viernes, 17 de abril de 2026

Why bother with plausible deniability?

Picture this scenario in a business: An employee, Brad, disclosed some information that wound up in the hands of a competitor. He may not have meant to, but he did, and a few people at the firm know this. So, at the next company meeting, another employee, Linda, looks pointedly at Brad and says, “I know that no one would ever dream of leaking information, intentionally or otherwise, from our discussions.”

Linda means the opposite of what she says, of course. She is letting people know that Brad is to blame. However, while Linda is making her message public, she also wants what we often call “plausible deniability” for her statement. If anyone asks later if she was insinuating anything about Brad, she can claim she was just making a general comment about the firm.

From the boardroom to the courtroom, the talk show, and beyond, people frequently seek plausible deniability for their statements. It seems to work, too. Indeed, to have plausible deniability, the denial need not be plausible.

“People can say, ‘That’s not what I meant,’ and completely get away with it, even though it’s totally obvious they’re lying,” says MIT philosopher Sam Berstler. “They wouldn’t be getting away with it in the same respect by putting the content in explicit words.”

She adds: “This should be very puzzling to us, because in both cases the intent is maximally obvious.”

So why does plausible deniability work, and work like this? And what does it tell us about how we interact? Berstler, who studies language and communication, has published a new paper on plausible deniability, examining these issues. It is part of a larger body of work Berstler is generating, focused on everyday interactions involving deception.

To understand plausible deniability, Berstler thinks we should recognize that our conversations cannot be understood simply by analyzing the words we use. Our interactions always take place in social contexts, often have a performative aspect, and occasionally intersect with “non-acknowedgement norms,” the practice of keeping quiet about what we all know. Plausible deniability is bound up with social practices that incentivize us to not be fully transparent.

“A lot of indirect speech is designed, as it were, to facilitate this kind of deniability,” Berstler says.

The paper, “Non-Epistemic Deniability,” is published in the journal MIND. Berstler, the Laurance S. Rockefeller Career Development Chair and assistant professor of philosophy at MIT, is the sole author.

Managing a personal “Cold War”

In Berstler’s view, there are multiple ways to create plausible deniability. One is through the practice of open secrets, the subject of one of her previous papers. An open secret is widely known information that is never acknowledged, for reasons of power or in-group identification, among other things. Indeed, no one even acknowledges that they are not acknowledging the open secret.

Examining open secrets led Berstler directly to her analysis of plausible deniability. However, the new paper focuses more on another way of creating plausible deniability, which she calls “two-tracking norms.” Two-tracking is when a group divides its communications into two parts: One track consists of official, limited, courteous interaction, and the second track consists more of informal, resentful, uncooperative interactions. Linda, in our example, is engaging in two-tracking.

But why do we two-track at all? Why not just be fully transparent? Well, in an office scenario, if Linda is mad that Brad divulged some company secrets, calling out Brad directly might lead to recriminations and conflict beyond what Linda is willing to tolerate for the sake of critizing Brad on the record.

“It's like a Cold War situation where we each have an interest in not letting the conflict go to a state where we’re firing warheads at each other, but we can’t just purely manage relations around the negotiating table because we’re adversaries,” Berstler says. “We’re going to aggress against each other, but in a limited way. In a two-track conversation, communicating in the second track is like fighting a proxy battle, but we’re also providing evidence to each other that we’re only going to engage in a proxy battle.”

In this way, Linda takes Brad to task and some people pick up on it, but Brad is not explicitly publicly shamed. And though he might be unhappy, he is less likely to wreck all company norms in an attempt to retaliate. The firm more or less rolls on as usual.

Waiting for Goffman

Where Berstler differs in part from other philosophers is in her emphasis on the extent to which social practices are integral to our ways of deploying deniability. Our interactions are not just limited to rhetoric, but have additional layers.

“What we mean can often be different from what we say, or enhanced from what we say,” Berstler says. “Sometimes we figure out what others mean by relying on what they say in literal language. But sometimes we’re relying on other things, like the context.”

So, back at the firm, the colleagues of Linda and Brad might have some knowledge of a confidentiality breach, or they might know that Linda does not usually speak up at meetings, or they might read things into her tone of voice and the way she appeared to look at Brad. There is more to be gleaned than her literal words.

In this kind of analysis, Berstler finds illumination in the work of the midcentury sociologist Erving Goffman, who studied in minute detail the performative parts of our everyday interactions and speech. Goffman, as Berstler notes in the paper, proposed that we have a ritualized, social self (or “face”) and that normal, everyday behavior generally allows us, and others, to keep this face intact.

Relatedly, Goffman and some of his intellectual followers concluded that habits such as two-tracking are very common in everyday life; the price we pay for saving face is a bit less transparency, and a bit more secrecy and deniability.

“What I’m suggesting is we have these other established practices like two-tracking and open secrecy, where the deniability is just a byproduct,” Berstler says.

What’s the solution?

By bringing sociological ideas into her work, Berstler is moving beyond the normal philosophical discussion of the subject. On the other hand, she is not directly disputing core ideas in linguistics or the philosophy of language; she is just suggesting we add another layer to our analysis of communication and meaning.

Digging into issues of plausible deniability also raises the question of what to do about it. There may be something pernicious in the practice, but calling out plausible deniability threatens to dismantle our social guardrails and break the “Cold War” norms used to help people co-exist.

Berstler, though, has another suggestion: Instead of calling out such subterfuge, we can become verbally and performatively skilled enough to counteract it.

