes cell proliferation and migration. The researchers also found that a high-fat diet is more likely to turn on this pathway, through the production of fatty molecules called ceramides.

Drugs that block ceramide production could offer a new way to help prevent metastasis in patients diagnosed with colon cancer, the researchers say.

“We’ve found a pathway that we think is druggable. If we shut down the enzymes that make ceramides, tumor cells can’t switch on this regenerative program, and they largely fail to seed metastases in the liver,” says Omer Yilmaz, director of the MIT Stem Cell Initiative, a professor of biology at MIT and a member of MIT’s Koch Institute for Integrative Cancer Research. He is also a gastrointestinal pathologist and director of translational research in pathology at Beth Israel Deaconess Medical Center.

Yilmaz, Nilay Sethi, an assistant professor of medicine at Harvard Medical School and Dana Farber Cancer Institute, and Alpaslan Tasdogan, head of the Institute for Tumor Metabolism and a professor in the Department of Dermatology at University Hospital Essen and the German Cancer Consortium (DKTK), are the senior authors of the study, which appears today in Science. MIT postdocs Swagata Goswami, Qiming Zhang, and Abdullah Burak Yildiz are the paper’s lead authors.

In the United States, colon cancer is usually diagnosed at stage 2 or 3 — before the cancer has spread. However, even after successful surgery, up to a third of these patients will relapse with metastatic disease.

While scientists have identified many genetic mutations that drive the development of colon cancer, it’s unknown exactly what prompts them to spread beyond the colon.

“Many studies have looked for a genetic driver of metastasis and come up empty,” Yilmaz says. “There isn’t a defining mutational signature that separates metastatic cells from the primary tumor, which points to metastasis being driven largely by changes in which genes are switched on and off, rather than by new mutations.”

In this study, the researchers sought to identify epigenetic programs that enable colon cancer cells to metastasize. Using tumor organoids from mouse models of several types of colon cancer and from patients with colorectal cancer, they found that metastatic cells shared one key feature: activation of the YAP1 program.

YAP1 is a protein that works with partner factors to switch on genes related to development, stem cell maintenance, and regeneration. In normal tissue, it is active during fetal development, and after injury, to promote healing.

In the gut, that repair response runs through a rare, fetal-like cell type, which normally appears only briefly to rebuild the intestinal lining after damage. YAP1 has been linked to cancer for years, but the new work shows that diet-derived lipids push tumor cells into this specific regenerative state — and that the state itself is what licenses metastasis.

“The regenerative program that we described is generally observed in the gut when there is severe injury or infection and the gut needs to regenerate. We see the tumor cells hijack this program to drive metastatic progression,” Goswami says.

Activation of this set of genes helps cancer cells to break free from the original tumor site and spread to other locations in the body. For colon cancer, the most common site of metastasis is the liver, followed by the lungs.

In mouse studies, the researchers also found that cancer cells in animals fed a high-fat diet turned on YAP1 to a greater extent than mice fed a healthy diet. A high-fat diet, the researchers found, triggers activation of enzymes that produce ceramides, a type of lipid. Ceramides then release the molecular brake that normally keeps YAP1 inactive, allowing it to move into the nucleus and switch on its target genes.

The researchers showed that genetically targeting YAP1, or the genes involved in ceramide production, markedly reduced the spread of colon cancer to the liver in mice.

To determine if YAP1 is also involved in metastasis in humans, the researchers analyzed RNA sequencing data from patients with colorectal cancer. They found that YAP1 was more active in metastatic cancer cells, and that patients with higher body mass index (BMI) showed higher expression of the genes activated by YAP1 than normal-weight patients. Patients with higher levels of those genes also had lower survival rates.

“We don’t think that the YAP1 program is specific to obesity. It’s just that it becomes accentuated in obesity, and that may account for why obesity is known to drive the progression of colorectal cancer,” Yilmaz says.

They now plan to develop drugs that inhibit two of the enzymes involved in ceramide production, DEGS1 and DEGS2, in hopes that such drugs could help prevent colon cancer metastasis.

The researchers caution that the findings do not yet translate into dietary advice for patients who have already been diagnosed, and that any drug targeting ceramide synthesis will have to clear a high bar for selectivity, since these lipids are also essential in healthy tissues.

The research was funded by the National Institutes of Health/National Cancer Institute, the MIT Stem Cell Initiative, a Koch Institute Frontier grant, and the NRW Junior Research Program.

One of the main reasons that ovarian cancer is among the deadliest forms of cancer is timing: When doctors catch it early, the five-year survival rate can be north of 90 percent. But when doctors catch it late, in stages 3 or 4, five-year survival is less than half that.

About 20 years ago, researchers studying ovarian cancer discovered that many cases of high-grade serous ovarian cancer, the most common type, originate in the fallopian tubes. Detecting the disease there remains challenging, in part because its precursor lesions can be microscopic and difficult to sample.

Now researchers in the group of MIT Professor Kripa Varanasi, working with colleagues at MIT and Johns Hopkins University, have developed a handheld device capable of gently collecting living cells from specific locations to test for ovarian and many other types of cancer. The researchers believe the technique could one day be used to catch cancers earlier and more effectively. It could also be used to create treatments based on individual patient samples.

In a study describing the system in the journal Device, the researchers showed their system enables targeted sampling of newly excised tissue, and they used it to recover living cells for cultivation and testing. The device holds a small microfluidic channel against the tissue and uses a syringe to drive fluid through the channel, applying a force parallel to the tissue surface to gently detach living cells from tiny sections of tissue.

“We wanted to collect living cells from specific regions of the fallopian tube while leaving the surrounding tissue intact,” says Varanasi, senior author of the study and the Maher A. Elmasri Professor of Mechanical Engineering. “Once we have these living cells, there are many things we can do with them. We can use them for diagnostics, grow them into organoids, and build living models of disease. Ultimately, this could allow us to test how an individual patient’s cells respond to different treatments and help us develop more personalized medicines.”

Joining Varanasi on the paper are co-first authors Domitille Avalle SM ’25, MIT postdoc Bert Vandereydt PhD ’26, and Sean Parks ’20, SM ’24. The other authors are MIT PhD candidate Huaiyao Peng; Rebecca Stone, the Johns Hopkins University School of Medicine Stoddard and O’Neil Professor in Gynecologic Oncology; and Angela Belcher, MIT’s James Mason Crafts Professor and a professor of biological engineering and of materials science and engineering.

Living cells for ovarian cancer research

The discovery that many high-grade serous ovarian cancers originate in the fallopian tubes has opened up new prevention options for women at increased risk, who can have their fallopian tubes removed after childbearing years, largely preserving normal hormone production.

Stone, a gynecologic oncologist at Johns Hopkins University, has long advocated for this procedure for certain women at increased risk of ovarian cancer. Belcher introduced Stone to Varanasi, and the three, together with other collaborators, received funding from Break Through Cancer, a foundation that brings together interdisciplinary teams to tackle some of the most challenging problems in cancer. Their project focuses on developing new approaches for the early detection of ovarian cancer, with the cell-collection technology forming one part of that broader effort.

The researchers began by asking whether they could collect living cells from specific regions of removed fallopian tubes to study the disease’s earliest stages.

"The idea was to see if we could find early signals from precancerous regions of concern,” Varanasi recalls.

The process traditionally involves placing surgically removed fallopian tubes in a chemical preservative and cutting the tissue into sections. The preservative maintains tissue structure, but the cells are no longer alive and cannot be grown in culture. A pathologist then looks for cancerous or precancerous regions in thin sections of the tissue under a microscope.

