Transistors, the building blocks of modern electronics, are typically made of silicon. Because it’s a semiconductor, this material can control the flow of electricity in a circuit. But silicon has fundamental physical limits that restrict how compact and energy-efficient a transistor can be.

MIT researchers have now replaced silicon with a magnetic semiconductor, creating a magnetic transistor that could enable smaller, faster, and more energy-efficient circuits. The material’s magnetism strongly influences its electronic behavior, leading to more efficient control of the flow of electricity. 

The team used a novel magnetic material and an optimization process that reduces the material’s defects, which boosts the transistor’s performance.

The material’s unique magnetic properties also allow for transistors with built-in memory, which would simplify circuit design and unlock new applications for high-performance electronics.

“People have known about magnets for thousands of years, but there are very limited ways to incorporate magnetism into electronics. We have shown a new way to efficiently utilize magnetism that opens up a lot of possibilities for future applications and research,” says Chung-Tao Chou, an MIT graduate student in the departments of Electrical Engineering and Computer Science (EECS) and Physics, and co-lead author of a paper on this advance.

Chou is joined on the paper by co-lead author Eugene Park, a graduate student in the Department of Materials Science and Engineering (DMSE); Julian Klein, a DMSE research scientist; Josep Ingla-Aynes, a postdoc in the MIT Plasma Science and Fusion Center; Jagadeesh S. Moodera, a senior research scientist in the Department of Physics; and senior authors Frances Ross, TDK Professor in DMSE; and Luqiao Liu, an associate professor in EECS, and a member of the Research Laboratory of Electronics; as well as others at the University of Chemistry and Technology in Prague. The paper appears today in Physical Review Letters.

Overcoming the limits

In an electronic device, silicon semiconductor transistors act like tiny light switches that turn a circuit on and off, or amplify weak signals in a communication system. They do this using a small input voltage.

But a fundamental physical limit of silicon semiconductors prevents a transistor from operating below a certain voltage, which hinders its energy efficiency.

To make more efficient electronics, researchers have spent decades working toward magnetic transistors that utilize electron spin to control the flow of electricity. Electron spin is a fundamental property that enables electrons to behave like tiny magnets.

So far, scientists have mostly been limited to using certain magnetic materials. These lack the favorable electronic properties of semiconductors, constraining device performance.

“In this work, we combine magnetism and semiconductor physics to realize useful spintronic devices,” Liu says.

The researchers replace the silicon in the surface layer of a transistor with chromium sulfur bromide, a two-dimensional material that acts as a magnetic semiconductor.

Due to the material’s structure, researchers can switch between two magnetic states very cleanly. This makes it ideal for use in a transistor that smoothly switches between “on” and “off.”

“One of the biggest challenges we faced was finding the right material. We tried many other materials that didn’t work,” Chou says.

They discovered that changing these magnetic states modifies the material’s electronic properties, enabling low-energy operation. And unlike many other 2D materials, chromium sulfur bromide remains stable in air.

To make a transistor, the researchers pattern electrodes onto a silicon substrate, then carefully align and transfer the 2D material on top. They use tape to pick up a tiny piece of material, only a few tens of nanometers thick, and place it onto the substrate.

“A lot of researchers will use solvents or glue to do the transfer, but transistors require a very clean surface. We eliminate all those risks by simplifying this step,” Chou says.

Leveraging magnetism

This lack of contamination enables their device to outperform existing magnetic transistors. Most others can only create a weak magnetic effect, changing the flow of current by a few percent or less. Their new transistor can switch or amplify the electric current by a factor of 10.

They use an external magnetic field to change the magnetic state of the material, switching the transistor using significantly less energy than would usually be required.

The material also allows them to control the magnetic states with electric current. This is important because engineers cannot apply magnetic fields to individual transistors in an electronic device. They need to control each one electrically.

The material’s magnetic properties could also enable transistors with built-in memory, simplifying the design of logic or memory circuits.

A typical memory device has a magnetic cell to store information and a transistor to read it out. Their method can combine both into one magnetic transistor.

“Now, not only are transistors turning on and off, they are also remembering information. And because we can switch the transistor with greater magnitude, the signal is much stronger so we can read out the information faster, and in a much more reliable way,” Liu says.

Building on this demonstration, the researchers plan to further study the use of electrical current to control the device. They are also working to make their method scalable so they can fabricate arrays of transistors.

This research was supported, in part, by the Semiconductor Research Corporation, the U.S. Defense Advanced Research Projects Agency (DARPA), the U.S. National Science Foundation (NSF), the U.S. Department of Energy, the U.S. Army Research Office, and the Czech Ministry of Education, Youth, and Sports. The work was partially carried out at the MIT.nano facilities.

Nature Climate Change, Published online: 10 November 2026; doi:10.1038/s41558-026-02821-8

This work projects a decline in global emissions in 2026 as a result of clean energy and in response to conflicts. Energy demand has increased, and will continue to, with electric vehicles and solar and wind deployment mitigating emissions; accelerated deployment is needed to reduce emissions.

Today, along with the ACLU, EFF filed an amicus brief in the Sixth Circuit court of appeals in United States v. Sturdivant, a case where police warrantlessly tracked a suspect’s movements using Automated License Plate Reader (ALPR) data. Our brief details the vast, interconnected network of ALPRs that constantly track drivers around the country and argues that this tracking is a “search” under the meaning of the Fourth Amendment.

Sturdivant involves a series of robberies in and around Cleveland, Ohio. Using surveillance camera footage, police identified the make, model and color of the robber’s car, but they did not know its license plate number. After running an open-ended search of two ALPR databases, Flock and ELSAG, officers learned a car belonging to the defendant had been tracked near sites of the robberies, eventually leading to his arrest. Faced with a motion to suppress the ALPR evidence, the district court held that Mr. Sturdivant had no reasonable expectation of privacy in his license plate number because it could be observed by members of the public, and that therefore he had no Fourth Amendment interest in not having his car tracked in public places.

But ALPRs are simply not comparable to observing an individual license plate on a passing car. As we’ve explained in many amicus briefs and in our lawsuits directly challenging ALPR use in San Jose, California and Wisconsin, ALPRs give law enforcement “a virtual time machine to view suspects’, or anyone else’s, past movements (and much more) with just the click of a button.” Just last month, a federal district court agreed and held that ALPRs are a “form of indiscriminate mass surveillance” that can violate the Fourth Amendment.

We hope the Sixth Circuit will reverse the lower court in this case and hold that ALPR searches require a warrant.

Related Cases: Wisconsin Council of Churches v. Milwaukee, Racine, Kenosha & Green BaySIREN and CAIR-CA v. San Jose

The number of petitions being filed at the Patent Trial and Appeal Board (PTAB) has collapsed. That’s not reason for celebration. The PTAB is an important line of defense against low-quality software patents being used in lawsuits against people and companies.

The drop didn’t happen because suddenly patents have become higher quality. It’s simply because USPTO leadership is increasingly refusing to consider good challenges to patents, for reasons unrelated to the merits. The process has become so unreliable that many would-be patent challengers aren’t even trying.

Inter partes review (IPR) is a process that allows people to challenge patents at the Patent Office, rather than having to fight an expensive lawsuit in federal court. It’s one of the most important ways to get bad patents out of the system.

In the third quarter of 2026, July through September, only 97 IPR petitions were filed at the PTAB, compared to 327 petitions during the same period in 2025. Similarly, only 37 IPR petitions were filed in September 2026, compared to 129 petitions in December 2024—the last full month before the current USPTO leadership began reshaping how the Board handles patent challenges.

This dramatic decline shouldn’t just concern patent lawyers. EFF has fought bad patents because so many software and internet patents make claims to the basic tools of free expression online, like sending email, using online ads, or publishing web pages. When bad patents go unchallenged, they affect everyone who cares about the freedom to write new code and make new technology.

