Chances are, you’ve already used a satellite today. Satellites make it possible for us to stream our favorite shows, call and text a friend, check weather and navigation apps, and make an online purchase. Satellites also monitor the Earth’s climate, the extent of agricultural crops, wildlife habitats, and impacts from natural disasters.

As we’ve found more uses for them, satellites have exploded in number. Today, there are more than 10,000 satellites operating in low-Earth orbit. Another 5,000 decommissioned satellites drift through this region, along with over 100 million pieces of debris comprising everything from spent rocket stages to flecks of spacecraft paint.

For MIT’s Richard Linares, the rapid ballooning of satellites raises pressing questions: How can we safely manage traffic and growing congestion in space? And at what point will we reach orbital capacity, where adding more satellites is not sustainable, and may in fact compromise spacecraft and the services that we rely on?

“It is a judgement that society has to make, of what value do we derive from launching more satellites,” says Linares, who recently received tenure as an associate professor in MIT’s Department of Aeronautics and Astronautics (AeroAstro). “One of the things we try to do is approach these questions of traffic management and orbital capacity as engineering problems.”

Linares leads the MIT Astrodynamics, Space Robotics, and Controls Lab (ARCLab), a research group that applies astrodynamics (the motion and trajectory of orbiting objects) to help track and manage the millions of objects in orbit around the Earth. The group also develops tools to predict how space traffic and debris will change as operators launch large satellite “mega-constellations” into space.

He is also exploring the effects of space weather on satellites, as well as how climate change on Earth may limit the number of satellites that can safely orbit in space. And, anticipating that satellites will have to be smarter and faster to navigate a more cluttered environment, Linares is looking into artificial intelligence to help satellites autonomously learn and reason to adapt to changing conditions and fix issues onboard.

“Our research is pretty diverse,” Linares says. “But overall, we want to enable all these economic opportunities that satellites give us. And we are figuring out engineering solutions to make that possible.”

Grounding practical problems

Linares was born and raised in Yonkers, New York. His parents both worked as school bus drivers to support their children, Linares being the youngest of six. He was an active kid and loved sports, playing football throughout high school.

“Sports was a way to stay focused and organized, and to develop a work ethic,” Linares says. “It taught me to work hard.”

When applying for colleges, rather than aim for Division I schools like some of his teammates, Linares looked for programs that were strong in science, specifically in aerospace. Growing up, he was fascinated with Carl Sagan’s “Cosmos” docuseries. And being close to Manhattan, he took regular trips to the Hayden Planetarium to take in the center’s immersive projections of space and the technologies used to explore it.

“My interest in science came from the universe and trying to understand our place within it,” Linares recalls.

Choosing to stay close to home, he applied to in-state schools with strong aeronautical engineering departments, and happily landed at the State University of New York at Buffalo (SUNY Buffalo), where he would ultimately earn his bachelor’s, master’s, and doctoral degrees, all in aerospace engineering.

As an undergraduate, Linares took on a research project in astrodynamics, looking to solve the problem of how to determine the relative orientation of satellites flying in formation.

“Formation flying was a big topic in the early 2000s,” Linares says. “I liked the flavor of the math involved, which allowed me to go a layer deeper toward a solution.”

He worked out the math to show that when three satellites fly together, they essentially form a triangle, the angles of which can be calculated to determine where each satellite is in relation to the other two at any moment in time. His work introduced a new controls approach to enable satellites to fly safely together. The research had direct applications for the U.S. Air Force, which helped to sponsor the work.

As he expanded the research into a master’s thesis, Linares also took opportunities to work directly with the Air Force on issues of satellite tracking and orientation. He served two internships with the U.S. Air Force Research Lab, one at Kirtland Air Force Base in Albuquerque, New Mexico, and the other in Maui, Hawaii.

“Being able to collaborate with the Air Force back then kind of grounded the research in practical problems,” Linares says.

For his PhD, he turned to another practical problem of “uncorrelated tracks.” At the time, the Air Force operated a network of telescopes to observe more than 20,000 objects in space, which they were working to label and record in a catalog to help them track the objects over time. But while detecting objects was relatively straightforward, the challenge came in correlating a detected object with what was already in the catalog. In other words, is what they were seeing something they had already seen?

Linares developed image analysis techniques to identify key characteristics of objects such as their shape and orientation, which helped the Air Force “fingerprint” satellites and pieces of space debris, and track their activity — and potential for collisions — over time.

