In Washington, where conversations about Russia often center on a single name, political science doctoral candidate Suzanne Freeman is busy redrawing the map of power in autocratic states. Her research upends prevailing narratives about Vladimir Putin’s Russia, asking us to look beyond the individual to understand the system that produced him.

“The standard view is that Putin originated Russia’s system of governance and the way it engages with the world,” Freeman explains. “My contention is that Putin is a product of a system rather than its author, and that his actions are very consistent with the foreign policy beliefs of the organization in which he was educated.”

That organization — the KGB and its successor agencies — stands at the center of Freeman’s dissertation, which examines how authoritarian intelligence agencies intervene in their own states’ foreign policy decision-making processes, particularly decisions about using military force.

Dismantling the “yes men” myth

Past scholarship has relied on an oversimplified characterization of intelligence agencies in authoritarian states. “The established belief that I’m challenging is essentially that autocrats surround themselves with ‘yes’ men,” Freeman says. She notes that this narrative stems in great part from a famous Soviet failure, when intelligence officers were too afraid to contradict Stalin’s belief that Nazi Germany wouldn’t invade in 1941.

Freeman’s research reveals a far more complex reality. Through extensive archival work, including newly declassified documents from Lithuania, Moldova, and Poland, she shows that intelligence agencies in authoritarian regimes actually have distinct foreign policy preferences and actively work to advance them.

“These intelligence agencies are motivated by their organizational interests, seeking to survive and hold power inside and beyond their own borders,” Freeman says.

When an international situation threatens those interests, authoritarian intelligence agencies may intervene in the policy process using strategies Freeman has categorized in an innovative typology: indirect manipulation (altering collected intelligence), direct manipulation (misrepresenting analyzed intelligence), preemption in the field (unauthorized actions that alter a foreign crisis), and coercion (threats against political leadership).

“By intervene, I mean behaving in some way that’s inappropriate in accordance with what their mandate is,” Freeman explains. That mandate includes providing policy advice. “But sometimes intelligence agencies want to make their policy advice look more attractive by manipulating information,” she notes. “They may change the facts out on the ground, or in very rare circumstances, coerce policymakers.”

From Soviet archives to modern Russia

Rather than studying contemporary Russia alone, Freeman uses historical case studies of the Soviet Union’s KGB. Her research into this agency’s policy intervention covers eight foreign policy crises between 1950 and 1981, including uprisings in Eastern Europe, the Sino-Soviet border dispute, and the Soviet-Afghan War.

What she discovered contradicts prior assumptions that the agency was primarily a passive information provider. “The KGB had always been important for Soviet foreign policy and gave policy advice about what they thought should be done,” she says. Intelligence agencies were especially likely to pursue policy intervention when facing a “dual threat:” domestic unrest sparked by foreign crises combined with the loss of intelligence networks abroad.

This organizational motivation, rather than simply following a leader’s preferences, drove policy recommendations in predictable ways.

Freeman sees striking parallels to Russia’s recent actions in Ukraine. “This dual organizational threat closely mirrors the threat that the KGB faced in Hungary in 1956, Czechoslovakia in 1968, and Poland from 1980 to 1981,” she explains. After 2014, Ukrainian intelligence reform weakened Russian intelligence networks in the country — a serious organizational threat to Russia’s security apparatus.

“Between 2014 and 2022, this network weakened,” Freeman notes. “We know that Russian intelligence had ties with a polling firm in Ukraine, where they had data saying that 84 percent of the population would view them as occupiers, that almost half of the Ukrainian population was willing to fight for Ukraine.” In spite of these polls, officers recommended going into Ukraine anyway.

This pattern resembles the KGB’s advocacy for invading Afghanistan using the manipulation of intelligence — a parallel that helps explain Russia’s foreign policy decisions beyond just Putin’s personal preferences.

Scholarly detective work

Freeman’s research innovations have allowed her to access previously unexplored material. “From a methodological perspective, it’s new archival material, but it’s also archival material from regions of a country, not the center,” she explains.

In Moldova, she examined previously classified KGB documents: huge amounts of newly available and unstructured documents that provided details into how anti-Soviet sentiment during foreign crises affected the KGB.

Freeman’s willingness to search beyond central archives distinguishes her approach, especially valuable as direct research in Russia becomes increasingly difficult. “People who want to study Russia or the Soviet Union who are unable to get to Russia can still learn very meaningful things, even about the central state, from these other countries and regions.”

From Boston to Moscow to MIT

Freeman grew up in Boston in an academic, science-oriented family; both her parents were immunologists. Going against the grain, she was drawn to history, particularly Russian and Soviet history, beginning in high school.

“I was always curious about the Soviet Union and why it fell apart, but I never got a clear answer from my teachers,” says Freeman. “This really made me want to learn more and solve that puzzle myself." 

At Columbia University, she majored in Slavic studies and completed a master’s degree at the School of International and Public Affairs. Her undergraduate thesis examined Russian military reform, a topic that gained new relevance after Russia’s 2014 invasion of Ukraine.

Before beginning her doctoral studies at MIT, Freeman worked at the Russia Maritime Studies Institute at the U.S. Naval War College, researching Russian military strategy and doctrine. There, surrounded by scholars with political science and history PhDs, she found her calling.

“I decided I wanted to be in an academic environment where I could do research that I thought would prove valuable,” she recalls.

Bridging academia and public education

Beyond her core research, Freeman has established herself as an innovator in war-gaming methodology. With fellow PhD student Benjamin Harris, she co-founded the MIT Wargaming Working Group, which has developed a partnership with the Naval Postgraduate School to bring mid-career military officers and academics together for annual simulations.

Their work on war-gaming as a pedagogical tool resulted in a peer-reviewed publication in PS: Political Science & Politics titled “Crossing a Virtual Divide: Wargaming as a Remote Teaching Tool.” This research demonstrates that war games are effective tools for active learning even in remote settings and can help bridge the civil-military divide.

When not conducting research, Freeman works as a tour guide at the International Spy Museum in Washington. “I think public education is important — plus they have a lot of really cool KGB objects,” she says. “I felt like working at the Spy Museum would help me keep thinking about my research in a more fun way and hopefully help me explain some of these things to people who aren’t academics.”

Looking beyond individual leaders

Freeman’s work offers vital insight for policymakers who too often focus exclusively on autocratic leaders, rather than the institutional systems surrounding them. “I hope to give people a new lens through which to view the way that policy is made,” she says. “The intelligence agency and the type of advice that it provides to political leadership can be very meaningful.”

As tensions with Russia continue, Freeman believes her research provides a crucial framework for understanding state behavior beyond individual personalities. “If you're going to be negotiating and competing with these authoritarian states, thinking about the leadership beyond the autocrat seems very important.”

Currently completing her dissertation as a predoctoral fellow at George Washington University’s Institute for Security and Conflict Studies, Freeman aims to contribute critical scholarship on Russia’s role in international security and inspire others to approach complex geopolitical questions with systematic research skills.

“In Russia and other authoritarian states, the intelligence system may endure well beyond a single leader’s reign,” Freeman notes. “This means we must focus not on the figures who dominate the headlines, but on the institutions that shape them.” 

In 15 TED Talk-style presentations, MIT faculty recently discussed their pioneering research that incorporates social, ethical, and technical considerations and expertise, each supported by seed grants established by the Social and Ethical Responsibilities of Computing (SERC), a cross-cutting initiative of the MIT Schwarzman College of Computing. The call for proposals last summer was met with nearly 70 applications. A committee with representatives from every MIT school and the college convened to select the winning projects that received up to $100,000 in funding.

“SERC is committed to driving progress at the intersection of computing, ethics, and society. The seed grants are designed to ignite bold, creative thinking around the complex challenges and possibilities in this space,” said Nikos Trichakis, co-associate dean of SERC and the J.C. Penney Professor of Management. “With the MIT Ethics of Computing Research Symposium, we felt it important to not just showcase the breadth and depth of the research that’s shaping the future of ethical computing, but to invite the community to be part of the conversation as well.”

“What you’re seeing here is kind of a collective community judgment about the most exciting work when it comes to research, in the social and ethical responsibilities of computing being done at MIT,” said Caspar Hare, co-associate dean of SERC and professor of philosophy.

The full-day symposium on May 1 was organized around four key themes: responsible health-care technology, artificial intelligence governance and ethics, technology in society and civic engagement, and digital inclusion and social justice. Speakers delivered thought-provoking presentations on a broad range of topics, including algorithmic bias, data privacy, the social implications of artificial intelligence, and the evolving relationship between humans and machines. The event also featured a poster session, where student researchers showcased projects they worked on throughout the year as SERC Scholars.

Highlights from the MIT Ethics of Computing Research Symposium in each of the theme areas, many of which are available to watch on YouTube, included:

Making the kidney transplant system fairer

Policies regulating the organ transplant system in the United States are made by a national committee that often takes more than six months to create, and then years to implement, a timeline that many on the waiting list simply can’t survive.

Dimitris Bertsimas, vice provost for open learning, associate dean of business analytics, and Boeing Professor of Operations Research, shared his latest work in analytics for fair and efficient kidney transplant allocation. Bertsimas’ new algorithm examines criteria like geographic location, mortality, and age in just 14 seconds, a monumental change from the usual six hours.

Bertsimas and his team work closely with the United Network for Organ Sharing (UNOS), a nonprofit that manages most of the national donation and transplant system through a contract with the federal government. During his presentation, Bertsimas shared a video from James Alcorn, senior policy strategist at UNOS, who offered this poignant summary of the impact the new algorithm has:

“This optimization radically changes the turnaround time for evaluating these different simulations of policy scenarios. It used to take us a couple months to look at a handful of different policy scenarios, and now it takes a matter of minutes to look at thousands and thousands of scenarios. We are able to make these changes much more rapidly, which ultimately means that we can improve the system for transplant candidates much more rapidly.”

The ethics of AI-generated social media content

As AI-generated content becomes more prevalent across social media platforms, what are the implications of disclosing (or not disclosing) that any part of a post was created by AI? Adam Berinsky, Mitsui Professor of Political Science, and Gabrielle Péloquin-Skulski, PhD student in the Department of Political Science, explored this question in a session that examined recent studies on the impact of various labels on AI-generated content.

In a series of surveys and experiments affixing labels to AI-generated posts, the researchers looked at how specific words and descriptions impacted users’ perception of deception, their intent to engage with the post, and ultimately if the post was true or false.

“The big takeaway from our initial set of findings is that one size doesn’t fit all,” said Péloquin-Skulski. “We found that labeling AI-generated images with a process-oriented label reduces belief in both false and true posts. This is quite problematic, as labeling intends to reduce people’s belief in false information, not necessarily true information. This suggests that labels combining both process and veracity might be better at countering AI-generated misinformation.”

Using AI to increase civil discourse online

“Our research aims to address how people increasingly want to have a say in the organizations and communities they belong to,” Lily Tsai explained in a session on experiments in generative AI and the future of digital democracy. Tsai, Ford Professor of Political Science and director of the MIT Governance Lab, is conducting ongoing research with Alex Pentland, Toshiba Professor of Media Arts arts Sciences, and a larger team.

Online deliberative platforms have recently been rising in popularity across the United States in both public- and private-sector settings. Tsai explained that with technology, it’s now possible for everyone to have a say — but doing so can be overwhelming, or even feel unsafe. First, too much information is available, and secondly, online discourse has become increasingly “uncivil.”

The group focuses on “how we can build on existing technologies and improve them with rigorous, interdisciplinary research, and how we can innovate by integrating generative AI to enhance the benefits of online spaces for deliberation.” They have developed their own AI-integrated platform for deliberative democracy, DELiberation.io, and rolled out four initial modules. All studies have been in the lab so far, but they are also working on a set of forthcoming field studies, the first of which will be in partnership with the government of the District of Columbia.

Tsai told the audience, “If you take nothing else from this presentation, I hope that you’ll take away this — that we should all be demanding that technologies that are being developed are assessed to see if they have positive downstream outcomes, rather than just focusing on maximizing the number of users.”

A public think tank that considers all aspects of AI

When Catherine D’Ignazio, associate professor of urban science and planning, and Nikko Stevens, postdoc at the Data + Feminism Lab at MIT, initially submitted their funding proposal, they weren’t intending to develop a think tank, but a framework — one that articulated how artificial intelligence and machine learning work could integrate community methods and utilize participatory design.

In the end, they created Liberatory AI, which they describe as a “rolling public think tank about all aspects of AI.” D’Ignazio and Stevens gathered 25 researchers from a diverse array of institutions and disciplines who authored more than 20 position papers examining the most current academic literature on AI systems and engagement. They intentionally grouped the papers into three distinct themes: the corporate AI landscape, dead ends, and ways forward.