That’s why EFF has defended IPR since it came on the scene more than a decade ago. It provides a faster and less expensive way to challenge patents that should never have been granted. Congress created IPRs because the Patent Office makes mistakes. District court litigation is an extraordinarily expensive way to correct them. Without a meaningful administrative review process, bad patents remain powerful weapons in the hands of patent trolls and others seeking to extract settlements from companies that often did nothing wrong.

Now, USPTO leadership is dismantling that safeguard.

Petitions and Institution Rates Are Falling

Since Congress created the IPR patent challenge process in 2012, the PTAB has always had some discretion in deciding which petitions deserve review. But institution rates today are much lower than their typical historical range.

Between fiscal years 2022 and 2024, the PTAB instituted between 66% and 68% of IPR petitions. In fiscal year 2026, which concluded in September 2026, the institution rate fell to 41%. And institution is only the first step of an IPR—it means the PTAB has agreed to hear the challenge, not that the challenger won, and the patent will be invalidated.

That decline helps explain why fewer petitions are being filed in the first place. Preparing an IPR petition takes substantial time and money. While cheaper than federal court, it’s still a complicated administrative process. Before filing, a challenger typically needs to search for prior art, hire experts, and develop detailed technical and legal arguments about why the patent never should have been granted.

Companies were willing to pay for IPRs because, historically, they could expect a predictable process. Petitions that met the statutory standard generally had a reasonable prospect of being heard and decided on the merits. But the USPTO has made it harder to get petitions heard and is throwing them out for reasons that have nothing to do with the merits of the challenges. Faced with these odds, many potential challengers aren’t filing at all.

Even After a Challenge Is Accepted, The USPTO Director Can Cancel It

When the PTAB decides to hear an IPR case, that means the petition has been “instituted”—but that has never meant that the petitioner will definitely win. It means the PTAB has determined the challenge warrants a full merits review, and the parties could expect the proceeding to continue toward a final written decision. But under the current USPTO leadership, that expectation has disappeared. The PTAB’s institution decisions are being repeatedly vacated by the Director.

USPTO Director John Squires has vacated at least 29 decisions granting institution of IPR proceedings, and subsequently denied the petitions, terminating those proceedings before a final written decision. These include cases that PTAB judges had already agreed to hear, as well as proceedings that Squires himself had previously allowed to proceed. This level of retroactive intervention is unprecedented.

In some cases, the parties had already invested substantial time and money litigating before the PTAB, only to have the proceeding terminated based on new reasoning by the Director’s office that arose after institution.

For example, in Revvo Technologies v. Cerebrum Sensor Technologies, the Director vacated an institution decision and remanded the case to the Board. The Board then instituted review again, but the Director reversed that decision. Using a new approach, the Director said that the petition had inconsistencies between claim construction positions taken before the PTAB and in district court. Since then, that decision has been cited as a basis for terminating several other instituted proceedings.

The result is another layer of uncertainty for people who want to challenge bad patents. Getting through the front door of an institution decision is much harder than it used to be—and now, even getting through that door doesn’t mean the PTAB will decide your challenge. Predictability is one of the things that made IPRs work.

Congress Created the PTAB To Avoid Costly Litigation

Congress created IPRs in part because challenging a questionable patent in federal court is enormously expensive and risky. Patent examiners work under serious time constraints, and some patents that shouldn’t have been granted will inevitably issue. Not every patent dispute requires years of expensive district court litigation. Congress recognized that, and created an administrative process, staffed with experts more capable of correcting those mistakes.

That system has benefited the public. Since 2012, it has helped invalidate thousands of patent claims that never should have been issued. The IPR process has protected startups, software developers, nonprofits, and small businesses that otherwise would have faced crushing litigation costs simply for building products or offering services that use everyday internet technology.

EFF supports a balanced IPR system because it should be easier to fight against bogus patents that should never have been issued. The cost of fighting a patent can be a weapon in itself, and weak patents don't just burden large technology companies. Patent trolls routinely target small businesses and independent developers, and have gone after podcasters, retailers, and even coffee shops and nonprofits. These organizations have neither the resources nor the desire to spend years litigating over potentially invalid patents in federal court. IPR offers a chance to challenge those patents before litigation costs get out of control.

If we weaken the IPR system, bad patents become much harder and more expensive to challenge.

We Shouldn't Accept This as the New Normal

Some defenders of the current approach argue that lower institution rates reflect more balance in the patent system. But institution rates at the PTAB have plummeted from 68% to 41% in just two years—a nearly 40% decline. IPR petition filings have reached historic lows. And even proceedings that have already been instituted can now be terminated months later, without a decision on the merits.

These developments send a troubling message to members of the public who might want to use the PTAB: don't bother. The PTAB doesn’t need to institute every petition. But it does need to provide a process that is transparent, predictable, and fair. Right now, confidence in the system is disappearing.

If companies, individuals, and nonprofits conclude that IPRs aren’t worth pursuing, the consequences won't stop at the PTAB. More weak patents that never should have been granted will survive, and patent trolls will use them. That means more defendants facing expensive district court litigation or paying settlements driven by the cost of litigation.

That's not what Congress intended when it created IPRs. It should not become the PTAB's “new normal.”

For the past two decades, the U.S. Supreme Court has been slowly weakening the Alien Tort Statute (ATS), a key human rights law that allows foreign nationals to sue persons or entities in civil court for harms resulting from violations of international law. The court’s recent ruling in Cisco Systems, Inc. v. Doe I dealt the law a final blow, foreclosing—absent new legislation by Congress—any future ability by plaintiffs to use the law to hold technology companies accountable for aiding and abetting human rights abuses. EFF filed multiple amicus briefs in the case, including most recently in the Supreme Court.

The Supreme Court Rejected New Causes of Action Under the ATS

The ATS was passed by the very first Congress in 1789 and in just a few words gives U.S. federal courts jurisdiction to hear civil claims brought by foreign nationals against defendants who violated “the law of nations or a treaty of the United States." There has been a longstanding question about what counts as a valid civil claim under “the law of nations.”

In Sosa v. Alvarez-Machain (2004), the Supreme Court had noted three historical international law claims that the First Congress likely had in mind: violation of safe conducts, infringement of the rights of ambassadors, and piracy, collectively known as the Blackstone Three.

The Sosa court also held that federal courts may recognize other substantive causes of action under the ATS if they reflect international norms that are “specific, universal, and obligatory,” stating that this “door is still ajar subject to vigilant doorkeeping, and thus open to a narrow class of international norms today.”

Thus, the Sosa court recognized that the ATS could be used to bring claims related to historical violations of international law as well as select newer claims reflecting modern concepts of human rights.

In Cisco, the Supreme Court overruled the second Sosa holding. The majority held that the current case, a technology company aiding and abetting torture and other human rights abuses, was beyond the scope of the original three ATS claims and it was not for the court to create new ways of enforcing international law. The court said:

[W]e close the door that Sosa cracked to judicially created ATS liability. We do not disturb Sosa’s holding that the ATS is a jurisdictional statute; nor do we revisit its assumption that causes of action are available for torts corresponding to the Blackstone three. We conclude only that Sosa was overly optimistic in its prediction that there might be a narrow class of cases in which courts may create ATS actions without infringing on the prerogatives of the political branches. In truth, this class is a null set.

What result for this case? Cisco argues that the Ninth Circuit erred in holding that aiding-and-abetting liability exists under the ATS for the torts alleged by the plaintiffs. Cisco is correct. Courts cannot create new rights of action to remedy violations of international law, so there is necessarily no liability for aiding and abetting such violations. Plaintiffs’ ATS claims against Cisco must be dismissed.

Cisco is Off the Hook for Aiding and Abetting Horrific Human Rights Abuses

The Supreme Court’s ending of the plaintiffs’ case against Cisco is hugely disappointing given that the case has been going on since 2011 and the Ninth Circuit in 2023 allowed the case to move forward in an excellent and well-reasoned opinion. The court held, in part:

We conclude that Plaintiffs’ allegations, accepted as true, are sufficient to state a plausible claim that Cisco provided essential technical assistance to the douzheng [violent struggle] of Falun Gong with awareness that the international law violations of torture, arbitrary detention, disappearance, and extrajudicial killing were substantially likely to take place. 