“It’s very time-consuming and destructive to the cells,” Varanasi says. “We wanted to bring new capabilities to pathology, so we can not only see what these cells look like, but also collect them alive and study how they behave.”

The MIT researchers saw the process firsthand while visiting surgeons in the operating room at Johns Hopkins.

“It inspired us,” Varanasi says. “We do a lot of work on fluid-surface interfaces in my lab, and we realized we could use a fluid instead of a scalpel or brush, because when you flow a fluid it applies shear stress at the interface. We thought it could work because we heard from surgeons that cells in some locations were loose and would come off during routine washing and other procedures.”

“This is exactly the kind of problem that benefits from bringing clinicians and engineers together,” Stone says. “We understand the clinical need, while the MIT team brings a very different perspective from fluid mechanics and engineering. That combination allowed us to approach the problem in a new way.”

The researchers’ new approach uses a 3D-printed microfluidic device that forms a vacuum seal with the tissue. The device confines liquid flow to a small region, where the flowing liquid creates shear stress that gently detaches living cells.

“We came up with this device where one syringe creates a vacuum that holds it against the tissue, and a second syringe pushes liquid through it,” Vandereydt says. “The vacuum creates a seal, so nothing leaks, and then we locally apply what is basically a microfluidic chip on the tissue that gently shears the cells off.”

The researchers showed they could tune the shear stress applied to the tissue and compared their approach to other cell collection workflows. They found the cells collected using their technique remained viable and grew in culture much more readily than cells detached using conventional approaches.

Finally, the researchers tested their device on fresh human fallopian tube samples, which required them to be on call for sample shipments from their collaborators at Johns Hopkins. After experiments, the samples were shipped back for conventional pathology.

“The samples could come at any time. Sometimes, we’d get an email from our collaborators at 11 p.m. saying ‘There are two fallopian tubes coming tomorrow,’” Vandereydt says. “We were able to collect living cells from those fallopian tubes and turn those into organoids, which is important for testing, disease modeling, and eventually developing personalized treatments.”

“We are developing optical approaches to identify suspicious regions of tissue, and this technology could allow us to collect living cells from exactly those locations,” Belcher says. “Being able to first see where the disease may be emerging and then collect those cells for further study could be very powerful.”

The researchers tested the device on different types of cells and found the approach can be tuned to collect cells of all types by applying different levels of shear stress.

“It’s agnostic to the disease,” Vandereydt says. “There are very loosely adherent prostate cancer cells that detach at 1 pascal [of stress], but if you look at bone cancer cells, only a few cells detach under as high as 5 pascals of stress.”

The researchers plan for the early use of their device to involve tissue that has already been removed from the body, as that offers an easier pathway to regulatory approval. But they would also like to see their device used to swab samples inside of patients for easier testing and earlier cancer detection.

“What is exciting about this technology is the ability to collect living cells from a specific area while preserving the tissue for pathology,” Stone says. “In the future, one could imagine integrating it into routine histopathology workflows, creating a powerful new way to study carcinogenesis and fundamental biology directly from human tissue.”

“Break Through Cancer brought together people working on not only ovarian cancer but also on pancreatic cancer, brain cancer, leukemia, and other cancers,” Varanasi says. “What we heard again and again is how valuable it would be to have better ways to obtain living cells from specific regions of tissue.”

The researchers hope that by making it possible to collect living cells from precise locations without removing or destroying the surrounding tissue, their approach could eventually help researchers and clinicians identify disease earlier and better understand how it develops.

“If this work can ultimately help women by enabling earlier detection of ovarian cancer, I would find that incredibly fulfilling,” Varanasi says. “That is really what motivates us — taking the science and engineering we develop in the lab and using it to make a difference in people’s lives.”

A few months ago, researchers from the MIT Senseable City Lab published a study about pollution in New York City featuring some new methods. For instance: With machine learning, they identified the types of vehicles appearing in 331 traffic cameras in the city, and estimated the emissions coming from each automobile. Given enough cameras, these visual artificial intelligence techniques could monitor emissions with an unprecedented combination of precision and scale.

For that matter, visual AI today can address all kinds of questions for urban planners. Why exactly is traffic snarling? What are the most dangerous aspects of different intersections? Which parts of plazas or parks attract the most people?

Across cities, more images means more data, more insight — and more concerns about privacy and fairness.

“We can treat these digital images as data and quantify features of the city,” says Fábio Duarte, an MIT researcher and co-author of a new book about visual AI and urban studies. “With computer vision techniques, each image is a dataset.” Still, he adds, “We have to be careful about it.”

And while urbanists have long used visual analysis to inform their thinking, now it’s possible to an unprecedented extent.

“Everybody has been observing the urban environment and trying to get some insight,” says Martina Mazzarello, an MIT scholar and a co-author of the new book. “But what if we can do that at a large scale and get some insight everywhere?”

The scholars explore these topics in “How AI Sees the City: Urban Visual Intelligence,” published this month by Routledge. The authors are Duarte, a principal research scientist and associate director of the MIT Senseable City Lab; Mazzarello, a research scientist and lead of MIT Senseable City Lab global initiatives; Carlo Ratti, a professor of the practice and founder and director of the MIT Senseable City Lab; and Fan Zhang, an assistant professor at the Institute of Remote Sensing and GIS at Peking University.

“Great urbanists such as Kevin Lynch and Willian H. Whyte showed us the extraordinary value of ‘looking’ at the city,” Ratti says, referring to two prominent thinkers about city dynamics whose work is described in the book. “Today, visual AI gives us new ways to build on that tradition — allowing us to observe cities at a scale and with a level of detail that was previously impossible.”

“How AI Sees the City” stems from the work of the MIT Senseable City Lab, founded in 2004, which uses data to better understand urban dynamics. As the authors discuss in the book, there is a long history of visual representations that shape the way we think about cities, from Romans building marble maps to the introduction of photography — which produced influential urban images about things like Hausmann’s reshaping of Paris or the crowding of tenements in New York City’s Lower East Side during the 19th century.

More recently, some scholars have used visual studies to better understand city form, including Lynch, a former MIT professor whose 1960 book, “The Image of the City,” influenced many scholars. Whyte, a sociologist famous for his book “The Organization Man,” then became an urbanist closely examining public spaces.

By explicitly placing AI in a continuum with these visual urban studies, the authors are making a point: Powerful as it might be, we can still think of AI primarily as a tool serving human purposes, as we seek to design and refine urban form.

“Kevin Lynch at MIT was only using paper and pen,” Duarte says. “We can now scale up what he was doing, with visual AI, while also looking at many different dimension of cities.”

There are extensive possibilities for applying visual AI to urban planning, ranging from emissions to traffic flow, safety, better imagery of street-level activity and sidewalks, and much more. The book also examines, for instance, urban greenery. While satellite imagery can show us how much tree cover and green spaces cities have, near-ubiquitous images from phones and other sources can also reveal to what extent people glimpse greenery in everyday life, a factor in reported wellness.

“The real promise of visual AI is not simply that computers can look at millions of images,” Zhang says. “It is that we can connect what is visible in those images — streets, buildings, greenery, traffic, public space — with larger questions about how cities function and how people experience them.”

Better image recognition by AI even extends to urban interiors. By using images from 400,000 AirBnB listings across the world, one recent Senseable City study shows that, contrary to some claims, interior design styles are not becoming globally more homogeneous, but reflect significant geographic differences.

