This game-playing AI is the new champ at Stratego

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

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

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

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

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

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

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

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

Hidden information

The world is full of imperfect information problems. 

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

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

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

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

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

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

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

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

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

A two-pronged approach

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

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

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

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

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

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

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

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

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

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

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

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

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

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Lung cancers can use two different mechanisms to evade KRAS-inhibiting drugs

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

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

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

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

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

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

Tissue transformation

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

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

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

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

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

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

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

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

Paths to resistance

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

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

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

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

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

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Climate Action Learning Lab bridges research and policy for effective climate solutions

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

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

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

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

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

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

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

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

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

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

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

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

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Who we become when we talk to machines

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

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

And yet, this kind of thing seems to be happening a lot today. 

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

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

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

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

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

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

The inner history of technology

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

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

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

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

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

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

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

Facing reality, but understanding the appeal

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

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

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

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

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

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

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

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

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

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

Don’t fear the friction

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

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

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

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

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

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

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

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

And “Artificial Intimacy” offers a recurring question for those readers.

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

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

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Pressurized experiments could help wind farms generate more power

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

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

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

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

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

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

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

Answers in the wind

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

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

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

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

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

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

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

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

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

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

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

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

Scaling the approach

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

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

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

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

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

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New formulation helps RNA vaccines withstand high temperatures

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

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

When Covid-19 vaccines carried by these particles were administered to mice, they generated just as strong an immune response as an RNA Covid-19 vaccine similar to one developed by Moderna. By using the AI algorithm to predict the optimal formulations for the particles, the researchers were able to cut down the number of experiments they needed to do, which rapidly sped up the development process.

“The real beauty of this algorithm is that we can use it with small data sets,” says Ana Jaklenec, a principal investigator in MIT’s Koch Institute for Integrative Cancer Research. “It’s really hard to run thousands of experiments, so this algorithm allows us to more easily achieve formulations with features that we want — in this case, stability.”

Jaklenec and Robert Langer, the David H. Koch Institute Professor, are the senior authors of the paper, which appears today in Nature Biotechnology. Graduate student Jinbi Tian and postdoc Khanh Tran are the lead authors of the paper.

Stable vaccines

RNA is a highly fragile molecule, so researchers stabilize it with lipid nanoparticles (LNPs) that protect the RNA from degradation and help it get into cells. However, these RNA-LNP vaccines still need to be kept cold (-20 to -80 degrees Celsius), which makes it difficult to ship them to regions that don’t have cold-storage facilities available.

Making these vaccines more heat-tolerant would not only enable them to be distributed more widely, but could also help researchers develop new vaccines that could be administered through novel methods such as microneedle patches. These patches contain hundreds of vaccine-filled microneedles, which dissolve when the patch is applied to the skin, releasing the vaccine.

To create more stable RNA vaccines, researchers have experimented with adding a variety of excipients — sugars, salts, or polymers — to the LNPs. Jaklenec and Langer recently developed polymer-stabilized LNPs that can withstand higher temperatures, but those LNPs were slightly different from the FDA-approved formulations that were used for the Moderna and Pfizer Covid-19 vaccines. 

In their new paper, the researchers wanted to see if they could find a way to make those FDA-approved formulations more stable at high temperatures.

They began by reusing some of the excipients that had worked in their earlier efforts, but they were “really getting stuck,” Jaklenec says. “We were trying to use and screen excipients that we’ve previously used successfully to stabilize LNPs, but it just wasn’t working. It was really frustrating for the team.” 

To speed up their progress, the researchers decided to try a machine-learning approach. Working with researchers at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL), they developed an algorithm that can make predictions based on very small datasets.

“We’d used our algorithms for various automated experimental design applications before, but never on a biological problem like vaccine stability,” says Mina Konaković Luković, an assistant professor of electrical engineering and computer science in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), who is also an author of the paper. “It was surprising to see how quickly the algorithm converged on a stable formulation — getting there in just a handful of iterations, rather than the exhaustive search that would normally be required.” 