“Instead of waiting for Open AI or Google to invite us to participate in the development of their products, we’ve come together to contest the status quo, think bigger-picture, and reorganize resources in this system in hopes of a larger societal transformation,” said D’Ignazio.

As more connected devices demand an increasing amount of bandwidth for tasks like teleworking and cloud computing, it will become extremely challenging to manage the finite amount of wireless spectrum available for all users to share.

Engineers are employing artificial intelligence to dynamically manage the available wireless spectrum, with an eye toward reducing latency and boosting performance. But most AI methods for classifying and processing wireless signals are power-hungry and can’t operate in real-time.

Now, MIT researchers have developed a novel AI hardware accelerator that is specifically designed for wireless signal processing. Their optical processor performs machine-learning computations at the speed of light, classifying wireless signals in a matter of nanoseconds.

The photonic chip is about 100 times faster than the best digital alternative, while converging to about 95 percent accuracy in signal classification. The new hardware accelerator is also scalable and flexible, so it could be used for a variety of high-performance computing applications. At the same time, it is smaller, lighter, cheaper, and more energy-efficient than digital AI hardware accelerators.

The device could be especially useful in future 6G wireless applications, such as cognitive radios that optimize data rates by adapting wireless modulation formats to the changing wireless environment.

By enabling an edge device to perform deep-learning computations in real-time, this new hardware accelerator could provide dramatic speedups in many applications beyond signal processing. For instance, it could help autonomous vehicles make split-second reactions to environmental changes or enable smart pacemakers to continuously monitor the health of a patient’s heart.

“There are many applications that would be enabled by edge devices that are capable of analyzing wireless signals. What we’ve presented in our paper could open up many possibilities for real-time and reliable AI inference. This work is the beginning of something that could be quite impactful,” says Dirk Englund, a professor in the MIT Department of Electrical Engineering and Computer Science, principal investigator in the Quantum Photonics and Artificial Intelligence Group and the Research Laboratory of Electronics (RLE), and senior author of the paper.

He is joined on the paper by lead author Ronald Davis III PhD ’24; Zaijun Chen, a former MIT postdoc who is now an assistant professor at the University of Southern California; and Ryan Hamerly, a visiting scientist at RLE and senior scientist at NTT Research. The research appears today in Science Advances.

Light-speed processing  

State-of-the-art digital AI accelerators for wireless signal processing convert the signal into an image and run it through a deep-learning model to classify it. While this approach is highly accurate, the computationally intensive nature of deep neural networks makes it infeasible for many time-sensitive applications.

Optical systems can accelerate deep neural networks by encoding and processing data using light, which is also less energy intensive than digital computing. But researchers have struggled to maximize the performance of general-purpose optical neural networks when used for signal processing, while ensuring the optical device is scalable.

By developing an optical neural network architecture specifically for signal processing, which they call a multiplicative analog frequency transform optical neural network (MAFT-ONN), the researchers tackled that problem head-on.

The MAFT-ONN addresses the problem of scalability by encoding all signal data and performing all machine-learning operations within what is known as the frequency domain — before the wireless signals are digitized.

The researchers designed their optical neural network to perform all linear and nonlinear operations in-line. Both types of operations are required for deep learning.

Thanks to this innovative design, they only need one MAFT-ONN device per layer for the entire optical neural network, as opposed to other methods that require one device for each individual computational unit, or “neuron.”

“We can fit 10,000 neurons onto a single device and compute the necessary multiplications in a single shot,” Davis says.   

The researchers accomplish this using a technique called photoelectric multiplication, which dramatically boosts efficiency. It also allows them to create an optical neural network that can be readily scaled up with additional layers without requiring extra overhead.

Results in nanoseconds

MAFT-ONN takes a wireless signal as input, processes the signal data, and passes the information along for later operations the edge device performs. For instance, by classifying a signal’s modulation, MAFT-ONN would enable a device to automatically infer the type of signal to extract the data it carries.

One of the biggest challenges the researchers faced when designing MAFT-ONN was determining how to map the machine-learning computations to the optical hardware.

“We couldn’t just take a normal machine-learning framework off the shelf and use it. We had to customize it to fit the hardware and figure out how to exploit the physics so it would perform the computations we wanted it to,” Davis says.

When they tested their architecture on signal classification in simulations, the optical neural network achieved 85 percent accuracy in a single shot, which can quickly converge to more than 99 percent accuracy using multiple measurements.  MAFT-ONN only required about 120 nanoseconds to perform entire process.

“The longer you measure, the higher accuracy you will get. Because MAFT-ONN computes inferences in nanoseconds, you don’t lose much speed to gain more accuracy,” Davis adds.

While state-of-the-art digital radio frequency devices can perform machine-learning inference in a microseconds, optics can do it in nanoseconds or even picoseconds.

Moving forward, the researchers want to employ what are known as multiplexing schemes so they could perform more computations and scale up the MAFT-ONN. They also want to extend their work into more complex deep learning architectures that could run transformer models or LLMs.

This work was funded, in part, by the U.S. Army Research Laboratory, the U.S. Air Force, MIT Lincoln Laboratory, Nippon Telegraph and Telephone, and the National Science Foundation.

Art restoration takes steady hands and a discerning eye. For centuries, conservators have restored paintings by identifying areas needing repair, then mixing an exact shade to fill in one area at a time. Often, a painting can have thousands of tiny regions requiring individual attention. Restoring a single painting can take anywhere from a few weeks to over a decade.

In recent years, digital restoration tools have opened a route to creating virtual representations of original, restored works. These tools apply techniques of computer vision, image recognition, and color matching, to generate a “digitally restored” version of a painting relatively quickly.

Still, there has been no way to translate digital restorations directly onto an original work, until now. In a paper appearing today in the journal Nature, Alex Kachkine, a mechanical engineering graduate student at MIT, presents a new method he’s developed to physically apply a digital restoration directly onto an original painting.

The restoration is printed on a very thin polymer film, in the form of a mask that can be aligned and adhered to an original painting. It can also be easily removed. Kachkine says that a digital file of the mask can be stored and referred to by future conservators, to see exactly what changes were made to restore the original painting.

“Because there’s a digital record of what mask was used, in 100 years, the next time someone is working with this, they’ll have an extremely clear understanding of what was done to the painting,” Kachkine says. “And that’s never really been possible in conservation before.”

As a demonstration, he applied the method to a highly damaged 15th century oil painting. The method automatically identified 5,612 separate regions in need of repair, and filled in these regions using 57,314 different colors. The entire process, from start to finish, took 3.5 hours, which he estimates is about 66 times faster than traditional restoration methods.

Kachkine acknowledges that, as with any restoration project, there are ethical issues to consider, in terms of whether a restored version is an appropriate representation of an artist’s original style and intent. Any application of his new method, he says, should be done in consultation with conservators with knowledge of a painting’s history and origins.

“There is a lot of damaged art in storage that might never be seen,” Kachkine says. “Hopefully with this new method, there’s a chance we’ll see more art, which I would be delighted by.”

Digital connections

The new restoration process started as a side project. In 2021, as Kachkine made his way to MIT to start his PhD program in mechanical engineering, he drove up the East Coast and made a point to visit as many art galleries as he could along the way.

“I’ve been into art for a very long time now, since I was a kid,” says Kachkine, who restores paintings as a hobby, using traditional hand-painting techniques. As he toured galleries, he came to realize that the art on the walls is only a fraction of the works that galleries hold. Much of the art that galleries acquire is stored away because the works are aged or damaged, and take time to properly restore.

“Restoring a painting is fun, and it’s great to sit down and infill things and have a nice evening,” Kachkine says. “But that’s a very slow process.”

As he has learned, digital tools can significantly speed up the restoration process. Researchers have developed artificial intelligence algorithms that quickly comb through huge amounts of data. The algorithms learn connections within this visual data, which they apply to generate a digitally restored version of a particular painting, in a way that closely resembles the style of an artist or time period. However, such digital restorations are usually displayed virtually or printed as stand-alone works and cannot be directly applied to retouch original art.

“All this made me think: If we could just restore a painting digitally, and effect the results physically, that would resolve a lot of pain points and drawbacks of a conventional manual process,” Kachkine says.

“Align and restore”

For the new study, Kachkine developed a method to physically apply a digital restoration onto an original painting, using a 15th-century painting that he acquired when he first came to MIT. His new method involves first using traditional techniques to clean a painting and remove any past restoration efforts.

“This painting is almost 600 years old and has gone through conservation many times,” he says. “In this case there was a fair amount of overpainting, all of which has to be cleaned off to see what’s actually there to begin with.”

He scanned the cleaned painting, including the many regions where paint had faded or cracked. He then used existing artificial intelligence algorithms to analyze the scan and create a virtual version of what the painting likely looked like in its original state.

Then, Kachkine developed software that creates a map of regions on the original painting that require infilling, along with the exact colors needed to match the digitally restored version. This map is then translated into a physical, two-layer mask that is printed onto thin polymer-based films. The first layer is printed in color, while the second layer is printed in the exact same pattern, but in white.

“In order to fully reproduce color, you need both white and color ink to get the full spectrum,” Kachkine explains. “If those two layers are misaligned, that’s very easy to see. So I also developed a few computational tools, based on what we know of human color perception, to determine how small of a region we can practically align and restore.”

Kachkine used high-fidelity commercial inkjets to print the mask’s two layers, which he carefully aligned and overlaid by hand onto the original painting and adhered with a thin spray of conventional varnish. The printed films are made from materials that can be easily dissolved with conservation-grade solutions, in case conservators need to reveal the original, damaged work. The digital file of the mask can also be saved as a detailed record of what was restored.

For the painting that Kachkine used, the method was able to fill in thousands of losses in just a few hours. “A few years ago, I was restoring this baroque Italian painting with probably the same order magnitude of losses, and it took me nine months of part-time work,” he recalls. “The more losses there are, the better this method is.”

He estimates that the new method can be orders of magnitude faster than traditional, hand-painted approaches. If the method is adopted widely, he emphasizes that conservators should be involved at every step in the process, to ensure that the final work is in keeping with an artist’s style and intent.

“It will take a lot of deliberation about the ethical challenges involved at every stage in this process to see how can this be applied in a way that’s most consistent with conservation principles,” he says. “We’re setting up a framework for developing further methods. As others work on this, we’ll end up with methods that are more precise.”

This work was supported, in part, by the John O. and Katherine A. Lutz Memorial Fund. The research was carried out, in part, through the use of equipment and facilities at MIT.Nano, with additional support from the MIT Microsystems Technology Laboratories, the MIT Department of Mechanical Engineering, and the MIT Libraries.

Today, 2.2 billion people in the world lack access to safe drinking water. In the United States, more than 46 million people experience water insecurity, living with either no running water or water that is unsafe to drink. The increasing need for drinking water is stretching traditional resources such as rivers, lakes, and reservoirs.

To improve access to safe and affordable drinking water, MIT engineers are tapping into an unconventional source: the air. The Earth’s atmosphere contains millions of billions of gallons of water in the form of vapor. If this vapor can be efficiently captured and condensed, it could supply clean drinking water in places where traditional water resources are inaccessible.

With that goal in mind, the MIT team has developed and tested a new atmospheric water harvester and shown that it efficiently captures water vapor and produces safe drinking water across a range of relative humidities, including dry desert air.

The new device is a black, window-sized vertical panel, made from a water-absorbent hydrogel material, enclosed in a glass chamber coated with a cooling layer. The hydrogel resembles black bubble wrap, with small dome-shaped structures that swell when the hydrogel soaks up water vapor. When the captured vapor evaporates, the domes shrink back down in an origami-like transformation. The evaporated vapor then condenses on the the glass, where it can flow down and out through a tube, as clean and drinkable water.

The system runs entirely on its own, without a power source, unlike other designs that require batteries, solar panels, or electricity from the grid. The team ran the device for over a week in Death Valley, California — the driest region in North America. Even in very low-humidity conditions, the device squeezed drinking water from the air at rates of up to 160 milliliters (about two-thirds of a cup) per day.

The team estimates that multiple vertical panels, set up in a small array, could passively supply a household with drinking water, even in arid desert environments. What’s more, the system’s water production should increase with humidity, supplying drinking water in temperate and tropical climates.

“We have built a meter-scale device that we hope to deploy in resource-limited regions, where even a solar cell is not very accessible,” says Xuanhe Zhao, the Uncas and Helen Whitaker Professor of Mechanical Engineering and Civil and Environmental Engineering at MIT. “It’s a test of feasibility in scaling up this water harvesting technology. Now people can build it even larger, or make it into parallel panels, to supply drinking water to people and achieve real impact.”

Zhao and his colleagues present the details of the new water harvesting design in a paper appearing today in the journal Nature Water. The study’s lead author is former MIT postdoc “Will” Chang Liu, who is currently an assistant professor at the National University of Singapore (NUS). MIT co-authors include Xiao-Yun Yan, Shucong Li, and Bolei Deng, along with collaborators from multiple other institutions.