“No matter what it is, we can learn from what we can see and then use it as urban designers, planners, policymakers, and citizens,” Mazzarello says. “It can be our eyes, or cameras with computers, but in the end it’s the same methodology, and now we are trying to optimize the ways we can use these tools.”

If the promise of visual AI for urban studies is vast, the pitfalls are concerning. In “How AI Sees the City,” the authors outline multiple potential problems with the technology, including the intrusiveness of widespread visual surveillance and the potential for bias being reinforced through AI systems.

The installation of ubiquitous cameras can quickly raise concerns about surveillance. London, an early adopter of CCTV, has about 210 cameras per square mile. But eight of the world’s 10 most camera-heavy cities are in China; Shanghai has over 5,000 cameras per square mile. Such surveillance practices have raised controversy in other parts of the world, with debate over the uses of traffic cameras bubbling up in the U.S. this year as well.

In evaluating the potential safety gains from intensive video recording, the authors write, “the benefits must be weighed against the significant erosion of personal freedom and the potential for abuse inherent in a system of constant monitoring.”

Meanwhile, AI systems can reinforce social biases as well, leading to the production of data that reinforce prior perceptions as much as underlying realities — about people, neighborhoods, and whole cities. If AI models are trained on majority population groups, they may not evaluate minority groups the same way.

“We need to teach AI to see, and depending on how you teach it, it will see what what is embedded in the culture,” Duarte says. “AI is not neutral.”

Still, as Mazzarello adds, “our eyes are not neutral, either. Every tool has to be guided in the right way, and trained in the best way.”

Other scholars have praised “How AI Sees the City.” Michael Batty of University College London has called it a “fascinating book” that “shows how we are beginning to interpret the world of urban design, suggesting ways in which we might improve design using urban analytics, AI and large language models.”

Ultimately, though the authors think there is great value in deploying visual AI to learn more about our cities, how they function, and how they might be improved. With caution and independent thinking, progress is possible. Or, as they conclude in the book, “We should explore this wisely, critically, and creatively.”

U.S. News and World Report has designated MIT as the top school in its annual rankings of the nation’s best universities, announced today.

Among the academic specialties that U.S. News evaluates, MIT’s engineering program continues to lead the rankings of undergraduate engineering programs at a doctoral institution. The Institute’s undergraduate computer science and economics programs also ranked No. 1, and its undergraduate business program ranked No. 2.

U.S. News ranked MIT highly in several other categories: The Institute is No. 1 for undergraduate research and creative projects, No. 2 on the list of most innovative schools, and No. 3 on the list of best value schools.

MIT placed first in five engineering specialties: aerospace/aeronautical/astronautical engineering; chemical engineering; electrical/electronic/communication engineering; materials engineering; and mechanical engineering. It placed second in computer engineering.

Other schools in the top five overall for undergraduate engineering programs are Georgia Tech, Stanford University, the University of California at Berkeley, and Caltech.

In computer science, MIT placed first in three specialties: biocomputing/bioinformatics/biotechnology (tied with Carnegie Mellon University); computer systems (tied with Carnegie Mellon); and theory. It placed second in three other disciplines: artificial intelligence, data analytics/science; and programming languages.

Other schools in the top five overall for undergraduate computer science programs are Carnegie Mellon and Stanford (both tied with MIT at No. 1), as well as UC Berkeley, Georgia Tech, Princeton University, and the University of Illinois at Urbana-Champaign.

In economics, MIT placed first in four specialties: development economics; econometrics; industrial organization (tied with Northwestern University); and microeconomics. It placed second in macroeconomics (tied with UC Berkeley).

Other schools in the top five overall for undergraduate economics programs are Harvard University, Princeton, and University of Chicago (all tied with MIT at No. 1), as well as Stanford, UC Berkeley, and Yale University.

Among undergraduate business specialties, the MIT Sloan School of Management led in three categories: analytics; production/operations management; and quantitative analysis. It placed second in entrepreneurship.

Other undergraduate business programs ranking in the top five include the University of Pennsylvania, UC Berkeley, New York University, and the University of Michigan at Ann Arbor.

Following World War II, the MIT faculty convened a committee to assess the Institute’s principles of education and their relevance “in a new era emerging from social upheaval and the disasters of war.” One of the outcomes of this pivotal report was the establishment of the School of Humanities, Arts, and Social Sciences (SHASS). MIT News convened a discussion with three SHASS faculty — David Kaiser, Heather Paxson, and Jonathan Gruber — about what makes the school special and why it’s a core part of the MIT experience. Listen to the conversation or read the transcript below.

Peter Dizikes: Welcome to MIT, everybody. My name is Peter Dizikes and I’m a writer for MIT News. Every year, over 1,000 undergraduates enroll at MIT. Once they’re here, they’ll spend at least a quarter of their time studying a set of core subjects such as music and theater arts, history, anthropology, and economics, linguistics and philosophy, literature, political science. These are all offered within MIT’s School of Humanities, Arts, and Social Sciences, known on campus as SHASS and a core piece of the university.

MIT is 165 years old. This month, SHASS turns 75 and is celebrating its anniversary with a two-day conference, September 24th and 25th. For this MIT News Roundtable, we’re delighted to have three distinguished faculty members from SHASS with us, historian and physicist David Kaiser, anthropologist Heather Paxson, and economist Jonathan Gruber, who are all here to talk about what makes SHASS a special place. Thank you all for joining us.

Peter Dizikes: If it’s all right, I’d like to just jump in and start by asking each of you a different question about SHASS. Perhaps we could start with you, David. David Kaiser, for our audience, is the Germeshausen Professor of the History of Science and Professor of Physics at MIT. He’s written numerous books about the history of physics and done scientific research focused on inflationary cosmology, the very rapid, very early expansion of our universe. He’s also edited a volume about the history of MIT and is, I think, the lead organizer of the SHASS 75 Conference.

David Kaiser: It has taken a village, but I’ve been lucky to work with a whole group.

Peter Dizikes: Very good. Well, on that note, the note being MIT history, right after World War II, MIT decided that it wanted to form SHASS. Why was that and how has that worked out for us?

David Kaiser: That’s right. So they didn’t decide very rapidly. And again, as people might know, there were some departments that we would now associate with SHASS that preexisted this. Economics, for example, had been taught at MIT as its own department from well before then. But there was this famous — famous for us, famous on campus — something called the “Lewis Report,” which you’ll hear about over and over again this year, I’m sure. And it was actually, I think, a three-year effort. So the group was put together very soon after the end of the second World War, starting in 1946, very soon. And they got together and studied almost everything you can imagine about life and the future at MIT. I mean, like parking lots and how far do the faculty commute, which is on my mind, as well as things like the undergraduate curriculum, dormitories, really every aspect, intellectual, residential, social, and beyond. It’s a really remarkable report and it’s easy to download from the web. It’s really worth reading even, I think, to this day.

But as you rightly know, when they completed the report in 1949, among their most significant recommendations was that MIT should not just have a few departments in humanities and social sciences, but have a concerted effort in what we would now call SHASS. Originally, it was the School of Humanities and Social Sciences. And of course, about 25 or so years ago, 50 years since the founding, we also very proudly added arts to our name.

