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

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

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

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

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

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

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

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

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

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

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

The study appears in Nature Microbiology.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Seeing the light

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

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

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

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

A Bonafide signal

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

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

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

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

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

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

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

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

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

This research was supported, in part, by NASA.

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

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

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

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

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

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

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

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

Investigating interference

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

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

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

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

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

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

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

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

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

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

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

Rethinking materials research

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Trailblazing theorist at DMSE

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

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

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

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

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

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

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

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

From superconductors to screenplays

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

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

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

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

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

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

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

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

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

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

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

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

Inspired by nature

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

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

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

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

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

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

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

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

Watery environments

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Department of Architecture

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

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

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

Department of Urban Studies and Planning (DUSP)

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

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

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

Program in Media Arts and Sciences

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Shell games

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

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

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

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

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

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

Through the looking glass

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Filling out the record

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

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

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

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

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

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

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

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

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

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

How much does public opinion matter?

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

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

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

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

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

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

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.