After completing his PhD, Linares worked as a postdoc at Los Alamos National Laboratory and the U.S. Naval Observatory. During that time he expanded his aerospace work to other areas including space weather, using satellite measurements to model how Earth’s ionosphere — the upper layer of the atmosphere that is ionized by the sun’s radiation — affects satellite drag.

He then accepted a position as assistant professor of aerospace engineering at the University of Minnesota at Minneapolis. For the next three years, he continued his research in modeling space weather, tracking space objects and coordinating satellites to fly in swarms.

Making space

In 2018, Linares made the move to MIT.

“I had a lot of respect for the people and for the history of the work that was done here,” says Linares, who was especially inspired by the legendary Charles Stark “Doc” Draper, who developed the first inertial guidance systems in the 1940s that would enable the self-navigation of airplanes, submarines, satellites, and spacecraft for decades to come. “This was essentially my field, and I knew MIT was the best place to continue my career.”

As a junior faculty member in AeroAstro, Linares spent his first years focused on an emerging challenge: space sustainability. Around that time, the first satellite constellations were launching into low-Earth orbit with SpaceX’s Starlink, which aimed to provide global internet coverage via a huge network of several thousand coordinating satellites. The launching of so many satellites, into orbits that already held other active and nonactive satellites, along with millions of pieces of space debris, raised questions about how to safely manage the satellite traffic and how much traffic an orbit can sustain.

“At what level do we reach a tipping point, where we have too many satellites in certain orbital regimes?” Linares says. “It was kind of a known problem at the time, but there weren’t many solutions.”

Linares’ group applied an understanding of astrodynamics, and the physics of how objects move in space, to figure out the best way to pack satellites in orbital “shells,” or lanes that would most likely prevent collisions. They also developed a state-of-the-art model of orbital traffic, that was able to simulate the trajectories of more than 10 million individual objects in space. Previous models were much more limited in the number of objects they could accurately simulate. Linares’ open-source model, called the MIT Orbital Capacity Assessment Tool, or MOCAT, could account for the millions of pieces of space debris, in addition to the many intact satellites in orbit.

The tools that his group has developed are used today by satellite operators to plan and predict safe spacecraft trajectories. His team is continuing to work on problems of space traffic management and orbital capacity. They are also branching out into space robotics. The team is testing ways to teleoperate a humanoid robot, which could potentially help to build future infrastructure and carry out long-duration tasks in space.

Linares is also exploring artificial intelligence, including ways that a satellite can autonomously “learn” from its experience and safely adapt to uncertain environments.

“Imagine if each satellite had a virtual Doc Draper onboard that could do the de-bugging that we did from the ground during the Apollo missions,” Linares says. “That way, satellites would become instantaneously more robust. And it’s not taking the human out of the equation. It’s allowing the human to be amplified. I think that’s within reach.”

Pretty fantastic video from Japan of a giant squid eating another squid.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Blog moderation policy.

In a dramatic middle-of-the-night stand off, a bipartisan set of lawmakers pushing for true reform and privacy protections for Americans bought us some more time to fight! They are holding out for, at a minimum, the requirement of an actual probable cause warrant for FBI access to information collected under the mass spying program known as 702.

A reauthorization with virtually no changes was defeated because a core group of lawmakers held strong; they know that people are hungry for real reform that protects the privacy of our communications. We now have a 10-day extension to continue to push Congress to pass a real reform bill. 

The Lawmakers rallied late Thursday night to reject a proposed amendment that made gestures at privacy protections, but it would not have improved on the status quo and would have reauthorized Section 702 for five more years to boot. 

Take action

TELL congress: 702 Needs Reform

Section 702 is rife with problems, loopholes, and compliance issues that need fixing. The National Security Agency collects full conversations being conducted by and with targets overseas – including by and with Americans in the U.S. –  and stores them in massive databases. The NSA then allows other agencies, including the Federal Bureau of Investigation, to access untold amounts of that information. In turn, the FBI takes a “finders keepers” approach to this data: they reason that since it's already collected under one law, it’s OK for them to see it. 

Under current practice, the FBI can query and even read the U.S. side of that communication without a warrant. What’s more, victims of this surveillance  won’t even know and have very few ways of finding out that their communications have been surveilled. EFF and other civil liberties advocates have been trying for years to know when data collected through Section 702 is used as evidence against them.  

Reforming Section 702 is even more urgent because of revelations hinted at by Senator Ron Wyden’s public statements concerning a “secret interpretation” of the law that enables surveillance of Americans, and a public  “Dear Colleague” letter he sent to fellow Senators about FBI abuse of Section 702. 