“I think the actual answer is becoming rhetorically clever,” Berstler says. “It’s being the person who uses indirect speech to respond strategically, without violating these norms. That is possible. It also means you have agency. You could become very good at verbal sparring.”

Besides, Berstler says, “Often that can be more powerful than just calling them out, and demonstrates your own verbal fluency. I think we admire it when we see it. Conversational skill is an important component of being morally good, in these cases by reprimanding someone in a way that’s not going to be counterproductive.”

She adds: “People who buy into the rhetoric of transparency can be setting back their own interests. Maybe speaking transparently is morally virtuous in some respects, but given the reality of our speech practices, transparency is not necessarily going to be the most effective way of handling things.”



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Jacob Andreas and Brett McGuire named Edgerton Award winners

MIT Associate Professor Jacob Andreas of the Department of Electrical Engineering and Computer Science [EECS] and MIT Associate Professor Brett McGuire of the Department of Chemistry have been selected as the winners of the 2026 Harold E. Edgerton Faculty Achievement Award. Established in 1982 as a permanent tribute to Institute Professor Emeritus Harold E. Edgerton’s great and enduring support for younger faculty members, this award is given annually in recognition of exceptional distinction in teaching, research, and service.

“The Department of Chemistry is extremely delighted to see Brett recognized for science that has changed how we think about carbon in space,” says Class of 1942 Professor of Chemistry and Department Head Matthew D. Shoulders. “Brett’s lab combines laboratory spectroscopy, radio astronomy, and sophisticated signal-analysis methods to pull definitive molecular fingerprints out of extraordinarily faint data. His discovery of polycyclic aromatic hydrocarbons in the cold interstellar medium has opened a powerful new window on astrochemistry. Moreover, Brett is inventing the creative and unique tools that make discoveries like this possible.”

“Jacob Andreas represents the very best of MIT EECS” says Asu Ozdaglar, EECS department head. “He is an innovative researcher whose work combines computational and linguistically informed approaches to build foundations of language learning. He is an extraordinary educator who has brought these forefront ideas into our core classes in natural language processing and machine learning. His ability to bridge foundational theory with real-world impact, while also advancing the social and ethical dimensions of computing, makes him truly deserving of the Edgerton Faculty Achievement Award.”

Andreas joined the MIT faculty in July 2019, and is affiliated with the Computer Science and Artificial Intelligence Laboratory. His work is in natural language processing (NLP), and more broadly in AI. He aims to understand the computational foundations of language learning, and to build intelligent systems that can learn from human guidance. Among other honors, Andreas has received Samsung’s AI Researcher of the Year award, MIT’s Kolokotrones and Junior Bose teaching awards, a 2024 Sloan Research Fellow award, and paper awards at the National Accrediting Agency for Clinical Laboratory Sciences, the International Conference on Machine Learning, and the Association for Computational Linguistics.

Andreas received his BS from Columbia University, his MPhil from Cambridge University (where he studied as a Churchill scholar), and his PhD in natural language processing from the University of California at Berkeley. His work in natural language processing has taken on thorny problems in the capability gap between humans and computers. “The defining feature of human language use is our capacity for compositional generalization,” explains Antonio Torralba, Delta Electronics Professor and faculty head of Artificial Intelligence and Decision-Making in the Department of EECS. “Many of the core challenges in natural language processing is addressed by simply training larger and larger neural models, but this kind of compositional generalization remains a persistent difficulty, and without the ability to generalize compositionally, the deep learning toolkit will never be robust enough for the most challenging real-world NLP tasks. Jacob’s work on compositional modeling draws new connections between NLP and work in computer vision and physics aimed at modeling systems governed by symmetries and other algebraic structures and, using them, they have been able to build NLP models exhibiting a number of new, human-like language acquisition behaviors, including one-shot word learning, learning via mutual exclusivity constraints, and learning of grammatical rules in extremely low-resource settings.”

Within EECS, Andreas has developed multiple advanced courses in natural language processing, as well as new exercises designed to get students to grapple with important social and ethical considerations in machine learning deployment. “Jacob has taken a leading role in completely modernizing and extending our course offerings in natural language processing,” says award nominator Leslie Pack Kaelbling, Panasonic Professor in the Department of EECS. “He has led the development of a modern two-course sequence, which is a cornerstone of the new AI+D [artificial intelligence and decision-making] major, routinely enrolling several hundred students each semester. His command of the area is broad and deep, and his classes integrate classical structural understanding of language with the most modern learning-based approaches. He has put MIT EECS on the worldwide map as a place to study natural language at every level.”

Brett McGuire joined the MIT faculty in 2020 and was promoted to associate professor in 2025. His research operates at the intersection of physical chemistry, molecular spectroscopy, and observational astrophysics, where he seeks to uncover how the chemical building blocks of life evolve alongside and help shape the birth of stars and planets. A former Jansky Fellow and then Hubble Postdoctoral Fellow at the National Radio Astronomy Observatory, McGuire has a BS in chemistry from the University of Illinois and a PhD in physical chemistry from Caltech. His honors include a 2026 Sloan Fellowship, the Beckman Young Investigator Award, the Helen B. Warner Prize for Astronomy, and the MIT Award for Teaching with Digital Technology.

The faculty who nominated McGuire for this award praised his extraordinary public outreach, his immediate willingness to take on teaching class 5.111 (Principles of Chemical Science), a General Institute Requirement (GIR) course comprised of 150–500 students, and his service to both the MIT and astrochemical communities.