Carrying capacity

Hydrogels are soft, porous materials that are made mainly from water and a microscopic network of interconnecting polymer fibers. Zhao’s group at MIT has primarily explored the use of hydrogels in biomedical applications, including adhesive coatings for medical implants, soft and flexible electrodes, and noninvasive imaging stickers.

“Through our work with soft materials, one property we know very well is the way hydrogel is very good at absorbing water from air,” Zhao says.

Researchers are exploring a number of ways to harvest water vapor for drinking water. Among the most efficient so far are devices made from metal-organic frameworks, or MOFs — ultra-porous materials that have also been shown to capture water from dry desert air. But the MOFs do not swell or stretch when absorbing water, and are limited in vapor-carrying capacity.

Water from air

The group’s new hydrogel-based water harvester addresses another key problem in similar designs. Other groups have designed water harvesters out of micro- or nano-porous hydrogels. But the water produced from these designs can be salty, requiring additional filtering. Salt is a naturally absorbent material, and researchers embed salts — typically, lithium chloride — in hydrogel to increase the material’s water absorption. The drawback, however, is that this salt can leak out with the water when it is eventually collected.

The team’s new design significantly limits salt leakage. Within the hydrogel itself, they included an extra ingredient: glycerol, a liquid compound that naturally stabilizes salt, keeping it within the gel rather than letting it crystallize and leak out with the water. The hydrogel itself has a microstructure that lacks nanoscale pores, which further prevents salt from escaping the material. The salt levels in the water they collected were below the standard threshold for safe drinking water, and significantly below the levels produced by many other hydrogel-based designs.

In addition to tuning the hydrogel’s composition, the researchers made improvements to its form. Rather than keeping the gel as a flat sheet, they molded it into a pattern of small domes resembling bubble wrap, that act to increase the gel’s surface area, along with the amount of water vapor it can absorb.

The researchers fabricated a half-square-meter of hydrogel and encased the material in a window-like glass chamber. They coated the exterior of the chamber with a special polymer film, which helps to cool the glass and stimulates any water vapor in the hydrogel to evaporate and condense onto the glass. They installed a simple tubing system to collect the water as it flows down the glass.

In November 2023, the team traveled to Death Valley, California, and set up the device as a vertical panel. Over seven days, they took measurements as the hydrogel absorbed water vapor during the night (the time of day when water vapor in the desert is highest). In the daytime, with help from the sun, the harvested water evaporated out from the hydrogel and condensed onto the glass.

Over this period, the device worked across a range of humidities, from 21 to 88 percent, and produced between 57 and 161.5 milliliters of drinking water per day. Even in the driest conditions, the device harvested more water than other passive and some actively powered designs.

“This is just a proof-of-concept design, and there are a lot of things we can optimize,” Liu says. “For instance, we could have a multipanel design. And we’re working on a next generation of the material to further improve its intrinsic properties.”

“We imagine that you could one day deploy an array of these panels, and the footprint is very small because they are all vertical,” says Zhao, who has plans to further test the panels in many resource-limited regions. “Then you could have many panels together, collecting water all the time, at household scale.”

This work was supported, in part, by the MIT J-WAFS Water and Food Seed Grant, the MIT-Chinese University of Hong Kong collaborative research program, and the UM6P-MIT collaborative research program.

The human brain is very good at solving complicated problems. One reason for that is that humans can break problems apart into manageable subtasks that are easy to solve one at a time.

This allows us to complete a daily task like going out for coffee by breaking it into steps: getting out of our office building, navigating to the coffee shop, and once there, obtaining the coffee. This strategy helps us to handle obstacles easily. For example, if the elevator is broken, we can revise how we get out of the building without changing the other steps.

While there is a great deal of behavioral evidence demonstrating humans’ skill at these complicated tasks, it has been difficult to devise experimental scenarios that allow precise characterization of the computational strategies we use to solve problems.

In a new study, MIT researchers have successfully modeled how people deploy different decision-making strategies to solve a complicated task — in this case, predicting how a ball will travel through a maze when the ball is hidden from view. The human brain cannot perform this task perfectly because it is impossible to track all of the possible trajectories in parallel, but the researchers found that people can perform reasonably well by flexibly adopting two strategies known as hierarchical reasoning and counterfactual reasoning.

The researchers were also able to determine the circumstances under which people choose each of those strategies.

“What humans are capable of doing is to break down the maze into subsections, and then solve each step using relatively simple algorithms. Effectively, when we don’t have the means to solve a complex problem, we manage by using simpler heuristics that get the job done,” says Mehrdad Jazayeri, a professor of brain and cognitive sciences, a member of MIT’s McGovern Institute for Brain Research, an investigator at the Howard Hughes Medical Institute, and the senior author of the study.

Mahdi Ramadan PhD ’24 and graduate student Cheng Tang are the lead authors of the paper, which appears today in Nature Human Behavior. Nicholas Watters PhD ’25 is also a co-author.

Rational strategies

When humans perform simple tasks that have a clear correct answer, such as categorizing objects, they perform extremely well. When tasks become more complex, such as planning a trip to your favorite cafe, there may no longer be one clearly superior answer. And, at each step, there are many things that could go wrong. In these cases, humans are very good at working out a solution that will get the task done, even though it may not be the optimal solution.

Those solutions often involve problem-solving shortcuts, or heuristics. Two prominent heuristics humans commonly rely on are hierarchical and counterfactual reasoning. Hierarchical reasoning is the process of breaking down a problem into layers, starting from the general and proceeding toward specifics. Counterfactual reasoning involves imagining what would have happened if you had made a different choice. While these strategies are well-known, scientists don’t know much about how the brain decides which one to use in a given situation.

“This is really a big question in cognitive science: How do we problem-solve in a suboptimal way, by coming up with clever heuristics that we chain together in a way that ends up getting us closer and closer until we solve the problem?” Jazayeri says.

To overcome this, Jazayeri and his colleagues devised a task that is just complex enough to require these strategies, yet simple enough that the outcomes and the calculations that go into them can be measured.

The task requires participants to predict the path of a ball as it moves through four possible trajectories in a maze. Once the ball enters the maze, people cannot see which path it travels. At two junctions in the maze, they hear an auditory cue when the ball reaches that point. Predicting the ball’s path is a task that is impossible for humans to solve with perfect accuracy.

“It requires four parallel simulations in your mind, and no human can do that. It’s analogous to having four conversations at a time,” Jazayeri says. “The task allows us to tap into this set of algorithms that the humans use, because you just can’t solve it optimally.”

The researchers recruited about 150 human volunteers to participate in the study. Before each subject began the ball-tracking task, the researchers evaluated how accurately they could estimate timespans of several hundred milliseconds, about the length of time it takes the ball to travel along one arm of the maze.

For each participant, the researchers created computational models that could predict the patterns of errors that would be seen for that participant (based on their timing skill) if they were running parallel simulations, using hierarchical reasoning alone, counterfactual reasoning alone, or combinations of the two reasoning strategies.

The researchers compared the subjects’ performance with the models’ predictions and found that for every subject, their performance was most closely associated with a model that used hierarchical reasoning but sometimes switched to counterfactual reasoning.

That suggests that instead of tracking all the possible paths that the ball could take, people broke up the task. First, they picked the direction (left or right), in which they thought the ball turned at the first junction, and continued to track the ball as it headed for the next turn. If the timing of the next sound they heard wasn’t compatible with the path they had chosen, they would go back and revise their first prediction — but only some of the time.

Switching back to the other side, which represents a shift to counterfactual reasoning, requires people to review their memory of the tones that they heard. However, it turns out that these memories are not always reliable, and the researchers found that people decided whether to go back or not based on how good they believed their memory to be.

“People rely on counterfactuals to the degree that it’s helpful,” Jazayeri says. “People who take a big performance loss when they do counterfactuals avoid doing them. But if you are someone who’s really good at retrieving information from the recent past, you may go back to the other side.”

Human limitations

To further validate their results, the researchers created a machine-learning neural network and trained it to complete the task. A machine-learning model trained on this task will track the ball’s path accurately and make the correct prediction every time, unless the researchers impose limitations on its performance.

When the researchers added cognitive limitations similar to those faced by humans, they found that the model altered its strategies. When they eliminated the model’s ability to follow all possible trajectories, it began to employ hierarchical and counterfactual strategies like humans do. If the researchers reduced the model’s memory recall ability, it began to switch to hierarchical only if it thought its recall would be good enough to get the right answer — just as humans do.

“What we found is that networks mimic human behavior when we impose on them those computational constraints that we found in human behavior,” Jazayeri says. “This is really saying that humans are acting rationally under the constraints that they have to function under.”

By slightly varying the amount of memory impairment programmed into the models, the researchers also saw hints that the switching of strategies appears to happen gradually, rather than at a distinct cut-off point. They are now performing further studies to try to determine what is happening in the brain as these shifts in strategy occur.

The research was funded by a Lisa K. Yang ICoN Fellowship, a Friends of the McGovern Institute Student Fellowship, a National Science Foundation Graduate Research Fellowship, the Simons Foundation, the Howard Hughes Medical Institute, and the McGovern Institute.

For many patients with schizophrenia, other psychiatric illnesses, or diseases such as hypertension and asthma, it can be difficult to take their medicine every day. To help overcome that challenge, MIT researchers have developed a pill that can be taken just once a week and gradually releases medication from within the stomach.

In a phase 3 clinical trial conducted by MIT spinout Lyndra Therapeutics, the researchers used the once-a-week pill to deliver a widely used medication for managing the symptoms of schizophrenia. They found that this treatment regimen maintained consistent levels of the drug in patients’ bodies and controlled their symptoms just as well as daily doses of the drug. The results are published today in Lancet Psychiatry.

“We’ve converted something that has to be taken once a day to once a week, orally, using a technology that can be adapted for a variety of medications,” 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, and an author of the study. “The ability to provide a sustained level of drug for a prolonged period, in an easy-to-administer system, makes it easier to ensure patients are receiving their medication.”

Traverso’s lab began developing the ingestible capsule studied in this trial more than 10 years ago, as part of an ongoing effort to make medications easier for patients to take. The capsule is about the size of a multivitamin, and once swallowed, it expands into a star shape that helps it remain in the stomach until all of the drug is released.

Richard Scranton, chief medical officer of Lyndra Therapeutics, is the senior author of the paper, and Leslie Citrome, a clinical professor of psychiatry and behavioral sciences at New York Medical College School of Medicine, is the lead author. Nayana Nagaraj, medical director at Lyndra Therapeutics, and Todd Dumas, senior director of pharmacometrics at Certara, are also authors.

Sustained delivery

Over the past decade, Traverso’s lab has been working on a variety of capsules that can be swallowed and remain in the digestive tract for days or weeks, slowly releasing their drug payload. In 2016, his team reported the star-shaped device, which was then further developed by Lyndra for clinical trials in patients with schizophrenia.

The device contains six arms that can be folded in, allowing it to fit inside a capsule. The capsule dissolves when the device reaches the stomach, allowing the arms to spring out. Once the arms are extended, the device becomes too large to pass through the pylorus (the exit of the stomach), so it remains freely floating in the stomach as drugs are slowly released from the arms. After about a week, the arms break off on their own, and each segment exits the stomach and passes through the digestive tract.

For the clinical trials, the capsule was loaded with risperidone, a commonly prescribed medication used to treat schizophrenia. Most patients take the drug orally once a day. There are also injectable versions that can be given every two weeks, every month, or every two months, but they require administration by a health care provider and are not always acceptable to patients.

The MIT and Lyndra team chose to focus on schizophrenia in hopes that a drug regimen that could be administered less frequently, through oral delivery, could make treatment easier for patients and their caregivers.

“One of the areas of unmet need that was recognized early on is neuropsychiatric conditions, where the illness can limit or impair one’s ability to remember to take their medication,” Traverso says. “With that in mind, one of the conditions that has been a big focus has been schizophrenia.”

The phase 3 trial was coordinated by researchers at Lyndra and enrolled 83 patients at five different sites around the United States. Forty-five of those patients completed the full five weeks of the study, in which they took one risperidone-loaded capsule per week.

Throughout the study, the researchers measured the amount of drug in each patient’s bloodstream. Each week, they found a sharp increase on the day the pill was given, followed by a slow decline over the next week. The levels were all within the optimal range, and there was less variation over time than is seen when patients take a pill each day.

Effective treatment

Using an evaluation known as the Positive and Negative Syndrome Scale (PANSS), the researchers also found that the patients’ symptoms remained stable throughout the study.

“One of the biggest obstacles in the care of people with chronic illnesses in general is that medications are not taken consistently. This leads to worsening symptoms, and in the case of schizophrenia, potential relapse and hospitalization,” Citrome says. “Having the option to take medication by mouth once a week represents an important option that can assist with adherence for the many patients who would prefer oral medications versus injectable formulations.”