That’s right—the way the government conducts mass surveillance is so secret and unaccountable even the way they interpret the law is classified. 

 “In many cases these will be law-abiding Americans having perfectly legitimate, often sensitive, conversations,” Wyden wrote. “These Americans could include journalists, foreign aid workers, people with family members overseas - even women trying to get abortion medication from an overseas provider. Congress has an obligation to protect our country from foreign threats and protect the rights of these and other Americans.” 

We have 10 days to make it clear to Congress: 702 needs real reforms. Not a blanket  reauthorization. Not lip service to change. Real reform.

Take action

TELL congress: 702 Needs Reform

MIT Professor Michael T. Laub as well as 21 MIT alumni have been elected as fellows of the American Association for the Advancement of Science (AAAS).

The 2025 class of AAAS Fellows includes 449 scientists, engineers, and innovators, spanning all 24 of AAAS disciplinary sections, who are recognized for their scientific achievements.

Laub, the Salvador E. Luria Professor in the MIT Department of Biology and an HHMI Investigator, studies the biological mechanisms and evolution of how cells process information to regulate their own growth and proliferation, using bacteria as a model organism to develop a deeper, fundamental understanding of how bacteria function and evolve. Laub was honored as a AAAS Fellow for distinguished contributions to the field of bacterial information processing, particularly to the understanding of coevolution of host-pathogen response and immunity.

“This year’s AAAS Fellows have demonstrated research excellence, made notable contributions to advance science, and delivered important services to their communities,” said Sudip S. Parikh, AAAS chief executive officer and executive publisher of the Science family of journals. “These fellows and their accomplishments validate the importance of investing in science and technology for the benefit of all.”

The following alumni were also named fellows of the AAAS:

• Debra Auguste ’99
• Julie Claycomb PhD ’04
• Chris Clifton ’85, SM ’86
• Kevin Crowston PhD ’91
• Maitreya Dunham ’99
• David Fike PhD ’07
• Jianping Fu PhD ’07
• Peter A. Gilman SM ’64, PhD ’66
• Diane M. Harper ’80, SM ’82
• Cherie R. Kagan PhD ’96
• Elizabeth A. Kensinger PhD ’03
• Kenro Kusumi PhD ’97
• Charla Lambert ’96
• Bennett A. Landman ’01, MNG ’02
• Michael E. Matheny SM ’06
• Paul David Ronney ScD ’83
• Steven Semken ’80, PhD ’89
• Sudipta Sengupta SM ’99, PhD ’06
• Lawrence R. Sita PhD ’86
• Jan M. Skotheim ’99
• Beverly Park Woolf ’66

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.”

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.”

Last week, Anthropic pulled back the curtain on Claude Mythos Preview, an AI model so capable at finding and exploiting software vulnerabilities that the company decided it was too dangerous to release to the public. Instead, access has been restricted to roughly 50 organizations—Microsoft, Apple, Amazon Web Services, CrowdStrike and other vendors of critical infrastructure—under an initiative called Project Glasswing.

The announcement was accompanied by a barrage of hair-raising anecdotes: thousands of vulnerabilities uncovered across every major...

Nations split over doubling down on fossil fuels or accelerating renewables.

Bipartisan legislation would bring summer cooling standards closer to the state's existing rules for winter heating.

In a letter, CenterPoint Energy Indiana labeled the plant "unreliable" and said it would need "substantial investment."

The Democratic senator is asking EPA to take enforcement actions against Elon Musk’s company.

The measure would let the state and insurers sue fossil fuel companies for damages tied to climate-driven disasters.

Federal courts have now rebuffed both administration attempts to stop states from suing Big Oil to pay for the effects of climate change.

Steel is one of Ukraine’s top sources of export revenue but is being hurt by carbon tariffs imposed by Europe, manufacturers claim.

Political tension could further delay legislation, which is aimed at securing the country's electricity supply with gas-fired plants when output from wind and solar sources run low.

In a “reasonable worst-case scenario," officials don’t foresee aisles of empty shelves but expect a lack of product variety if the Strait of Hormuz has not reopened by June.

The Midwest, which in recent days has been hit by storms, large hail and tornadoes, may face a more significant storm outbreak Friday.

Nature Climate Change, Published online: 17 April 2026; doi:10.1038/s41558-026-02618-9

The sea–land breeze acts to counter urban heat in many coastal cities. Here the authors simulate how this circulation changes with warming ocean water, showing that it decreases in most of them, adding heat stress to urban areas.

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.”

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.