The plaintiffs sued Cisco for the company’s role in building and deploying the “Golden Shield,” also referred to as “The Great Firewall of China”—a vast surveillance system that Cisco began building in the late 1990s and that the company customized to enable the Chinese government to target disfavored minorities, including members of the Falun Gong religion.

The thirteen plaintiffs—Falun Gong practitioners—alleged arrest, detention, and torture, including of themselves and family members, at least one of whom died by beating while being detained. The allegations were horrific: the plaintiffs were placed in forced labor camps, beaten with steel rods, shocked with electric batons, and endured sleep deprivation and violent force-feeding.

Importantly, the plaintiffs also alleged that their private emails, text messages, and other information—intercepted by the Golden Shield—were shown to them and used as part of their torture and forced conversion sessions, which included threats to their family members and others who had communicated with them. 

And now, Cisco is off the hook for all of this.

Congress Must Act to Hold American Tech Companies Accountable for Aiding and Abetting Human Rights Abuses

As we argued in our amicus brief to the Supreme Court, a robust federal statute is necessary given that voluntary and informal mechanisms of corporate human rights accountability are inadequate.

The plaintiffs filed their lawsuit in 2011. The following year, in 2012, Cisco first published its Global Human Rights Policy. Since then, the company has never provided redress to the plaintiffs, despite its currently operative human rights policy stating:

Where we have identified that we have caused or contributed to an adverse human rights impact, we are committed to providing access to and cooperating in remediation for affected individuals, workers, and communities through legitimate processes.

What’s more, the Associated Press conducted an extensive 2025 investigation, which recently won the Pulitzer Prize, further mapping Cisco’s and other American tech companies’ involvement in creating China’s surveillance state over the past three decades. When questioned by the AP reporters, the companies dismissed any notion of human rights obligations and simply “said they adhere to relevant export controls, laws and regulations where they operate.”

In short, Cisco’s commitment to human rights is a fiction.

Given that the U.S. Supreme Court has effectively read the ATS into non-existence, Congress must now step up. While there might be other avenues for redress including under state law, there must also be a comprehensive federal human rights law allowing any plaintiff to hold American companies—especially technology companies—accountable for aiding and abetting human rights abuses, with knowledge or intent that those abuses would occur, and whether the victims suffered those abuses in the U.S. or in foreign countries.

The implications of this gap in U.S. human rights law are significant. As Prof. Harold Koh considered:

[T]he Justices should ask whether affording such immunity in Cisco would implicitly signal to U.S. companies that they could not be held liable for assisting the U.S. government in committing gross violations. Such violations could include constructing a mass surveillance system that may be used to surveil, persecute, retaliate against, and punish real and perceived opponents and constructing systems that autonomously commit war crimes.

American companies should not have a free pass to build out surveillance states (or other technologically sophisticated systems) that not only violate personal privacy or facilitate censorship, but that can also lead to real-world physical violence and even death.

Related Cases: Doe I v. Cisco

Two weeks ago, I attended the [un]prompted AI security conference in San Francisco. It was an excellent event: three days of very dense talks about all aspects of AI security. (My talk was about integrous AI, and I will post it when it is online.)

AI security can be divided into three broad topics. It’s important for researchers to understand which one they are working on. And it’s important for us as a community to work on them all. They are: security of AI, security with AI, and security from AI. [un]prompted had good talks about all three...

Nature Climate Change, Published online: 09 November 2026; doi:10.1038/s41558-026-02797-5

It has been surprisingly difficult to calculate how much warming different regions of the Earth have experienced since pre-industrial times. Now, research shows that in most land regions, the temperature thresholds that governments agreed to in Paris to keep below are already dangerously close.

Nature Climate Change, Published online: 09 November 2026; doi:10.1038/s41558-026-02790-y

Climate targets are set for global temperatures, but what they mean regionally is less clear. Here the authors quantify the amount of human-induced warming in individual IPCC regions and find that, by 2025, they had reached 90–94% (65–76%) of their expected warming levels at the 1.5 °C (2 °C) global warming thresholds.

Today, a scientist who wants to simulate a physical process must pick the right tool for that one job, such as a solver built for flowing water or another designed to model the way a protein folds. Each one is hand-coded around the specific equations that dictate how that process behaves.

Researchers at MIT, the University of British Columbia (UBC), and elsewhere have now replaced that patchwork with a single deep-learning system, called WorldParticle, that could help engineers model different physical phenomena, from sliding sand to folding proteins, with one unified artificial intelligence architecture.

Scientists and engineers could use this system to model processes that typically require separate simulators, such as a fluid sloshing against a deformable solid. This could offer a faster, more accurate way to simulate how sloshing oil might impact an elastic valve in a storage tank, for instance. It could also help engineers study where and how granular materials, like sand or soil, could move when a soft robot touches them.

The system represents every material it simulates, from a dune of sand to a protein molecule, as tiny particles and predicts where those particles will move one step at a time. Because it learns the underlying dynamics rather than being programmed with an equation, the model can adapt to materials, forces, and environments it never encountered during training.  

In the long term, the researchers hope this work helps scientists discover new materials and explore physical processes they would be unable to effectively study with existing simulation methods.

“We want to have a GPT moment for simulation,” says Peter Yichen Chen, a former MIT postdoc who is now an assistant professor at UBC, and is a co-corresponding author of an open-access paper on this technique. “Every time people want to build a simulator for a particular physical phenomenon, they need to code it up from scratch. Our vision is to have a foundation model that works out of the box for any scenario you want to simulate. This is a first step in that direction.”

Chen is joined by co-corresponding and co-lead author Minghao Guo, an MIT postdoc; co-lead authors Caoliwen Wang and Siyuan Chen, UBC graduate students; Wojciech Matusik, an MIT professor of electrical engineering and computer science who leads the Computational Design and Fabrication Group within the Computer Science and Artificial Intelligence Laboratory (CSAIL); and others at UBC, Georgia Tech, Inria, Meta, the University of Pennsylvania, the University of Utah, the University of California at Los Angeles, and the University of Hong Kong. The research will be presented at the ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia.

Unified simulation

For this work, the researchers drew inspiration from world models. These powerful AI systems learn the rules that govern the physical world so they can predict how objects or environments will change over time in response to certain actions or conditions.

The researchers applied these concepts to develop a unified AI architecture that can simulate multiple types of physical phenomena. 

“If you study deformable objects and fluids, you would need two simulators — one for each physical regime. But if you unify them into one model, maybe you could couple the physics of these two processes and gain insights into new problems?” Guo says.

WorldParticle breaks an object into thousands or millions of tiny, three-dimensional dots, or particles, using what is known as a Lagrangian representation. It tracks the movement and velocity of the particles over space and time to simulate the material’s dynamics.

Typically, when scientists model materials and physical processes using Lagrangian particles, they use a governing equation that defines the fundamental laws which control how that material behaves over time and space. They must convert that complex equation into a formula a numerical solver can handle, repeating these steps for each material and process they want to model.

Instead, the researchers designed a neural network that learns to solve governing equations in general, eliminating the need to design a new model and solver to handle each one. 

Once WorldParticle converts an object into particles, a neural network takes the 3D positions and physical properties of the particles as inputs. It outputs their positions and properties for the next frame of the simulation, predicting the material’s motion over time and space in one-second increments. 

“It is a similar architecture to ChatGPT. But instead of predicting one word at a time to write a piece of text, we are predicting how these particles will move, one step at a time,” Chen says.

Before each simulation step, WorldParticle separates the external forces that affect the physical system, like impulse forces that affect an ocean wave when it slams into a seawall. 