Side effects from the treatment were minimal, the researchers found. Some patients experienced mild acid reflux and constipation early in the study, but these did not last long. The results, showing effectiveness of the capsule and few side effects, represent a major milestone in this approach to drug delivery, Traverso says.

“This really demonstrates that what we had hypothesized a decade ago, which is that a single capsule providing a drug depot within the GI tract could be possible,” he says. “Here what you see is that the capsule can achieve the drug levels that were predicted, and also control symptoms in a sizeable cohort of patients with schizophrenia.”

The investigators now hope to complete larger phase 3 studies before applying for FDA approval of this delivery approach for risperidone. They are also preparing for phase 1 trials using this capsule to deliver other drugs, including contraceptives.

“We are delighted that this technology which started at MIT has reached the point of phase 3 clinical trials,” says Robert Langer, the David H. Koch Institute Professor at MIT, who was an author of the original study on the star capsule and is a co-founder of Lyndra Therapeutics.

The research was funded by Lyndra Therapeutics.

As the frequency and severity of extreme weather events grow, it may become increasingly necessary to employ a bolder approach to climate change, warned Emily A. Carter, the Gerhard R. Andlinger Professor in Energy and the Environment at Princeton University. Carter made her case for why the energy transition is no longer enough in the face of climate change while speaking at the MIT Energy Initiative (MITEI) Presents: Advancing the Energy Transition seminar on the MIT campus.

“If all we do is take care of what we did in the past — but we don’t change what we do in the future — then we’re still going to be left with very serious problems,” she said. Our approach to climate change mitigation must comprise transformation, intervention, and adaption strategies, said Carter. 

Transitioning to a decarbonized electricity system is one piece of the puzzle. Growing amounts of solar and wind energy — along with nuclear, hydropower, and geothermal — are slowly transforming the energy electricity landscape, but Carter noted that there are new technologies farther down the pipeline.  

“Advanced geothermal may come on in the next couple of decades. Fusion will only really start to play a role later in the century, but could provide firm electricity such that we can start to decommission nuclear,” said Carter, who is also a senior strategic advisor and associate laboratory director at the Department of Energy’s Princeton Plasma Physics Laboratory. 

Taking this a step further, Carter outlined how this carbon-free electricity should then be used to electrify everything we can. She highlighted the industrial sector as a critical area for transformation: “The energy transition is about transitioning off of fossil fuels. If you look at the manufacturing industries, they are driven by fossil fuels right now. They are driven by fossil fuel-driven thermal processes.” Carter noted that thermal energy is much less efficient than electricity and highlighted electricity-driven strategies that could replace heat in manufacturing, such as electrolysis, plasmas, light-emitting diodes (LEDs) for photocatalysis, and joule heating. 

The transportation sector is also a key area for electrification, Carter said. While electric vehicles have become increasingly common in recent years, heavy-duty transportation is not as easily electrified. The solution? “Carbon-neutral fuels for heavy-duty aviation and shipping,” she said, emphasizing that these fuels will need to become part of the circular economy. “We know that when we burn those fuels, they’re going to produce CO2 [carbon dioxide] again. They need to come from a source of CO2 that is not fossil-based.” 

The next step is intervention in the form of carbon dioxide removal, which then necessitates methods of storage and utilization, according to Carter. “There’s a lot of talk about building large numbers of pipelines to capture the CO2 — from fossil fuel-driven power plants, cement plants, steel plants, all sorts of industrial places that emit CO2 — and then piping it and storing it in underground aquifers,” she explained. Offshore pipelines are much more expensive than those on land, but can mitigate public concerns over their safety. Europe is exclusively focusing their efforts offshore for this very reason, and the same could be true for the United States, Carter said.  

Once carbon dioxide is captured, commercial utilization may provide economic leverage to accelerate sequestration, even if only a few gigatons are used per year, Carter noted. Through mineralization, CO2 can be converted into carbonates, which could be used in building materials such as concrete and road-paving materials.  

There is another form of intervention that Carter currently views as a last resort: solar geoengineering, sometimes known as solar radiation management or SRM. In 1991, Mount Pinatubo in the Philippines erupted and released sulfur dioxide into the stratosphere, which caused a temporary cooling of the Earth by approximately 0.5 degree Celsius for over a year. SRM seeks to recreate that cooling effect by injecting particles into the atmosphere that reflect sunlight. According to Carter, there are three main strategies: stratospheric aerosol injection, cirrus cloud thinning (thinning clouds to let more infrared radiation emitted by the earth escape to space), and marine cloud brightening (brightening clouds with sea salt so they reflect more light).  

“My view is, I hope we don't ever have to do it, but I sure think we should understand what would happen in case somebody else just decides to do it. It’s a global security issue,” said Carter. “In principle, it’s not so difficult technologically, so we’d like to really understand and to be able to predict what would happen if that happened.” 

With any technology, stakeholder and community engagement is essential for deployment, Carter said. She emphasized the importance of both respectfully listening to concerns and thoroughly addressing them, stating, “Hopefully, there’s enough information given to assuage their fears. We have to gain the trust of people before any deployment can be considered.” 

A crucial component of this trust starts with the responsibility of the scientific community to be transparent and critique each other’s work, Carter said. “Skepticism is good. You should have to prove your proof of principle.” 

MITEI Presents: Advancing the Energy Transition is an MIT Energy Initiative speaker series highlighting energy experts and leaders at the forefront of the scientific, technological, and policy solutions needed to transform our energy systems. The series will continue in fall 2025. For more information on this and additional events, visit the MITEI website.

Travel agents help to provide end-to-end logistics — like transportation, accommodations, meals, and lodging — for businesspeople, vacationers, and everyone in between. For those looking to make their own arrangements, large language models (LLMs) seem like they would be a strong tool to employ for this task because of their ability to iteratively interact using natural language, provide some commonsense reasoning, collect information, and call other tools in to help with the task at hand. However, recent work has found that state-of-the-art LLMs struggle with complex logistical and mathematical reasoning, as well as problems with multiple constraints, like trip planning, where they’ve been found to provide viable solutions 4 percent or less of the time, even with additional tools and application programming interfaces (APIs).

Subsequently, a research team from MIT and the MIT-IBM Watson AI Lab reframed the issue to see if they could increase the success rate of LLM solutions for complex problems. “We believe a lot of these planning problems are naturally a combinatorial optimization problem,” where you need to satisfy several constraints in a certifiable way, says Chuchu Fan, associate professor in the MIT Department of Aeronautics and Astronautics (AeroAstro) and the Laboratory for Information and Decision Systems (LIDS). She is also a researcher in the MIT-IBM Watson AI Lab. Her team applies machine learning, control theory, and formal methods to develop safe and verifiable control systems for robotics, autonomous systems, controllers, and human-machine interactions.

Noting the transferable nature of their work for travel planning, the group sought to create a user-friendly framework that can act as an AI travel broker to help develop realistic, logical, and complete travel plans. To achieve this, the researchers combined common LLMs with algorithms and a complete satisfiability solver. Solvers are mathematical tools that rigorously check if criteria can be met and how, but they require complex computer programming for use. This makes them natural companions to LLMs for problems like these, where users want help planning in a timely manner, without the need for programming knowledge or research into travel options. Further, if a user’s constraint cannot be met, the new technique can identify and articulate where the issue lies and propose alternative measures to the user, who can then choose to accept, reject, or modify them until a valid plan is formulated, if one exists.

“Different complexities of travel planning are something everyone will have to deal with at some point. There are different needs, requirements, constraints, and real-world information that you can collect,” says Fan. “Our idea is not to ask LLMs to propose a travel plan. Instead, an LLM here is acting as a translator to translate this natural language description of the problem into a problem that a solver can handle [and then provide that to the user],” says Fan.

Co-authoring a paper on the work with Fan are Yang Zhang of MIT-IBM Watson AI Lab, AeroAstro graduate student Yilun Hao, and graduate student Yongchao Chen of MIT LIDS and Harvard University. This work was recently presented at the Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics.

Breaking down the solver

Math tends to be domain-specific. For example, in natural language processing, LLMs perform regressions to predict the next token, a.k.a. “word,” in a series to analyze or create a document. This works well for generalizing diverse human inputs. LLMs alone, however, wouldn’t work for formal verification applications, like in aerospace or cybersecurity, where circuit connections and constraint tasks need to be complete and proven, otherwise loopholes and vulnerabilities can sneak by and cause critical safety issues. Here, solvers excel, but they need fixed formatting inputs and struggle with unsatisfiable queries.  A hybrid technique, however, provides an opportunity to develop solutions for complex problems, like trip planning, in a way that’s intuitive for everyday people.

“The solver is really the key here, because when we develop these algorithms, we know exactly how the problem is being solved as an optimization problem,” says Fan. Specifically, the research group used a solver called satisfiability modulo theories (SMT), which determines whether a formula can be satisfied. “With this particular solver, it’s not just doing optimization. It’s doing reasoning over a lot of different algorithms there to understand whether the planning problem is possible or not to solve. That’s a pretty significant thing in travel planning. It’s not a very traditional mathematical optimization problem because people come up with all these limitations, constraints, restrictions,” notes Fan.

Translation in action

The “travel agent” works in four steps that can be repeated, as needed. The researchers used GPT-4, Claude-3, or Mistral-Large as the method’s LLM. First, the LLM parses a user’s requested travel plan prompt into planning steps, noting preferences for budget, hotels, transportation, destinations, attractions, restaurants, and trip duration in days, as well as any other user prescriptions. Those steps are then converted into executable Python code (with a natural language annotation for each of the constraints), which calls APIs like CitySearch, FlightSearch, etc. to collect data, and the SMT solver to begin executing the steps laid out in the constraint satisfaction problem. If a sound and complete solution can be found, the solver outputs the result to the LLM, which then provides a coherent itinerary to the user.

If one or more constraints cannot be met, the framework begins looking for an alternative. The solver outputs code identifying the conflicting constraints (with its corresponding annotation) that the LLM then provides to the user with a potential remedy. The user can then decide how to proceed, until a solution (or the maximum number of iterations) is reached.

Generalizable and robust planning

The researchers tested their method using the aforementioned LLMs against other baselines: GPT-4 by itself, OpenAI o1-preview by itself, GPT-4 with a tool to collect information, and a search algorithm that optimizes for total cost. Using the TravelPlanner dataset, which includes data for viable plans, the team looked at multiple performance metrics: how frequently a method could deliver a solution, if the solution satisfied commonsense criteria like not visiting two cities in one day, the method’s ability to meet one or more constraints, and a final pass rate indicating that it could meet all constraints. The new technique generally achieved over a 90 percent pass rate, compared to 10 percent or lower for the baselines. The team also explored the addition of a JSON representation within the query step, which further made it easier for the method to provide solutions with 84.4-98.9 percent pass rates.

The MIT-IBM team posed additional challenges for their method. They looked at how important each component of their solution was — such as removing human feedback or the solver — and how that affected plan adjustments to unsatisfiable queries within 10 or 20 iterations using a new dataset they created called UnsatChristmas, which includes unseen constraints, and a modified version of TravelPlanner. On average, the MIT-IBM group’s framework achieved 78.6  and 85 percent success, which rises to 81.6 and 91.7 percent with additional plan modification rounds. The researchers analyzed how well it handled new, unseen constraints and paraphrased query-step and step-code prompts. In both cases, it performed very well, especially with an 86.7 percent pass rate for the paraphrasing trial.

Lastly, the MIT-IBM researchers applied their framework to other domains with tasks like block picking, task allocation, the traveling salesman problem, and warehouse. Here, the method must select numbered, colored blocks and maximize its score; optimize robot task assignment for different scenarios; plan trips minimizing distance traveled; and robot task completion and optimization.

“I think this is a very strong and innovative framework that can save a lot of time for humans, and also, it’s a very novel combination of the LLM and the solver,” says Hao.

This work was funded, in part, by the Office of Naval Research and the MIT-IBM Watson AI Lab.

Research that crosses the traditional boundaries of academic disciplines, and boundaries between academia, industry, and government, is increasingly widespread, and has sometimes led to the spawning of significant new disciplines. But Munther Dahleh, a professor of electrical engineering and computer science at MIT, says that such multidisciplinary and interdisciplinary work often suffers from a number of shortcomings and handicaps compared to more traditionally focused disciplinary work.

But increasingly, he says, the profound challenges that face us in the modern world — including climate change, biodiversity loss, how to control and regulate artificial intelligence systems, and the identification and control of pandemics — require such meshing of expertise from very different areas, including engineering, policy, economics, and data analysis. That realization is what guided him, a decade ago, in the creation of MIT’s pioneering Institute for Data, Systems and Society (IDSS), aiming to foster a more deeply integrated and lasting set of collaborations than the usual temporary and ad hoc associations that occur for such work.

