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.”

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

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

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

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

Griffith, professor of biological and mechanical engineering and director of CGR, says the Fairbairn Fund will permit the illumination of “the enormous sex-based differences in human immunity” and advance next-generation drug-discovery technologies.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

As countries across the world experience a resurgence in nuclear energy projects, the questions of where and how to dispose of nuclear waste remain as politically fraught as ever. The United States, for instance, has indefinitely stalled its only long-term underground nuclear waste repository. Scientists are using both modeling and experimental methods to study the effects of underground nuclear waste disposal and ultimately, they hope, build public trust in the decision-making process.

New research from scientists at MIT, Lawrence Berkeley National Lab, and the University of Orléans makes progress in that direction. The study shows that simulations of underground nuclear waste interactions, generated by new, high-performance-computing software, aligned well with experimental results from a research facility in Switzerland.

The study, which was co-authored by MIT PhD student Dauren Sarsenbayev and Assistant Professor Haruko Wainwright, along with Christophe Tournassat and Carl Steefel, appears in the journal PNAS.

“These powerful new computational tools, coupled with real-world experiments like those at the Mont Terri research site in Switzerland, help us understand how radionuclides will migrate in coupled underground systems,” says Sarsenbayev, who is first author of the new study.

The authors hope the research will improve confidence among policymakers and the public in the long-term safety of underground nuclear waste disposal.

“This research — coupling both computation and experiments — is important to improve our confidence in waste disposal safety assessments,” says Wainwright. “With nuclear energy re-emerging as a key source for tackling climate change and ensuring energy security, it is critical to validate disposal pathways.”

Comparing simulations with experiments

Disposing of nuclear waste in deep underground geological formations is currently considered the safest long-term solution for managing high-level radioactive waste. As such, much effort has been put into studying the migration behaviors of radionuclides from nuclear waste within various natural and engineered geological materials.

Since its founding in 1996, the Mont Terri research site in northern Switzerland has served as an important test bed for an international consortium of researchers interested in studying materials like Opalinus clay — a thick, water-tight claystone abundant in the tunneled areas of the mountain.

“It is widely regarded as one of the most valuable real-world experiment sites because it provides us with decades of datasets around the interactions of cement and clay, and those are the key materials proposed to be used by countries across the world for engineered barrier systems and geological repositories for nuclear waste,” explains Sarsenbayev.

For their study, Sarsenbayev and Wainwright collaborated with co-authors Tournassat and Steefel, who have developed high-performance computing software to improve modeling of interactions between the nuclear waste and both engineered and natural materials.

To date, several challenges have limited scientists’ understanding of how nuclear waste reacts with cement-clay barriers. For one thing, the barriers are made up of irregularly mixed materials deep underground. Additionally, the existing class of models commonly used to simulate radionuclide interactions with cement-clay do not take into account electrostatic effects associated with the negatively charged clay minerals in the barriers.

Tournassat and Steefel’s new software accounts for electrostatic effects, making it the only one that can simulate those interactions in three-dimensional space. The software, called CrunchODiTi, was developed from established software known as CrunchFlow and was most recently updated this year. It is designed to be run on many high-performance computers at once in parallel.

For the study, the researchers looked at a 13-year-old experiment, with an initial focus on cement-clay rock interactions. Within the last several years, a mix of both negatively and positively charged ions were added to the borehole located near the center of the cement emplaced in the formation. The researchers focused on a 1-centimeter-thick zone between the radionuclides and cement-clay referred to as the “skin.” They compared their experimental results to the software simulation, finding the two datasets aligned.

“The results are quite significant because previously, these models wouldn’t fit field data very well,” Sarsenbayev says. “It’s interesting how fine-scale phenomena at the ‘skin’ between cement and clay, the physical and chemical properties of which changes over time, could be used to reconcile the experimental and simulation data.” 

The experimental results showed the model successfully accounted for electrostatic effects associated with the clay-rich formation and the interaction between materials in Mont Terri over time.

“This is all driven by decades of work to understand what happens at these interfaces,” Sarsenbayev says. “It’s been hypothesized that there is mineral precipitation and porosity clogging at this interface, and our results strongly suggest that.”

“This application requires millions of degrees of freedom because these multibarrier systems require high resolution and a lot of computational power,” Sarsenbayev says. “This software is really ideal for the Mont Terri experiment.”

Assessing waste disposal plans

The new model could now replace older models that have been used to conduct safety and performance assessments of underground geological repositories.

“If the U.S. eventually decides to dispose nuclear waste in a geological repository, then these models could dictate the most appropriate materials to use,” Sarsenbayev says. “For instance, right now clay is considered an appropriate storage material, but salt formations are another potential medium that could be used. These models allow us to see the fate of radionuclides over millennia. We can use them to understand interactions at timespans that vary from months to years to many millions of years.”

Sarsenbayev says the model is reasonably accessible to other researchers and that future efforts may focus on the use of machine learning to develop less computationally expensive surrogate models.

Further data from the experiment will be available later this month. The team plans to compare those data to additional simulations.

“Our collaborators will basically get this block of cement and clay, and they’ll be able to run experiments to determine the exact thickness of the skin along with all of the minerals and processes present at this interface,” Sarsenbayev says. “It’s a huge project and it takes time, but we wanted to share initial data and this software as soon as we could.”

For now, the researchers hope their study leads to a long-term solution for storing nuclear waste that policymakers and the public can support.

“This is an interdisciplinary study that includes real world experiments showing we’re able to predict radionuclides’ fate in the subsurface,” Sarsenbayev says. “The motto of MIT’s Department of Nuclear Science and Engineering is ‘Science. Systems. Society.’ I think this merges all three domains.”

Growing up in Paris, Vincent Rollet was exposed to the world beyond France from an early age. His dad was an engineer who traveled around the globe to set up electrical infrastructure, and he moved the family to the United States for two years when Rollet was a small child. His father’s work sparked Rollet’s interest in international development and growth. “It made me want to see and learn how things work in other parts of the world,” he says.

Today, Rollet is a fifth-year PhD student in MIT’s Department of Economics, studying how cities evolve — and how they may become constrained by their past. “Cities constantly need to adapt to economic changes,” he explains. “For example, you might need more housing as populations grow, or want to transform manufacturing spaces into modern lab facilities. With the rise of remote work, many cities now have excess office space that could potentially become residential housing.” Ultimately, Rollet hopes his research can influence urban policymakers to better serve city residents.

A happy accident

Rollet’s first exposure to economics was almost accidental. As a teenager, he stumbled upon the lecture videos of a game theory course at Yale University. “I randomly clicked on the available courses,” he said, “and I watched the videos, and I found it interesting.”

In high school and college, he focused on math and physics. “It’s the kind of training you’re typically pushed to do in France,” he says. But at the end of his first year at École Polytechnique — mandatory military training for all students — he remembered the Yale course that he had watched in high school. He had spent that year helping run a military service program for disadvantaged youth. “I was looking for an enjoyable way to start studying again,” he says. “So I went back to game theory.”

Rollet decided to take a game theory course with an economics professor, Pierre Boyer, who would play a key role in his academic path. Through conversations with Boyer, Rollet learned that economics could provide a rigorous, mathematical approach to understanding the topics around international development and international politics that had long fascinated him. Boyer introduced Rollet to two MIT-trained economists, professors Vincent Pons and Benjamin Marx, with whom he continues to collaborate today. A research visit to the U.S. in 2019 to work with them solidified his interest in pursuing graduate school. Shortly thereafter, he began his PhD at MIT.

Why cities get “stuck”

Rollet’s research explores why cities struggle to adapt their built environments as economic conditions shift, and why certain urban spaces become “stuck” in outdated patterns of development. He’s drawn to cities because they are a microcosm of different interacting systems in economics. “To understand cities, you need to understand how labor markets work, how the housing market works, and how transportation works,” he notes.

Rollet has spent most of his PhD focusing on New York City. By examining detailed data on building permits, real estate transactions, rents, and zoning changes, he has tracked the evolution of every building in the city over nearly two decades, studying when and why developers choose to demolish buildings and construct new ones, and how these decisions are influenced by economic, regulatory, and technological constraints. By combining computational theory and data — which often includes information on natural experiments (i.e., What happens when a city changes a regulation?) — Rollet aims to reveal generalizable principles underlying how cities grow and evolve.