After predicting the initial states of the particles, WorldParticle integrates the external forces back into the simulation, correcting its particle predictions based on how those forces affect the object’s behavior. In this way, the system can generalize to unseen materials, changing environmental conditions, and varied forces.

A general, scalable framework

But because a complex object could be broken into millions of particles, the problem quickly becomes too computationally intensive for the model to handle. 

To streamline processing, the researchers employed a merging technique to combine and compress particles while still allowing WorldParticle to capture fine-grained details of their behavior.

“We needed to do a lot of detailed engineering and employ many tweaks to the architecture for it to be truly generalizable and scalable,” Guo says.

They demonstrated WorldParticle by modeling six different physical phenomena, including deformable objects, granular materials, and proteins, with higher accuracy than numerical simulation methods. 

For instance, WorldParticle effectively captured how a granular object would collapse into a pile while some baseline methods left sand particles floating in the air. Their method also modeled how protein structures change over time about 100 times faster than numerical simulators, Guo adds. 

The general-purpose framework could be applied to many materials and processes, including soft robot manipulation tasks, RNA evolution, and astrophysical phenomena like the merging of galaxies, the researchers say.

Moving forward, they want to adapt WorldParticle to simulate these and other physical systems. They also want to build on this work toward the development of a full, general-purpose simulation pipeline for scientific discovery. 

 “We see the unification of computation and science giving us new insights and tools to discover physical phenomena or explain some unexplainable physical phenomena,” Guo says. “With a unified model, we could unlock many discoveries that we can’t even imagine.”

Humans are intuitive by nature. We can sense meaning beyond the literal words that we encounter. Now, a new artificial intelligence-assisted learning platform developed by MIT engineers is enabling robots to read larger meaning into a human’s natural language commands. 

In a study to be presented at the annual Conference on Robot Learning this month, researchers from MIT and New York University introduce FLEX, a flexible feature-editing pipeline for robot motion-planning. 

With FLEX, humans can interact with a robot by giving it instructions in natural, conversational language. The robot can not only follow the instructions literally but also read into the words to draw larger generalizations that help to improve its motion-planning task. 

The team demonstrated FLEX in several simulated scenarios, including one in which a robot serves a cup of coffee. In these virtual scenarios, a person interacts with a robotic arm by typing commands in natural language. As the robot moves the coffee cup over a cluttered table, the person might type “Don’t spill on the laptop!” In response, the robot will reason about why it should avoid the laptop. For this, FLEX incorporates a large language model (LLM) — a type of AI system that is trained on enormous libraries of text, including social media posts, video transcriptions, and web content, which the LLM uses to identify larger trends, patterns, and generalizations. 

“LLMs have seen so many texts from the internet,” says study author Nathan Dennler, a postdoc in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). “People might have written about, ‘Oh, my phone dropped in the water and now it doesn’t work,’ or ‘I spilled coffee on my computer and now it doesn’t work.’ So LLMs pick up on the idea that liquids plus devices leads to things not working.”

Through the LLM, the robot reasons that it should also avoid bumping into all electronics. In its updated path, the robot adjusts to avoid both the laptop and a nearby phone, without the person having to explicitly spell this out.

“The idea is that there is some higher-level intention behind the words that you say,” Dennler says. “People naturally get this. But you have to carefully explain that to a robot, which takes time and effort. FLEX gives you an interface of communication with a robot to say, ‘When people say this, they mean that.’”

The study was led by Andreea Bobu, assistant professor of aeronautics and astronautics at MIT. Co-authors are Audrey Lee SM ’26 and Ilia Sucholutsky of New York University.

Beyond what we say

At MIT, Bobu heads up the Collaborative Learning and Autonomy Research (CLEAR) Lab, where her group develops autonomous agents that learn to work for, with, and around people. She is focused on developing assistive and household robots that can carry out physical tasks, and reason and adapt in real-time, in response to a person’s natural, conversational commands. 

“Robots use math to do their trajectory planning. But we can’t expect a consumer to learn to do this math in order to tell a robot what to do,” says Dennler, who is a member of the CLEAR Lab. “Instead of having to get people to learn how to use a robot in a specific way, the goal is to interact with the robot through natural language.”

There is a large effort in the robotics community to incorporate natural language into robotic learning pipelines. Various strategies include using language to clarify a task’s goal, redirect a robot’s trajectory, and update its understanding of what is a safe path to take. These existing methods use natural language to tell the robot how to physically perform a task. But a robot could potentially learn much more from what we tell it. 

Bobu and her colleagues are expanding what robots can learn from natural language commands. In their new study, they focused on enabling a robot to interpret a person’s underlying, unspecified preference from limited natural language commands.  

Absorbing common sense

The team’s new robot learning platform, FLEX, is a three-step process that helps a robot read more into a natural language command, beyond what it is explicitly told to do. 

The first step, “Add,” instructs a robot to add a feature to its list of things in its environment that matter for the task at hand, based on a user’s natural language command. For instance, as a robot is moving a bowling ball from one location to another, a person might interject with “Don’t drop it on my mug!” The robot would then add the mug to a list of things to avoid in its path. This first step is how previous methods have worked. Every time a person makes a correction, the robot adds to its features list, and updates its trajectory to avoid those features. 

“The problem happens when your feature list becomes super long, and some features are no longer relevant because your environment may have changed,” says Dennler, who notes that this approach can grow tiresome if a person has to specify every specific feature to avoid. 

The second step, “Generalize,” is the new solution that FLEX provides. This step incorporates an LLM — in this case, GPT-4o — that considers the mug, along with any other features that the person has so far specified to avoid. The LLM automatically searches through its vast cache of data for patterns and connections among these features, to ultimately make a generalization that it should avoid objects that are fragile, even when the person has not explicitly said so. 

“LLMs have seen so many texts from the internet, from all sorts of people and places,” Dennler says. “Because they’ve trained so much on those things, some of that common sense gets absorbed into the LLM.”

In the third step, “Mask,” the robot’s standard computer vision system assesses its environment to determine what objects are no longer present, and then deletes those objects from its features list. This entire three-step process takes about five seconds. 

The team tested FLEX in simulations of a robot arm carrying out three different tasks: serving a cup of coffee, moving a candle, and carrying a bowling ball. In all three scenarios, volunteers interacted with the virtual robot by typing natural language commands, such as “Don’t spill on my laptop,” “Stay away from the stack of paper,” and “Don’t drop on the mug.” With FLEX, the robot followed all three commands literally and also reasoned at a higher level to not spill on electronics, stay away from flammable objects, and not drop its cargo on fragile things. 

“Everyone’s homes are different, and everyone cares about different things, so no robot comes out of the box knowing what matters to you,” Bobu says. “What excites me about FLEX is that it can pick that up from a sentence or two instead of asking you to spell out every rule — and that’s the difference between a robot people put up with and one they actually want in their home.” 

This work was partly supported by the MIT Generative AI Impact Consortium and the MIT Schwarzman College of Computing’s Social and Ethical Responsibilities of Computing program.

News story: “Scientists discover a bizarre Jurassic ‘squid’ unlike anything ever seen.”

Scientists have identified the “fattest” belemnite ever discovered, a bizarre, barrel-shaped squid relative that lived in Wyoming’s ancient seas 160 million years ago. The exceptionally rare fossils reveal a creature unlike anything previously known, potentially a predator of smaller squid-like animals and a surprising survivor of a supposedly vanished family.

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

Topology optimization is a computer design method that can help engineers design beams using reduced amounts of concrete and steel. But those intricate shapes produced from the tool are often too complex or expensive to pour into conventional rigid molds. The few that have been built have mostly relied on specialized equipment, such as concrete 3D printers or machine-carved molds, equipment that is out of reach in regions where new construction is expected to grow.  

Now, MIT engineers have shown that highly efficient beams can be cast in something more accessible: sewn fabric. In a paper published in the Journal of Building Engineering, the team designed and built a 3-meter (about 10-foot) test beam using fabric molds created through an automated pattern-making process. Compared with a conventional beam designed to carry the same weight, the optimized beam used about one-third as much concrete and half as much steel reinforcement. When tested, its measured response was within about 2 percent of what the team's computer model predicted.