“Brett is at the very top of astrochemical scientists in his age group due to his discovery of fused carbon ring compounds in the cold region of the ISM [interstellar medium], an observation that provides a route for carbon incorporation in planets,” says Sylvia Ceyer, the John C. Sheehan Professor of Chemistry in her nomination statement. “His extensive involvement in service-oriented activities within the astrochemical/physical community is highly unusual for a junior scientist, and is testament to the value that the astronomical community places in his wisdom and judgement. His phenomenal organizational skills have made his contributions to graduate admission protocols and seminar administration at MIT the envy of the department. And most importantly, Brett is a superb teacher, who cares deeply about students’ understanding and success, not only in his course, but in their future endeavors.”

“As an assistant professor, Brett volunteered to teach 5.111, a large GIR course with 150–500 students, and has received some of the best teaching evaluations among all faculty who have led the subject,” says Mei Hong, the David A. Leighty Professor of Chemistry. “He has a natural talent in explaining abstract physical chemistry concepts in an engaging manner. His slides, which he prepared from scratch instead of modifying from previous years’ material from other professors, are clear, and … the combination of lucid explanation and humor has generated great enthusiasm and interest in chemistry among students.”

Subject evaluations from McGuire’s courses praised his humor, the clarity of his explanations, and his ability to transform a lecture into a “science show.” “I haven't felt this sort of desire for the depth of understanding in a subject beyond just a straight grade [in some time],” says one student. “Brett definitely stimulated that love of learning for me.” 

“Brett is an outstanding faculty member who is dedicated to fostering student learning and success,” says Jennifer Weisman, assistant director of academic programs in chemistry. “He is thoughtful, caring, and goes above and beyond to help his colleagues, students, and staff.”

“I’m thrilled to be selected for the Edgerton Award this year,” says McGuire. “The award is nominally for teaching, research, and service; MIT and the chemistry department in particular have been an incredible place to learn and grow in all these areas. I’m incredibly grateful for the mentorship, enthusiasm, and support I have received from my colleagues, from my students both in the lab and in the classroom, and from the MIT community during my time here. I look forward to many more years of exciting discovery together with this one-of-a-kind community.”



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jueves, 16 de abril de 2026

Bringing AI-driven protein-design tools to biologists everywhere

Artificial intelligence is already proving it can accelerate drug development and improve our understanding of disease. But to turn AI into novel treatments we need to get the latest, most powerful models into the hands of scientists.

The problem is that most scientists aren’t machine-learning experts. Now the company OpenProtein.AI is helping scientists stay on the cutting edge of AI with a no-code platform that gives them access to powerful foundation models and a suite of tools for designing proteins, predicting protein structure and function, and training models.

The company, founded by Tristan Bepler PhD ’20 and former MIT associate professor Tim Lu PhD ’07, is already equipping researchers in pharmaceutical and biotech companies of all sizes with its tools, including internally developed foundation models for protein engineering. OpenProtein.AI also offers its platform to scientists in academia for free.

“It’s a really exciting time right now because these models can not only make protein engineering more efficient — which shortens development cycles for therapeutics and industrial uses — they can also enhance our ability to design new proteins with specific traits,” Bepler says. “We’re also thinking about applying these approaches to non-protein modalities. The big picture is we’re creating a language for describing biological systems.”

Advancing biology with AI

Bepler came to MIT in 2014 as part of the Computational and Systems Biology PhD Program, studying under Bonnie Berger, MIT’s Simons Professor of Applied Mathematics. It was there that he realized how little we understand about the molecules that make up the building blocks of biology.

“We hadn’t characterized biomolecules and proteins well enough to create good predictive models of what, say, a whole genome circuit will do, or how a protein interaction network will behave,” Bepler recalls. “It got me interested in understanding proteins at a more fine-grained level.”

Bepler began exploring ways to predict the chains of amino acids that make up proteins by analyzing evolutionary data. This was before Google released AlphaFold, a powerful prediction model for protein structure. The work led to one of the first generative AI models for understanding and designing proteins — what the team calls a protein language model.

“I was really excited about the classical framework of proteins and the relationships between their sequence, structure, and function. We don’t understand those links well,” Bepler says. “So how could we use these foundation models to skip the ‘structure’ component and go straight from sequence to function?”

After earning his PhD in 2020, Bepler entered Lu’s lab in MIT’s Department of Biological Engineering as a postdoc.

“This was around the time when the idea of integrating AI with biology was starting to pick up,” Lu recalls. “Tristan helped us build better computational models for biologic design. We also realized there’s a disconnect between the most cutting-edge tools available and the biologists, who would love to use these things but don’t know how to code. OpenProtein came from the idea of broadening access to these tools.”

Bepler had worked at the forefront of AI as part of his PhD. He knew the technology could help scientists accelerate their work.

“We started with the idea to build a general-purpose platform for doing machine learning-in-the-loop protein engineering,” Bepler says. “We wanted to build something that was user friendly because machine-learning ideas are kind of esoteric. They require implementation, GPUs, fine-tuning, designing libraries of sequences. Especially at that time, it was a lot for biologists to learn.”

OpenProtein’s platform, in contrast, features an intuitive web interface for biologists to upload data and conduct protein engineering work with machine learning. It features a range of open-source models, including PoET, OpenProtein’s flagship protein language model.

PoET, short for Protein Evolutionary Transformer, was trained on protein groups to generate sets of related proteins. Bepler and his collaborators showed it could generalize about evolutionary constraints on proteins and incorporate new information on protein sequences without retraining, allowing other researchers to add experimental data to improve the model.