Originally shaped as a manufacturing hub with dense commercial centers and sprawling residential outskirts, New York’s physical structure has been largely frozen since zoning regulations were imposed in the 1960s. Despite dramatic shifts in population and economic activity, the city’s regulations have barely budged, creating profound mismatches: soaring housing costs, overcrowded residential areas, and underutilized commercial spaces. The buildings are expensive to replace, and regulations are notoriously hard to change once they are established.

Rollet’s findings reveal critical inefficiencies. In cities like New York or Boston, housing often sells for hundreds of thousands of dollars more than it costs to build. This large gap suggests that demand far outpaces supply: There simply aren’t enough homes being built. “When the housing supply is too constrained, we are effectively wasting resources, making housing unnecessarily expensive,” he explains.

But implementing any kind of action or policy to alleviate these inefficiencies has downstream effects. For example, it can have different impacts on different groups of people. “There will be winners and losers,” Rollet explains. “One reason is that you might directly care about the welfare of a certain group, like directly providing housing for lower-income households. Another reason is that if there are sufficiently many people who are losers of a certain policy, or if they’re sufficiently powerful, they’re going to be able to block the policy change, and this poses a political constraint.”

So what makes a city “stuck”? “Much of the time,” Rollet says, “it’s policy.” But the effects of policy changes take time to materialize and might be difficult for people to detect. Rollet cites Cambridge’s recent zoning reform allowing the construction of six-story buildings as a case in point. “These policy changes can benefit a lot of people, by reducing the housing prices a bit for everyone,” he says, “but individual people won’t know it. This makes collective action very hard.”

Economics, however, provides a toolkit to characterize and quantify these effects. “What economists can bring to the table is to give policymakers more information on the likely consequences of their policy actions,” Rollet says.

Striving to “improve things”

As Rollet enters the home stretch of his PhD, he’s grateful to his advisors in the economics department for helping him develop a foundation for the diverse set of tools necessary for his work. From professors Dave Donaldson and David Atkin, he learned how to adapt methods traditionally used in the study of international trade, to analyze the movement of people across neighborhoods and cities. From Professor Tobias Salz, he gained insights into modeling the behavior of firms over time, which he now applies to understanding the actions of real estate developers. “The training here pushes you to produce research that truly stands out,“ he says. “The courses helped me discover a new set of fields and methods.”

Beyond research, Rollet actively contributes to his department, including serving as the co-president of the Graduate Economics Association. “MIT is truly the best place for economics, not just because of their courses, but because it’s a really friendly department where people help each other out,” he says. “The Graduate Economics Association helps to build that sense of community, and I wanted to be a part of that.” In addition, he is a member of a mental health and peer support group in the department.

Rollet also enjoys teaching. He has been a teaching assistant for microeconomics and international trade courses and has built an impressive writing repertoire explaining complex concepts in several fields. In high school, one of Rollet’s hobbies was writing quantum theory explainers on the internet for general audiences. Some publishers found his writing and contacted him about turning it into a book. The book was published, and has sold more than 14,000 copies. As a college student, Rollet worked on two books: one on game theory for general audiences, and an intro to economics textbook that two professors recruited him to co-author. It’s still the standard textbook at École Polytechnique today. “It was my Covid activity,” Rollet laughs.

Looking forward, Rollet aims to pursue a career in research and teaching. His immediate goal remains clear: develop research that meaningfully impacts policy, by shedding light on how cities can overcome constraints and evolve in ways that better serve their residents. He’s excited about how, in the future, more fine-grained and detailed data sources could shed light on how micro behavior can lead to macro outcomes.

"Housing and cities — these markets are failing in important ways in many parts of the world. There’s real potential for policy to improve things.”

Like almost any MIT student, Mason Estrada wants to take what he learned on campus and apply it to the working world.

Unlike any other MIT student, Estrada will soon be going to work on a pitcher’s mound, and some day Dodger Stadium might be his office.

Estrada, the star pitcher for MIT’s baseball team, is signing a contract with the Los Angeles Dodgers organization, after the team selected him in the 7th round of the Major League Baseball draft on July 14. The right-hander, whose stellar stuff earned significant attention from MLB scouts, will be reporting soon to the Dodgers’ instructional camp in Arizona.

“I’m definitely excited,” says Estrada, who was projected as a likely draft pick but did not know he would be selected by the Dodgers, Major League Baseball’s defending champions.

From the outside, MIT might seem like an atypical starting point for a pitching career, but it has helped Estrada in multiple ways: by providing a strong baseball program in itself, and, more subtly, by reinforcing the value of systematic improvement, at a time when baseball pitching increasingly resembles, well, engineering.

On the first count, Estrada praises his MIT coaches and teammates for the baseball environment they have helped provide.

“It was really awesome,” Estrada says about playing baseball at the Institute. “I was surrounded by a bunch of guys that wanted to win. There was a great team culture of grinding and working hard.”

Meanwhile, pitching in professional baseball more than ever involves “pitch design” or “pitch shaping.” For a decade now, major-league teams have used high-speed cameras to determine which pitches work best. In turn, pitchers are often reverse-engineering parts of their arsenals, by starting with the desired outcome, then finding the combination of velocity and movement to stymie hitters.

Into this setting, enter Estrada, an MIT aeronautics and astronautics major — although, he makes clear, pitching at MIT has never involved transferring aerodynamic knowledge from the classroom to the mound. Rather, what counts is using feedback and analysis to get better.

“It’s not necessarily based on the subject I was studying,” Estrada says. “It’s learning to think like an engineer generally, learning to think through problems the right way, and finding the best solution.”

This season, Estrada went 6-0 with a 2.21 ERA for MIT, striking out 66 and allowing a paltry 22 hits in 40 2/3 innings on the season. There are additional numbers that hint at his potential: Estrada’s fastball has hit 96 miles per hour, and he throws two types of sliders, with velocity in the upper 80s while producing up to 2,700 rotations per minute, in line with big-league metrics.

On the mound, Estrada uses his lower body to generate significant drive toward the plate — “I have to rely on my strength,” he says. Pitchers who share elements of this approach include Spencer Strider of the Atlanta Braves, although, Estrada emphasizes, “Everybody at the professional level is different.”

MIT’s baseball coaches praise Estrada’s dedication to the sport.

“Mason’s work ethic is through the roof,” says Todd Carroll, MIT’s pitching coach and recruiting coordinator, now in his 13th season at the Institute. Carroll thinks Estrada’s fastball and sliders could translate well to the professional game. The forward drive of Estrada’s motion, Carroll also notes, means that when Estrada delivers a pitch, “It’s on a hitter quick.”

Carroll concurs that the engineering mindset on campus actively helps players improve over time.

“MIT students are problem-solvers,” he says. “MIT is a place where people can do that as well as anywhere in the world. When a pitcher here misses the strike zone, that’s a problem they want to solve.”

Inevitably, all the off-field work, analysis, and preparation, is designed to let Estrada simply be himself on the diamond. For athletes, some parts of the brain are best put on pause when competing.

“In games, I’m just focused on getting the hitter out,” Estrada says. “I’m staying in the moment.”

As it happens, baseball’s relatively new world of pitch shaping and pitch design has been enabled by MIT-linked technology. The kind of high-speed video camera many teams use, the Edgertronic, is manufactured by Sanstreak Corp., founded by Mike Matter ’84, a graduate of what is now the Department of Electrical Engineering and Computer Science. If the camera name sounds familiar, it should: Matter named it in homage to Harold “Doc” Edgerton, the legendary MIT pioneer of high-speed photography, whom Matter counted as a mentor.

Estrada is the fifth MIT undergraduate selected in baseball’s draft, which dates to 1966, and the highest-drafted player in MIT history at 225th overall. The others are Alan Dopfel ’72, selected by the California Angels; Jason Szuminski ’00, drafted by the San Diego Padres; Austin Filiere ’18, picked by the Chicago Cubs; and David Hesslink ’17, chosen by the Seattle Mariners. Of those players, Szuminski reached the majors, with the Padres.

At least two major-league pitchers also earned MIT degrees after finishing long baseball careers: Chris Capuano MBA ’19, a former All-Star with the Brewers, who received his master’s degree in management as part of the MIT Sloan Fellows program, and Skip Lockwood SM ’83.

As a Dodger, Estrada joins an organization famed for great pitching: Since the team moved to Los Angeles in 1958, their star pitchers have included Sandy Koufax, Don Drysdale, Fernando Valenzuela, Orel Hershiser, and Clayton Kershaw.

Beyond that, the Dodgers are known for investing considerable resources in player development, staying on the leading edge of analytics while bulking up their staff in order to help players improve. They have won the World Series twice this decade, in 2020 and 2024.