So the point is the Lewis Committee said, in essence, there are so many striking, dramatic, literally world-changing developments that we can associate with what we might now call STEM, or science and technology, and they had in mind things like the Manhattan Project and nuclear weapons, which had been used to such dramatic effect just not long before they wrote the report. And they were concerned that changes that could be that rapid and that far-reaching simply require an informed leadership, an informed citizenry, more generally, of people who can try to think critically and carefully and kind of contextually and not only understand neutron diffusion, but also understand the flow of people and ideas and cultures and politics and beyond.

They argued not just that SHASS should be founded, it should be, as they said, “A co-equal school to the existing schools.” The report was very clear: This needs to be as central to MIT’s existence and experience as School of Science, School of Engineering, and of course there are other great schools as well. And it really was, there’s too much at stake, changing too rapidly with too far-ranging implications for our students and our faculty and staff and the broader community not to have the toolkit to think about history, governance, economics, culture, identity, human expression — that these were inescapable parts of being an educated member and a responsible member of the new nuclear age.

Peter Dizikes: Surely we still have enough challenges today that that rationale would hold up, we think?

David Kaiser: I think we’re done! No, we haven’t nailed it. There’s a few more things to worry about. And so I think when I say I return to that Lewis Report, I really do because some parts will seem quaint — what were the concerns in 1949 might not always resonate today — but a lot of the concerns sound actually quite contemporary. And with only a little bit of keyword swapping, I think we’ll get to it, I’m sure, in our discussion, no shortage of topics today that are filling a kind of intellectual role that the disruptions of the second World War had played for that earlier generation.

Peter Dizikes: Right. Thank you. Heather, I’d like to toss a question to you as well. Heather Paxson, for our audience, is the William R. Kenan, Jr. Professor of Anthropology at MIT, a former head of the MIT program in anthropology, and she is currently associate dean for faculty in SHASS. You’ve written multiple books, including “The Life of Cheese,” which I can vouch goes very deep into the American psyche. Heather, given that you are dean and I think have a lot of visibility into what’s going on SHASS-wide, in a sense, could you just say a little bit more for us about the breadth of everything that happens in SHASS?

Heather Paxson: Thank you, Peter. So putting the humanities and the arts and the social sciences together in a school is actually quite unusual among our peer institutions and does make for some really fun collaborations and convenings.

So just to give you a little taste of that breadth, just this week yesterday, our colleagues in political science, Adam Berinsky and Charles Stewart, and research that they’re doing in collaboration with Chara Podimata, who is an operations research specialist in the Sloan School of Management. They are using AI to study AI. They did a huge study or are in the midst of a huge study of looking at how AI chatbots are providing information to citizens about elections that may or may not be biased and tailored to the asker and what the effects that will have on our midterms coming up. So very timely, amazing work. That’s social sciences.

In the arts, yesterday I saw our colleague Jay Scheib, who’s the head of Music and Theater Arts. He’s a stage director and he’s just back from Germany where he’s been staging a production of Wagner in Germany. So, really, just a lot of fun stuff.

Peter Dizikes: It is actually amazing the breadth of people circulating around here. Jon, I have a question for you as well on a slightly different note. So Jon Gruber is Ford Professor of Economics at MIT, a former head of the Department of Economics, he’s published over 200 research papers, I think I can say is one of the most influential figures in the expansion of health care access in the U.S. You’re also the only person here right now who’s been an MIT student. You were an undergrad here. Could you just say a few words about what was significant about your student experience, what you took with you from being a student?

Jonathan Gruber: One thing that’s sort of embarrassing is when I started as undergrad here, I was closer to the Lewis Report than we are today. And the Lewis Report was still, in many ways, being implemented when I was undergraduate. I would say SHASS was much more of a second-class citizen then than it is now. It was sort of embarrassing to say one was a SHASS major without saying a double major, but it was really viewed as a service organization, something kids took so they could get on with their courses that mattered.

I really think that’s changed. I think the MIT student body’s changed from when I was here. We’re a much more well-rounded student body. We’re now competing with these Ivy League institutions that we were very separate from when I was a student, and that wouldn’t be possible without SHASS. But I think what’s important to recognize is MIT is no longer a school that just competes with engineering schools. We’re a school that competes with all universities. And the only way to do that is a well-rounded education. Folks aren’t going to come here if they can’t have a well-rounded education, if it’s just a science education. So SHASS has developed to become so much more integral into the life of MIT. The respect level of SHASS, everything has just really improved.

Peter Dizikes: Were there particular classes or courses that jump out in retrospect?

Jonathan Gruber: Well, I think I’m a great story for SHASS in the sense that I came to MIT as someone who’s good at math, but didn’t like math. I was just good at it, but I wasn’t someone who was doing proofs in my basement. I just didn’t find it appealing. But I came to MIT because it was a math-y school and it was the best school I got into and I was good at math. And then I took 14.01, which is our Intro to Economics class, and I was like, “Oh my God, I can use math for something interesting. I can actually take this math I love to answer questions I really want to answer and on topics I really care about in the real world.” And that was just eye-opening to me. I literally can picture standing at the crosswalk at 77 Mass Ave with my then girlfriend telling her how excited I was. I can picture that moment, what 14.01 had opened up for me. So you can imagine it’s incredibly thrilling for me now to get to teach 14.01 and hopefully inspire some of those students the way that I was inspired.

Peter Dizikes: What’s interesting is many people here have slightly indirect paths to what they ended up doing, right? So you didn’t come here expecting for that to happen, but it happened.

Jonathan Gruber: That’s exactly right. I think one thing that’s very important at part of the university education is to open yourself up to learning new things and heading in new directions. One concern I always have about MIT is that students come here too predetermined to do X. I think that’s almost more of a problem here than other universities. I think that’s why SHASS is so important. Because we want to open their minds to the fact that even if they move from science major X to science major Y, along the way they’re exposed to a range of things that allow them to choose what’s going to give them the most fulfilling future, not just what they thought was interesting in high school.

Peter Dizikes: And when I said at the outset that a quarter of the time they’ll be spending on some of these subjects is this is one of the MIT requirements, is that people need eight classes from SHASS during their four years here. So hopefully they do get that kind of exposure.

Jonathan Gruber: That is why we have that requirement and hopefully they take those classes seriously and are open-eyed and can really… I’ll tell you, Peter, one of the things that distresses me most is the number of juniors and seniors I have taking 14.01 saying, “God, I wish I took this freshman year. I would’ve studied more economics.” Which makes me feel good about my class, but a little disappointed that it’s taken that long to find it.

Heather Paxson: Oh, we thought that was just anthropology! They don’t even know how to find econ?

Peter Dizikes: Well, stepping back for one second. In daily life, what is special about being at SHASS? Teaching and learning is one of those things, but if you had to cite a couple of things about the qualities and characteristics of being here, the students, your colleagues, what would you say?

Jonathan Gruber: I mean, I would say that what’s special and unique about SHASS at MIT is the fact that we are at MIT and that we are the place that can bring together the science and the social sciences and humanities and arts in a productive way, which is so important right now. The conversation cannot go on without talking about AI, but basically the fundamental central issue in AI right now is how do we think about it ethically? How do we regulate it? And there’s no place better to think about that than MIT, where you’ve got the people developing the frontier AI models next to the people who can help you think about how to regulate and think ethically about those models. And so I think this world is increasingly becoming STEM-based, and I think, as a result, the most productive place to learn about topics from anthropology to history to economics is a place where you’ll learn about that alongside STEM.