Dahleh has now written a book detailing the process of analyzing the landscape of existing disciplinary divisions at MIT and conceiving of a way to create a structure aimed at breaking down some of those barriers in a lasting and meaningful way, in order to bring about this new institute. The book, “Data, Systems, and Society: Harnessing AI for Societal Good,” was published this March by Cambridge University Press.

The book, Dahleh says, is his attempt “to describe our thinking that led us to the vision of the institute. What was the driving vision behind it?” It is aimed at a number of different audiences, he says, but in particular, “I’m targeting students who are coming to do research that they want to address societal challenges of different types, but utilizing AI and data science. How should they be thinking about these problems?”

A key concept that has guided the structure of the institute is something he refers to as “the triangle.” This refers to the interaction of three components: physical systems, people interacting with those physical systems, and then regulation and policy regarding those systems. Each of these affects, and is affected by, the others in various ways, he explains. “You get a complex interaction among these three components, and then there is data on all these pieces. Data is sort of like a circle that sits in the middle of this triangle and connects all these pieces,” he says.

When tackling any big, complex problem, he suggests, it is useful to think in terms of this triangle. “If you’re tackling a societal problem, it’s very important to understand the impact of your solution on society, on the people, and the role of people in the success of your system,” he says. Often, he says, “solutions and technology have actually marginalized certain groups of people and have ignored them. So the big message is always to think about the interaction between these components as you think about how to solve problems.”

As a specific example, he cites the Covid-19 pandemic. That was a perfect example of a big societal problem, he says, and illustrates the three sides of the triangle: there’s the biology, which was little understood at first and was subject to intensive research efforts; there was the contagion effect, having to do with social behavior and interactions among people; and there was the decision-making by political leaders and institutions, in terms of shutting down schools and companies or requiring masks, and so on. “The complex problem we faced was the interaction of all these components happening in real-time, when the data wasn’t all available,” he says.

Making a decision, for example shutting schools or businesses, based on controlling the spread of the disease, had immediate effects on economics and social well-being and health and education, “so we had to weigh all these things back into the formula,” he says. “The triangle came alive for us during the pandemic.” As a result, IDSS “became a convening place, partly because of all the different aspects of the problem that we were interested in.”

Examples of such interactions abound, he says. Social media and e-commerce platforms are another case of “systems built for people, and they have a regulation aspect, and they fit into the same story if you’re trying to understand misinformation or the monitoring of misinformation.”

The book presents many examples of ethical issues in AI, stressing that they must be handled with great care. He cites self-driving cars as an example, where programming decisions in dangerous situations can appear ethical but lead to negative economic and humanitarian outcomes. For instance, while most Americans support the idea that a car should sacrifice its driver rather than kill an innocent person, they wouldn’t buy such a car. This reluctance lowers adoption rates and ultimately increases casualties.

In the book, he explains the difference, as he sees it, between the concept of “transdisciplinary” versus typical cross-disciplinary or interdisciplinary research. “They all have different roles, and they have been successful in different ways,” he says. The key is that most such efforts tend to be transitory, and that can limit their societal impact. The fact is that even if people from different departments work together on projects, they lack a structure of shared journals, conferences, common spaces and infrastructure, and a sense of community. Creating an academic entity in the form of IDSS that explicitly crosses these boundaries in a fixed and lasting way was an attempt to address that lack. “It was primarily about creating a culture for people to think about all these components at the same time.”

He hastens to add that of course such interactions were already happening at MIT, “but we didn’t have one place where all the students are all interacting with all of these principles at the same time.” In the IDSS doctoral program, for instance, there are 12 required core courses — half of them from statistics and optimization theory and computation, and half from the social sciences and humanities.

Dahleh stepped down from the leadership of IDSS two years ago to return to teaching and to continue his research. But as he reflected on the work of that institute and his role in bringing it into being, he realized that unlike his own academic research, in which every step along the way is carefully documented in published papers, “I haven’t left a trail” to document the creation of the institute and the thinking behind it. “Nobody knows what we thought about, how we thought about it, how we built it.” Now, with this book, they do.

The book, he says, is “kind of leading people into how all of this came together, in hindsight. I want to have people read this and sort of understand it from a historical perspective, how something like this happened, and I did my best to make it as understandable and simple as I could.”

Suppose you were shown that an artificial intelligence tool offers accurate predictions about some stocks you own. How would you feel about using it? Now, suppose you are applying for a job at a company where the HR department uses an AI system to screen resumes. Would you be comfortable with that?

A new study finds that people are neither entirely enthusiastic nor totally averse to AI. Rather than falling into camps of techno-optimists and Luddites, people are discerning about the practical upshot of using AI, case by case.

“We propose that AI appreciation occurs when AI is perceived as being more capable than humans and personalization is perceived as being unnecessary in a given decision context,” says MIT Professor Jackson Lu, co-author of a newly published paper detailing the study’s results. “AI aversion occurs when either of these conditions is not met, and AI appreciation occurs only when both conditions are satisfied.”

The paper, “AI Aversion or Appreciation? A Capability–Personalization Framework and a Meta-Analytic Review,” appears in Psychological Bulletin. The paper has eight co-authors, including Lu, who is the Career Development Associate Professor of Work and Organization Studies at the MIT Sloan School of Management.

New framework adds insight

People’s reactions to AI have long been subject to extensive debate, often producing seemingly disparate findings. An influential 2015 paper on “algorithm aversion” found that people are less forgiving of AI-generated errors than of human errors, whereas a widely noted 2019 paper on “algorithm appreciation” found that people preferred advice from AI, compared to advice from humans.

To reconcile these mixed findings, Lu and his co-authors conducted a meta-analysis of 163 prior studies that compared people’s preferences for AI versus humans. The researchers tested whether the data supported their proposed “Capability–Personalization Framework” — the idea that in a given context, both the perceived capability of AI and the perceived necessity for personalization shape our preferences for either AI or humans.

Across the 163 studies, the research team analyzed over 82,000 reactions to 93 distinct “decision contexts” — for instance, whether or not participants would feel comfortable with AI being used in cancer diagnoses. The analysis confirmed that the Capability–Personalization Framework indeed helps account for people’s preferences.

“The meta-analysis supported our theoretical framework,” Lu says. “Both dimensions are important: Individuals evaluate whether or not AI is more capable than people at a given task, and whether the task calls for personalization. People will prefer AI only if they think the AI is more capable than humans and the task is nonpersonal.”

He adds: “The key idea here is that high perceived capability alone does not guarantee AI appreciation. Personalization matters too.”

For example, people tend to favor AI when it comes to detecting fraud or sorting large datasets — areas where AI’s abilities exceed those of humans in speed and scale, and personalization is not required. But they are more resistant to AI in contexts like therapy, job interviews, or medical diagnoses, where they feel a human is better able to recognize their unique circumstances.

“People have a fundamental desire to see themselves as unique and distinct from other people,” Lu says. “AI is often viewed as impersonal and operating in a rote manner. Even if the AI is trained on a wealth of data, people feel AI can’t grasp their personal situations. They want a human recruiter, a human doctor who can see them as distinct from other people.”

Context also matters: From tangibility to unemployment

The study also uncovered other factors that influence individuals’ preferences for AI. For instance, AI appreciation is more pronounced for tangible robots than for intangible algorithms.

Economic context also matters. In countries with lower unemployment, AI appreciation is more pronounced.

“It makes intuitive sense,” Lu says. “If you worry about being replaced by AI, you’re less likely to embrace it.”  

Lu is continuing to examine people’s complex and evolving attitudes toward AI. While he does not view the current meta-analysis as the last word on the matter, he hopes the Capability–Personalization Framework offers a valuable lens for understanding how people evaluate AI across different contexts.

“We’re not claiming perceived capability and personalization are the only two dimensions that matter, but according to our meta-analysis, these two dimensions capture much of what shapes people’s preferences for AI versus humans across a wide range of studies,” Lu concludes.

In addition to Lu, the paper’s co-authors are Xin Qin, Chen Chen, Hansen Zhou, Xiaowei Dong, and Limei Cao of Sun Yat-sen University; Xiang Zhou of Shenzhen University; and Dongyuan Wu of Fudan University.

The research was supported, in part, by grants to Qin and Wu from the National Natural Science Foundation of China. 

MIT has an unparalleled history of bringing together interdisciplinary teams to solve pressing problems — think of the development of radar during World War II, or leading the international coalition that cracked the code of the human genome — but the challenge of climate change could demand a scale of collaboration unlike any that’s come before at MIT.

“Solving climate change is not just about new technologies or better models. It’s about forging new partnerships across campus and beyond — between scientists and economists, between architects and data scientists, between policymakers and physicists, between anthropologists and engineers, and more,” MIT Vice President for Energy and Climate Evelyn Wang told an energetic crowd of faculty, students, and staff on May 6. “Each of us holds a piece of the solution — but only together can we see the whole.”

Undeterred by heavy rain, approximately 300 campus community members filled the atrium in the Tina and Hamid Moghadam Building (Building 55) for a spring gathering hosted by Wang and the Climate Project at MIT. The initiative seeks to direct the full strength of MIT to address climate change, which Wang described as one of the defining challenges of this moment in history — and one of its greatest opportunities.

“It calls on us to rethink how we power our world, how we build, how we live — and how we work together,” Wang said. “And there is no better place than MIT to lead this kind of bold, integrated effort. Our culture of curiosity, rigor, and relentless experimentation makes us uniquely suited to cross boundaries — to break down silos and build something new.”

The Climate Project is organized around six missions, thematic areas in which MIT aims to make significant impact, ranging from decarbonizing industry to new policy approaches to designing resilient cities. The faculty leaders of these missions posed challenges to the crowd before circulating among the crowd to share their perspectives and to discuss community questions and ideas.

Wang and the Climate Project team were joined by a number of research groups, startups, and MIT offices conducting relevant work today on issues related to energy and climate. For example, the MIT Office of Sustainability showcased efforts to use the MIT campus as a living laboratory; MIT spinouts such as Forma Systems, which is developing high-performance, low-carbon building systems, and Addis Energy, which envisions using the earth as a reactor to produce clean ammonia, presented their technologies; and visitors learned about current projects in MIT labs, including DebunkBot, an artificial intelligence-powered chatbot that can persuade people to shift their attitudes about conspiracies, developed by David Rand, the Erwin H. Schell Professor at the MIT Sloan School of Management.

Benedetto Marelli, an associate professor in the Department of Civil and Environmental Engineering who leads the Wild Cards Mission, said the energy and enthusiasm that filled the room was inspiring — but that the individual conversations were equally valuable.

“I was especially pleased to see so many students come out. I also spoke with other faculty, talked to staff from across the Institute, and met representatives of external companies interested in collaborating with MIT,” Marelli said. “You could see connections being made all around the room, which is exactly what we need as we build momentum for the Climate Project.”

Researchers from the Disruptive and Sustainable Technologies for Agricultural Precision (DiSTAP) interdisciplinary research group within the Singapore-MIT Alliance for Research and Technology have developed the world’s first near-infrared fluorescent nanosensor capable of real-time, nondestructive, and species-agnostic detection of indole-3-acetic acid (IAA) — the primary bioactive auxin hormone that controls the way plants develop, grow, and respond to stress.

Auxins, particularly IAA, play a central role in regulating key plant processes such as cell division, elongation, root and shoot development, and response to environmental cues like light, heat, and drought. External factors like light affect how auxin moves within the plant, temperature influences how much is produced, and a lack of water can disrupt hormone balance. When plants cannot effectively regulate auxins, they may not grow well, adapt to changing conditions, or produce as much food. 

Existing IAA detection methods, such as liquid chromatography, require taking plant samples from the plant — which harms or removes part of it. Conventional methods also measure the effects of IAA rather than detecting it directly, and cannot be used universally across different plant types. In addition, since IAA are small molecules that cannot be easily tracked in real time, biosensors that contain fluorescent proteins need to be inserted into the plant’s genome to measure auxin, making it emit a fluorescent signal for live imaging.

SMART’s newly developed nanosensor enables direct, real-time tracking of auxin levels in living plants with high precision. The sensor uses near infrared imaging to monitor IAA fluctuations non-invasively across tissues like leaves, roots, and cotyledons, and it is capable of bypassing chlorophyll interference to ensure highly reliable readings even in densely pigmented tissues. The technology does not require genetic modification and can be integrated with existing agricultural systems — offering a scalable precision tool to advance both crop optimization and fundamental plant physiology research. 

By providing real-time, precise measurements of auxin, the sensor empowers farmers with earlier and more accurate insights into plant health. With these insights and comprehensive data, farmers can make smarter, data-driven decisions on irrigation, nutrient delivery, and pruning, tailored to the plant’s actual needs — ultimately improving crop growth, boosting stress resilience, and increasing yields.

