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

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

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

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

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

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

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

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

Putting the pieces together

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

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

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

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

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

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

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

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

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

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

Quantifying lost carbon

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

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

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

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

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

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

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

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

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

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

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

On Thursday, June 5, 11 individuals and four teams were awarded MIT Excellence Awards — the highest awards for staff at the Institute. Cheers from colleagues holding brightly colored signs and pompoms rang out in Kresge Auditorium in celebration of the honorees. In addition to the Excellence Awards, staff members received the Collier Medal, the Staff Award for Distinction in Service, and the Gordon Y. Billard Award.  

The Collier Medal honors the memory of Officer Sean Collier, who gave his life protecting and serving MIT. The medal recognizes an individual or group whose actions demonstrate the importance of community, and whose contributions exceed the boundaries of their profession. The Staff Award for Distinction in Service is presented to an individual whose service results in a positive, lasting impact on the MIT community. The Gordon Y. Billard Award is given to staff or faculty members, or MIT-affiliated individuals, who provide "special service of outstanding merit performed for the Institute."

The 2025 MIT Excellence Award recipients and their award categories are:

Bringing Out the Best

• Timothy Collard
• Whitney Cornforth
• Roger Khazan

Embracing Inclusion

• Denise Phillips

Innovative Solutions

• Ari Jacobovits
• Stephanie Tran
• MIT Health Rebranding Team, Office of the Executive Vice President and Treasurer: Ann Adelsberger, Amy Ciarametaro, Kimberly Schive, Emily Wade

Outstanding Contributor

• Sharon Clarke
• Charles "Chip" Coldwell
• Jeremy Mineweaser
• Christopher "Petey" Peterson
• MIT Health Accreditation Team, Office of the Executive Vice President and Treasurer: Christianne Garcia, David Podradchik, Janis Puibello, Kristen Raymond
• MIT Museum Visitor Experience Supervisor Team, Associate Provost for the Arts: Mariah Crowley, Brianna Vega

Serving Our Community

• Nada Miqdadi El-Alami
• MIT International Scholars Office, Office of the Vice President for Research: Portia Brummitt-Vachon, Amanda Doran, Brianna L. Drakos, Fumiko Futai, Bay Heidrich, Benjamin Hull, Penny Rosser, Henry Rotchford, Patricia Toledo, Makiko Wada
• Building 68 Kitchen Staff, Department of Biology, School of Science: Brikti Abera, AnnMarie Budhai, Nicholas Budhai, Daniel Honiker, Janet Katin, Umme Khan, Shuming Lin, Kelly McKinnon, Karen O'Leary

The 2025 Collier Medal recipient was Kathleen Monagle, associate dean and director of disability and access services, student support, and wellbeing in the Division of Student Life. Monagle oversees a team that supports almost 600 undergraduate, graduate, and MITx students with more than 4,000 accommodations. She works with faculty to ensure those students have the best possible learning experience — both in MIT’s classrooms and online.

This year’s recipient of the 2025 Staff Award for Distinction in Service was Stu Schmill, dean of admissions and student financial services in the Office of the Vice Chancellor. Schmill graduated from MIT in 1986 and has since served the Institute in a variety of roles. His colleagues admire his passion for sharing knowledge; his insight and integrity; and his deep love for MIT’s culture, values, and people.

Three community members were honored with a 2025 Gordon Y. Billard Award. 

• William "Bill" Cormier, project technician, Department of Mechanical Engineering, School of Engineering

• John E. Fernández, professor, Department of Architecture, School of Architecture and Planning; and director of MIT Environmental Solutions Initiative, Office of the Vice President for Research

• Tony Lee, coach, MIT Women's Volleyball Club, Student Organizations, Leadership, and Engagement, Division of Student Life

Presenters included President Sally Kornbluth; MIT Chief of Police John DiFava and Deputy Chief Steven DeMarco; Dean of the School of Science Nergis Mavalvala; Vice President for Human Resources Ramona Allen; Executive Vice President and Treasurer Glen Shor; Lincoln Laboratory Assistant Director Justin Brooke; Chancellor Melissa Nobles; and Provost Anantha Chandrakasan.

Visit the MIT Human Resources website for more information about the award recipients, categories, and to view photos and video of the event. 

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

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

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

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

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

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

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

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

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

Building better blends

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

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

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

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

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

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

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

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

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

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

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

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

Accelerating discovery

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Cold confinement

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

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

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

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

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

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

Tuning fuzz

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

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

Springs away

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

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

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

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

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

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

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

In 2023, students from Calistoga Junior/Senior High School in California entered a year-long invention project run by the Lemelson-MIT Program. Tasked with finding problems to solve in their community, the students settled on an invention to keep firefighters and agricultural workers cool in hot working conditions.

Over the next 12 months, the students learned more about the problem from the workers, developed a prototype cooling system, and filed a patent for their invention. After presenting their solution at the program’s capstone Eurekafest event at MIT, the students were invited to the California State Capitol to share their work with lawmakers, and they went on to be selected as finalists in the student SXSW Innovation Awards.

For 20 years, the Lemelson-MIT InvenTeams Grant Initiative has inspired high school students across the country by supporting them through an extracurricular invention program that culminates in presentations on MIT’s campus each spring. The students select their own problems and invent their own solutions, receiving $7,500 in grants from Lemelson-MIT, along with mentorship, technical consultation, and more support to turn their ideas into reality.