Peter Dizikes: Since you mentioned that everything is affected by AI, I’m interested in what everybody’s favorite teaching experiences have been here, but you’re also probably having to be a little bit mindful of how to make sure that everybody is doing their own work and putting in the hard work and the hard thinking that it takes to really get what you want out of MIT. So those are two questions. From pre-AI days, do you have a particular favorite kind of teaching experience? What made it great? And then how are we adapting now?

David Kaiser: One of the courses I really love teaching here, I’ve been teaching it on and off, really, for 20 plus years, is cross-listed in our program in Science, Technology, and Society, my home department, also in Physics, and it counts as another one of these, I think, very important requirements that all the undergraduates must take. It’s a communications-intensive course in the major for the physics major. So they have to learn to write essays and express themselves coherently as part of their physics education, as well as, of course, throughout their SHASS coursework.

And so it’s predominantly students who are, like Jon had been, very interested in math and math-y things and physics and all those things, but they also have to come in there and not just rely on their, frankly, fabulous calculating skills. They have to practice reading stuff that might look a little unfamiliar or unexpected to them and they have to practice really composing coherent arguments about that. And the arguments sometimes are about the intellectual work, what was Einstein’s thinking in 1905 and how do we know and why does it matter?

A lot of it in this class turns to the things like I think were on the minds of those authors of the Lewis Report. What are educated people’s responsibilities under very complicated disruptive times like wartime, like the escalation of fighting of Vietnam? The list is long, just within recent history. What does it mean to take a remarkable education in a variety of fields and do something with that that is consistent with what you think you want to do as a person and as a member of a larger group? And to watch our physics majors wrestle with this creatively, and there’s no single answer that they’re racing toward, I think that’s just incredibly rewarding.

And a lot of them, I hear over and over again from seniors who are about to go to very fancy PhD programs in physics, “I never really paused to think about time dilation until I had write an essay about it. Oh, yeah, there’s kind of a reason for that.” Or, “I never really got my head around quantum theory, I could solve my problem sets, but there’s something really strange happening in the universe and it’s not only captured by these very, very complicated mathematical expressions, so that’s essential too.” So I have this collection of favorite moments of these kinds of “aha” where the eyes light up and the jaw drops at least a little bit and you say, “I didn’t even know that was a thing I didn’t know.” And it’s really fun to see that.

David Kaiser: It has them reading text, and not only a textbook, and then really having to make their own argument based on their own selection of primary and secondary sources, the way we would teach to do in our other courses.

Heather Paxson: I’ve been teaching here for quite a few years now, but before I got here, I probably taught at five other colleges and universities, so lots of different teaching experience in different sorts of institutions. And for many years I would say, MIT students, it’s just different. It is just so much more fun to teach anthropology with MIT students because they came to class having approached the texts, reading them, not to decide whether they agreed with the text or not, they were needed to be persuaded by the argument, and it really made for a very rich conversation in the classroom.

I think it’s interesting because the moments in the classroom that I can think about or the assignments or the engagements that I can think about are actually things that I think we are all trying to steer more towards today. So the things that I’m doing in class or trying to do in class today, more experience-based projects, more hands-on, are the things that actually, thinking back, I’ve done for a long time and are the most memorable.

So just one example, a class I haven’t taught in a very long time, but a colleague is teaching it now, a class called Art Craft Science, which is really fun to teach here. The assignment was to make mozzarella cheese. So I gave them instructions straight from the box of this mozzarella making kit and the instructions were not very well written. They were predicated on a knowledge of cooking and so forth. That was the point. So they had to go home, I gave them the ingredients, they made cheese, and then they write it up as a lab. I figured they knew how to do that, write it up as a lab. And the discussion of the lab was to reflect on the skills that they relied on to be able to enact these really poorly written instructions. So it was all about tacit knowledge. And so that was the lesson.

And that’s the kind of thing I think we’re all trying to reinvent now in the age of AI, but I’m sure we’ve all been doing it, we just didn’t have as much sense of attention to it. But that is MIT, the “mens et manus” thing. It’s all over our curriculum. It always has been, but now it does have this new, I think, shiny coin value to it. So that’s what’s fun.

Peter Dizikes: Our motto “mens et manus” is “mind and hand,” and I’m sure there is going to have to be a lot of continual reinventing of these kinds of exercises going forward. Do you have?

Jonathan Gruber: I would say there are two things that make me happiest as a teacher. One is when I illustrate the power of economics through counterintuitive lessons, when I can see the kids are like, “Wow, that’s really cool. I didn’t think of it that way till I took this class.” That’s really great. When it just can change the way that they think, they can think about things somewhat differently. And that’s what I hope the kids take from the class. I always say, “I don’t care if you remember certain terms, I just want you to think like an economist.” And when I see that happening, it’s wonderful. But most enjoyable is when they laugh at my jokes. My wife can tell if I’ve had a good lecture day, a bad lecture day, what percent of my jokes they laugh at, which is always below 10%, by the way. But the question is, is it 10% or 1%? And that’s really the most important thing to me.

David Kaiser: Jon, quick question. Does the proportion rise closer to midterms? Are they gaming the system?

Peter Dizikes: You haven’t had anyone come in and really study that empirically, though?

Jonathan Gruber: No, but the best review I ever got, now this was many years before he got famous for a different reason, was that I was viewed as a “well-dressed Pee-wee Herman.”

Peter Dizikes: Students will say if a professor makes them laugh, they’ll take that class when they’re shopping around, right?

Peter Dizikes: Slightly different kind of question here, which is: How has being at SHASS perhaps influenced your careers? You’re all people who’ve done different things in the same career. Heather, you’ve written about some very different topics. Jon, you’ve been very involved in research and also public policy. And Dave, you’ve had two careers in one as a physicist and a historian. So what is it about this place that maybe encourages you to try different things and follow through with them?

David Kaiser: Oh, okay. It’s buy one, get one free, I think. So one example comes to mind, Peter. I think many will eventually. But a number of years ago I wrote a book as an historian that I just loved immersing myself in all the things historians do, finding dusty old papers and interviewing people around. And it was called “How the Hippies Saved Physics.” It was a kind of an obnoxious title or funny title. And it was really who cared about certain obscure sounding questions in quantum physics before the whole field knew we had to care about them. It was really, I think, to me, at least an engaging and fun story about people on the margins who made contributions there.

Where I’m going with this is because I’m here and very lucky to live in more than one department and interact with all kinds of folks, one of the extremely gifted postdocs in physics who had just come to MIT to work with me on the physics side, read the book on a lark because it had a funny cover, I think is why he probably picked it up. And the upshot is that got us thinking more about our own physics projects because the historical study said, “Oh, I never thought that’s where these ideas came from, and I see what they did then and we’ve learned a lot more about these things in the interim. Let’s try this something new.” So we put a little group together and that became a five-year, really, adventure for me on the physics side that grew entirely, at least for me, from the fact that I’d spent several years writing this kind of deep-dive historical study.

The ability to have one lead to the other, to have these conversations happening close in time and close on campus to each other, I mean, that’s extraordinary and I’m very lucky, and I don’t know that I would have that at many other places where I could have been or where our friends are. So I think that the boundaries are not actually that high between our various parts of campus. They can feel high at times, but there really is the kind of cross-campus traffic, and we’re trying to get more of that going with recent initiatives. I think we really can just bring questions together without saying, “Oh, but you’re in that department, I’m in this department.”