“Researchers can use their own data to train models and optimize protein sequences, and then they can use our other tools to analyze those proteins,” Bepler says. “People are generating libraries of protein sequences in silico [on computers] and then running them through predictive models to get validation and structural predictors. It’s basically a no-code front-end, but we also have APIs for people who want to access it with code.”

The models help researchers design proteins faster, then decide which ones are promising enough for further lab testing. Researchers can also input proteins of interest, and the models can generate new ones with similar properties.

Since its founding, OpenProtein’s team has continued to add tools to its platform for researchers regardless of their lab size or resources.

“We’ve tried really hard to make the platform an open-ended toolbox,” Bepler says. “It has specific workflows, but it’s not tied specifically to one protein function or class of proteins. One of the great things about these models is they are very good at understanding proteins broadly. They learn about the whole space of possible proteins.”

Enabling the next generation of therapies

The large pharmaceutical company Boehringer Ingelheim began using OpenProtein’s platform in early 2025. Recently, the companies announced an expanded collaboration that will see OpenProtein’s platform and models embedded into Boehringer Ingelheim’s work as it engineers proteins to treat diseases like cancer and autoimmune or inflammatory conditions.

Last year, OpenProtein also released a new version of its protein language model, PoET-2, that outperforms much larger models while using a small fraction of the computing resources and experimental data.

“We really want to solve the question of how we describe proteins,” Bepler says. “What’s the meaningful, domain-specific language of protein constraints we use as we generate them? How can we bring in more evolutionary constraints? How can we describe an enzymatic reaction a protein carries out such that a model can generate sequences to do that reaction?”

Moving forward, the founders are hoping to make models that factor in the changing, interconnected nature of protein function.

“The area I am excited about is going beyond protein binding events to use these models to predict and design dynamic features, where the protein has to engage two, three, or four biological mechanisms at the same time, or change its function after binding,” says Lu, who currently serves in an advisory role for the company.

As progress in AI races forward, OpenProtein continues to see its mission as giving scientists the best tools to develop new treatments faster.

“As work gets more complex, with approaches incorporating things like protein logic and dynamic therapies, the existing experimental toolsets become limiting,” Lu says. “It’s really important to create open ecosystems around AI and biology. There’s a risk that AI resources could get so concentrated that the average researcher can’t use them. Open access is super important for the scientific field to make progress.”



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With navigating nematodes, scientists map out how brains implement behaviors

Animal behavior reflects a complex interplay between an animal’s brain and its sensory surroundings. Only rarely have scientists been able to discern how actions emerge from this interaction. A new open-access study in Nature Neuroscience by researchers in The Picower Institute for Learning and Memory at MIT offers one example by revealing how circuits of neurons within C. elegans nematode worms respond to odors and generate movement as they pursue of smells they like and evade ones they don’t.

“Across the animal kingdom, there are just so many remarkable behaviors,” says study senior author Steven Flavell, associate professor in the Picower Institute and MIT’s Department of Brain and Cognitive Sciences and an investigator of the Howard Hughes Medical Institute. “With modern neuroscience tools, we are finally gaining the ability to map their mechanistic underpinnings.”

By the end of the study, which former graduate student Talya Kramer PhD ’25 led as her doctoral thesis research, the team was able to show exactly which neurons in the worm’s brain did which of the jobs needed to sense where smells were coming from, plan turns toward or away from them, shift to reverse (like old-fashioned radio-controlled cars, C. elegans worms turn in reverse), execute the turns, and then go back to moving forward. Not only did the study reveal the sequence and each neuron’s role in it, but it also demonstrated that worms are more skillful and intentional in these actions than perhaps they’ve received credit for. And finally, the study demonstrated that it’s all coordinated by the neuromodulatory chemical tyramine.

“One thing that really excited us about this study is that we were able to see what a sensorimotor arc looks like at the scale of a whole nervous system: all the bits and pieces, from responses to the sensory cue until the behavioral response is implemented,” Flavell says.

Seeing the sequence

To do the research, Kramer put worms in dishes with spots of odors they’d either want to navigate toward or slither away from. With the lab’s custom microscopes and software, she and her co-authors could track how the worms navigated and all the electrical activity of more than 100 neurons in their brains during those behaviors (the worms only have 302 neurons total).

The surveillance enabled Kramer, Flavell, and their colleagues to observe that the worms weren’t just ambling randomly until they happened to get where they’d want to be. Instead, the worms would execute turns with advantageous timing and at well-chosen angles. The worms seemed to know what they were doing as they navigated along the gradients of the odors.

Inside their heads, patterns of electrical activity among a cohort of 10 neurons (indicated by flashing green light tied to the flux of calcium ions in the cells), revealed the sequence of neural activation that enabled the worms to execute these sensible sensory-guided motions: forward, then into reverse, then into the turn, and then back to forward. Particular neurons guided each of these steps, including detecting the odors, planning the turn, switching into reverse, and then executing the turns.

A couple of neurons stood out as key gears in the sequence. A neuron called SAA proved pivotal for integrating odor detection with planning movement, as its activity predicted the direction of the eventual turn. Several neurons were flexible enough to show different activity patterns depending on factors such as where the odors were and whether the worm was moving forward or in reverse.

And if the neurons are indeed turning and shifting gears, then the neuromodulator tyramine (the worm analog of norepinephrine) was the signal essential to switch their gears. After the worms started moving in reverse, tyramine from the neuron RIM enabled other neurons in the sequence to change their activity appropriately to execute the turns. In several experiments the scientists knocked out RIM tyramine and saw that the navigation behaviors and the sequence of neural activity largely fell apart.

“The neuromodulator tyramine plays a central role in organizing these sequential brain activity patterns,” Flavell says.