Whatever happens on the diamond, Estrada wants to return to MIT to complete his degree. Before the draft, he had made plans to temporarily transfer to the University of Tennessee to play Division I baseball next season, with the plan of returning to MIT as a student. However, Estrada will not be doing that now that he is signing with the Dodgers.

As things now stand, Estrada is taking a leave of absence from the Institute while his professional career starts to unfold.

“I just want to be clear I’m very thankful to MIT and to the MIT baseball staff for all they’ve done,” Estrada says.

And now, campus experience in hand, Estrada is off to his very distinctive work environment. 

Teaching a robot new skills used to require coding expertise. But a new generation of robots could potentially learn from just about anyone.

Engineers are designing robotic helpers that can “learn from demonstration.” This more natural training strategy enables a person to lead a robot through a task, typically in one of three ways: via remote control, such as operating a joystick to remotely maneuver a robot; by physically moving the robot through the motions; or by performing the task themselves while the robot watches and mimics.

Learning-by-doing robots usually train in just one of these three demonstration approaches. But MIT engineers have now developed a three-in-one training interface that allows a robot to learn a task through any of the three training methods. The interface is in the form of a handheld, sensor-equipped tool that can attach to many common collaborative robotic arms. A person can use the attachment to teach a robot to carry out a task by remotely controlling the robot, physically manipulating it, or demonstrating the task themselves — whichever style they prefer or best suits the task at hand.

The MIT team tested the new tool, which they call a “versatile demonstration interface,” on a standard collaborative robotic arm. Volunteers with manufacturing expertise used the interface to perform two manual tasks that are commonly carried out on factory floors.

The researchers say the new interface offers increased training flexibility that could expand the type of users and “teachers” who interact with robots. It may also enable robots to learn a wider set of skills. For instance, a person could remotely train a robot to handle toxic substances, while further down the production line another person could physically move the robot through the motions of boxing up a product, and at the end of the line, someone else could use the attachment to draw a company logo as the robot watches and learns to do the same.

“We are trying to create highly intelligent and skilled teammates that can effectively work with humans to get complex work done,” says Mike Hagenow, a postdoc at MIT in the Department of Aeronautics and Astronautics. “We believe flexible demonstration tools can help far beyond the manufacturing floor, in other domains where we hope to see increased robot adoption, such as home or caregiving settings.”

Hagenow will present a paper detailing the new interface, at the IEEE Intelligent Robots and Systems (IROS) conference in October. The paper’s MIT co-authors are Dimosthenis Kontogiorgos, a postdoc at the MIT Computer Science and Artificial Intelligence Lab (CSAIL); Yanwei Wang PhD ’25, who recently earned a doctorate in electrical engineering and computer science; and Julie Shah, MIT professor and head of the Department of Aeronautics and Astronautics.

Training together

Shah’s group at MIT designs robots that can work alongside humans in the workplace, in hospitals, and at home. A main focus of her research is developing systems that enable people to teach robots new tasks or skills “on the job,” as it were. Such systems would, for instance, help a factory floor worker quickly and naturally adjust a robot’s maneuvers to improve its task in the moment, rather than pausing to reprogram the robot’s software from scratch — a skill that a worker may not necessarily have.

The team’s new work builds on an emerging strategy in robot learning called “learning from demonstration,” or LfD, in which robots are designed to be trained in more natural, intuitive ways. In looking through the LfD literature, Hagenow and Shah found LfD training methods developed so far fall generally into the three main categories of teleoperation, kinesthetic training, and natural teaching.

One training method may work better than the other two for a particular person or task. Shah and Hagenow wondered whether they could design a tool that combines all three methods to enable a robot to learn more tasks from more people.

“If we could bring together these three different ways someone might want to interact with a robot, it may bring benefits for different tasks and different people,” Hagenow says.

Tasks at hand

With that goal in mind, the team engineered a new versatile demonstration interface (VDI). The interface is a handheld attachment that can fit onto the arm of a typical collaborative robotic arm. The attachment is equipped with a camera and markers that track the tool’s position and movements over time, along with force sensors to measure the amount of pressure applied during a given task.

When the interface is attached to a robot, the entire robot can be controlled remotely, and the interface’s camera records the robot’s movements, which the robot can use as training data to learn the task on its own. Similarly, a person can physically move the robot through a task, with the interface attached. The VDI can also be detached and physically held by a person to perform the desired task. The camera records the VDI’s motions, which the robot can also use to mimic the task when the VBI is reattached.

To test the attachment’s usability, the team brought the interface, along with a collaborative robotic arm, to a local innovation center where manufacturing experts learn about and test technology that can improve factory-floor processes. The researchers set up an experiment where they asked volunteers at the center to use the robot and all three of the interface’s training methods to complete two common manufacturing tasks: press-fitting and molding. In press-fitting, the user trained the robot to press and fit pegs into holes, similar to many fastening tasks. For molding, a volunteer trained the robot to push and roll a rubbery, dough-like substance evenly around the surface of a center rod, similar to some thermomolding tasks.

For each of the two tasks, the volunteers were asked to use each of the three training methods, first teleoperating the robot using a joystick, then kinesthetically manipulating the robot, and finally, detaching the robot’s attachment and using it to “naturally” perform the task as the robot recorded the attachment’s force and movements.

The researchers found the volunteers generally preferred the natural method over teleoperation and kinesthetic training. The users, who were all experts in manufacturing, did offer scenarios in which each method might have advantages over the others. Teleoperation, for instance, may be preferable in training a robot to handle hazardous or toxic substances. Kinesthetic training could help workers adjust the positioning of a robot that is tasked with moving heavy packages. And natural teaching could be beneficial in demonstrating tasks that involve delicate and precise maneuvers.

“We imagine using our demonstration interface in flexible manufacturing environments where one robot might assist across a range of tasks that benefit from specific types of demonstrations,” says Hagenow, who plans to refine the attachment’s design based on user feedback and will use the new design to test robot learning. “We view this study as demonstrating how greater flexibility in collaborative robots can be achieved through interfaces that expand the ways that end-users interact with robots during teaching.”

This work was supported, in part, by the MIT Postdoctoral Fellowship Program for Engineering Excellence and the Wallenberg Foundation Postdoctoral Research Fellowship.

Large language models (LLMs) excel at using textual reasoning to understand the context of a document and provide a logical answer about its contents. But these same LLMs often struggle to correctly answer even the simplest math problems.

Textual reasoning is usually a less-than-ideal way to deliberate over computational or algorithmic tasks. While some LLMs can generate code like Python to handle symbolic queries, the models don’t always know when to use code, or what kind of code would work best.

LLMs, it seems, may need a coach to steer them toward the best technique.

Enter CodeSteer, a smart assistant developed by MIT researchers that guides an LLM to switch between code and text generation until it correctly answers a query.

CodeSteer, itself a smaller LLM, automatically generates a series of prompts to iteratively steer a larger LLM. It reviews the model’s current and previous answers after each round and provides guidance for how it can fix or refine that solution until it deems the answer is correct.

The researchers found that augmenting a larger LLM with CodeSteer boosted its accuracy on symbolic tasks, like multiplying numbers, playing Sudoku, and stacking blocks, by more than 30 percent. It also enabled less sophisticated models to outperform more advanced models with enhanced reasoning skills.

This advance could improve the problem-solving capabilities of LLMs for complex tasks that are especially difficult to solve with textual reasoning alone, such as generating paths for robots in uncertain environments or scheduling shipments in an international supply chain.

“There is a race to develop better and better models that are capable of doing everything, but we’ve taken a complementary approach. Researchers have spent years developing effective technologies and tools to tackle problems in many domains. We want to enable LLMs to select the right tools and methods, and make use of others’ expertise to enhance their own capabilities,” says Chuchu Fan, an associate professor of aeronautics and astronautics (AeroAstro) and principal investigator in the MIT Laboratory for Information and Decision Systems (LIDS).

Fan, the senior author of the study, is joined on a paper about the work by LIDS graduate student Yongchao Chen; AeroAstro graduate student Yilun Hao; University of Illinois at Urbana-Champaign graduate student Yueying Liu; and MIT-IBM Watson AI Lab Research Scientist Yang Zhang. The research will be presented at the International Conference on Machine Learning.

An LLM “trainer”  

Ask an LLM which number is bigger, 9.11 or 9.9, and it will often give the wrong answer by using textual reasoning. But ask it to use code to answer the same question, and it can generate and execute a Python script to compare the two numbers, easily solving the problem.