Fabric formwork, in which concrete is poured into a mold made of textile, has been explored for years to create beams that vary in depth, rather than in rectangular shapes common in construction. But because fabric deforms under the weight of wet concrete, that flexibility has made the final shape hard to predict.

“The key realization was that this flexibility is not just a limitation to manage; with the right computational pattern design, it can become a way to produce optimized structural forms,” says Josephine Carstensen, the Gilbert W. Winslow (1937) Career Development Professor in Civil Engineering and senior author of the study.

Fabric molds can also be inexpensive, lightweight, and fast compared to wood or steel molds. “You could sew a formwork together off site, pack it into a box, and bring it to a job site very easily,” says Jackson Jewett PhD ’25, co-author of the study. “We joke that we are developing the H&M of the construction industry.” 

Less material, lower carbon

Cement production accounts for roughly 8 percent of human-caused carbon emissions. 

“This work addresses concrete’s climate footprint primarily by reducing material demand,” Carstensen says. “Cement production is highly carbon-intensive, so using less concrete is an important part of decarbonizing construction.”

Where new construction happens matters, too. The world’s number of buildings is expected to nearly double by 2050, with about 85 percent of that growth in developing countries, where access to advanced construction equipment is often limited. That is why, Carstensen says, the team focused on an accessible, cost-conscious fabrication approach.

Designing the beam

Concrete is strong when compressed, but weak when pulled, so it’s reinforced with steel that can handle the pulling forces. The researchers first mapped a path for the steel reinforcement using a method called graphic statics, which can generate structural designs almost instantly. They used it to shape the steel bar so that it carries an equal share of the weight along its entire length to keep the reinforcement simple enough for builders to make. Topology optimization, which works more slowly but can place material freely, was used to determine where concrete was needed and where material could be removed from areas where it contributes little to the structural performance.

“Both methods are good at different things, so we tried to combine the strengths of both into a single methodology,” says Jewett.

The resulting beam has a top section, narrow angled legs, and a thin bottom section for the steel reinforcement. The team’s automated process translated the 3D beam design into flat fabric pieces that can be cut and sewn into a mold, accounting for how the fabric will deform once concrete is poured in. A simple timber frame supported the finished fabric mold.

Watching the beam take shape

For Mark Hellrich SM ’26, the study’s lead author, the main hands-on challenge was getting the concrete mix right. Woven cotton fabric lets water seep through, so a mix that is too runny loses water through the mold. It also presses harder against the fabric, pushing it away from the intended shape.

The team used a moderately wet, sand-rich mix with only small stones, so the concrete could flow through the beam’s narrow, angled legs and thin bottom section without getting stuck. They then packed it in by hand, prodding each layer and tapping and kneading the fabric from the outside.

“This is where the flexibility of the mold becomes an advantage: you can work the concrete through the interconnected passages without a vibrator, which a rigid mold of this shape would need,” says Hellrich. A concrete vibrator is a tool typically used to shake air pockets out of freshly poured concrete in rigid molds. 

Once the concrete was in place, the wet fabric revealed the beam’s final shape. “All three beams were free of wrinkles and creases,” says Hellrich. The top surface stayed close to the design, while the main difference was a downward sag along the bottom, with the angled legs folding slightly inward.

Finding the right fabric mold

To find out how much sewing precision the beam needs, the team built three molds for the same design, made from 36, 56, and 76 fabric pieces. Comparing the 3D scans of the cast beams with the target design, the researchers found that more seams gave finer control over the details, but did not necessarily make the beam’s overall shape more accurate.

“More seams improve local control because they divide the fabric into smaller, more constrained panels. But a more complex pattern also has more pieces to align and more seam length, so small cutting, sewing, and assembly inaccuracies can accumulate across the mold, explains Carstensen.

The 56-piece mold demonstrated the best balance, capturing the important design geometry without the added time, effort, and potential errors associated with the most complex pattern.

The researchers then load-tested one of the beams, gradually stacking 36 water-filled buckets on top of it, up to about 30 percent of its design load to reflect everyday service conditions. Weighted lines hung from the beam’s underside to precise measuring gauges below, tracking how far the beam bent as the load was added and removed. At the beam’s center, the measured deflection — its downward movement under the load — was within about 2 percent of the computer model’s prediction.

Where it fits in the real world

The team also compared the cost of a single fabric-formed beam with a conventional one and found that location makes all the difference. In the Global South, where materials tend to cost more relative to labor, all three fabric versions were cheaper, with savings of about 10 to 20 percent. In the Global North, higher labor costs for sewing reversed that advantage, making the fabric versions about 16 to 55 percent more expensive.

The findings suggest that fabric formwork is most promising for custom-shaped concrete components produced as a one-off or in small batches, such as exposed beams, roof or long-span framing, pedestrian structures, and landscape elements. For the repetitive, rectangular beams found in most buildings, conventional reusable molds remain the more economical choice where labor is expensive.

“To get started, a builder would need the digital pattern for the target component, a sewing machine and suitable fabric, and a simple timber support frame,” Carstensen says. “That is a much lower equipment threshold than robotic fabrication, concrete 3D printing, or CNC-milled molds.”

The researchers describe the 3-meter beam as a proof of concept comparable to a short building span. It is also a big step up from the 1.2-meter beam the team built in a previous project, Jewett notes.  

Building bigger isn’t just a matter of enlarging the design, Carstensen notes. As a beam gets longer, its own weight and the pressure on the fabric mold change, so a larger beam would need a new design. The next step, Carstensen says, is “to test multiple beams to failure.”  

“The nice thing about these optimization methods is they generate structures that look organic,” Jewett says. “Their geometries are clear expressions of how the forces move through the structural system. There is elegance to them. It makes you ask why we don’t design all our structures in this way.”

Joining Carstensen, Jewett, and Hellrich on the paper are co-authors Abhinit Sati, Edward Chien, and Emily Whiting, of Boston University’s Department of Computer Science.

The work was partially supported by the U.S. National Science Foundation.

Google is rightfully facing blowback for forcing K-12 schools to enable AI if they wanted to keep using Google search. The company quickly backtracked and decoupled and turned off the services by default, but made it clear search will continue to be just another AI chatbot. This mandate forces an ultimatum on educators: accept generative AI or de-google your school.

Students, teachers, and parents across the U.S. are now years into an argument about how schools should respond to the growth of AI-enabled tools. Google decided to skip this debate and take this choice from schools. In August it activated AI chatbot Gemini for 150 million K-12 students on Google Classroom, leaving educators scrambling to respond. A few months later, Google introduced a new way to turn it off — but now administrators would also be turning off Google search.

Google walked this back somewhat, clarifying that Gemini, Gemini notebook, and Search will have their own controls and will be off by default. But because of Google’s broader approach to force AI on all search users, this change is still an example of a tech monopolist pushing unwanted changes into the classroom.

Tying AI to Search

It’s no secret that the biggest companies in AI  are operating at a huge loss, in the hope they can follow Uber’s enshittification playbook. Offer a cheap convenience at a major loss until old systems atrophy, then flip the script to reduce wages and raise consumer costs—whether that’s paying with cash or with data. 

This puts Google in the odd position of an incumbent, with around 90% of search traffic. It needs to not only make good on its own AI investment, but protect its dominance from new AI tools. It’s not just enough to offer its own AI mode, it needs to turn search into AI mode, while engaging in clumsy self-preferencing that is eating its own ad revenue.

The problem with this approach is the public is split on AI, to put it mildly. Some users will welcome these changes, but the rest are stuck in an all-or-nothing bargain. Anyone concerned  about the accuracy of AI overviews or their impact on click-through rates for online communities and small publishers can just deal with it. The bet is any inconvenience in switching to an alternative is enough to keep you trapped.