“We need new technologies to address the problems of food insecurity and climate change worldwide. Auxin is a central growth signal within living plants, and this work gives us a way to tap it to give new information to farmers and researchers,” says Michael Strano, co-lead principal investigator at DiSTAP, Carbon P. Dubbs Professor of Chemical Engineering at MIT, and co-corresponding author of the paper. “The applications are many, including early detection of plant stress, allowing for timely interventions to safeguard crops. For urban and indoor farms, where light, water, and nutrients are already tightly controlled, this sensor can be a valuable tool in fine-tuning growth conditions with even greater precision to optimize yield and sustainability.”

The research team documented the nanosensor’s development in a paper titled, “A Near-Infrared Fluorescent Nanosensor for Direct and Real-Time Measurement of Indole-3-Acetic Acid in Plants,” published in the journal ACS Nano. The sensor comprises single-walled carbon nanotubes wrapped in a specially designed polymer, which enables it to detect IAA through changes in near infrared fluorescence intensity. Successfully tested across multiple species, including Arabidopsis, Nicotiana benthamiana, choy sum, and spinach, the nanosensor can map IAA responses under various environmental conditions such as shade, low light, and heat stress. 

“This sensor builds on DiSTAP’s ongoing work in nanotechnology and the CoPhMoRe technique, which has already been used to develop other sensors that can detect important plant compounds such as gibberellins and hydrogen peroxide. By adapting this approach for IAA, we’re adding to our inventory of novel, precise, and nondestructive tools for monitoring plant health. Eventually, these sensors can be multiplexed, or combined, to monitor a spectrum of plant growth markers for more complete insights into plant physiology,” says Duc Thinh Khong, research scientist at DiSTAP and co-first author of the paper.

“This small but mighty nanosensor tackles a long-standing challenge in agriculture: the need for a universal, real-time, and noninvasive tool to monitor plant health across various species. Our collaborative achievement not only empowers researchers and farmers to optimize growth conditions and improve crop yield and resilience, but also advances our scientific understanding of hormone pathways and plant-environment interactions,” says In-Cheol Jang, senior principal investigator at TLL, principal investigator at DiSTAP, and co-corresponding author of the paper.

Looking ahead, the research team is looking to combine multiple sensing platforms to simultaneously detect IAA and its related metabolites to create a comprehensive hormone signaling profile, offering deeper insights into plant stress responses and enhancing precision agriculture. They are also working on using microneedles for highly localized, tissue-specific sensing, and collaborating with industrial urban farming partners to translate the technology into practical, field-ready solutions. 

The research was carried out by SMART, and supported by the National Research Foundation of Singapore under its Campus for Research Excellence And Technological Enterprise program.

An autonomous drone carrying water to help extinguish a wildfire in the Sierra Nevada might encounter swirling Santa Ana winds that threaten to push it off course. Rapidly adapting to these unknown disturbances inflight presents an enormous challenge for the drone’s flight control system.

To help such a drone stay on target, MIT researchers developed a new, machine learning-based adaptive control algorithm that could minimize its deviation from its intended trajectory in the face of unpredictable forces like gusty winds.

Unlike standard approaches, the new technique does not require the person programming the autonomous drone to know anything in advance about the structure of these uncertain disturbances. Instead, the control system’s artificial intelligence model learns all it needs to know from a small amount of observational data collected from 15 minutes of flight time.

Importantly, the technique automatically determines which optimization algorithm it should use to adapt to the disturbances, which improves tracking performance. It chooses the algorithm that best suits the geometry of specific disturbances this drone is facing.

The researchers train their control system to do both things simultaneously using a technique called meta-learning, which teaches the system how to adapt to different types of disturbances.

Taken together, these ingredients enable their adaptive control system to achieve 50 percent less trajectory tracking error than baseline methods in simulations and perform better with new wind speeds it didn’t see during training.

In the future, this adaptive control system could help autonomous drones more efficiently deliver heavy parcels despite strong winds or monitor fire-prone areas of a national park.

“The concurrent learning of these components is what gives our method its strength. By leveraging meta-learning, our controller can automatically make choices that will be best for quick adaptation,” says Navid Azizan, who is the Esther and Harold E. Edgerton Assistant Professor in the MIT Department of Mechanical Engineering and the Institute for Data, Systems, and Society (IDSS), a principal investigator of the Laboratory for Information and Decision Systems (LIDS), and the senior author of a paper on this control system.

Azizan is joined on the paper by lead author Sunbochen Tang, a graduate student in the Department of Aeronautics and Astronautics, and Haoyuan Sun, a graduate student in the Department of Electrical Engineering and Computer Science. The research was recently presented at the Learning for Dynamics and Control Conference.

Finding the right algorithm

Typically, a control system incorporates a function that models the drone and its environment, and includes some existing information on the structure of potential disturbances. But in a real world filled with uncertain conditions, it is often impossible to hand-design this structure in advance.

Many control systems use an adaptation method based on a popular optimization algorithm, known as gradient descent, to estimate the unknown parts of the problem and determine how to keep the drone as close as possible to its target trajectory during flight. However, gradient descent is only one algorithm in a larger family of algorithms available to choose, known as mirror descent.

“Mirror descent is a general family of algorithms, and for any given problem, one of these algorithms can be more suitable than others. The name of the game is how to choose the particular algorithm that is right for your problem. In our method, we automate this choice,” Azizan says.

In their control system, the researchers replaced the function that contains some structure of potential disturbances with a neural network model that learns to approximate them from data. In this way, they don’t need to have an a priori structure of the wind speeds this drone could encounter in advance.

Their method also uses an algorithm to automatically select the right mirror-descent function while learning the neural network model from data, rather than assuming a user has the ideal function picked out already. The researchers give this algorithm a range of functions to pick from, and it finds the one that best fits the problem at hand.

“Choosing a good distance-generating function to construct the right mirror-descent adaptation matters a lot in getting the right algorithm to reduce the tracking error,” Tang adds.

Learning to adapt

While the wind speeds the drone may encounter could change every time it takes flight, the controller’s neural network and mirror function should stay the same so they don’t need to be recomputed each time.

To make their controller more flexible, the researchers use meta-learning, teaching it to adapt by showing it a range of wind speed families during training.

“Our method can cope with different objectives because, using meta-learning, we can learn a shared representation through different scenarios efficiently from data,” Tang explains.

In the end, the user feeds the control system a target trajectory and it continuously recalculates, in real-time, how the drone should produce thrust to keep it as close as possible to that trajectory while accommodating the uncertain disturbance it encounters.

In both simulations and real-world experiments, the researchers showed that their method led to significantly less trajectory tracking error than baseline approaches with every wind speed they tested.

“Even if the wind disturbances are much stronger than we had seen during training, our technique shows that it can still handle them successfully,” Azizan adds.

In addition, the margin by which their method outperformed the baselines grew as the wind speeds intensified, showing that it can adapt to challenging environments.

The team is now performing hardware experiments to test their control system on real drones with varying wind conditions and other disturbances.

They also want to extend their method so it can handle disturbances from multiple sources at once. For instance, changing wind speeds could cause the weight of a parcel the drone is carrying to shift in flight, especially when the drone is carrying sloshing payloads.

They also want to explore continual learning, so the drone could adapt to new disturbances without the need to also be retrained on the data it has seen so far.

“Navid and his collaborators have developed breakthrough work that combines meta-learning with conventional adaptive control to learn nonlinear features from data. Key to their approach is the use of mirror descent techniques that exploit the underlying geometry of the problem in ways prior art could not. Their work can contribute significantly to the design of autonomous systems that need to operate in complex and uncertain environments,” says Babak Hassibi, the Mose and Lillian S. Bohn Professor of Electrical Engineering and Computing and Mathematical Sciences at Caltech, who was not involved with this work.

This research was supported, in part, by MathWorks, the MIT-IBM Watson AI Lab, the MIT-Amazon Science Hub, and the MIT-Google Program for Computing Innovation.

Will the perfect storm of potentially life-changing, artificial intelligence-driven health care and the desire to increase profits through subscription models alienate vulnerable patients?

For the third year in a row, MIT's Envisioning the Future of Computing Prize asked students to describe, in 3,000 words or fewer, how advancements in computing could shape human society for the better or worse. All entries were eligible to win a number of cash prizes.
 
Inspired by recent research on the greater effect microbiomes have on overall health, MIT-WHOI Joint Program in Oceanography and Applied Ocean Science and Engineering PhD candidate Annaliese Meyer created the concept of “B-Bots,” a synthetic bacterial mimic designed to regulate gut biomes and activated by Bluetooth.  
 
For the contest, which challenges MIT students to articulate their musings for what a future driven by advances in computing holds, Meyer submitted a work of speculative fiction about how recipients of a revolutionary new health-care technology find their treatment in jeopardy with the introduction of a subscription-based pay model.

In her winning paper, titled “(Pre/Sub)scribe,” Meyer chronicles the usage of B-Bots from the perspective of both their creator and a B-Bots user named Briar. They celebrate the effects of the supplement, helping them manage vitamin deficiencies and chronic conditions like acid reflux and irritable bowel syndrome. Meyer says that the introduction of a B-Bots subscription model “seemed like a perfect opportunity to hopefully make clear that in a for-profit health-care system, even medical advances that would, in theory, be revolutionary for human health can end up causing more harm than good for the many people on the losing side of the massive wealth disparity in modern society.”

As a Canadian, Meyer has experienced the differences between the health care systems in the United States and Canada. She recounts her mother’s recent cancer treatments, emphasizing the cost and coverage of treatments in British Columbia when compared to the U.S.

Aside from a cautionary tale of equity in the American health care system, Meyer hopes readers take away an additional scientific message on the complexity of gut microbiomes. Inspired by her thesis work in ocean metaproteomics, Meyer says, “I think a lot about when and why microbes produce different proteins to adapt to environmental changes, and how that depends on the rest of the microbial community and the exchange of metabolic products between organisms.”

Meyer had hoped to participate in the previous year’s contest, but the time constraints of her lab work put her submission on hold. Now in the midst of thesis work, she saw the contest as a way to add some variety to what she was writing while keeping engaged with her scientific interests. However, writing has always been a passion. “I wrote a lot as a kid (‘author’ actually often preceded ‘scientist’ as my dream job while I was in elementary school), and I still write fiction in my spare time,” she says.

Named the winner of the $10,000 grand prize, Meyer says the essay and presentation preparation were extremely rewarding.

“The chance to explore a new topic area which, though related to my field, was definitely out of my comfort zone, really pushed me as a writer and a scientist. It got me reading papers I’d never have found before, and digging into concepts that I’d barely ever encountered. (Did I have any real understanding of the patent process prior to this? Absolutely not.) The presentation dinner itself was a ton of fun; it was great to both be able to celebrate with my friends and colleagues as well as meet people from a bunch of different fields and departments around MIT.”
 

Envisioning the future of the computing prize
 

Co-sponsored by the Social and Ethical Responsibilities of Computing (SERC), a cross-cutting initiative of the MIT Schwarzman College of Computing and the School of Humanities, Arts, and Social Sciences (SHASS), with support from MAC3 Philanthropies, the contest this year attracted 65 submissions from undergraduate and graduate students across various majors, including brain and cognitive sciences, economics, electrical engineering and computer science, physics, anthropology, and others.

Caspar Hare, associate dean of SERC and professor of philosophy, launched the prize in 2023. He says that the object of the prize was “to encourage MIT students to think about what they’re doing, not just in terms of advancing computing-related technologies, but also in terms of how the decisions they make may or may not work to our collective benefit.”

He emphasized that the Envisioning the Future of Computing prize will continue to remain “interesting and important” to the MIT community. There are plans in place to tweak next year’s contest, offering more opportunities for workshops and guidance for those interested in submitting essays.

“Everyone is excited to continue this for as long as it remains relevant, which could be forever,” he says, suggesting that in years to come the prize could give us a series of historical snapshots of what computing-related technologies MIT students found most compelling.

“Computing-related technology is going to be transforming and changing the world. MIT students will remain a big part of that.”

Crowning a winner

As part of a two-stage evaluation process, all the submitted essays were reviewed anonymously by a committee of faculty members from the college, SHASS, and the Department of Urban Studies and Planning. The judges moved forward three finalists based on the papers that were deemed to be the most articulate, thorough, grounded, imaginative, and inspiring.
 
In early May, a live awards ceremony was held where the finalists were invited to give 20-minute presentations on their entries and took questions from the audience. Nearly 140 MIT community members, family members, and friends attended the ceremony in support of the finalists. The audience members and judging panel asked the presenters challenging and thoughtful questions on the societal impact of their fictional computing technologies.
 
A final tally, which comprised 75 percent of their essay score and 25 percent of their presentation score, determined the winner.