Initially trained to understand and predict human language, LLMs are more likely to answer queries using text, even when code would be more effective. And while they have learned to generate code through fine-tuning, these models often generate an incorrect or less efficient version of the code.

Rather than trying to retrain a powerful LLM like GPT-4 or Claude to improve these capabilities, the MIT researchers fine-tune a smaller, lightweight LLM to guide a larger model between text and code. Fine-tuning a smaller model doesn’t change the larger LLM, so there is no risk it would undermine the larger model’s other abilities.

“We were also inspired by humans. In sports, a trainer may not be better than the star athlete on the team, but the trainer can still give helpful suggestions to guide the athlete. This steering method works for LLMs, too,” Chen says.

This trainer, CodeSteer, works in conjunction with the larger LLM. It first reviews a query and determines whether text or code is suitable for this problem, and which sort of code would be best.

Then it generates a prompt for the larger LLM, telling it to use a coding method or textual reasoning to answer the query. The larger model follows this prompt to answer the query and sends the result back to CodeSteer, which reviews it.

If the answer is not correct, CodeSteer will continue prompting the LLM to try different things that might fix the problem, such as incorporating a search algorithm or constraint into its Python code, until the answer is correct.

“We found that oftentimes, the larger LLM will try to be lazy and use a shorter, less efficient code that will not carry the correct symbolic calculation. We’ve designed CodeSteer to avoid this phenomenon,” Chen says.

A symbolic checker evaluates the code’s complexity and sends a signal to CodeSteer if it is too simple or inefficient. The researchers also incorporate a self-answer checker into CodeSteer, which prompts the LLM to generate code that calculates the answer to verify it is correct.

Tackling complex tasks

As the researchers designed CodeSteer, they couldn’t find suitable symbolic datasets to fine-tune and test the model, since many existing benchmarks don’t point out whether a certain query could be best solved with text or code.

So, they gathered a corpus of 37 complex symbolic tasks, including spatial reasoning, mathematics, order reasoning, and optimization, and built their own dataset, called SymBench. They implemented a fine-tuning approach that leverages SymBench to maximize the performance of CodeSteer.

In their experiments, CodeSteer outperformed all nine baseline methods they evaluated and boosted average accuracy from 53.3 percent to 86.4 percent. It maintains similar performance even on unseen tasks, and on a variety of LLMs.

In addition, a general-purpose model augmented with CodeSteer can achieve higher accuracy than state-of-the-art models designed to focus on complex reasoning and planning, while requiring much less computation.

“Our method uses an LLM’s own capabilities. By augmenting an LLM with the ability to smartly use coding, we can take a model that is already very strong and improve its performance even more,” Chen says.

In the future, the researchers want to streamline CodeSteer to speed up its iterative prompting process. In addition, they are studying how to effectively fine-tune a unified model with the ability to switch between textual reasoning and code generation, rather than relying on a separate assistant.

“The authors present an elegant solution to the critical challenge of tool utilization in LLMs. This simple yet impactful method enables state-of-the-art LLMs to achieve significant performance improvements without requiring direct fine-tuning,” says Jinsung Yoon, a staff research scientist at Google Cloud AI, who was not involved with this work. “This research represents a substantial contribution that promises to significantly enhance the application of LLMs to a diverse range of tasks with which they currently struggle.”

“Their success in training a smaller, specialized model to strategically guide larger, advanced models is particularly impactful,” adds Chi Wang, a senior staff scientist at Google DeepMind who was not involved with this work. “This intelligent collaboration among diverse AI ‘agents’ paves the way for more robust and versatile applications in complex real-world scenarios.”

This research is supported, in part, by the U.S. Office of Naval Research and the MIT-IBM Watson AI Lab.

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

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

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

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

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

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

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

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

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

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

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

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

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

MIT equips students with the tools to advance science and engineering — but a new class aims to ensure they also develop their own values and learn how to navigate conflicting viewpoints.

Offered as a pilot this past spring, the multidisciplinary class 21.01 (Compass Course: Love, Death, and Taxes: How to Think — and Talk to Others — About Being Human), invites students to wrestle with difficult questions like:

• What do we value (and why)?
• What do we know (and how do we know it)?
• What do we owe to each other (and what should we do about it)?

The class is part of the Compass Initiative, which is led by faculty from across the MIT School of Humanities, Arts, and Social Sciences (SHASS). 

Lily L. Tsai, Ford Professor of Political Science and lead faculty for Compass, says the new course is meant to help students use the humanities and social sciences as their guide to thinking about the kind of humans they want to be and what kind of society they want to help create.

"At MIT, we're some of the people who are creating the technologies that are accelerating change and leading to more unpredictability in the world. We have a special responsibility to envision and reimagine a moral and civic education that enables people to navigate it," says Tsai.

The course is the result of a multi-year collaboration involving over 30 faculty from 19 departments, ranging from Philosophy and Literature to Brain and Cognitive Sciences and Electrical Engineering and Computer Science, all led by a core team of 14 faculty from SHASS and a student advisory board.

During its initial run in the spring, Compass followed an arc that began with students investigating questions of value. Early in the semester, students explored what makes a genius, using Beethoven's "Symphony No. 9" as a case study, accompanied by lectures from Emily Richmond Pollock, associate professor of music, and a podcast conversation with Larry Guth, professor of mathematics, and David Kaiser, professor of physics and science, technology, and society. 

Students then grappled with the concept of a merit-based society by digging into the example of the imperial Chinese civil service exam, guided by professor of history Tristan Brown. Next, they questioned what humans really know to be true by examining the universality of language through lectures by professor of linguistics Adam Albright, and the philosophy of truth and knowledge through lectures by professor of philosophy Alex Byrne.

The semester ended with challenging debates about what humans owe one another, including a class designed by Nobel laureate and professor of economics Esther Duflo on taxation and climate burdens. 

More than anything, Tsai says, she hopes that Compass prepares students to navigate dorm hallways, the family Thanksgiving table, or future labs or boardroom tables, and learn how to express opinions and actively listen to others with whom they may disagree — all without canceling one another. 

The class takes a "flipped classroom" approach: Students watch recorded lectures at home and come to class prepared for discussion and debate. Each section is co-taught by two faculty members, combining disciplines and perspectives.

Second-year mechanical engineering major Kayode Dada signed up because it fulfilled a communications-intensive requirement and offered cross-departmental exposure. But Compass ultimately became more than that to him. "College isn't just about learning science stuff — it's also about how we grow as people," he says. Dada was assigned to a section co-taught by Tsai and professor of literature Arthur Bahr. 

Forming a social contract

In the first week, students draft a Rousseau-inspired social compact and learn firsthand how to build a classroom community. "We knew these were deep topics," Dada says. "To get the most out of the class, we had to open up, respect each other, and keep conversations confidential."

One early exercise was especially impactful. After watching lectures by Ford Professor of Philosophy and Women’s and Gender Studies Sally Haslanger on value, students were asked to draw a map representing their values, with arrows pointing from ones that were more instrumental to ones that were fundamental.

At first, Dada felt stuck. Growing up in Kentucky, the son of a Nigerian immigrant who had dreamed of attending MIT himself, Dada had focused for years on gaining admission to the Institute. "I thought getting into MIT would make me feel fulfilled," he admits. "But once I got here, I realized the work alone wasn't enough."

The values exercise helped him reorient. He identified practicing Christianity, hard work, helping others, and contributing to society as central to his belief system. The exercise influenced Dada, leading him to choose to volunteer at a robotics camp for kids in Louisville to share his MIT education with others.

Who governs science? 

Later in the semester, Dada was animatedly representing a figure whose views contradicted his own: James D. Watson, the Nobel Prize winner who co-discovered DNA's structure — and is also a controversial figure. 

That week, each student had been assigned a persona from a 1976 Cambridge City Council hearing debating recombinant DNA research. The class, designed by Associate Professor Robin Scheffler, was investigating the question: Who governs science — scientists, the government, those who fund research, or the public?

They revisited a real-life debate around recombinant DNA research and the dangers for biological weapons development and other threats to the public that citizens of that time believed it posed when carried out in MIT and Harvard University labs. Pioneered in the 1970s, the technique involved the splicing of genes related to the E. coli bacterium. In the Compass classroom, students argued different sides from their personas: banning the research, moving labs outside city limits, or proceeding without government interference.

Dada notes how faculty intentionally seeded conflicting viewpoints. "It taught me how to negotiate with someone who has different values and come to a resolution that respects everyone involved," he says. "That's something I want to keep exploring."