Peter Dizikes: Having read “How the Hippies Saved Physics,” which came out in 2011, I would say you were writing about figures who, even at the time, were semi-overlooked, but since then have gone on to win major awards, and in a way the whole area of study there has been elevated.

David Kaiser: Well, that’s right. One of what I like to call “my hippies,” shared the Nobel Prize in physics in 2022. And in fact, one of the colleagues that I got to do the physics work as a follow-on with shared that same Nobel Prize, I think it’s, frankly, because he began working with me. Anton hasn’t gone on record, but I think the record speaks for itself. Anyway, the point is it’s now sort of extraordinarily exciting work that came from just 50+ years earlier from really being on the margin and being denigrated. And that kind of arc in the span of a single human lifetime or a career is really rapid change. Anyway, to be able to sit and watch that from many facets, it was a great adventure.

David Kaiser: I mean, look, I’m not keeping score, Peter, but I know where it’s going to be at the end.

Jonathan Gruber: I would say two things. So one is, going back to my undergrad days, I think many students here are head down, do the work, don’t necessarily engage with a lot of what’s going on in the world. I had a political science professor named Louis Menand who changed my life, who made me engage. He’d worked in the great society. He really was very opinionated, but in a way that he could defend it. It really opened my eyes and he began by getting involved in working on policy at MIT. So I was the first student representative to the committee on the undergraduate program when Margaret MacVicar set it up in 1985. I was the first student representative. And then it grew into my interest in just policy in general, so that was very exciting for me.

And then the other thing was the way I’ve been involved in policy is a particularly MIT way, which is that I’m the numbers guy when health care policy gets made. I develop computer models and mathematical models to help folks understand how their policies will affect people. But those models themselves don’t do any good unless we can explain what they’re doing in clear terms. So it’s really that crosswalk of why it’s great to be at MIT, which is I have the math skills to do it and I have the incredible students to help me, I mean, the work in this area has been helped by so many amazing students, but to have the SHASS skills and the communication skills to be able to explain what I’m doing and why it’s important, that is really kind of where SHASS is perfect for me.

Peter Dizikes: And also noteworthy that you had such an influential class that was not in econ, as important as you found those to be, but this is a political science class as well that helped feed into it.

Peter Dizikes: Heather, on maybe a slightly different note, how do you keep this healthy, productive culture going in all these different departments? We have this famous culture in the Department of Economics and in many other departments throughout SHASS where there’s this culture of openness to inquiry and elevating interest in students, but how does one, over 75 years, keep that going?

Heather Paxson: Well, thanks for asking the anthropologist about culture. I think we often think of culture in terms of ideas and values, a shared set of ideas and values, but my one word answer to that is actually “participation.” I love that, Jon, you were a student rep on an institute committee. I mean, it’s that kind of participation in the workings of our organizations and the workings of our departments, of our deciding what gets included in the curriculum, that participation is what creates a sense of belonging and certainly is the stuff of culture.

Peter Dizikes: So the things we study over 75 years are going to evolve and change, the things we believe are going to evolve and change.

Heather Paxson: So like an institution’s culture is what mediates between what changes and what stays constant.

Peter Dizikes: Also, you’re now batting 100% on jokes as well. Dave, what can we expect from the conference which is coming up in the very near future?

David Kaiser: Very near future. I’m really excited about it. It’s been a lot of work from really, genuinely a very large, wonderful, hardworking committee. I’m most excited because we have 40 plus speakers, including Jon, and Heather’s going to share us a panel. We’re going to hear from early career scholars, from more experienced scholars, we’re going to hear from people representing every single unit in SHASS, from alumni, including Jon, more recent alumni, done different things with their SHASS and MIT educations out in the broader world. We’re going to have a session I’m especially excited about, a showcase put together by Music and Theater Arts, original musical compositions, a dance performance, the jazz ensemble play. I mean, this is just fantastic. For free, really? Plus really good food. It’s going to be great.

It’s going to be an exhausting, but, I think, very, very exciting two days. I think the goal really is to showcase how we’re thrilled to be doing things in our own fields, advancing knowledge in the way that we and our immediate colleagues are most excited about, and it’s not only limited to that. And I think part of the message will be, and has been, as we began the discussion with, practically every challenge we might tick off on our finger is the biggies that keep us up at night. None of those will be solved by a technical fix alone, or frankly, a little tweak on a humanistic side or social science either. We really, really have to continue getting even better at doing the kinds of collaborative work across fields and across departments.

None of these challenges has a single or simple answer. If they did, they wouldn’t be persistent challenges. So the more that we can share with ourselves across our departments with MIT and beyond, it’s open to the public, the symposium is, that this is really a place where we can enter together with humility and experience, both, and try to build teams that couldn’t do these things on their own. And I think we’ve been doing more and more of that with the presidential initiatives, MITHIC and the whole series of them. I think we just have to keep building that as a muscle we can flex and get used to using more often. And if the symposium can help recenter that emphasis for our own colleagues and beyond, I think that’d be a great, great success.

Jonathan Gruber: Peter, I think this raised a really important issue, which is in economics, we have the concept of the public good. What’s the public good? That’s a good where one person’s efforts benefit everyone. In this world of incredibly intense academic pressure and pressure to earn a good living, it’s hard to come to university and focus on the public good as opposed to private good. SHASS is the place at MIT that focuses students on the public good.

You have people like David and Heather who spend so much time dedicated to so many different committees and making MIT function, and that’s led by SHASS. Not that there aren’t great participants all around the university, but SHASS is really the participation leader. And universities need that. That’s the lifeblood of this university, is that kind of volunteerism and participation. I hope that students by being exposed our courses get the value of the public good, that they realize that maybe it’s not as valuable to them, per se, but that there’s a value to the institution and the world of them doing the kind of volunteering that Heather and David do.

Stubbs, who currently serves as audit assistant director for IT Audit and Advisory Services, has been a member of the MIT community for more than 11 years and brings deep institutional knowledge, highly salient management experience, and a forward-looking vision to the role. Stubbs will succeed Michael Moody, who has served as Institute auditor for 12 years and will retire from MIT in October.

Executive Vice President and Treasurer Glen Shor announced the news today in a letter to MIT’s Academic Council.

“Carter stood out in a competitive field of candidates thanks to his impressive audit and IT expertise, collaborative leadership style, and robust understanding of MIT’s complex operations,” Shor says. “He has earned the trust and admiration of colleagues inside and outside the division and is well-positioned to write its next chapter.”

As Institute auditor, Stubbs will lead a team of internal auditors responsible for independently evaluating MIT’s academic, research, and administrative processes, including operations at Lincoln Laboratory. He will oversee a comprehensive, risk-based audit and advisory program spanning financial, operational, compliance, and technology reviews across the Institute.

The MIT Audit Division maintains a dual reporting structure to ensure its independence. Stubbs and the audit team work for the MIT Corporation Risk and Audit Committee, but receive administrative support from the MIT Office of the Executive Vice President and Treasurer.

“Carter’s strong technical command of IT auditing and hands-on experience auditing and advising on major systems implementations will be especially valuable as the Institute continues to advance its business and digital transformation roadmap,” says Pat Callahan, the chair of the Risk and Audit Committee. “The committee will be well-served by his experience with our current audit program, his demonstrated leadership and sound judgment, and his wide-ranging knowledge of the Institute.”