Google can and should offer a way to disable AI overviews. Smaller services like DuckDuckGo* and Brave search have simple and persisting AI settings which don’t require login. These services also don't mince words and make a distinction between generated AI summaries, suspected AI results, and the backend use of AI to curate links. 

It’s not enough to point to opaque workarounds, either. Adding “-ai” to your Google search will usually remove AI Overview, but so will “-unicorn”. This trick isn’t meant to be an AI off-switch, it’s a quirk of how AI Overview was implemented and can change at any time. These tricks also don’t generalize well to settings for system administrators. Hacks might work for now, but everyone—including administrators—deserve clear set-and-forget controls for the tools they rely on.

Pushing AI on Schools

De-googling your life isn’t easy, and for schools it can be even more difficult. For decades Google has gone after schools to foster life-long Google users—resulting in many institutions being deeply entrenched in its ecosystem despite data privacy concerns. That’s because schools face major switching costs when moving to a competitor. It takes more than flipping a setting—it means a lengthy contract process, staff retraining, and a cumbersome migration of past work. 

The difficulty of switching makes granular controls even more essential, particularly for a hot-button issue like generative AI. Few parents would send their kids to AI-driven schools, but they may want their kids to learn tools like search as they exist outside of the classroom. However, many parents and teachers concerned with AI disrupting learning are pushing major school districts to implement temporary AI moratoriums for young students. These pauses are meant to make sure any introduction of AI fits the needs of students and is done in a way that actually makes sure educators are prepared. 

But in either camp, this is a choice that must be made by schools, not Google. Teachers know more about what their students need than a trillion-dollar tech company, and this expertise should be respected in their own classrooms. 

Google’s self-preferencing and desperate attempt to eventually recoup on an AI money pit must be tempered with more fine-tune controls over generative AI. It’s in Google’s own interest to stop abusing its dominance to piss off users, advertisers, and school communities. It’s also Google’s responsibility as an adjudicated monopolist.

*DuckDuckGo is an EFF donor. EFF donors do not influence our agenda or our positions.

Discovering new molecules for specific uses is a major driver of progress in a wide variety of industries, whether it’s pharmaceutical companies looking for new drugs, or electronics or energy companies seeking new materials with particular properties. But it’s also largely a trial-and-error process, because there is not yet any way to perfectly predict how a given chemical compound will behave in terms of the myriad different properties and responses materials can exhibit.

Recent progress has been made on using artificial intelligence and machine learning to supplement or speed up laborious calculations based on physical principles that can at least provide some initial guidance on which compounds might be worth synthesizing and testing. Now, a new method developed at MIT shows that when there are few data on some technologically relevant property, such as ligand affinities for a recently discovered cellular target for a potential drug, this approach can outperform other existing methods for winnowing down the field of possible molecules that might be worth looking at.

The findings were reported in the Sept. 14 issue of the Journal of Chemical Information and Modeling, in a paper by MIT graduate student Akshat Shirish Zalte and recent graduate Jackson Burns PhD ’26, Research Scientist Charlles Abreu, Professor William Green, and three researchers from the chemical company BASF. 

An earlier version of the paper has been online since last year, and the open-source model the team developed, called CheMeleon, has already found regular use by several pharmaceutical companies. CheMeleon was built using ChemProp, an open-source machine-learning package also developed and maintained by the Green group at MIT, and anyone can download CheMeleon through ChemProp and fine-tune it for their own purposes.

Earlier this year, there was a contest organized by OpenADMET for trying to predict the properties of a variety of new molecules that had not previously been synthesized, says Green, who is the Hoyt Hottel Professor of Chemical Engineering. “It was a big contest, a lot of people participated,” he says. When the winners were announced, it turned out that nine of the top 20 entries had used CheMeleon in their process. “So then we started getting all kinds of inquiries from the pharma companies about this, but our paper had just been submitted; it wasn’t even peer-reviewed or anything yet.”

While the paper went through that review process, its influence was already spreading. At least one pharma company, Green says, “sent a message to all their people, and said ‘Anytime you’re modeling a property, start with CheMeleon, that’s our new company policy’” when making initial decisions about which molecules to pursue.

The key to CheMeleon was pre-training a machine-learning model with detailed data about a million different molecules, so that it gets to learn correlations between the structure of a given molecule and how it behaves with respect to a catalog of about 1,600 descriptors that have been developed by chemists over decades. CheMeleon can then be fine-tuned to predict well-known properties like solubility and viscosity, to more esoteric properties such as bioactivity or reactivity, with a certain protein or enzyme.

Those million molecules are a minuscule fraction of the innumerable ways that atoms can bond together, but they provide enough examples so that once the AI system has been trained on them, it can work out on its own many underlying patterns and relationships, and then apply those to a new, unknown molecule that’s presented to it.

“Most of today’s deep-learning models require more data than traditional methods,” says Zalte. Some successful models use extensive data from the published literature in chemical journals, he says. “We don’t look at the literature at all. We just calculate a bunch of molecular descriptors like molecular weight or number of oxygens, among others.” Then predicting those descriptors forms the basis for their pre-training task.

CheMeleon is a pre-trained foundation model. Inspired by the well-known foundation models in language such as ChatGPT and Claide, which essentially are developed by feeding them massive amounts of information from the internet, CheMeleon uses molecular descriptors of a million molecules from PubChem, a large, freely available database of chemical information maintained by the U.S. National Institutes of Health. 

With classical expert-driven models, on the other hand, specialists in a field manually input detailed knowledge about their subject. In developing these classical models, he says, for example, people know from experiments that a certain property of interest, say solubility, may relate to particular descriptors of the same molecules, “like molecular weight, or the number of oxygens that are present, or how branchy or bulky the molecule is.” 

With this team’s approach, they can start with an existing neural network architecture and train it to learn underlying principles. “We’re not training it for a specific property, but as a general model that understands some level of chemistry, so that even if you fine-tune it with just a few data points for the task you care about, you can still get some good performance,” he says. That’s thanks to the fact that the model is “not having to learn everything from scratch.”

To verify its usefulness, “We compared our model not only against other foundation models that are out there, but also with classical models leveraging expert-designed principles,” Zalte says. Comparing its performance across 58 test cases, “we see that our model comes out on top.”

Green adds, “The CheMeleon foundation model drastically reduces the number of molecules that need to be tested experimentally to accurately predict the properties of new molecules. This is a huge advantage, since synthesizing and testing a large number of molecules is slow and expensive."

Besides the MIT members, the team also included Jochen Sieg, Christian Feldmann, and Miriam Mathea, researchers at BASF SE in Germany. The work was supported by the Machine Learning for Pharmaceutical Discovery and Synthesis consortium, along with fellowship support from U.S. Department of Energy and MathWorks.

What does the digital future look like? Who gets to imagine it, shape it, and make it real?

Building a freer, more inclusive digital age requires not only challenging systems of surveillance and control, but also making space for creativity, community, and radical imagination. 

That's why earlier this year with the queer arts collective, we put out a call for submissions inviting people to explore the intersections of technology, identity, and digital liberation—whether that involved pointing the lens towards an underrepresented issue in digital justice efforts; sharing personal accounts of joy, pleasure, or sorrow under surveillance; or painting wide imaginations for our communities using technology for good instead of carcerality and doom.

And we’re excited to share that the project is now live! Desire Lines: Encoding Queer Digital Futures explores a digital world that dreams wilder and exists louder. Seven artists share their visions of digital liberation, spanning from early internet-coded yearning to escapist steamy chat rooms and seed-laced battle suits for revolution. 

These essays and stories remind us that digital rights aren't just about the existence of technology and its deployment as existing in a vacuum, but instead about how tech tools are a way for people to express themselves freely, find communities, explore identities, and build spaces to exist and seek joy without fear. Too often, conversations about technology focus on innovation, efficiency, and the next big thing while overlooking the people and communities most affected by the systems being built. This project was an effort to break containment and make space for something different: a chance to imagine digital futures rooted in queer creativity, resistance, and possibility.