When Dada closed his presentation with frantically-Googled sentimental music piped unexpectedly from his phone, his classmates laughed in appreciation. The atmosphere was more intimate than academic — an ethos Tsai hoped to cultivate. "They really built intellectual relationships based on trust," she says. "There was a lot of laughter. They took joy in disagreeing and debating."

Changing opinions 

First-year student-athlete Shannon Cordle, who is majoring in mechanical engineering, didn't know what to expect from Compass. Since it was new, there were no student reviews. What stood out to her was the grading system: 15 percent of the final grade is based on a rubric each student created for themselves.

Cordle's goal was to become more comfortable expressing an opinion — even before she's fully formed it. "It's easy to stay quiet when you're unsure," she says. "Compass helped me practice speaking up and being willing to be wrong, because that's how you learn."

One week, the class debated whether a meritocracy creates a just society — an especially relevant topic at MIT, given its famously selective admissions process. 

Students were able to pick their stance beforehand, and then invited to change it as they gained more perspectives during the debate.

"This helps students grasp not only the flaws in another viewpoint, but also how to strengthen their arguments," Tsai says.

Cordle, who hopes to go into prosthetics, views her future field as representing the perfect balance between creativity and ethics. "The humanities challenge how we view our fields as scientists and engineers," she says.

A compass helps travelers find their way — but it's most useful when they need to reorient and change direction. In that spirit, Compass prepares students not just to ask big questions, but to keep asking — and keep adapting — as their lives and careers evolve.

“Bringing these unexpected class elements together with students and faculty generated magical alchemy — a kind of transformation that we didn't even know we could create,” Tsai says.

In addition to the class, the MIT Compass Podcast engages in these fundamental questions with guests from across the MIT schools of Science and Engineering. There are also plans to adapt the residential version of this class for online learners on MITx.

In addition to philanthropic support from MIT Corporation life member emeritus Ray Stata '57, the initiative is supported by the Office of the Vice Chancellor and the MIT Human Insight Collaborative's SHASS Education Innovation Fund, which promotes new, transformative educational approaches in SHASS fields.

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

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

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

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

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

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

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

Simultaneous treatments

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

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

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

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

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

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

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

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

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

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

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

Optimizing dosages, minimizing error

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

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

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

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

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

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

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

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

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

Extensive turnover

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

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

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

What are the rules?

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

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

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

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

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

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

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

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

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

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

Daniel Kleppner, the Lester Wolfe Professor Emeritus of Physics at MIT whose work in experimental atomic physics made an immense mark on the field, died on June 16 at the age of 92, in Palo Alto, California.

Kleppner’s varied research examined the interactions of atoms with static electric and magnetic fields and radiation. His work included creating precision measurements with hydrogen masers, including the co-invention of the hydrogen maser atomic clock; his research into the physics of Rydberg atoms and cavity quantum electrodynamics; and his pioneering work in Bose-Einstein condensation (BEC).

Kleppner, who retired in 2003 after 37 years at MIT, was a highly literate and articulate scientist whose exacting research and communication skills helped set the direction of modern atomic, molecular, and optical (AMO) physics. From 1987 to 2000, he was associate director of the MIT Research Laboratory of Electronics (RLE), and served as interim director in 2001. He also co-founded the MIT-Harvard Center for Ultracold Atoms (CUA) in 2000, where he was co-director until 2006.

While he was never awarded a Nobel Prize, Kleppner's impact on the field of atomic physics and quantum optics, and his generous mentorship, enabled the Nobel achievements of many others. His patient and exacting pursuit of discovery led to basic research insights that led to major achievements. His extensive research into the tiny atom provided the fundamental knowledge necessary for the huge: the eventual development of groundbreaking technologies such as the global positioning system (GPS), magnetic resonance imaging (MRI), and quantum computing.

“He was a leader in the department, and a leader in the American Physical Society,” says Wolfgang Ketterle, the John D. MacArthur Professor of Physics at MIT and a 2001 Nobel laureate. “He was a statesman of science. He was this eloquent person, this master of words who could express things in memorable ways, and at the same time he has this sense of humility.”

“Dan Kleppner was a giant in the area of AMO physics, and in science more broadly,” says John Doyle PhD ’91, Harvard Quantum Initiative co-director and Kleppner advisee who helped Kleppner create the Bose-Einstein condensate from atomic hydrogen. “Perhaps his most impactful legacy is leading a culture of respect and supportive community actions that all scientists in the area of AMO physics enjoy today. Not only did his science lay the path for current research directions, his kindness, erudition, and commitment to community — and community service — are now ever-expanding waves that guide AMO physics. He was a mentor and friend to me."

Kleppner’s daughter Sofie Kleppner notes: “People who worked on early lasers never imagined we would be scanning groceries at the checkout counter. When they developed the hydrogen maser, they were a bunch of nerdy people who really wanted to understand Einstein’s theory of relativity. This was the basis for GPS, this is how our flights run on time. Our dad was convinced that basic research today could lead to all sorts of valuable things down the road.”

Early life and career

Born in Manhattan on Dec. 16, 1932, Kleppner was the son of Vienna native and advertising agency founder Otto Kleppner, who wrote the best-selling book “Advertising Procedure.” His mother, Beatrice (Taub) Kleppner, grew up in New Jersey and was a graduate of Barnard College. She helped with Otto’s manuscripts. Daniel Kleppner was the second of three siblings; his brother, the late Adam Kleppner, was a professor of mathematics at the University of Maryland, and his sister, Susan Folkman, was a research psychologist at the University of California at Berkeley.

“As a teenager, I just liked building things,” Kleppner once said. “And that turned out to be very useful when I went on to become an experimental physicist. I had a crystal radio, so I could listen to the radio over earphones. And the thought that the signals were just coming out of the atmosphere, I remember thinking: totally remarkable. And actually, I still do. In fact, the idea of the electromagnetic field, although it’s very well understood in physics, always seems like a miracle to me.”

In high school, he was inspired by his physics teacher, Arthur Hussey, who allowed Kleppner to work all hours in the labs. “There was one time when the whole school was having a pep rally, and I wasn’t that interested in cheering football, so I stayed up and worked in the lab, and the high school principal noticed that I was in there and called me in and gave me a dressing down for lack of school spirit.”

He didn’t care. Hussey talked with Kleppner about quantum mechanics, and “that sort of put a bee in my bonnet on that,” and taught him a little calculus. “In those years, physics was extremely fashionable. These were the post-war years, and physicists were considered heroes for having brought the war to conclusion with the atom bomb, and … the development of radar.”

He knew by then that he was “destined to spend a life in physics,” he said in a video interview for InfiniteMIT. “It was an easy era to become delighted by physics, and I was.”

Studying physics at Williams College, he was drawn to Albert Einstein’s theory of general relativity. He built a programmable machine that he called a forerunner of cybernetics. Williams also instilled in him a lifelong love of literature, and he almost became an English major. However, he didn’t appreciate what he called the school fraternities’ “playboy” and “anti-intellectual” atmosphere, and worked to graduate quickly within three years, in 1953.

He deferred his acceptance to Harvard University with a Fulbright Fellowship to Cambridge University, where he met the young physicist Kenneth Smith, whose research was with atomic beam resonance. Smith introduced him to the book “Nuclear Moments,” by Harvard professor Norman Ramsey, and presented a proposal by Ramsey’s advisor I.I. Rabi, who invented a technique that could make an atomic clock so precise “that you could see the effect of gravity on time that Einstein predicted,” said Kleppner.

“I found that utterly astonishing,” Kleppner noted. “The thought that gravity affects time: I had a hard time just visualizing that.”

When Kleppner wandered Harvard’s halls in 1955, he was excited to see a door with Ramsey’s name on it. He was interested in Ramsey’s research on molecular beam magnetic resonance, atomic clocks, and precision measurements. “Fortunately, I came along at a time when he had an opening in his research group,” Kleppner recalled.

A new atomic clock

As Kleppner’s advisor, Ramsey encouraged him to create a new type of atomic clock, believing that cesium and ammonia masers, a technology of amplified microwaves, were not precise enough to measure the effect of gravity on time.

Kleppner’s thesis was on using the concepts behind an ammonia maser to advance toward a hydrogen maser, which uses the natural microwave frequency of hydrogen atoms and amplifies it through stimulated emission of radiation. Kleppner discovered that coherent cesium atoms can bounce from properly prepared surfaces without losing their coherence.

After his 1959 PhD, Kleppner stayed on at Harvard, becoming an assistant professor in 1962.