The researchers used this algorithm to analyze nearly 50 FDA-approved excipients. For each excipient, the researchers first measured how well it stabilized RNA when incorporated into an LNP. They used these particles to deliver mRNA encoding a protein called firefly luciferase, which produces bioluminescence, into cells. By measuring how much light was emitted by the cells, the researchers could determine how effectively each excipient protected the mRNA.

The researchers chose five of the most promising excipients and used their AI algorithm to predict ratios of those excipients that would best stabilize LNPs similar to those used by Moderna. Based on those predictions, the researchers tested two formulations at a time in cells, fed those results back into the algorithm, and generated more predictions. After several rounds, they chose one formulation that appeared promising enough to test in animal studies.

This process took only a few weeks, much less than it would have taken without guidance from the AI algorithm.

“Before we implemented the AI algorithm, we spent several months testing different combinations and also doing the prescreening of all the excipients that we could find, but nothing would get us to 100 percent stability,” Tran says.

Robust immune responses

To test the heat resistance of their new LNP formulation, the researchers used the particles to package Covid-19 mRNA antigens, then dehydrated them using a process called vacuum drying. These particles were then stored at 37 degrees Celsius (98 degrees Fahrenheit) for two months, or at room temperature for one year. Mice that were vaccinated with these particles, even after long-term storage, showed equivalent immune responses to mice that received vaccines carried by LNPs similar to the original Moderna formulation.

The researchers also used their new heat-resistant formulation to create solid microneedle patches that could deliver a SARS-CoV-2 antigen. These patches generated an immune response similar to that produced by the injectable RNA vaccines.  

“Our approach broadens the application of not only mRNA vaccines, but also therapeutics or advanced drug-delivery platforms like controlled-release particles or microneedle patches, which requires the formulation to either be in solid state or to be stable at higher temperature,” Tian says. 

The researchers also showed that they could use their algorithm to stabilize other LNP formulations, including one similar to those used by Pfizer to deliver its Covid-19 vaccine. This formulation uses the same excipients as the one the MIT team developed for the Moderna LNP, but in a different ratio. For each LNP, once a heat-resistant formulation has been developed, it could be adapted to deliver any type of mRNA payload, the researchers say. 

The research was, in part, funded by the Gates Foundation.

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MIT students gain a humanist lens on technical innovation in Tulsa, Oklahoma

When MIT mechanical engineering student Daphne Wang arrived for her internship at the Muscogee Creek Nation Department of Health in Tulsa, Oklahoma last summer, she expected to be writing code. To her surprise, she found herself analyzing tribal history and the implications for technical innovation. 

“I learned so much that I think should be required curriculum for every single person in this country,” says Wang. “I think tech for good is genuinely getting out into communities and learning about people who aren't you, creating worldly perspectives that you ultimately can take into everything else that you do.”

Wang and fellow intern Lucy Sun, who is majoring in artificial intelligence and decision making, spent the summer supporting the development of the Muscogee Nation's first Pregnancy Risk Assessment Monitoring System (PRAMS) survey to capture maternal health data specific to Muscogee women. The project required completing literature reviews, analyzing datasets, and drafting survey questions, while considering issues of data sovereignty and governance structures specific to the Muscogee.

“It was this change of pace — 180 [degrees] from MIT, completely,” Wang reflects. At MIT, she says, course instructors routinely prepare students for big technical challenges, but there are fewer opportunities to “work with communities, [learn] how to think about the cultural and historical dimensions of the work, or how to consider the people who will be affected.” 

Wang’s supervisor, epidemiologist Breanna McNaughton-Long, was effusive about Wang and Sun’s engagement. “They were instrumental. They did so much work that I don't think we would have been able to do as quickly,” she says. “And they brought this sense that we were doing something that mattered.”

Experiential learning at the confluence of humanities and engineering 

Wang was one of 10 MIT undergraduates to participate in the PKG Center for Social Impact’s 2026 Code.Tulsa program. Now in its second year, Code.Tulsa is made possible by support from the Patrick J. McGovern Foundation and the George Kaiser Family Foundation. 

Students live at the University of Tulsa for the summer. They intern with the Muscogee and Cherokee nations, as well as local nonprofits Black Tech Street and Urban Coders Guild, completing AI, data science, and other technical projects. 