Together with the queer arts collective, we were excited to launch the magazine and celebrate the artists and ideas that brought it to life through our session at Mozilla Festival last week, ‘Privacy x Queer Desire,’ where we started out by co-curating a community privacy plan grounded in digital fairness before launching the magazine. 

If the stories in Encoding Queer Digital Futures teach us one thing, it’s that we don't have to accept the digital future handed to us by oppressive governments and tech companies. We already have pre-Big Tech internet experiences encoded in our fondest memories that can inform our dreams for something better, and together we can build a new reality.

Read Desire Lines: Encoding Queer Digital Futures here.

Three MIT affiliates — Anthea Coster of MIT Haystack Observatory, Carolyn Ruppel ’86, SM ’86, PhD ’92 of the MIT Energy Initiative, and César Terrer of the Department of Civil and Environmental Engineering — have been elected as fellows of the American Geophysical Union (AGU) for 2026. 

The AGU also honored eight additional MIT alumni as new fellows: Claudia Benitez-Nelson PhD ’99; Robert Howarth PhD ’79; Douglas J. Jerolmack PhD ’06; Tamara Shapiro Ledley; Galen A. McKinley PhD ’02; Mark David Schmitz PhD ’02; Frederik J. Simons PhD ’02; and Dawn Y. Sumner PhD ’95.

With some 60,000 members in more than 135 countries, the AGU is the world’s largest professional organization for Earth and space scientists and annually elects less than 0.1% of its membership to fellow status. Fellows are chosen from among AGU members who “have made exceptional contributions to Earth and space science through a breakthrough, discovery, or innovation in their field.” AGU Fellows are recognized for their scientific eminence, demonstrated through breakthroughs, discoveries, or innovations that advance the Earth and space sciences. Fellows also act as external experts, advising government agencies and other organizations outside the sciences.

Anthea Coster

Anthea J. Coster is an MIT principal research scientist and Haystack Observatory assistant director emerita.

Coster joined MIT in 1984, originally at MIT Lincoln Laboratory. At Haystack, her career work focuses on atmospheric physics across the ionosphere, magnetosphere, and thermosphere, along with space weather effects and coupling across these different parts of the complex Earth system. She has been responsible for numerous breakthrough contributions in global navigation satellite signal (GNSS) applications to geospace research, including the first GNSS observations of storm-enhanced density across the heavily populated continental United States. She is a recent winner of the Appleton Prize from the International Radio Science Union.

One of the nomination letters for Coster’s AGU Fellowship states, “recognizing that high-level conferences alone were insufficient to provide the necessary training for researchers in developing nations, she co-founded the annual GNSS workshop series (since 2009) in partnership with Boston College and ICTP [the Abdus Salam International Center for Theoretical Physics], training 40–50 participants yearly from developing countries … Through the U.N.-sponsored International Space Weather Initiative (ISWI) and other programs, she has participated in numerous public outreach initiatives, consistently working to make space science accessible to diverse audiences worldwide.” Her nomination also mentioned “[h]er efforts in bringing space physics to South America, Africa, and tribal Alaska ... as does her work with advocating for opportunities for women and underserved populations,” and highlighted her integrity, work excellence, respect for colleagues, and collaboration efforts, as well as her public outreach activities.

Carolyn Ruppel

Carolyn Ruppel, who earned three degrees from the MIT Department of Earth, Atmospheric and Planetary Sciences, is currently the deputy director of science and technology at the MIT Energy Initiative (MITEI). Ruppel, a life member of the AGU, is an expert on gas hydrates, an icelike combination of water and concentrated methane that sequesters an estimated 15% of Earth’s massive methane budget. Before joining MITEI in 2025, she held positions at Woods Hole Oceanographic Institution (1992-93), Georgia Tech (1994-2006), and the U.S. Geological Survey’s (USGS’s) Woods Hole coastal and marine research center (2006-2025). In her current position, Ruppel supports Director William H. Green in leading MITEI’s strategic and technical activities. She coordinates among member companies, MIT researchers, and MITEI’s internal teams to manage its portfolio of energy research, education, and outreach.

Starting in 2010, Ruppel led the renowned USGS Gas Hydrates Project, focusing on the resource potential of these shallowly buried and widely distributed deposits, as well as possible feedbacks among warming climate, gas hydrate breakdown, and release of methane (a greenhouse gas) to the ocean and atmosphere. In 2011, Ruppel wrote the supplementary paper on gas hydrates for the MITEI study on “The Future of Natural Gas.” Ruppel also led the influential study debunking catastrophic scenarios for massive gas hydrate breakdown due to global warming and co-discovered over 700 seafloor methane seeps on the U.S. Atlantic margin. Ruppel’s other scientific contributions have been in the fields of continental rifting, subsea permafrost distribution, hydrate reservoir dynamics, and marine heat flow and acoustics.  

César Terrer

César Terrer is the TianFu Group Career Development Professor and associate professor of civil and environmental engineering at MIT.

Terrer studies the land carbon sink: The roughly 30 percent of annual carbon dioxide emissions that plants and soils absorb each year, and whether that will hold through this century. It is one of the largest uncertainties in climate projection. Earth system models disagree about the fate of the land sink by an amount comparable to decades of emissions, largely because they represent the biology that governs it through assumptions rather than measurements. Terrer’s group replaces those assumptions with data, combining global observations — CO₂ experiments, soil inventories, forest plots and satellites. That work has shown that soil nutrients limit how much extra carbon plants take up as CO₂ rises, about three times less than models had assumed; that plant and soil carbon trade off against one another rather than accumulating together as models assume; and that animals are part of the carbon cycle, with tropical forests that have lost their seed dispersers regrowing less than a quarter as much carbon as intact ones. His group is now testing whether soils, rather than vegetation, account for most of the recent land sink. 

All of Terrer’s first-author papers are cited in the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. NASA selected him to co-lead the modeling and data synthesis working group for PANGEA, the scoping study shaping the agency’s next tropical field campaign, and Apple, Cargill, PepsiCo, Inditex, and other organizations have sought his advice on natural climate solutions.

Alumni honored

The eight additional MIT alumni honorees elected AGU Fellows graduated from the following departments:

Claudia Benitez-Nelson PhD ’99 — Earth, Atmospheric and Planetary Sciences (with WHOI);Robert Howarth PhD ’79 — Biology (with WHOI);Douglas J. Jerolmack PhD ’06 — Earth, Atmospheric and Planetary Sciences;Tamara Shapiro Ledley PhD ’83 — Meteorology;Galen A. McKinley PhD ’02 — Earth, Atmospheric and Planetary Sciences;Mark David Schmitz PhD ’02 — Earth, Atmospheric, and Planetary Sciences;Frederik J. Simons PhD ’02 — Earth, Atmospheric, and Planetary Sciences; andDawn Y. Sumner PhD ’95 — Earth, Atmospheric, and Planetary Sciences.

AGU Fellow awards will be presented at the AGU Fall Meeting in San Francisco in December.

“The safety of young people in the digital environment cannot be assessed without also considering the safety of young people in offline environments. For millions of young people around the world, the digital environment is their safe place, their haven from an unsafe physical environment, community, or family situation. The digital environment may be the best, perhaps only, place for them to find critical safety information and community, to explore ideas that are controversial in their families and larger physical environments, and to otherwise nourish their personal autonomy.

“For these young people, regulatory efforts that limit their access to online services or to certain features they find valuable, make their lives, in total, less safe, even if there has been some marginal safety benefit to their digital environment.

“The OHCHR must thus consider the whole of young persons' lives in any recommendations that come out of this process and not treat ‘safety, physical or mental health, and rights in the digital environment’ as disconnected from safety, physical or mental health, and rights elsewhere.”