Kleppner’s research on hydrogen led to a method to keep hydrogen atoms locked in a glass container for study over a longer period of time. The result, featuring hydrogen atoms bouncing within a microwave cavity, is used to stabilize the frequency of a clock to a precision better than one microsecond in a year.

In 1960, he and Ramsey successfully created a new atomic clock whose significant stability could confirm the minute effects of gravity on time, as predicted by Einstein’s theory of general relativity.

The current generation of optical clocks “are good enough to see the gravitational red shift for a few centimeters in height, so that’s quite extraordinary, and it’s had an extraordinary result,” said Kleppner. “We got to rethink just what we mean by time.”

While the hydrogen maser did verify Einstein’s conjecture about time and gravity, it took more than a decade before being widely used, at first by radio astronomers. Today, atomic clocks such as the hydrogen maser are used in applications requiring high short-term stability, such as the synchronization of ground-based timing systems that track global positioning satellites, for timekeeping and communication by naval observatories to maintain a precise and stable time reference known as UTC (USNO); very long-baseline microwave interferometry (VLBI) that enables astronomers to achieve very high resolution and study distant radio sources, including black holes; and, indirectly, in magnetic resonance imaging.

“When we first set out to make these atomic clocks, our goals were about the least practical you can think of,” Kleppner said in an interview with the MIT Physics Department. “From being a rather abstract idea that you’d like to somehow witness, it becomes a very urgent thing for the conduct of human affairs.”

Ramsey went on to win the Nobel Prize in Physics in 1989 for his work on the separated oscillatory fields method and its application in the hydrogen maser and atomic clocks.

MIT, ultracold gases, and BEC advancements

Kleppner figured he wouldn’t get tenure at Harvard, “because no matter how generous and good-spirited Norman was, he casts a long shadow, and it was good for me to be at just the right distance. When I came to MIT, I had a pallet of experiments that I wanted to pursue, and some ideas about teaching that I wanted to pursue, and the transition was very simple.”

Kleppner joined the Institute in 1966, and his Harvard PhD student (and current MIT professor post-tenure) David Pritchard followed him, to work on scattering experiments: Kleppner worked with pulsed lasers, and Pritchard with continuous-wave (CW) lasers.

“He was young, he was verbal, and he seemed to have new ideas about what to do,” says Pritchard. “We foresaw how important lasers would become. For a long time, it was just Dan and myself. That was actually the era in which lasers took over. Dan and I started off, we both got into lasers, and he did Rydberg atoms, and I did collisions and spectroscopy of weakly bound molecules and two-photon spectroscopy.”

Kleppner led the tiny MIT Atomic Physics Group to eventually become the US News and World Report’s No. 1 nationally ranked atomic physics group in 2012. “Dan was the leader on this,” recalled Pritchard. “To start from non-tenure and build it into the number-one ranked department in your subfield, that’s a lifetime achievement.”

The group became what Pritchard called “the supergroup” of laser developers that included Charles Townes, who won the Nobel for his work; Ali Javan, who established a major laser research center at MIT; and Dolly Shibles. Pritchard joined the faculty in 1970, and Ketterle joined in 1990 as his postdoc. “We were pioneers, and the result was of course that our total group had a bigger impact.”

“He’s not just the father figure of the field, he is my scientific father,” says Pritchard. “When I’m writing something and it’s not going very well, I would sort of think to myself, ‘What would Dan say? What would he advise you?”

With MIT low-temperature physicist Tom Greytak ’63, PhD ’67, Kleppner developed two revolutionary techniques — magnetic trapping and evaporative cooling. When the scientific community combined these techniques with laser cooling, atomic physics went into a major new direction.

In 1995, a group of researchers, led by Kleppner's former students Eric Cornell PhD ’90 and Carl Weiman ’73, made a BEC using rubidium atoms, and Ketterle succeeded with sodium atoms. For this achievement, they received the 2001 Nobel Prize in Physics. Kleppner called BEC “the most exciting advance in atomic physics for decades.” 

At a conference on BEC in 1996, Ketterle recalls Kleppner describing his own contributions: “'I feel like Moses, who showed his people the Holy Land, but he never reached it himself.' This was exactly what Dan did. He showed us the Holy Land of Bose-Einstein condensation. He showed us what is possible … He was the godfather of Bose-Einstein condensation.”

But he did reach the Holy Land. In 1998, when only a few groups had been able to create BECs, Kleppner and Greytak realized a hydrogen BEC. When he presented their work at the summer school in Varenna soon afterward, he received a long-lasting standing ovation — after 20 years of hard work, he had reached his goal.

“It is an irony that when Dan started this work, hydrogen was the only choice to reach the low temperatures for BEC,” says Ketterle. But in the end, it turned out that hydrogen has special properties that made it much harder to reach BEC than with other atoms. 

Rydberg atoms

In 1976, Kleppner pioneered the field of Rydberg atoms, a highly excited atom that shares the simple properties that characterize hydrogen. Kleppner showed that these states could be excited by a tunable laser and easily detected with field ionization. He then mapped out their response in high electric and magnetic fields, which he used to provide new physical insights into the connections between quantum mechanics and classical chaos.

In 1989, his research into atomic energy levels, under conditions where the corresponding classical motion is chaotic, mapped out the positions of thousands of quantum levels as a function of laser frequency and applied field using high-resolution laser spectroscopy. His observations gave new physical insight into the implications of classical chaos on quantum systems.

“I see Dan as being the inventor of Rydberg atoms,” says Dan’s former student William Phillips PhD ’76, physicist at the Institute of Standards and Technology (NIST). “Of course, Rydberg atoms is something that nature gives you, but Dan was the one who really understood this was something that you could use to do really new and wonderful things.”

Such atoms have proved to be useful for studying the transition between quantum mechanics and classical chaos. Kleppner’s 1976 paper on Rydberg atoms’ strong interactions, long lifetimes, and sensitivity to external fields has led to current scientific research and multimillion-dollar startups interested in developing the promising Rydberg quantum computer; highly accurate measurements of electric and magnetic fields; and in quantum optics experiments.

“Largely due to Dan’s seminal roadmap, Rydberg atoms have become atomic physics’ E. coli for investigating the interaction of radiation with matter,” wrote Ketterle in his nomination for Kleppner’s 2017 APS Medal for Exceptional Achievement in Research. “They are being used by others in quests for experimental systems to realize Schrödinger’s cat, as well as for making a quantum computer.”

In 1981, Kleppner suggested in a theoretical paper the possibility of suppressing spontaneous emission with a cavity: excited atoms cannot decay when the cavity lacks the oscillatory modes to receive their emissions. This was followed by his demonstration of this effect, and launched the field of cavity quantum electrodynamics (cQED), the study of how light confined within a reflective cavity interacts with atoms or other particles. This field has led to the creation of new lasers and photonic devices.

“This work fundamentally changed the way physicists regard the process of spontaneous emission by showing that it is not a fixed property of a quantum state, but can be modified and controlled,” said Ketterle. “Current applications of these principles, which Dan terms ‘wrecking the vacuum,’ include thresholdless lasers and the construction of photonic bandgap materials in which light propagation is forbidden at certain frequencies.”

MIT-Harvard Center for Ultracold Atoms

In 2000, Kleppner secured National Science Foundation funding to co-found the Center for Ultracold Atoms (CUA), an MIT-Harvard collaboration that linked RLE with the Harvard Department of Physics to explore the physics of ultracold atoms and quantum gases. Kleppner served as its first director until 2006, and was a member of a group that included MIT professors Ketterle, Pritchard, Vladan Vuletic, Martin W. Zwierlein, Paola Cappellaro PhD ’06, and Isaac Chuang ’90.

“Many centers disappear after 10 to 20 years; sometimes their mission is fulfilled,” says Ketterle, the CUA director from 2006 to 2023. “But given the excitement and the rapid evolution in atomic physics, the CUA is a super-active center brimming with excitement, and we just recently got renewed. That’s partially due to the efforts of Dan. He created the tradition of atomic physics at MIT. We are one of the best atomic physics groups in the world. And we are really a family.”

Boost-phase intercept report

Kleppner co-authored a highly influential 2003 report that examined the technical feasibility of boost-phase intercept, a concept central to President George H.W. Bush’s proposed controversial Strategic Defense Initiative (SDI), nicknamed "Star Wars,” which purportedly would render nuclear weapons obsolete. The focus of the APS Study on Boost-Phase Intercept for National Missile Defense, published as a special supplement to Reviews of Modern Physics, was on the physics and engineering challenges of intercepting a missile during its boost phase.