This year’s judging panel included:

• Marzyeh Ghassemi, associate professor in electrical engineering and computer science;
• Caspar Hare, associate dean of SERC and professor of philosophy;
• Jason Jackson, associate professor in political economy and urban planning;
• Brad Skow, professor of philosophy;
• Armando Solar-Lezama, associate director and chief operating officer of the MIT Computer Science and Artificial Intelligence Laboratory; and
• Nikos Trichakis, interim associate dean of SERC and associate professor of operations management.

The judges also awarded $5,000 to the two runners-up: Martin Staadecker, a graduate student in the Technology and Policy Program in the Institute for Data, Systems, and Society, for his essay on a fictional token-based system to track fossil fuels, and Juan Santoyo, a PhD candidate in the Department of Brain and Cognitive Sciences, for his short story of a field-deployed AI designed to help the mental health of soldiers in times of conflict. In addition, eight honorable mentions were recognized, with each receiving a cash prize of $1,000.

Data should drive every decision a modern business makes. But most businesses have a massive blind spot: They don’t know what’s happening in their visual data.

Coactive is working to change that. The company, founded by Cody Coleman ’13, MEng ’15 and William Gaviria Rojas ’13, has created an artificial intelligence-powered platform that can make sense of data like images, audio, and video to unlock new insights.

Coactive’s platform can instantly search, organize, and analyze unstructured visual content to help businesses make faster, better decisions.

“In the first big data revolution, businesses got better at getting value out of their structured data,” Coleman says, referring to data from tables and spreadsheets. “But now, approximately 80 to 90 percent of the data in the world is unstructured. In the next chapter of big data, companies will have to process data like images, video, and audio at scale, and AI is a key piece of unlocking that capability.”

Coactive is already working with several large media and retail companies to help them understand their visual content without relying on manual sorting and tagging. That’s helping them get the right content to users faster, remove explicit content from their platforms, and uncover how specific content influences user behavior.

More broadly, the founders believe Coactive serves as an example of how AI can empower humans to work more efficiently and solve new problems.

“The word coactive means to work together concurrently, and that’s our grand vision: helping humans and machines work together,” Coleman says. “We believe that vision is more important now than ever because AI can either pull us apart or bring us together. We want Coactive to be an agent that pulls us together and gives human beings a new set of superpowers.”

Giving computers vision

Coleman met Gaviria Rojas in the summer before their first yearthrough the MIT Interphase Edge program. Both would go on to major in electrical engineering and computer science and work on bringing MIT OpenCourseWare content to Mexican universities, among other projects.

“That was a great example of entrepreneurship,” Coleman recalls of the OpenCourseWare project. “It was really empowering to be responsible for the business and the software development. It led me to start my own small web-development businesses afterward, and to take [the MIT course] Founder’s Journey.”

Coleman first explored the power of AI at MIT while working as a graduate researcher with the Office of Digital Learning (now MIT Open Learning), where he used machine learning to study how humans learn on MITx, which hosts massive, open online courses created by MIT faculty and instructors.

“It was really amazing to me that you could democratize this transformational journey that I went through at MIT with digital learning — and that you could apply AI and machine learning to create adaptive systems that not only help us understand how humans learn, but also deliver more personalized learning experiences to people around the world,” Coleman says of MITx. “That was also the first time I got to explore video content and apply AI to it.”

After MIT, Coleman went to Stanford University for his PhD, where he worked on lowering barriers to using AI. The research led him to work with companies like Pinterest and Meta on AI and machine-learning applications.

“That’s where I was able to see around the corner into the future of what people wanted to do with AI and their content,” Coleman recalls. “I was seeing how leading companies were using AI to drive business value, and that’s where the initial spark for Coactive came from. I thought, ‘What if we create an enterprise-grade operating system for content and multimodal AI to make that easy?’”

Meanwhile, Gaviria Rojas moved to the Bay Area in 2020 and started working as a data scientist at eBay. As part of the move, he needed help transporting his couch, and Coleman was the lucky friend he called.

“On the car ride, we realized we both saw an explosion happening around data and AI,” Gaviria Rojas says. “At MIT, we got a front row seat to the big data revolution, and we saw people inventing technologies to unlock value from that data at scale. Cody and I realized we had another powder keg about to explode with enterprises collecting tremendous amount of data, but this time it was multimodal data like images, video, audio, and text. There was a missing technology to unlock it at scale. That was AI.”

The platform the founders went on to build — what Coleman describes as an “AI operating system” — is model agnostic, meaning the company can swap out the AI systems under the hood as models continue to improve. Coactive’s platform includes prebuilt applications that business customers can use to do things like search through their content, generate metadata, and conduct analytics to extract insights.

“Before AI, computers would see the world through bytes, whereas humans would see the world through vision,” Coleman says. “Now with AI, machines can finally see the world like we do, and that’s going to cause the digital and physical worlds to blur.”

Improving the human-computer interface

Reuters’ database of images supplies the world’s journalists with millions of photos. Before Coactive, the company relied on reporters manually entering tags with each photo so that the right images would show up when journalists searched for certain subjects.

“It was incredible slow and expensive to go through all of these raw assets, so people just didn’t add tags,” Coleman says. “That meant when you searched for things, there were limited results even if relevant photos were in the database.”

Now, when journalists on Reuters’ website select ‘Enable AI Search,’ Coactive can pull up relevant content based on its AI system’s understanding of the details in each image and video.

“It’s vastly improving the quality of results for reporters, which enables them to tell better, more accurate stories than ever before,” Coleman says.

Reuters is not alone in struggling to manage all of its content. Digital asset management is a huge component of many media and retail companies, who today often rely on manually entered metadata for sorting and searching through that content.

Another Coactive customer is Fandom, which is one of the world’s largest platforms for information around TV shows, videogames, and movies with more than 300 million monthly active users. Fandom is using Coactive to understand visual data in their online communities and help remove excessive gore and sexualized content.

“It used to take 24 to 48 hours for Fandom to review each new piece of content,” Coleman says. “Now with Coactive, they’ve codified their community guidelines and can generate finer-grain information in an average of about 500 milliseconds.”

With every use case, the founders see Coactive as enabling a new paradigm in the ways humans work with machines.

“Throughout the history of human-computer interaction, we’ve had to bend over a keyboard and mouse to input information in a way that machines could understand,” Coleman says. “Now, for the first time, we can just speak naturally, we can share images and video with AI, and it can understand that content. That’s a fundamental change in the way we think about human-computer interactions. The core vision of Coactive is because of that change, we need a new operating system and a new way of working with content and AI.”

When navigating a place that we’re only somewhat familiar with, we often rely on unique landmarks to help make our way. However, if we’re looking for an office in a brick building, and there are many brick buildings along our route, we might use a rule like looking for the second building on a street, rather than relying on distinguishing the building itself.

Until that ambiguity is resolved, we must hold in mind that there are multiple possibilities (or hypotheses) for where we are in relation to our destination. In a study of mice, MIT neuroscientists have now discovered that these hypotheses are explicitly represented in the brain by distinct neural activity patterns.

This is the first time that neural activity patterns that encode simultaneous hypotheses have been seen in the brain. The researchers found that these representations, which were observed in the brain’s retrosplenial cortex (RSC), not only encode hypotheses but also could be used by the animals to choose the correct way to go.

“As far as we know, no one has shown in a complex reasoning task that there’s an area in association cortex that holds two hypotheses in mind and then uses one of those hypotheses, once it gets more information, to actually complete the task,” says Mark Harnett, an associate professor of brain and cognitive sciences, a member of MIT’s McGovern Institute for Brain Research, and the senior author of the study.

Jakob Voigts PhD ’17, a former postdoc in Harnett’s lab and now a group leader at the Howard Hughes Medical Institute Janelia Research Campus, is the lead author of the paper, which appears today in Nature Neuroscience.

Ambiguous landmarks

The RSC receives input from the visual cortex, the hippocampal formation, and the anterior thalamus, which it integrates to help guide navigation.

In a 2020 paper, Harnett’s lab found that the RSC uses both visual and spatial information to encode landmarks used for navigation. In that study, the researchers showed that neurons in the RSC of mice integrate visual information about the surrounding environment with spatial feedback of the mice’s own position along a track, allowing them to learn where to find a reward based on landmarks that they saw.

In their new study, the researchers wanted to delve further into how the RSC uses spatial information and situational context to guide navigational decision-making. To do that, the researchers devised a much more complicated navigational task than typically used in mouse studies. They set up a large, round arena, with 16 small openings, or ports, along the side walls. One of these openings would give the mice a reward when they stuck their nose through it. In the first set of experiments, the researchers trained the mice to go to different reward ports indicated by dots of light on the floor that were only visible when the mice get close to them.

Once the mice learned to perform this relatively simple task, the researchers added a second dot. The two dots were always the same distance from each other and from the center of the arena. But now the mice had to go to the port by the counterclockwise dot to get the reward. Because the dots were identical and only became visible at close distances, the mice could never see both dots at once and could not immediately determine which dot was which.

To solve this task, mice therefore had to remember where they expected a dot to show up, integrating their own body position, the direction they were heading, and path they took to figure out which landmark is which. By measuring RSC activity as the mice approached the ambiguous landmarks, the researchers could determine whether the RSC encodes hypotheses about spatial location. The task was carefully designed to require the mice to use the visual landmarks to obtain rewards, instead of other strategies like odor cues or dead reckoning.

“What is important about the behavior in this case is that mice need to remember something and then use that to interpret future input,” says Voigts, who worked on this study while a postdoc in Harnett’s lab. “It’s not just remembering something, but remembering it in such a way that you can act on it.”

The researchers found that as the mice accumulated information about which dot might be which, populations of RSC neurons displayed distinct activity patterns for incomplete information. Each of these patterns appears to correspond to a hypothesis about where the mouse thought it was with respect to the reward.

When the mice get close enough to figure out which dot was indicating the reward port, these patterns collapsed into the one that represents the correct hypothesis. The findings suggest that these patterns not only passively store hypotheses, they can also be used to compute how to get to the correct location, the researchers say.

“We show that RSC has the required information for using this short-term memory to distinguish the ambiguous landmarks. And we show that this type of hypothesis is encoded and processed in a way that allows the RSC to use it to solve the computation,” Voigts says.

Interconnected neurons

When analyzing their initial results, Harnett and Voigts consulted with MIT Professor Ila Fiete, who had run a study about 10 years ago using an artificial neural network to perform a similar navigation task.

That study, previously published on bioRxiv, showed that the neural network displayed activity patterns that were conceptually similar to those seen in the animal studies run by Harnett’s lab. The neurons of the artificial neural network ended up forming highly interconnected low-dimensional networks, like the neurons of the RSC.

“That interconnectivity seems, in ways that we still don’t understand, to be key to how these dynamics emerge and how they’re controlled. And it’s a key feature of how the RSC holds these two hypotheses in mind at the same time,” Harnett says.

In his lab at Janelia, Voigts now plans to investigate how other brain areas involved in navigation, such as the prefrontal cortex, are engaged as mice explore and forage in a more naturalistic way, without being trained on a specific task.

“We’re looking into whether there are general principles by which tasks are learned,” Voigts says. “We have a lot of knowledge in neuroscience about how brains operate once the animal has learned a task, but in comparison we know extremely little about how mice learn tasks or what they choose to learn when given freedom to behave naturally.”

The research was funded, in part, by the National Institutes of Health, a Simons Center for the Social Brain at MIT postdoctoral fellowship, the National Institute of General Medical Sciences, and the Center for Brains, Minds, and Machines at MIT, funded by the National Science Foundation.

Three years ago, Massachusetts passed a law prohibiting the disposal of used clothing and textiles. The law aims to reduce waste and promote recycling and repurposing. While many are unaware of the nascent law, MIT students at the helm of Infinite Threads were happy to see its passage.

Infinite Threads is a spinoff of the Undergraduate Association Sustainability Committee — a group of students running reuse-related events since 2013. With new leadership and a new focus, Infinite Threads went from holding three to four popup sales a year to nine.

A group of students collects lightly used clothing from MIT community members and sells the items at deeply discounted prices at popup sales held several times each semester. Sales take place outside of the Student Center to optimize the high foot traffic in the area. Anyone can purchase items at the sales, and Infinite Threads also accepts clothing donations at the popups as well.

Administrators Cameron Dougal ’25, a recent graduate who majored in urban science and planning with computer science (Course 11-6), and Erin Hovendon, a rising senior in mechanical engineering (Course 2), led the small student-run organization for much of the year 2024-25 academic year.

“Our mission is to reduce material waste. We collect a lot of clothing at the end of the spring semester when students are moving out of their residence halls. We then sell items such as shirts, jackets, pants, and jeans at the popup sales for $2 to $6,” says Dougal, adding “we often have a lot of leftover T-shirts from residence hall events and career fairs that we give away for free. These MIT-related items demonstrate the importance of a hyperlocal reuse ecosystem. As soon as these types of items leave campus, there is a much lower chance that they will find a new home.”