Stubbs joined MIT in 2015 as a senior auditor of information technology, steadily assuming increasing responsibility for information technology, data analytics, and advisory services. He now leads those functions for the Audit Division and serves on the division’s management team. Working closely with the Institute auditor, Stubbs shapes annual risk assessment work, audit planning, and broader division strategy while managing the oversight of complex engagements; contributing to quality assurance and advancing the division’s capabilities; and proactively responding to emerging institutional needs. Stubbs collaborates with leaders from across MIT’s academic, research, administrative, and technology units, including Lincoln Laboratory, and facilitates communications with Institute governance.

During his time at MIT, Stubbs has built an extensive network of partners and developed a multifaceted understanding of the Institute’s operating model, higher education and research risks, and the leadership judgment necessary to navigate complex institutional matters. He has helped steer cross-Institute efforts involving research data management, artificial intelligence, cybersecurity, and digital transformation. A graduate of the 2025 MIT Leader to Leader program, Stubbs served as an advisor to the MIT Working Group on Artificial Intelligence in Administration and Operations and is a member of the MIT Data Incident Response Team.

“I am honored to serve as MIT’s next Institute auditor,” says Stubbs. “The Audit Division plays an essential role in advancing the Institute’s mission of education and research through independent insight, trusted partnership, and thoughtful perspective on risk. I look forward to building on the division’s strong foundation and helping the Institute navigate an increasingly complex regulatory and risk environment.”

Prior to joining MIT, Stubbs held audit roles at Clean Harbors Environmental Services, Denbury Resources, and PricewaterhouseCoopers, where he developed broad expertise in IT and business process controls across multiple industries. He holds certifications as both a certified internal auditor and certified information systems auditor and earned a BBA in information and operations management from Texas A&M University.

Using materials safe for human consumption, MIT researchers have created tiny batteries that could be used to power ingestible electronic devices. Such batteries could make the devices safer for patients and minimize the environmental impact of the batteries after they are excreted.

In a new study, the researchers showed that the batteries, which generate 1.84 volts, could power two different types of devices: an RFID tag that can transmit from the stomach, and a capsule that produces a small electrical current that stimulates production of ghrelin, the hunger hormone.

This type of battery, which contains electrodes made from magnesium and molybdenum trioxide, could also be deployed in other ingestible devices for sensing or therapeutic applications, the researchers say.

“For many of the systems we’re developing, we need power, and we power the system through different ways,” says Giovanni Traverso, a professor of mechanical engineering at MIT, a gastroenterologist at Brigham and Women’s Hospital, and an associate member of the Broad Institute of MIT and Harvard. “Often, we use batteries, so the question here was: Could we develop a battery that was bioresorbable, and then apply that across a range of application areas?”

Not all of these devices require a power source. For those that do, the researchers have powered the devices from an external source that wirelessly transmits power, harvested power from the GI tract, or used small coin batteries. However, those batteries, which usually contain lithium, silver oxide, or other metals, could pose a safety risk if the battery’s protective coating was damaged while traveling through the GI tract.

To create a safer battery and allow the systems to be fully self-contained with no external power needed, the researchers turned to metals that can act as electrodes but are safe for human consumption in small amounts — magnesium and molybdenum trioxide.

“Those materials are known to be relatively safe. That was the biggest driver, thinking about materials that can be tolerated by humans,” Traverso says.

The researchers used magnesium to create the battery’s anode and molybdenum trioxide for the cathode. The battery also contains an ionic liquid gel electrolyte, and the entire system is bioresorbable, meaning that it can be fully broken down and absorbed by the body. The researchers designed two different versions of the battery that could be used for different applications —a disc 7.5 millimeters in diameter and a rectangular bar 24 millimeters long.

To test how the batteries would behave in the GI tract, the researchers first exposed them to a highly acidic solution similar to gastric juice. They found that the batteries function normally for about three days, then their performance begins to slowly decline. Within a few weeks, they break down completely.

The researchers then incorporated the rectangular battery into a degradable device they first reported in 2023, which is designed to deliver a small electrical current to the lining of the stomach. In their earlier work, Traverso’s lab showed that this jolt could stimulate endocrine cells in the stomach to produce ghrelin.

Stimulating ghrelin secretion could prove useful for treating diseases that involve nausea or loss of appetite, such as cachexia (loss of body mass that can occur in patients with cancer or other chronic diseases).

The initial version of that device was powered by two silver oxide coin batteries, similar to those used in FDA-approved ingestible devices. By replacing those with the new magnesium-molybdenum oxide batteries, the researchers made nearly the entire device — with the exception of a printed circuit board — bioresorbable. Any components that aren’t absorbed can be passed through the GI tract and excreted.

In the new study, the researchers showed that new battery was strong enough to generate continuous electrical stimulation for up to three days. Tests in animals showed that 20 minutes of stimulation within the stomach could boost ghrelin levels by about 50 percent.

“What makes this work exciting is that we were able to show that a bioresorbable battery is not just a concept. It can actually power clinically relevant functions inside the gastrointestinal tract and then simply dissolve,” Say says.

The researchers then incorporated the battery into a RFID device, which they designed to help patients adhere to their medication schedules. This capsule can transmit its location from within the GI tract via a bioresorbable RFID tag made from molybdenum and cellulose.

An earlier RFID system, known as SAFARI and reported by Traverso’s lab in January, used passive RFID tags, powered by harvested energy, which limits the communication range.

In the new study, tests in animals showed that RFID tags could be effectively powered by a disc-shaped bioresorbable battery. With the new battery, the device could transmit continuously from the GI tract, and with a longer range (up to 1.5 meters).

The researchers are now planning a clinical trial for the SAFARI system, which they expect will begin in about two years. Such systems could not only be safer for patients, but also would reduce the environmental impact of batteries that would eventually be excreted into the sewage system.

“The benefits are twofold: one, the ability to be bioresorbable, but also the potential to minimize environmental impact because the materials will be degraded in the environment as well,” Traverso says.

The research was funded by Novo Nordisk, the Karl van Tassel Career Development Professorship, MIT’s Department of Mechanical Engineering, the Brigham and Women’s Hospital Division of Gastroenterology, and the U.S. Advanced Research Projects Agency for Health (ARPA-H).

As we age, some of the cells in our body enter a state of senescence, in which they stop dividing but do not die. Those senescent cells can contribute to age-related disorders such as cancer, tissue degeneration, and inflammatory diseases.

In an advance that could lead to better ways to diagnose and treat those diseases, MIT researchers have developed a noninvasive way to detect biomarkers of senescence. Their method is based on Raman microscopy, which can reveal the biochemical composition of cells without harming them.

By combining Raman microscopy with gene expression data at single-cell resolution from the same cells, the researchers were able to identify unique “barcodes” that can be used to quickly identify senescent cells. This study was done in mouse cells, but the researchers are now working on adapting it for use with human tissue.

“You can imagine that one day we may develop an endoscope that can look inside your body and identify cellular senescence,” says Jeon Woong Kang, an MIT research scientist and one of the senior authors of the study.

The research is part of a National Institutes of Health initiative called the Cellular Senescence Network, which is pursuing a deeper understanding of senescence in hopes of developing therapies that could combat some of the tissue-damaging effects of senescent cells.

Peter So, director of the MIT Laser Biomedical Research Center (LBCR) and an MIT professor of biological engineering and mechanical engineering, and Jian Shu, an assistant professor at Massachusetts General Hospital (MGH) and Harvard Medical School, and an associate member of the Broad Institute and Ragon Institute, are also senior authors of the paper, which appears today in Nature Aging. Lead authors of the paper are Ke Zhang, an instructor at MGH and Harvard Medical School; Xingjian Chen, a postdoc at MGH and Harvard Medical School; Francesco Monticolo, a postdoc at MGH and Harvard Medical School; and Salvatore Sorrentino, a postdoc at MIT.