“This was a subject on which I had no technical background at all,” Kleppner recalled, so he expressed gratitude for the skills of co-chair Fred Lamb of the University of Illinois. “But the APS [American Physical Society] felt that it was important to have information for the public … and no one knew anything about it. It was the point in my life where I could do that. And I feel that you have an obligation when the need arises and you can do it, to do that.”

The result? “Technically, it really would not succeed, except in very limited circumstances,” Kleppner said. Added Pritchard, “It vastly changed the path of the nation.”

“He was the perfect person to chair the committee,” says Ketterle. “He excelled in being neutral and unbiased, and to create a no-nonsense report. I think the APS was very proud of this report. It shows how physicists analyze something which was at that moment of immense political and societal importance. This report helped to understand what laser weapons cannot do and what they can do. The fact that (SDI) eventually, slowly, disappeared, the report may have contributed to that.”

Dedicated educator

Kleppner trained generations of physicists, including as advisor to 23 PhD students who have gone on to attain positions in major universities and achieve major scientific awards.

He was awarded the Oersted Medal of the American Association of Physics Teachers in 1997, and earned the Institute’s prestigious 1995-1996 James R. Killian, Jr. Faculty Achievement Award for his service to MIT and society on behalf of atomic physics. “He has given generously of his time and effort to the formation of national science policy, and he has served the Institute with distinction as teacher, administrator and counselor,” the Killian committee wrote.

Kleppner and Ramsey wrote the widely used text “Quick Calculus” in 1972 — the third edition of the book was updated in 2022 edition with MIT Department of Physics’ Peter Dourmashkin. With Robert J. Kolenkow, Kleppner also wrote “An Introduction to Mechanics” in 1973, and its second edition in 2013. Physics department head Deepto Chakrabarty ’88 called it “a masterpiece:” “It has formed the foundation of our freshman 8.012 course for potential physics majors for over 50 years and has provided a deep, elegant, and mathematically sophisticated introduction to classical mechanics of physics majors across the U.S. It was my own introduction to serious physics as an MIT freshman in 1984.”

Recently, while Kleppner was being wheeled into surgery, one of the medical personnel noticed that his patient was the author of that book and blurted out, “Oh my God, I still am wondering about one of those problems that I found so difficult,” recalls his wife, Bea, laughing.

Kleppner called his method of teaching “an engagement with the students and with the subject.” He said that his role model for teaching was his wife, who taught psychology at Beaver Country Day High School. “Fortunately, at MIT, the students are so great. There’s nothing tough about teaching here, except trying to stay ahead of the students.”

He leaves a legacy of grateful physicists impacted by his generous teaching style.

“I’ve always felt that I’ve just been incredibly lucky to be part of Dan’s group,” says Phillips, who was at Princeton when his research into magnetic resonance caught Kleppner’s attention, and invited him to MIT. “Dan extended this idea to putting this hydrogen maser in a much higher magnetic field. Not that many people are trained by somebody like Dan Kleppner in the art of precision measurement.”

Kleppner also gifted Phillips an apparatus he built for his thesis, which shaved years off the laser cooling experiments that led to Phillips’ Nobel.

Ketterle credited Kleppner’s mentorship for his career at MIT. “He was an older, experienced person who believed in me. He had more trust in me than I had initially myself. I felt whenever I was at a crossroads, I could go to Dan and ask him for advice. When I gave him a paper to edit … there was red ink all over it, but he was absolutely right on almost everything.’”

In 2003, Kleppner was dismayed at the statistic that over 60 percent of middle and high school teachers teaching physics have no background in the subject. He started the CUA’s Teaching Opportunities in Physical Science summer program with his then-postdoc Ted Ducas to train physics majors to prepare and teach physics material to middle and high school students. In its 14-year run, they worked with 112 students.

According to Ducas, one survey “indicates over 60 percent of our undergraduates have gone into, or plan to go into, pre-college teaching — a higher percentage than expected, because physics majors have so many other career opportunities often paying significantly more. The potential positive impact of that number of highly qualified and motivated teachers is dramatic.”

Kleppner also partnered with Japanese mathematician Heisuke Hironaka on the mentoring program Japanese Association for Mathematical Sciences (JAMS), which connected American college science students with their Japanese counterparts. “His interest in ensuring that future generations also see the value of international communities was reflected in JAMS,” says Sofie Kleppner.

Recognitions and public service

Kleppner was promoted to professor in 1974 and headed the physics department’s Division of Atomic, Plasma and Condensed Matter Physics from 1976 to 1979. He was named the Lester Wolfe Professor of Physics in 1985.

Active in the interface between physics and public policy, he served on more than 30 committees. For the APS, he was on the Panel on Public Affairs (POPA), chaired the Physics Planning Committee and the Division of Atomic, Molecular and Optical Physics, and contributed to a study on the growth and mentorship of young physics professors. He chaired a report for the National Academy of Sciences on atomic physics that he presented on various congressional committees, served on the National Research Council's Physics Survey Committee, and was chair of the International Union of Pure and Applied Physics’ Commission on Atomic and Molecular Physics. At MIT, he was also an ombuds of the Physics Department.

Kleppner was a fellow of the American Academy of Arts and Sciences, American Association for the Advancement of Science, OSA (now Optica), French Academy of Sciences, and the American Philosophical Society; a member of the National Academy of Sciences; and a Phi Beta Kappa lecturer.

His interest in literature at Williams bloomed into a secondary career as a writer, including decades of writing witty and insightful, yet accessible, pieces for Physics Today, including his “Reference Frame” columns on physics history and policy.

Kleppner was a recipient of many awards, including the prestigious Wolf Prize in 2005 “for groundbreaking work in atomic physics of hydrogenic systems, including research on the hydrogen maser, Rydberg atoms, and Bose-Einstein condensation.” Other accolades include a 2014 Benjamin Franklin Medal and a 2006 National Medal of Science, presented by U.S. President George W. Bush. He also received the Frederic Ives Medal (2007), the William F. Meggers Award (1991), the Lilienfeld Prize (1991), and the Davisson-Germer Prize (1986).

His articles, congressional testimony, and advocating on behalf of physicists around the world at one point inspired his Physics Planning Committee colleagues to present him with a Little League trophy of a golden baseball player, with the inscription “Dan Kleppner — Who Went to Bat for Atomic Physics.”

Kleppner said that he was inspired by his mentor, Ramsey, to get involved in the scientific community. “It’s a privilege to be a scientist in this country,” said Kleppner. “And I think that one has some obligation to pay for the privilege, when you can.”

He wrote, “Any scenario for a decent future of our nation and the world must include a reasonable component of science that is devoted to the search for new knowledge. We cannot afford to abandon this vision under a barrage of criticism, no matter how eloquent or powerful the critics.”

Family and retired life

Kleppner met his future wife, Beatrice Spencer, in 1954 on the USS United States, when both were England-bound and in their second year of studying at Cambridge. They began as friends, and eventually married in 1958, in Ipswich, Massachusetts. They raised their three children, Sofie, Paul, and Andrew, at their home in Belmont, Massachusetts, and their vacation home in Vermont.

Kleppner’s family described him as an optimist who didn’t believe in lying, worrying, or unethical behavior. He and Bea generously invited into their home anyone in need. “When we were growing up, we had the international community in our house,” recalls Sofie. “He was just a tremendously generous person. At my father’s 80th birthday celebration at MIT, there were three hours of five-minute reminiscences. It was really moving to hear the number of people who felt that just having the open door at my parents’ house meant the difference to them as they went through difficult times.”

In his retirement, Kleppner continued with his woodworking projects, including building beds, lamps, cabinets, a beautiful spiral staircase, a cradle curved like the hull of a boat, and bookcases featuring super ellipses, a closed curve that blends elements of an ellipse and a rectangle.

“I enjoy designing,” he said in one video. “It’s the same instinct for making things work in experimental physics. It’s lovely to make a piece of apparatus that starts functioning, and even if the experiment doesn’t do what you want it to do. There’s always a lot of jubilation when the apparatus is first turned on and first works.”

His last article for Physics Today was in 2020. In his later years, he kept in touch with his colleagues, swapping book ideas with Ketterle’s wife, Michele Plott, and, since the Covid-19 pandemic, maintained regular Zoom meetings with a group of his former students, hosted by Mike Kash; and another, what they called “The Famous Physicists,” that included Phillips and  their Brazilian colleague Vanderlei Bagnato.