In addition to Flavell and Kramer, the paper’s other authors are Flossie Wan, Sara Pugliese, Adam Atanas, Sreeparna Pradhan, Alex Hiser, Lillie Godinez, Jinyue Luo, Eric Bueno, and Thomas Felt.

A MathWorks Science Fellowship, the National Institutes of Health, the National Science Foundation, The McKnight Foundation, The Alfred P. Sloan Foundation, the Freedom Together Foundation, and HHMI provided funding to support the work.



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miércoles, 15 de abril de 2026

Waves hit different on other planets

On a calm day, a light breeze might barely ripple the surface of a lake on Earth. But on Saturn’s largest moon Titan, a similar mild wind would kick up 10-foot-tall waves.

This otherworldly behavior is one prediction from a new wave model developed by scientists at MIT. The model is the first to capture the full dynamics of waves and what it takes to whip them up under different planetary conditions.

In a study published in the Journal of Geophysical Research: Planets, the MIT team introduces the model, which they’ve aptly coined “PlanetWaves.” They apply the model to predict how waves behave on planetary bodies that might host liquid lakes and oceans, including Titan, ancient Mars, and three planets beyond the solar system.

The model predicts that a gentle wind would be enough to stir up huge waves on Titan, where lakes are filled with light liquid hydrocarbons. In contrast, it would take hurricane-force winds to barely move the surface of a lake on the exoplanet 55-Cancri e, which is thought to be a lava world covered in hot, dense liquid rock. 

“On Earth, we get accustomed to certain wave dynamics,” says study author Andrew Ashton, associate scientist at the Woods Hole Oceanographic Institution (WHOI) and faculty member of the MIT-WHOI Joint Program. “But with this model, we can see how waves behave on planets with different liquids, atmospheres, and gravity, which can kind of challenge our intuition.”

The team is particularly keen to understand how waves form on Titan. The large moon is the only other planetary body in the solar system other than the Earth that is known to currently host liquid lakes.

“Anywhere there’s a liquid surface with wind moving over it, there’s potential to make waves,” says Taylor Perron, the Cecil and Ida Green Professor of Earth, Atmospheric and Planetary Sciences at MIT. “For Titan, the tantalizing thing is that we don’t have any direct observation of what these lakes look like. So we don’t know for sure what kind of waves might exist there. Now this model gives us an idea.”

If humans were to one day to send a probe to Titan’s lakes, the team’s new model could inform the design of wave-resilient spacecraft.

“You would want to build something that can withstand the energy of the waves,” says lead author Una Schneck, a graduate student in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS). “So it’s important to know what kind of waves these instruments would be up against.”

The study’s co-authors include Charlene Detelich and Alexander Hayes of Cornell University and Milan Curcic of the University of Miami.

“The first puff”

When wind blows over water, it creates waves that can be strong enough to carve out coastlines and redistribute sediment brought to the coast by rivers. Through this process, waves can be a significant force in shaping a landscape over time. Schneck and her colleagues, who study landscape evolution on Earth and other planets, wondered how waves might behave on other worlds where gravity, atmospheric conditions, and liquid compositions can be very different from what is found on Earth.

“There have been attempts in the past to predict how gravity will affect waves on other planets,” Schneck says. “But they don’t quantify other factors such as the composition of the liquid that is making waves. That was the big leap with this project.”

She and her colleagues developed a full wave model that takes into account not just a planet’s gravity, but also properties of its surface liquid, such as its density, viscosity, and surface tension, or how resistant a liquid is to rippling. The team also incorporated the effect of a planet’s atmospheric pressure. With this model, they aimed to predict how a planet’s liquid surface would evolve in response to winds of a given speed.

“Imagine a completely still lake,” Ashton offers. “We’re trying to figure out the first puff that will make those first little tiny ripples, on up to a full ocean wave.”

Making waves

The team first tested their new model with wave data on Earth. They used measurements of waves that were collected by buoys across Lake Superior over 20 years. They found that the model, which took into account Earth’s gravity, the composition of liquid (water), and atmospheric conditions, was able to accurately predict what windspeeds it would take to generate waves across the lake, and how high the waves grew with a given wind strength.

The researchers then applied the model to predict how waves would behave on other planetary bodies that are known to host liquid on their surface. They looked first to Titan, where NASA’s Cassini mission previously captured radar images of lake formations, which scientists suspect are currently filled with liquid methane and ethane. The team used the new model to calculate the moon’s wave dynamics given its gravity, atmospheric pressure, and liquid composition.

They found that on Titan, it’s surprisingly easy to make waves. The relatively light liquid, combined with low gravity and atmospheric pressure, means that even a gentle wind can stir up huge waves.

“It kind of looks like tall waves moving in slow motion,” Schneck says. “If you were standing on the shore of this lake, you might feel only a soft breeze but you would see these enormous waves flowing toward you, which is not what we would expect on Earth.”

The researchers also considered wave activity on ancient Mars. The Red Planet hosts many impact basins that may have once been filled with water, before the planet’s atmosphere dissipated and the water evaporated away. One of those basins is Jezero Crater, which is currently being explored by NASA’s Perseverance rover. With the new model, the team showed that as Mars’ atmosphere gradually disappeared, reducing its pressure over time, it would have required stronger winds to make the same waves.

Beyond the solar system, the researchers applied the model to three different exoplanets. The first, LHS1140b, is a “cool super-Earth,” meaning that it is colder and larger than Earth. The planet hosts liquid water, though because it is so large, it has a stronger gravity. The model showed that the same wind on Earth would generate much smaller waves of water on the super-Earth, due to its difference in gravity.