Cell senescence is often triggered by DNA damage, which leads to an irreversible arrest of the cell cycle. These cells don’t die, but they undergo significant changes to their shape, metabolic processes, and gene expression profiles.

The immune system is responsible for clearing out these “zombie cells,” but as people age, this process becomes less efficient. When senescent cells accumulate, they may contribute to sagging skin, muscle weakness, and chronic conditions such as osteoarthritis and type 2 diabetes.

Cellular senescence also has beneficial effects, playing critical roles in embryonic development and tissue regeneration.

“Senescence is not just a pathological condition,” So says. “The idea behind the NIH Cellular Senescence Network is to take a very comprehensive approach to understand senescence and identify senescent cells, because it plays a role in so many normal physiological conditions and many pathological conditions.”

Scientists have already identified a few biomarkers for senescence, including two proteins called p16 and p21, which are involved in halting the cell cycle. However, those proteins can only be identified using a process that ends up destroying the cells.

The MIT team wanted to find a way to noninvasively identify senescent cells using Raman microscopy. Unlike RNA-sequencing, which consumes the cells as it analyzes them, Raman microscopy is a nondestructive technique that reveals the chemical composition of tissues or cells by shining near-infrared or visible light on them.

In the new study, the researchers used Raman microscopy in conjunction with spatial RNA sequencing — a technique that reveals where genes are active within a tissue — to identify new markers of senescence. By combining these two techniques, they were able to generate a much broader picture of the distinctive features of senescent cells, including gene expression levels, spatial location, and other biochemical information.

“Our idea was to look at many different features to characterize senescence. That’s why we wanted to combine both single-cell gene expression and Raman microscopy, so that we can characterize the senescence from two complementary views,” Shu says.

Using both methods of analysis, the researchers examined skin and lung tissue from 2-month-old mice and 26-month-old mice.

One of the most dramatic changes seen in both lung and skin cells was an increase in lipid synthesis in older cells, along with accumulation of lipids. How this affects the physiology of the cells is not yet known, the researchers say.

The researchers also found some effects that were specific to each tissue. In senescent skin cells, they discovered that cellular pathways associated with muscle contraction and with remodeling of collagen and the extracellular matrix were significantly affected. And in aged lung tissue, they found increased activity of genes involved in immune activation and inflammation.

In future work, the researchers hope to study further what role these changes play in senescent cells.

Using these data, the researchers were able to identify combinations of Raman peaks that correlate with senescence. These peaks, which represent specific chemical bonds, are linked to the presence of certain lipids, proteins, or other molecules.

“Combining the most important Raman features with the most important gene signatures, we were able to create a barcode that can help us to identify senescent cells in a more unbiased way,” Sorrentino says. “Using this barcode, we can focus on a few Raman bands that emerged as the most informative in this work.” Using these bands, it could be possible to identify senescent cells by looking for just those bands of the Raman spectrum. This could help to enable diagnostics that would detect cells that have become senescent.

To help make that possible, the researchers are now working on a higher-speed version of their Raman imaging system. Currently, it takes about 30 hours to analyze a tissue sample about one square millimeter in size, but they hope to develop a system that can quickly pick out the Raman barcodes they identified from larger samples.

The research was funded by the National Institutes of Health and Massachusetts General Hospital.

President Sally Kornbluth took to GBH’s “Boston Public Radio” (BPR) to discuss the value of scientific research amid cuts to federal funding and the new endowment tax impacting MIT and other universities. In her wide-ranging conversation with hosts Jim Braude and Margery Eagan, Kornbluth highlighted ‘Curiosity on a mission,’ MIT’s support for quantum technology, artificial intelligence and research—and where to get soft serve ice cream.

“The best science is scientists just following their instincts, trying to find the answers to fundamental questions,” said Kornbluth.

Scientific discovery is born from a long sequence of experiments, funded largely by federal grants, “but it's not just scientists doing [research] for their own sake,” she explains. “It's all these discoveries on a mission to do important things in the world, whether it's curing diseases or finding new materials or finding new ways to construct buildings.”

Currently, MIT has seen a 20% decline in federal funding, limiting graduate admissions and threatening future breakthroughs. Kornbluth emphasized, “[Researchers] have to go down a lot of blind alleyways following your curiosity to hit the things that are going to have major impact down the road.”

Kornbluth also touched on the impact of the endowment tax on MIT. “We've had to make a lot of budgetary cuts to accommodate the endowment tax and the losses in federal funding, so if you put it together, people have really had to tighten their belts…and really focus to keep our research mission,” she said.

Referencing the impacts of long-term research free from short-term pressures of politics and profit, Kornbluth mentioned Prof. Robert Weinberg’s discovery that RAS genes can mutate into many different cancers, including pancreatic cancer. “It's now been shown that RAS could be druggable,” explained Kornbluth. “So, this discovery that was made by Bob 45 years ago is now being translated into RAS targeting drugs that could lead to treatments of cancers like pancreatic cancer.”

Future treatments for cancer, and other diseases, are based on the discoveries made today. “If you look at some of the stuff like immunotherapy for cancer now, that was 40, 50, years of immunology research,” said Kornbluth. “People might not see the impact [of funding cuts] immediately, but you're going to see the impact in 10 years, and your children and your children's children will see the impact.”

Restrictions by the government are not limited to funding cuts. About 40% of MIT’s graduate student population are international students. Kornbluth explained to the hosts how the four-year cap on student visas by the Trump administration is incompatible with the amount of time it takes to obtain a PhD, which is usually at least five to six years.

“This [four-year cap] is untenable for PhD students,” said Kornbluth. She emphasized the many ways international students contribute to research and the economy in the U.S.

“I trained so many fabulous students from all over the world who are now professors in American universities, or working for American industries, or starting their own American companies.”

Though cuts to funding threaten the research pipeline, Kornbluth is confident that technologies such as AI and quantum can advance work at MIT and beyond. She believes that AI will change, not replace, the work of PhD students. “[AI] is going to make each graduate student sort of like a mini faculty member,” said Kornbluth. “They're going to get to have AI agents that can explore multiple ideas with them.”

Looking ahead, Kornbluth outlined her work with Governor Maura Healey to advance quantum technology in Massachusetts, which is “the future in so many things… It's much more efficient in computing, much more sensitive at sensing things like molecules in the blood, like viewing from a canopy what's happening underneath,” she explained.

In May, Healey and Kornbluth announced plans for the Quantum Systems Laboratory at MIT, a shared space for academic and commercial development in quantum, open to researchers both from and beyond MIT.

“We need to make sure that Massachusetts is at the forefront, and so we're building this quantum systems laboratory that is going to have quantum computing but also figure out an interconnect between different forms of quantum computing,” Kornbluth expressed.

Kornbluth shouted out the soft serve at Eastern Edge, MIT’s new food hall, that was recently featured in “Soft Serve with Scientists,” a new feature with the host of GBH’s “Curiosity Desk.”

“I was really lobbying hard for the soft serve machine,” revealed Kornbluth.

Off campus? Kornbluth recommends Momma’s Grocery located near Porter Square and the Charles River Speedway. Her favorite is “the maple creamy with the maple sprinkles.”

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