“In recent years, I would still go to Dan for advice about difficult questions,” says Phillips, “sometimes about physics, sometimes just about life and public policy, because maybe I always felt that if there was anything you wanted done in which physics or science was part of the question that Dan would be the best person to do it.”

His family says that Kleppner suddenly fell ill at a Father’s Day dinner. According to his wife, his last words before being rushed to the hospital were a toast to his grandson, who recently graduated high school: “To Darwin and all youth who have new and exciting ideas.”

Says Bea, “​​He always said that you have to be optimistic to be a scientist, because you have to be patient. Things don’t work out and they’re fiddly, and there are lots of things that go wrong. His last words were ones that make you feel there’s hope for the future.”

The National Academy of Sciences (NAS) has elected 120 members and 30 international members, including five MIT faculty members and 13 MIT alumni. Professors Rodney Brooks, Parag Pathak, Scott Sheffield, Benjamin Weiss, and Yukiko Yamashita were elected in recognition of their “distinguished and continuing achievements in original research.” Membership to the National Academy of Sciences is one of the highest honors a scientist can receive in their career.

Elected MIT alumni include: David Altshuler ’86, Rafael Camerini-Otero ’66, Kathleen Collins PhD ’92, George Daley PhD ’89, Scott Doney PhD ’91, John Doyle PhD ’91, Jonathan Ellman ’84, Shanhui Fan PhD ’97, Julia Greer ’97, Greg Lemke ’78, Stanley Perlman PhD ’72, David Reichman PhD ’97, and Risa Wechsler ’96. 

Those elected this year bring the total number of active members to 2,662, with 556 international members. The NAS is a private, nonprofit institution that was established under a congressional charter signed by President Abraham Lincoln in 1863. It recognizes achievement in science by election to membership, and — with the National Academy of Engineering and the National Academy of Medicine — provides science, engineering, and health policy advice to the federal government and other organizations.

Rodney Brooks

Rodney A. Brooks is the Panasonic Professor of Robotics Emeritus at MIT and the chief technical officer and co-founder of Robust AI. Previously, he was founder, chair, and CTO of Rethink Robotics and founder and CTO of iRobot Corp. He is also the former director of the MIT Artificial Intelligence Laboratory and the MIT Computer Science and Artificial Intelligence Laboratory. Brooks received degrees in pure mathematics from the Flinders University of South Australia and a PhD in computer science from Stanford University in 1981. He held research positions at Carnegie Mellon University and MIT, and a faculty position at Stanford before joining the faculty of MIT in 1984.

Brooks’ research is concerned with both the engineering of intelligent robots to operate in unstructured environments, and with understanding human intelligence through building humanoid robots. He has published papers and books in model-based computer vision, path planning, uncertainty analysis, robot assembly, active vision, autonomous robots, micro-robots, micro-actuators, planetary exploration, representation, artificial life, humanoid robots, and compiler design.

Brooks is a member of the National Academy of Engineering, a founding fellow of the Association for the Advancement of Artificial Intelligence, a fellow of the American Academy of Arts and Sciences, the American Association for the Advancement of Science, the Association for Computing Machinery, a foreign fellow of The Australian Academy of Technological Sciences and Engineering, and a corresponding member of the Australian Academy of Science. He won the Computers and Thought Award at the 1991 International Joint Conference on Artificial Intelligence, and the IEEE Founders Medal in 2023.

Parag Pathak

Parag Pathak is the Class of 1922 Professor of Economics and a founder and director of MIT’s Blueprint Labs. He joined the MIT faculty in 2008 after completing his PhD in business economics and his master’s and bachelor’s degrees in applied mathematics, all at Harvard University.

Pathak is best known for his work on market design and education. His research has informed student placement and school choice mechanisms across the United States, including in Boston, New York City, Chicago, and Washington, and his recent work applies ideas from market design to the rationing of vital medical resources. Pathak has also authored leading studies on school quality, charter schools, and affirmative action. In urban economics, he has measured the effects of foreclosures on house prices and how the housing market reacted to the end of rent control in Cambridge, Massachusetts.

Pathak’s research on market design was recognized with the 2018 John Bates Clark Medal, given by the American Economic Association to the economist under 40 whose work is judged to have made the most significant contribution to the field. He is a fellow of the American Academy of Arts and Sciences, the Econometric Society, and the Society for the Advancement of Economic Theory. Pathak is also the founding co-director of the market design working group at the National Bureau of Economic Research, and a co-founder of Avela Education.

Scott Sheffield

Scott Sheffield, Leighton Family Professor of Mathematics, joined the MIT faculty in 2008 after a faculty appointment at the Courant Institute at New York University. He received a PhD in mathematics from Stanford University in 2003 under the supervision of Amir Dembo, and completed BA and MA degrees in mathematics from Harvard University in 1998.

Sheffield is a probability theorist, working on geometrical questions that arise in such areas as statistical physics, game theory, and metric spaces, as well as long-standing problems in percolation theory and the theory of random surfaces.

In 2017, Sheffield received the Clay Research Award with Jason Miller, “in recognition of their groundbreaking and conceptually novel work on the geometry of Gaussian free field and its application to the solution of open problems in the theory of two-dimensional random structures.” In 2023, he received the Leonard Eisenbud Prize with Jason Miller “for works on random two-dimensional geometries, and in particular on Liouville Quantum Gravity.” Later in 2023, Sheffield received the Frontiers of Science Award with Jason Miller for the paper “Liouville quantum gravity and the Brownian map I: the QLE(8/3,0) metric.” Sheffield is a fellow of the American Academy of Arts and Science.

Benjamin Weiss

Benjamin Weiss is the Robert R. Schrock Professor of Earth and Planetary Sciences. He studied physics at Amherst College as an undergraduate and went on to study planetary science and geology at Caltech, where he earned a master’s degree in 2001 and PhD in 2003. Weiss’ doctoral dissertation on Martian meteorite ALH 84001 revealed records of the ancient Martian climate and magnetic field, and provided evidence some meteorites could transfer materials from Mars to Earth without heat-sterilization. Weiss became a member of the Department of Earth, Atmospheric and Planetary Sciences faculty in 2004 and is currently chair of the Program in Planetary Science.

A specialist in magnetometry, Weiss seeks to understand the formation and evolution of the Earth, terrestrial planets, and small solar system bodies through laboratory analysis, spacecraft observations, and fieldwork. He is known for key insights into the history of our solar system, including discoveries about the early nebular magnetic field, the moon’s long-lived core dynamo, and asteroids that generated core dynamos in the past. In addition to leadership roles on current, active NASA missions — as deputy principal investigator for Psyche, and co-investigator for Mars Perseverance and Europa Clipper — Weiss has also been part of science teams for the SpaceIL Beresheet, JAXA Hayabusa 2, and ESA Rosetta spacecraft.

As principal investigator of the MIT Planetary Magnetism Laboratory, Weiss works to develop high-sensitivity, high-resolution techniques in magnetic microscopy to image the magnetic fields embedded in rock samples collected from meteorites, the lunar surface, and sites around the Earth. Studying these magnetic signatures can help answer questions about the conditions of the early solar system, past climates on Earth and Mars, and factors that promote habitability.

Yukiko Yamashita

Yukiko Yamashita is a professor of biology at MIT, a core member of the Whitehead Institute for Biomedical Research, and an investigator at the Howard Hughes Medical Institute (HHMI). Yamashita earned her BS in biology in 1994 and her PhD in biophysics in 1999 from Kyoto University. From 2001 to 2006, she did postdoctoral research at Stanford University. She was appointed to the University of Michigan faculty in 2007 and was named an HHMI Investigator in 2014. She became a member of the Whitehead Institute and a professor of biology at MIT in 2020.

Yukiko Yamashita studies two fundamental aspects of multicellular organisms: how cell fates are diversified via asymmetric cell division, and how genetic information is transmitted through generations via the germline.

Two remarkable feats of multicellular organisms are generation of many distinct cell types via asymmetric cell division and transmission of the germline genome to the next generation, essentially in eternity. Studying these processes using the Drosophila male germline as a model system has led us to venture into new areas of study, such as functions of satellite DNA, “genomic junk,” and how they might be involved in speciation.

Yamashita is a member of the American Academy of Arts and Sciences, a fellow of the American Society for Cell Biology, and the winner of the Tsuneko and Reiji Okazaki Award in 2016. She was named a MacArthur Fellow in 2011.

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

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

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

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

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

Boosting cell defense

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

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

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

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

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

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

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

Fighting infection

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Addition by multiplication

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

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

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

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

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

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

The future of PhysicsGen

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

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

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

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

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

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

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

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