The PKG Center’s assistant dean for community-based programs, Vippy Yee, complements students’ professional experience with education on the historical and cultural significance of tech-based development in the Tulsa region from the perspective of local leaders. 

“As with all PKG Center programming, our aim is to help students integrate an engineer’s approach to problem-solving with a humanist lens on the nature of social challenges, and by extension interventions,” says Alison Badgett, the PKG Center’s director. 

“Getting to speak to people who were either very educated on the issues or have experienced them has been a massive help in reshaping the way that I think about social issues,” says Chenise Harper, an electrical engineering with computing major who interned with the Urban Coders Guild. 

Harper puts this dynamic candidly: “I hated history, but I learned a lot about why it is useful for making social change and how to go about researching it. I now feel a lot more confident that I can make an impact.”

A student’s vision for increasing STEM access

Code.Tulsa was the brainchild of sophomore Jack Carson, an electrical engineering and computer science (EECS) student who approached the PKG Center with the idea for Code.Tulsa as an incoming first-year student. Carson, who is from Tulsa and a member of the Cherokee Nation, saw firsthand that rural Native students who could benefit greatly from STEM education were unlikely to have access to it. Carson proposed developing a weeklong STEM camp for members of federally recognized tribes held at the University of Tulsa, with he and Code.Tulsa interns serving as instructors. 

Carson devised a nomination system to attract promising high school students, selecting 25 campers from 10 tribes out of 160 applications representing 25 Native nations. 

To develop the curriculum, Carson enlisted Harvard University student Allie Zong, whom he had met at Campus Preview Weekend. The Tulsa Advanced Sciences Camp (TASC) offers intensive, interactive track time in AI engineering, DNA technology, physics, and chemistry and energy. This is complemented by philosophical workshops to help students think more ambitiously and deliberately about what they can and should achieve in the near and long term. As Zong explains, “We teach them not knowledge, but curiosity, and the skills to teach themselves."

The TASC experience “kind of propelled me to self-study calculus,” says Alyssa Theofanidis, a rising high school senior from Houston who is a citizen of the Cherokee Nation. Theofanidis participated in the camp’s first year, returning this summer as a teaching assistant. Like many TASC campers, Theofanidis is eager to use her developing STEM expertise to benefit her Native community. 

TASC “got us to think 1,000 times more ambitiously about the impact we could have,” said another camper, who was inspired by guest speakers “like the nuclear fusion person at the top of their field,” referring to physicist Alexandra LeViness of MIT spinout Commonwealth Fusion Systems, who helped develop TASC’s chemistry and energy track. Campers were also inspired by “the different worldviews” of MIT interns. “I thought, maybe this is what MIT looks like,” said one camper, with several expressing the intent to apply to MIT as a result of the camp. 

Cherokee Nation Principal Chief Chuck Hoskin Jr. also delivered remarks at TASC, encouraging students as future leaders to take a public interest in technology. 

“Technology can be used for good, or it can be used for harm. Your generation has the opportunity to bend that arc toward something good,” Hoskin said. “It's a very special relationship that the Cherokee Nation has with MIT. As we reach out a hand in friendship, we have a hand reaching back. We are thankful for Cherokee citizen Jack Carson, a former secretary for the Cherokee Nation tribal youth council, for his leadership role in organizing this effort.”

Making a positive long-term social impact 

Like TASC campers, Code.Tulsa interns came away from the experience motivated to make a positive impact. While most MIT students won’t go on to full-time professional roles traditionally associated with social impact, PKG Center programming like Code.Tulsa helps students recognize they can promote the public interest no matter their career. As Elvis Chipiro, a junior in computer science and engineering, reflected after interning with the Cherokee Nation, “Social impact is not separate from mainstream technology; rather, it is embedded in the choices engineers make every day … Ultimately, meaningful social change is not driven by technology alone, but by people willing to design systems with empathy, responsibility, and inclusion at the center.”

For others, Code.Tulsa helps them reconnect with a sense of public purpose. “Remembering that … I could use my MIT education to help others was a big reason I applied to MIT in the first place,” says Harper. “But I forgot my own mission in the stress of school. Code.Tulsa really re-opened my eyes to why I am here.”

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