In total, high school InvenTeams have been granted 19 U.S. patents since the program’s start, with many more teams, like the one from Calistoga, continuing work on their inventions after the program. Students often report an increased sense of confidence and interest in STEM subjects following their InvenTeams experience. In some cases, that new mindset changes students’ life trajectories.

“In a traditional school setting, students don’t always get the chance to show what they can do,” says Calistoga High School teacher Heather Brooks, who sponsored the 2023 team. “I was blown away by the students’ power and creativity.”

Turning students into inventors

The Lemelson Prize program started in 2004 with one $500,000 award given to a prolific inventor each year and smaller prizes given to inventor teams from MIT. In 2006, following a National Science Foundation report on the best ways to foster and support inventors, the program started awarding smaller grants to teams of high school students across the country.

“[Program founder] Jerome Lemelson wanted to inspire young people to become inventors and had a deep belief that America’s strength and innovation was driven by invention,” says Lemelson-MIT Executive Director Stephanie Couch. “He wanted young people to celebrate inventors like they celebrate rock stars and football players.”

When Couch arrived at MIT nine years ago, her research showed that giving small grants to younger students was the most successful way to increase students’ interest in STEM subjects.

Each year, the InvenTeams program receives between 50 and 80 applications from student teams across the country. From there, 20 to 30 teams are selected for Excite Awards. Those teams submit an in-depth application in which they describe the problem they’re solving, conduct patent research, and share early ideas for their solution. They also outline plans for community engagement, budget allocation, and additional background research.

Judges with a range of expertise select the finalists, who submit monthly updates throughout the year. Teams also meet with the community members they are inventing solutions for regularly.

“We see invention as a practice in empathy,” says Edwin Marrero, the interim invention education manager of the Lemelson-MIT program. “When you’re inventing, you’re inventing for somebody — and we like to say you’re inventing with somebody. Students learn to communicate and work in their communities. It’s a good skill to learn early in life.”

The final event at MIT, dubbed Eurekafest, is held every June. It features live presentations at the Stata Center that are open to the public and allow the students to showcase their inventions. Students stay in MIT dormitories for a few days leading up to the presentations and participate in a series of networking opportunities.

“The presentations are my favorite part, because people are peppering students with questions, and their depth of understanding, along with the confidence they project, is totally unlike anything you’ve ever seen from a high schooler,” Couch says.

This year’s teams presented ways to detect contamination in drinking water, help visually impaired people communicate, treat groundwater for use in agriculture, and more. Finalist teams hailed from Lubbock, Texas; Edison, New Jersey; Nitro, West Virginia, and — for the first time in the program’s history — MIT’s backyard of Cambridge, Massachusetts. The team from Cambridge invented a communication device for rowers on crew teams so they can hear from their coaches. They filed a patent for their invention.

“We’ve learned from our research that this one-year program really does transform the students’ perceptions of themselves, what they’re capable of, and what they’ll do next,” Couch says. “Also, by letting them pick what problem they want to solve and for whom they want to solve it, we’re giving them agency and tapping into that intrinsic motivation in life — to find meaning and purpose. How often in school do you get to find a problem versus being told which one to work on?”

Scaling invention education

There are many stories about the impact of the InvenTeam program on students. In 2016, a team of students on the autism spectrum developed a treadmill device and app to detect lameness in cows on dairy farms — a way to catch injury or disease in the animals. The students filed a patent for the device, which cost far less than other solutions on the market.

In 2018, a team from Garey High School in California developed a sensor device to help monitor foot health in diabetic patients and prevent amputations.

“Our school is one of the lowest-performing academically, and 99 percent of our students are low income,” says Antonio Gamboa, the school district’s former science department chair. “Before the Lemelson-MIT InvenTeams grant, district administrators said they didn’t have money to support science. Once they saw what these students could do, that turned around — not just in our school, but across the district.”

The InvenTeams program has been so successful the Lemelson-MIT program created a membership program, called Partners in Invention Education, to help many more schools adopt invention education. The curriculum stretches from kindergarten all the way to the first two years of college.

“As a middle school math teacher in New York City Public Schools, I noticed kids are falling out of love with these STEM subjects at an early age,” Marrero says. “I think a big reason for that is it’s not taught in a way that meaningful to them. There often aren’t real-world applications in lessons. Lemelson-MIT’s invention education makes STEM subjects relevant to kids. They’re the drivers of the learning. They might discover they need math or science skills to solve the problem they’re working on, and it creates a different level of motivation.”

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Look and learn

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

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

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

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

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

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

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

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

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

The future is soft

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Meet you at the Met

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

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

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

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

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

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

Fewer outdoor groups: Anomie or Starbucks?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The study appears in Nature Microbiology.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Seeing the light

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

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

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

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

A Bonafide signal

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

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

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

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

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

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

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

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

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

This research was supported, in part, by NASA.

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

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

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

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

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

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

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

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

Investigating interference

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

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

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

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

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

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

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

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

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

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

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

Rethinking materials research

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Trailblazing theorist at DMSE

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

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

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

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

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

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

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

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

From superconductors to screenplays

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

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

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

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

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

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

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

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

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

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

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

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

Inspired by nature

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

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

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

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

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

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

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

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

Watery environments

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Department of Architecture

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

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

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

Department of Urban Studies and Planning (DUSP)

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

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

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

Program in Media Arts and Sciences

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Shell games

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

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

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

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

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

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

Through the looking glass

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Filling out the record

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

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

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

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

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

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

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

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

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

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

How much does public opinion matter?

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

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

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

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

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

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