So begins EFF’s submission to the global Call for Inputs on Child Rights and Safety in the Digital Environment conducted by the United Nations Office of the High Commissioner on Human Rights. The OHCHR is preparing a report on the issue to be presented to the UN Human Rights Council at its 64th session in the first quarter of 2027. The Human Rights Council requested the OHCHR to solicit input from a wide range of stakeholders, including child and youth perspectives, and convene five regional consultations to assess the risks to the safety of the child in the digital environment and best practices to address these risks in different geographical areas, bearing in mind current and emerging business models. 

The OHCHR acknowledges that young people today navigate a digital environment with both “unprecedented opportunities” and “serious and evolving risks,” and sets out a rights-based framing whereby “Children's 'safety' is therefore addressed through the full framework of their rights — with freedom from violence (Art. 19) and protection from exploitation and abuse (Arts. 34–36) as clearly defined obligations, situated alongside privacy (Art. 16), access to information (Arts. 13 and 17), expression (Art. 13), participation (Art. 12), and development (Art. 6). Protection and the full range of children's rights are understood as reinforcing rather than competing imperatives, and measures taken in the name of safety must not be used to restrict the enjoyment of other rights, such as the right to privacy and the right to participate.”

In light of this, EFF’s submission focused on the current general disregard for young people's speech and privacy rights by regulators around the world, all of whom seem far more interested in empowering parents rather than young people themselves, or meaningfully involving young people in the regulatory process. We emphasized the human rights-centered uses young people make of social media that were not specifically set out in the concept note. We also explained that too often, policy proposals aiming to remedy the issues of online safety for young people introduce limitations on access to online services and content rather than tackling the specific problems that people experience. 

More specifically, our submission looks at:

1. the most significant risks to young people’s safety in the digital environment
2. how digital product design and business models drive risks to children's rights online, including in AI-mediated services
3. the inclusion of young people’s views in the design, governance, and regulation of digital environments 
4. how equality and non-discrimination in access and protection can be ensured
5. the promotion of digital literacy, skills, and empowerment for young people 
6. the impact of legislative and regulatory measures from the United States to Indonesia and Australia 
7. importance of broader, more systemic interventions that can address address online harms
8. how global instruments and processes can protect young people online. 

The internet is where young people learn, find community, explore their identities, access important information, and express themselves. Curtailing these opportunities doesn't make them safer. And requiring everyone to hand over sensitive personal information just to access online spaces creates privacy risks for all users.

As the OHCHR has acknowledged, the solution to the issue of online safety should not require a choice between protecting young people from danger or protecting their rights. Instead of restricting access, governments should focus on the systems that create these risks in the first place; tech companies must be accountable for their design choices, data practices, and surveillance-driven business models; and young people need to be included in the decisions that affect them. 

Read our full submission here. 

The MIT Quilt Show, put on as a collaboration between the Crafters’ Circle, MIT Makerspaces, and the MIT Women’s League, and funded by the Council for the Arts at MIT, ran for three days in Lobby 13 Oct. 5-7 and drew 950 visitors.

“We got wonderful feedback throughout the show, and it was tremendously gratifying to see the MIT community turn out en masse to support this art form,” says Jane Halpern, show organizer and communications officer for the Department of Electrical Engineering and Computer Science.

The exhibit featured around 40 quilts and quilt-adjacent works from about 20 members of the MIT community, plus collaborative works such as an astrophysics community quilt, assembled by graduate student Swati Ravi, and an American flag featuring the embroidery work of refugees, assembled by Anh Vu Sawyer MBA ’20 and her husband Philip.

While the quilt show was a one-time event, Halpern encourages MIT community members who are interested in adding more quilts to their lives to join the Crafters’ Circle Slack channel; check for class listings in the Makerspaces; and visit the New England Quilt Museum in nearby Lowell, Massachusetts.

This is fascinating:

The video describes a technique that can reveal where an otherwise unidentifiable recording was made by analyzing its audio for the faint sounds of passing aircraft. The technique is both fascinating and frightening.

It works by looking at Doppler curves, which plot the way a plane’s sound changes as it passes overhead because of the Doppler effect. This is the same familiar Doppler effect that causes the frequency of a siren to rise as it approaches and then fall as it passes by and moves away. More generally, it’s the phenomenon whereby the frequency of a wave perceived by an observer changes when the observer and the source of the wave are moving in relation to one another...

On November 3, our colleagues at the Software Freedom Law Center, India (SFLC.in) received notifications from X that the Delhi Police had issued takedown requests targeting 42 URLs. Among the accounts and posts targeted were those belonging to SFLC.in, the Internet Freedom Foundation (IFF), and X account @geoblockedindia, as well as posts documenting legal notices and instances of online censorship. 

Takedown requests targeting SFLC.in and other digital rights groups threaten not only their work, but the public’s ability to know when and how online speech is being restricted. 

The requests cite grounds including hate speech, alleged defamation of the Hindu nationalist organization Rashtriya Swayamsevak Sangh (RSS), and concerns relating to safety and security. But the context in which these requests have emerged is particularly troubling: they target organizations and individuals whose work involves documenting restrictions on online expression and making those restrictions visible to the public. 

In other words, the organizations being targeted are among those trying to tell the public when and how its speech is being censored. 

SFLC.in is one of India's most important digital rights organizations and promotes innovation and open access to knowledge by helping developers make great free and open-source software, protects privacy and civil liberties for Indians by educating and providing free legal advice, and helps policymakers make informed and just decisions about use of technology. In 2025, we recognized their work with an EFF Award for Defending Digital Freedoms. 

IFF (the Internet Freedom Foundation) is another of EFF's close allies in India. Like EFF, IFF combines advocacy campaigns and strategic litigation to advance digital rights. They write frequently about internet shutdowns and have filed several lawsuits contesting them. 

Among SFLC.in's initiatives is its Free Speech Tracker, which documents instances of online censorship, content restrictions, and account takedowns in India. In a country where government orders to restrict online content are often opaque, this work is absolutely essential. 
 
Recent legal notices object to the tracker itself, as well as the organization’s documentation of restricted content and geo-blocked material. The allegations against SFLC.in include claims that it is "continuously opposing government directions" and publicly circulating information about restricted content, thereby undermining measures taken in the name of national security and public order. 

But documenting government censorship is not the same as undermining public safety. Reporting that content has been restricted is not an endorsement of that content, nor does it constitute an attempt to circumvent the law. It is an exercise in transparency and accountability, and one that allows journalists, researchers, civil society organizations, and the public to understand how governments exercise their power over the information landscape. 

Censorship thrives in the absence of transparency 

The use of legal complaints and platform takedown mechanisms against organizations documenting censorship creates a particularly troubling dynamic. When those who monitor restrictions on expression are threatened with having their accounts or reporting removed, the result is a chilling effect that extends far beyond the immediate targets. Other organizations or journalists may think twice before documenting controversial takedowns or challenging government orders, and the public loses access to an independent record of decisions that affect its ability to speak and access information online. 

There is also an important role here for platforms like X. Platforms must be transparent about the requests they receive, provide meaningful notice to affected users, and ensure that any restrictions are subject to appropriate scrutiny and avenues for appeal. When governments seek to restrict speech, platforms have a responsibility to avoid becoming opaque intermediaries through which censorship is quietly carried out. 

These developments are part of a broader pattern of pressure on independent civil society organizations working to defend freedom of expression and access to information in India. Digital rights groups play an indispensable role in documenting the effects of government policies, identifying patterns of censorship, and challenging restrictions that undermine fundamental rights. Their ability to do so independently is not a peripheral concern; it is a cornerstone of a healthy democratic society. 

The right to freedom of expression includes the right to scrutinize the exercise of government power. That includes the right to document censorship, question the reasons given for it, and make that information available to the public.  

EFF stands in solidarity with SFLC.in, the Internet Freedom Foundation, @geoblockedindia, and all those working to document and challenge restrictions on online expression in India. We urge the authorities in India to respect the rights of digital rights organizations to conduct their work without intimidation or retaliation, and to ensure that any restrictions on online expression comply with international human rights standards.