The team also considered Kepler 1649b, a Venus-like planet, which has a gravity similar to Earth’s, with lakes of sulfuric acid, which is about twice as dense as water. Under these conditions, the researchers found that it would take strong winds to make even a ripple on the exo-Venus, compared to on Earth.

This effect is even more pronounced for the third planet, 55-Cancri e — a lava world that has both a higher gravity than Earth and a much denser, more viscous surface liquid. Scientists suspect that the planet hosts oceans of liquefied rock. In this environment, the model predicts that hurricane-force winds on Earth, of about 80 miles per hour, would generate only small waves of a few centimeters in height on the lava world.

Aside from illuminating new ways that waves can behave on other planets, Perron hopes the model will answer longstanding questions of planetary landscape formation.

“Unlike on Earth where there is often a delta where a river meets the coast, on Titan there are very few things that look like deltas, even though there are plenty of rivers and coasts. Could waves be responsible for this?” Perron wonders. “These are the kinds of mysteries that this model will help us solve.”

This work was supported, in part, by NASA and the National Science Foundation.



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Geothermal energy turns red hot

Drill deep and drill differently. That’s what’s needed to exploit the nearly bottomless promise of geothermal energy in the United States and around the globe, according to participants at the 2026 Spring Symposium, titled “Next-generation geothermal energy for firm power.” 

Sponsored by the MIT Energy Initiative (MITEI), the March 4 event drew 120 people, including MIT faculty and students, investors, and representatives from startups, multinational energy companies, and zero-carbon advocacy groups.

“The time feels right to pull together good policy, great corporate partners, and the research and technological innovations … to make significant advances in the widespread utilization of this incredible resource,” said Karen Knutson, the vice president for government affairs at MIT, in welcoming attendees.

Technology from the oil and gas industry helped usher in a first wave of geothermal energy. But chewing vertical holes through rocks in traditional ways can’t deliver on the full potential of this resource. And the real treasure — geologic formations radiating heat at 374 degrees Celsius and above — lies kilometers beneath Earth’s surface, far beyond the reach of most conventional drilling rigs.

Panelists explored the many innovations in accessing and circulating subsurface heat, as well as digging to unprecedented depths through extremely challenging geological conditions, discussing advanced drilling technologies, materials, and subsurface imaging.

This work is needed urgently, as demand for firm (always-on) power skyrockets in response to the electrification of industry and rise of data centers, said Pablo Dueñas‑Martínez, a MITEI research scientist. “We cannot get through this only with solar and wind; we need dense, deployable energy like geothermal.”

From “minuscule” to “almost inexhaustible” energy

In her opening remarks, Carolyn Ruppel, MITEI’s deputy director of science and technology, noted that despite decades of successful projects in places like the United States, Kenya, Iceland, Indonesia, and Turkey, geothermal still contributes only a “minuscule” share of global electricity. “The tremendous heat beneath our feet remains largely untouched,” she said.

Citing MIT’s milestone 2006 study “The Future of Geothermal Energy,” keynote speaker John McLennan, a professor at the University of Utah and co–principal investigator of the U.S. Department of Energy’s Utah FORGE enhanced geothermal systems (EGS) field laboratory, reminded attendees that the continental crust holds enough accessible heat to supply power for generations. “For practical purposes, it’s almost inexhaustible,” he said.

The question now, he said, is how to access that resource economically and responsibly.

At the Utah FORGE test site, McLennan has been part of a team investigating one method — adapting the oil and gas industry’s drilling and reservoir engineering expertise for hot, relatively impermeable rocks.

The project has drilled multiple deep wells into crystalline granitic rock, including a pair of wells that have been hydraulically stimulated and connected. In a recent circulation test, cold water was pumped down one well, flowed through fractures, and returned hot through the other.

“On a commercial basis … this hot water would be converted to electricity at the surface,” McLennan said. “This has now been demonstrated at Utah FORGE.”

The basic physics, in other words, work. The harder problems now are cost, repeatability, and scale.

Geothermal on the grid

Several panels highlighted the fact that next-generation geothermal is already beginning to deliver firm power.

At Lightning Dock, New Mexico, geothermal company Zanskar used a probabilistic modeling framework that simulated thousands of possible subsurface configurations to identify where to drill a new production well at an underperforming geothermal field. By thermal power delivered, the resulting well is now “the most-productive pumped geothermal well in the country,” said Joel Edwards, Zanskar’s co-founder and chief technology officer — powering the entire 15 megawatt (MW) Lightning Dock plant from a single well.

This data-driven approach enables the company to find and develop new resources faster and more cheaply than traditional methods, said Edwards.

José Bona, the director of next-generation geothermal at Turboden, explained how his company’s technology uses specialized turbines to circulate organic fluids that conserve heat better than water, and then convert that heat efficiently into electrical power. This closed-cycle technology can utilize low- to medium-temperature heat sources. Turboden is supplying its technology both to the Lightning Dock geothermal facility in New Mexcio and to Fervo Energy’s Cape Station in southwest Utah, an EGS project that will begin delivering 100 MW of baseload, clean electricity to the grid this year, aiming for 500 MW by 2028.

In Geretsried, Germany, Eavor has developed its own proprietary closed-loop system by creating a kind of underground radiator.

“We drilled to about 4.5 kilometers vertical depth, completed six horizontal multilateral pairs, and we delivered the first power to the grid in December,” said Christian Besoiu, the team lead of technology development at Eavor. The project will ultimately be capable of supplying 8.2 MW of electricity to the 32,000 households in the Bavarian town of Geretsried and 64 MW of thermal energy to the district in which the town lies, prioritizing heat when needed.