Hovendon, who has an interest in sustainability and hopes to pursue a career in renewable energy, joined the group after seeing an email sent to DormSpam. “It was a great opportunity to jump into a sustainability leadership role while also helping the MIT community. We aim to offer affordable clothing options, and we get a lot of positive feedback about the thrift popups — I love hearing from students that they got clothing items they now wear frequently from one of our sales,” says Hovendon.

“Any money made at the popups is used to pay the student workers and to rent the U-Haul we use to bring the clothing we store at MIT’s Furniture Exchange warehouse to the Student Center. Our goal is simple: we want to keep clothing out of landfills, which in return helps the planet,” says Dougal.

Studies show that a pair of cotton denim jeans can take up to a year to decompose, while jeans or items of clothing made with polyester can take 40-200 years to decompose. According to the Environmental Protection Agency, blue jeans account for 5 percent of landfill space. Infinite Threads saves clothing items from ending up in landfills.

Hovendon agrees. “We don’t make a lot of money at the sales — it’s not our goal. Our goal is to help the environment. We received some seed funding from the MIT Women's League, the Office of Sustainability, and the MIT Fabric Innovation Hub.”

Infinite Threads also collaborates with the MIT Office of Sustainability (MITOS) to bring awareness to their work.

“Infinite Threads is a fantastic model for how students can directly take action, empower individuals, and leverage the collective community to design out clothing waste and climate impacts through the reuse culture. MIT students, like Cameron and Erin, are well-positioned to tackle sustainability challenges on campus and out in the world as they bring a willingness to solve complex challenges, experiment with many solutions, and grapple with operational realities,” says Brian Goldberg, assistant director of MITOS.

In 2024-25, the club sold over 1,000 clothing items. Any clothing that does not sell at the thrift shop is given to Helpsy, an organization that helps keep clothing out of the trash and landfills while also creating jobs. Dougal and Hovendon say they have diverted about 750 pounds of textiles to Helpsy in 2024-25 alone.

Lauren Higgins, a rising senior majoring in political science who took over managing Infinite Threads from Dougal earlier this year, says, “I originally joined as one of the staff for Infinite Threads, and I love being able to help out with waste reduction and sustainability efforts on campus. It's been great to see our impact, and I hope we're able to continue that this upcoming year.”

Animators could create more realistic bouncy, stretchy, and squishy characters for movies and video games thanks to a new simulation method developed by researchers at MIT.

Their approach allows animators to simulate rubbery and elastic materials in a way that preserves the physical properties of the material and avoids pitfalls like instability.

The technique simulates elastic objects for animation and other applications, with improved reliability compared to other methods. In comparison, many existing simulation techniques can produce elastic animations that become erratic or sluggish or can even break down entirely.

To achieve this improvement, the MIT researchers uncovered a hidden mathematical structure in equations that capture how elastic materials deform on a computer. By leveraging this property, known as convexity, they designed a method that consistently produces accurate, physically faithful simulations.

“The way animations look often depends on how accurately we simulate the physics of the problem,” says Leticia Mattos Da Silva, an MIT graduate student and lead author of a paper on this research. “Our method aims to stay true to physical laws while giving more control and stability to animation artists.”

Beyond 3D animation, the researchers also see potential future uses in the design of real elastic objects, such as flexible shoes, garments, or toys. The method could be extended to help engineers explore how stretchy objects will perform before they are built.

She is joined on the paper by Silvia Sellán, an assistant professor of computer science at Columbia University; Natalia Pacheco-Tallaj, an MIT graduate student; and senior author Justin Solomon, an associate professor in the MIT Department of Electrical Engineering and Computer Science and leader of the Geometric Data Processing Group in the Computer Science and Artificial Intelligence Laboratory (CSAIL). The research will be presented at the SIGGRAPH conference.

Truthful to physics

If you drop a rubber ball on a wooden floor, it bounces back up. Viewers expect to see the same behavior in an animated world, but recreating such dynamics convincingly can be difficult. Many existing techniques simulate elastic objects using fast solvers that trade physical realism for speed, which can result in excessive energy loss or even simulation failure.

More accurate approaches, including a class of techniques called variational integrators, preserve the physical properties of the object, such as its total energy or momentum, and, in this way, mimic real-world behavior more closely. But these methods are often unreliable because they depend on complex equations that are hard to solve efficiently.

The MIT researchers tackled this problem by rewriting the equations of variational integrators to reveal a hidden convex structure. They broke the deformation of elastic materials into a stretch component and a rotation component, and found that the stretch portion forms a convex problem that is well-suited for stable optimization algorithms.

“If you just look at the original formulation, it seems fully non-convex. But because we can rewrite it so that is convex in at least some of its variables, we can inherit some advantages of convex optimization algorithms,” she says.

These convex optimization algorithms, when applied under the right conditions, come with guarantees of convergence, meaning they are more likely to find the correct answer to the problem. This generates more stable simulations over time, avoiding issues like a bouncing rubber ball losing too much energy or exploding mid-animation.

One of the biggest challenges the researchers faced was reinterpreting the formulation so they could extract that hidden convexity. Some other works explored hidden convexity in static problems, but it was not clear whether the structures remained solid for dynamic problems like simulating elastic objects in motion, Mattos Da Silva says.

Stability and efficiency

In experiments, their solver was able to simulate a wide range of elastic behavior, from bouncing shapes to squishy characters, with preservation of important physical properties and stability over long periods of time. Other simulation methods quickly ran into trouble: Some became unstable, causing erratic behavior, while others showed visible damping.

“Because our method demonstrates more stability, it can give animators more reliability and confidence when simulating anything elastic, whether it’s something from the real world or even something completely imaginary,” she says.

While the solver is not as fast as some simulation tools that prioritize speed over accuracy, it avoids many of the trade-offs those methods make. Compared to other physics-based approaches, it also avoids the need for complex, nonlinear solvers that can be sensitive and prone to failure.

In the future, the researchers want to explore techniques to further reduce computational cost. In addition, they want to explore applications of this technique in fabrication and engineering, where reliable simulations of elastic materials could support the design of real-world objects, like garments and toys.

“We were able to revive an old class of integrators in our work. My guess is there are other examples where researchers can revisit a problem to find a hidden convexity structure that could offer a lot of advantages,” she says.

This research is funded, in part, by a MathWorks Engineering Fellowship, the Army Research Office, the National Science Foundation, the CSAIL Future of Data Program, the MIT-IBM Watson AI Laboratory, the Wistron Corporation, and the Toyota-CSAIL Joint Research Center.

Academic research groups and startups are essential drivers of scientific progress. But some projects, like the Hubble Space Telescope or the Human Genome Project, are too big for any one academic lab or loose consortium. They’re also not immediately profitable enough for industry to take on.

That’s the gap researchers at MIT were trying to fill when they created the concept of focused research organizations, or FROs. They describe a FRO as a new type of entity, often philanthropically funded, that undertakes large research efforts using tightly coordinated teams to create a public good that accelerates scientific progress.

The original idea for focused research organizations came out of talks among researchers, most of whom were working to map the brain in MIT Professor Ed Boyden’s lab. After they began publishing their ideas, however, the researchers realized FROs could be a powerful tool to unlock scientific advances across many other applications.

“We were quite pleasantly surprised by the range of fields where we see FRO-shaped problems,” says Adam Marblestone, a former MIT research scientist who co-founded the nonprofit Convergent Research to help launch FROs in 2021. “Convergent has FRO proposals from climate, materials science, chemistry, biology — we even have launched a FRO on software for math. You wouldn’t expect math to be something with a large-scale technological research bottleneck, but it turns out even there, we found a software engineering bottleneck that needed to be solved.”

Marblestone helped formulate the idea for focused research organizations at MIT with a group including Andrew Payne SM ’17, PhD ’21 and Sam Rodriques PhD ’19, who were PhD students in Boyden’s lab at the time. Since then, the FRO concept has caught on. Convergent has helped attract philanthropic funding for FROs working to decode the immune system, identify the unintended targets of approved drugs, and understand the impacts of carbon dioxide removal in our oceans.

In total, Convergent has supported the creation of 10 FROs since its founding in 2021. Many of those groups have already released important tools for better understanding our world — and their leaders believe the best is yet to come.

“We’re starting to see these first open-source tools released in important areas,” Marblestone says. “We’re seeing the first concrete evidence that FROs are effective, because no other entity could have released these tools, and I think 2025 is going to be a significant year in terms of our newer FROs putting out new datasets and tools.”

A new model

Marblestone joined Boyden’s lab in 2014 as a research scientist after completing his PhD at Harvard University. He also worked in a new position called director of scientific architecting at the MIT Media Lab, which Boyden helped create, through which he tried to organize individual research efforts into larger projects. His own research focused on overcoming the challenges of measuring brain activity across large scales.

Marblestone discussed this and other large-scale neuroscience problems with Payne and Rodriques, and the researchers began thinking about gaps in scientific funding more broadly.

“The combination of myself, Sam, Andrew, Ed, and others’ experiences trying to start various large brain-mapping projects convinced us of the gap in support for medium-sized science and engineering teams with startup-inspired structures, built for the nonprofit purpose of building scientific infrastructure,” Marblestone says.

Through MIT, the researchers also connected with Tom Kalil, who was at the time working as the U.S. deputy director for technology and innovation. Rodriques wrote about the concept of a focused research organization as the last chapter of his PhD thesis in 2019.

“Ed always encouraged us to dream very, very big,” Rodriques says. “We were always trying to think about the hardest problems in biology and how to tackle them. My thesis basically ended with me explaining why we needed a new structure that is like a company, but nonprofit and dedicated to science.”

As part of a fellowship with the Federation of American Scientists in 2020, and working with Kalil, Marblestone interviewed scientists in dozens of fields outside of neuroscience and learned that the funding gap existed across disciplines.

When Rodriques and Marblestone published an essay about their findings, it helped attract philanthropic funding, which Marblestone, Kalil, and co-founder Anastasia Gamick used to launch Convergent Research, a nonprofit science studio for launching FROs.

“I see Ed’s lab as a melting pot where myself, Ed, Sam, and others worked on articulating a need and identifying specific projects that might make sense as FROs,” Marblestone says. “All those ideas later got crystallized when we created Convergent Research.”

In 2021, Convergent helped launch the first FROs: E11 Bio, which is led by Payne and committed to developing tools to understand how the brain is wired, and Cultivarium, an FRO making microorganisms more accessible for work in synthetic biology.

“From our brain mapping work we started asking the question, ‘Are there other projects that look like this that aren’t getting funded?’” Payne says. “We realized there was a gap in the research ecosystem, where some of these interdisciplinary, team science projects were being systematically overlooked. We knew a lot of amazing things would come out of getting those projects funded.”

Tools to advance science

Early progress from the first focused research organizations has strengthened Marblestone’s conviction that they’re filling a gap.

[C]Worthy is the FRO building tools to ensure safe, ocean-based carbon dioxide removal. It recently released an interactive map of alkaline activity to improve our understanding of one method for sequestering carbon known as ocean alkalinity enhancement. Last year, a math FRO, Lean, released a programming language and proof assistant that was used by Google’s DeepMind AI lab to solve problems in the International Mathematical Olympiad, achieving the same level as a silver medalist in the competition for the first time. The synthetic biology FRO Cultivarium, in turn, has already released software that can predict growth conditions for microbes based on their genome.

Last year, E11 Bio previewed a new method for mapping the brain called PRISM, which it has used to map out a portion of the mouse hippocampus. It will be making the data and mapping tool available to all researchers in coming months.

“A lot of this early work has proven you can put a really talented team together and move fast to go from zero to one,” Payne says. “The next phase is proving FROs can continue to build on that momentum and develop even more datasets and tools, establish even bigger collaborations, and scale their impact.”

Payne credits Boyden for fostering an ecosystem where researchers could think about problems beyond their narrow area of study.

“Ed’s lab was a really intellectually stimulating, collaborative environment,” Payne says. “He trains his students to think about impact first and work backward. It was a bunch of people thinking about how they were going to change the world, and that made it a particularly good place to develop the FRO idea.”

Marblestone says supporting FROs has been the highest-impact thing he’s been able to do in his career. Still, he believes the success of FROs should be judged over closer to 10-year periods and will depend on not just the tools they produce but also whether they spin out companies, partner with other institutes, and create larger, long-lasting initiatives to deploy what they built.

“We were initially worried people wouldn’t be willing to join these organizations because it doesn’t offer tenure and it doesn’t offer equity in a startup,” Marblestone says. “But we’ve been able to recruit excellent leaders, scientists, engineers, and others to create highly motivated teams. That’s good evidence this is working. As we get strong projects and good results, I hope it will create this flywheel where it becomes easier to fund these ideas, more scientists will come up with them, and I think we’re starting to get there.”