Beyond oil and gas technology

Early geothermal exploration typically targeted preexisting faults using vertical wells left by oil and gas drilling. Today, companies are experimenting with rock fracturing at multiple subsurface levels and creating heat reservoirs in previously untenable formations by using propping materials.

“Instead of vertical wells, we’re going to horizontal wells, we’re going to cased wells, we’re introducing proppants [solid materials that hold open hydraulically fractured rock] … we do dozens of stages with these designs,” said Koenraad Beckers, the geothermal engineering lead at ResFrac. This shale-style approach has already yielded much higher flow rates and more-reliable performance than earlier EGS.

Some current geothermal wells manage to achieve depths close to 15,000 feet using the oil and gas industry’s polycrystalline diamond compact drill bits, which can bore through hard rock like granite at more than 100 feet per hour. But these bits and the rigs that drive them are no match for conditions six or more kilometers down — and it is at those depths that the heat on hand begins to make an overwhelming economic case for geothermal.

“If we go to around 300 to 350 degrees, your power potential increases 10 times,” said Lev Ring, CEO of Sage Geosystems. “At that point, with reasonable CAPEX [capital expenditure] assumptions, levelized cost of electricity [a metric for comparing the cost of electricity across different generation technologies] is around 4 cents, and geothermal becomes cheaper than any other alternative.”

But “at 10 kilometers down … the largest land rigs in existence today cannot handle it,” Ring added. “We need alternatives — new materials, new ways to handle pressure, maybe even welding on the rig … a whole space that has not been addressed yet.”

One panel, featuring Quaise Energy, an MIT spinout with MITEI roots, spotlighted just how radically drilling might change. Co-founder Matt Houde described the company’s millimeter-wave drilling approach, which uses high-frequency electromagnetic waves derived from fusion research to vaporize rock instead of grinding it, as with conventional drilling. In a recent Texas field test, the team drilled 100 meters of hard basement rock in about a month, and is now planning kilometer-scale trials aimed at reaching superhot rock temperatures around 400 C, where each well could deliver many times the power of today’s geothermal projects.

Innovations for deep drilling

Moderating a panel on “MIT innovations for next-generation geothermal,” Andrew Inglis, the venture builder in residence with MIT Proto Ventures, whose position is sponsored by the U.S. Department of Energy GEODE program, framed the Institute’s role in getting such hard-tech ideas out of the lab and into the field. “The way MIT thinks about tech development, uniquely from other universities, can play a very singular role in geothermal commercial liftoff,” he said.

Materials researchers on that panel illustrated the point. Matěj Peč, an associate professor of geophysics in the Department of Earth, Atmospheric and Planetary Sciences, outlined work to build sensors that survive up to 900 C so that rock deformation and fracturing can be studied at supercritical conditions. Michael Short, the Class of 1941 Professor in the Department of Nuclear Science and Engineering, and C. Cem Tasan, the POSCO Associate Professor of Metallurgy in the Department of Materials Science and Engineering, respectively described coatings and alloys designed to resist corrosion, fouling, and cracking in extreme environments. In response to audience questions after their talks, Tasan made an important point, highlighting how academics need input from industry to understand the real-world problems (e.g., corrosion of pipes by geofluids) that require engineering solutions.

Other researchers are rethinking how to detect geothermal resources: Wanju Yuan, a research scientist with the Geological Survey of Canada at Natural Resources Canada, is using satellite imagery and thermal infrared sensing to screen vast regions for subtle hot spots and structures, processing thousands of images to identify promising sites in just a few months of work. “It’s a very efficient way to screen potential areas before more expensive exploration, thus reducing exploration and drilling risks,” he said.

Policy as backdrop, not center stage

Policy loomed in the background of many discussions — from bipartisan support for geothermal exploration and tax incentives to issues of regulation and permitting.

For Ruppel, that was by design.

“We wanted this meeting to showcase what’s technically possible and what’s already happening on the ground,” she said. “The policy world is starting to pay attention. Our job is to make sure that when that spotlight turns our way, next-generation geothermal is ready.”

MITEI’s Spring Symposium was followed by a gathering of geothermal entrepreneurs, investors, and energy industry experts co-hosted by MITEI and the Clean Air Task Force. “GeoTech Summit: Accelerating geothermal technology, projects, and deal flow” explored the financing challenges and opportunities of geothermal energy today.



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MIT faculty, alumni receive 2025-26 American Physical Society honors

The American Physical Society (APS) recently honored two MIT faculty members — professors Yoel Fink PhD ’00 and Mehran Kardar PhD ’83 — as well as six alumni with prizes and awards for their contributions to physics and academic leadership.

In addition, several MIT faculty members — Professor Jorn Dunkel, Professor Yen-Jie Lee PhD ’11, Associate Professor Mingda Li PhD ’15, and Associate Professor Julien Tailleur — as well as 12 additional alumni were named APS Fellows.

Yoel Fink PhD ’00, the Danae and Vasilis (1961) Salapatas Professor in the Department of Materials Science and Engineering, received the Andrei Sakharov Prize “for defending the academic freedom and human rights of scientists working in the U.S.”

The prize, named for physicist and human rights advocate Andrei Sakharov, recognizes scientists whose leadership and impact advance the principles of intellectual freedom and human dignity. Fink’s research focuses on “computing fabrics” — fibers and textiles that sense, communicate, store, and process information. By embedding functionality at the fiber level, fabrics become computing systems that can infer human activity and context while keeping the traditional qualities of garments. These textiles enable noninvasive monitoring of physiological and health conditions, with applications ranging from fetal and maternal health to human performance analytics, injury prevention in challenging environments, and defense.

Mehran Kardar PhD ’83, the Francis Friedman Professor of Physics, received the Lars Onsager Prize “for ground-breaking contributions to statistical physics, including the Kardar-Parisi-Zhang equation, Casimir forces, active matter, and aspects of biological physics.”

Kardar’s research focuses on how complex behavior emerges from simple interactions in systems both in and far from equilibrium, including stable ones like a still pond and rapidly changing ones such as growing surfaces. The Kardar-Parisi-Zhang equation, which he helped develop, provides a unifying framework for understanding how randomness and fluctuations shape evolving phenomena, from fluids and interfaces to biological and quantum systems. His work has also advanced the theoretical understanding of disordered materials, soft matter such as polymers and gels, and fluctuation-induced forces — including Casimir forces arising from quantum and thermal effects. More recently, he has applied these ideas to active matter — systems of self-driven units — and biological systems, helping reveal patterns in living and evolving systems.

Alumni receiving awards

Joel Butler PhD ’75 was presented the W.K.H. Panofsky Prize in Experimental Particle Physics “for wide-ranging scientific, technical, and strategic contributions to particle physics, particularly exceptional leadership in fixed-target quark flavor experiments at Fermilab and collider physics at the Large Hadron Collider.”

Anthony Duncan PhD ’75 is the recipient of the Abraham Pais Prize for History of Physics “for research on the history of quantum physics between 1900 and 1927 that culminated in 'Constructing Quantum Mechanics,' an exemplary work that uses primary sources masterfully and employs scaffold and arch metaphors to describe developments in the quantum revolution.”

Laura A. Lopez ’04 was presented the Edward A. Bouchet Award “for pioneering contributions to X-ray astronomy, including foundational studies of supernova remnants, compact objects, and stellar feedback in galaxies, and for transformative leadership in advancing equity and inclusion in physics through innovative mentorship programs, national advocacy, and unwavering support for students from historically marginalized communities.”

Zhiquan Sun PhD ’25 is the recipient of the J.J. and Noriko Sakurai Dissertation Award in Theoretical Particle Physics “for applying effective field theory to advance our understanding of QCD [quantum chromodynamics], including establishing a new formalism to study heavy quark fragmentation, determining how confinement affects energy correlators, and revealing an overlooked complexity of the axion solution to the strong CP [charge conjugation symmetry and parity symmetry] problem.”

Charles B. Thorn III ’68 received the Dannie Heineman Prize for Mathematical Physics for “fundamental contributions to elementary particle physics, primarily the theory of strong interactions and the development of string theory.”

Christina Wang ’19 received the Mitsuyoshi Tanaka Dissertation Award in Experimental Particle Physics “for pioneering a novel technique using CMS [Compact Muon Solenoid] muon chambers to search for weakly-coupled sub-GeV [giga-electronvolt] mass dark matter using long-lived particle searches, and for groundbreaking work in quantum sensing to enable new probes of dark matter.”

APS Fellows

Several MIT faculty were elected 2025 APS Fellows:

Jorn Dunkel, MathWorks Professor of Mathematics, is the recipient of the Division of Statistical and Nonlinear Physics Fellowship “for pioneering contributions to statistical, nonlinear, and biological physics, notably in understanding pattern formation in soft matter and biology, cell positioning in tissues, and turbulence in active media.”

Yen-Jie Lee PhD '11, professor of physics, received the Division of Nuclear Physics Fellowship “for pioneering measurements of jet quenching, medium response and heavy-quark diffusion in the quark-gluon plasma, and for using electron-positron collisions as an innovative control to understand collectivity in small collision systems.”

Mingda Li PhD '15, associate professor of nuclear science and engineering, is the recipient of the Topical Group on Data Science Fellowship “for pioneering the integration of artificial intelligence with scattering and spectroscopy, enabling breakthroughs in phonons, topological states, optical and time-resolved spectra, and data-driven discovery for quantum and energy applications.”

Julien Tailleur, associate professor of physics, is the recipient of the Division of Soft Matter Fellowship “for foundational theoretical work on motility-induced phase separation and emergent collective behavior in scalar active matter.”

The following additional MIT alumni were also honored as APS Fellows:

Andrew Cross SM ’05, PhD ’08 (EECS), Division of Quantum Information Fellowship 

Kevin D. Dorfman SM '01, PhD '02 (ChemE), Division of Polymer Physics Fellowship

Geoffroy Hautier PhD '11 (DMSE), Division of Computational Physics Fellowship

Douglas J. Jerolmack PhD '06 (EAPS), Division of Statistical and Nonlinear Physics Fellowship

Brian Lantz '92, PhD '99 (Physics), Division of Gravitational Physics Fellowship

Valerio Lucarini SM '03 (EAPS), Topical Group on Physics of Climate Fellowship

Giles Novak '81 (Physics), Division of Astrophysics Fellowship

Steve Presse PhD '08 (Physics), Division of Biological Physics Fellowship

Jonathan Rothstein PhD '01 (MechE), Division of Fluid Dynamics Fellowship

Gray Rybka PhD '07 (Physics), Division of Particles and Fields Fellowship

Sarah Sheldon '08, PhD '13 (Physics, NSE), Forum on Industrial and Applied Physics Fellowship

Lian Shen ScD '01 (MechE), Division of Fluid Dynamics Fellowship



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