New research:

One reason the early years of squids has been such a mystery is because squids’ lack of hard shells made their fossils hard to come by. Undeterred, the team instead focused on finding ancient squid beaks—hard mouthparts with high fossilization potential that could help the team figure out how squids evolved.

With that in mind, the team developed an advanced fossil discovery technique that completely digitized rocks with all their embedded fossils in complete 3D form. Upon using that technique on Late Cretaceous rocks from Japan, the team identified 1,000 fossilized cephalopod beaks hidden inside the rocks, which included 263 squid specimens and 40 previously unknown squid species...

When ChatGPT or Gemini give what seems to be an expert response to your burning questions, you may not realize how much information it relies on to give that reply. Like other popular generative artificial intelligence (AI) models, these chatbots rely on backbone systems called foundation models that train on billions, or even trillions, of data points.

In a similar vein, engineers are hoping to build foundation models that train a range of robots on new skills like picking up, moving, and putting down objects in places like homes and factories. The problem is that it’s difficult to collect and transfer instructional data across robotic systems. You could teach your system by teleoperating the hardware step-by-step using technology like virtual reality (VR), but that can be time-consuming. Training on videos from the internet is less instructive, since the clips don’t provide a step-by-step, specialized task walk-through for particular robots.

A simulation-driven approach called “PhysicsGen” from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Robotics and AI Institute customizes robot training data to help robots find the most efficient movements for a task. The system can multiply a few dozen VR demonstrations into nearly 3,000 simulations per machine. These high-quality instructions are then mapped to the precise configurations of mechanical companions like robotic arms and hands. 

PhysicsGen creates data that generalize to specific robots and condition via a three-step process. First, a VR headset tracks how humans manipulate objects like blocks using their hands. These interactions are mapped in a 3D physics simulator at the same time, visualizing the key points of our hands as small spheres that mirror our gestures. For example, if you flipped a toy over, you’d see 3D shapes representing different parts of your hands rotating a virtual version of that object.

The pipeline then remaps these points to a 3D model of the setup of a specific machine (like a robotic arm), moving them to the precise “joints” where a system twists and turns. Finally, PhysicsGen uses trajectory optimization — essentially simulating the most efficient motions to complete a task — so the robot knows the best ways to do things like repositioning a box.

Each simulation is a detailed training data point that walks a robot through potential ways to handle objects. When implemented into a policy (or the action plan that the robot follows), the machine has a variety of ways to approach a task, and can try out different motions if one doesn’t work.

“We’re creating robot-specific data without needing humans to re-record specialized demonstrations for each machine,” says Lujie Yang, an MIT PhD student in electrical engineering and computer science and CSAIL affiliate who is the lead author of a new paper introducing the project. “We’re scaling up the data in an autonomous and efficient way, making task instructions useful to a wider range of machines.”

Generating so many instructional trajectories for robots could eventually help engineers build a massive dataset to guide machines like robotic arms and dexterous hands. For example, the pipeline might help two robotic arms collaborate on picking up warehouse items and placing them in the right boxes for deliveries. The system may also guide two robots to work together in a household on tasks like putting away cups.

PhysicsGen’s potential also extends to converting data designed for older robots or different environments into useful instructions for new machines. “Despite being collected for a specific type of robot, we can revive these prior datasets to make them more generally useful,” adds Yang.

Addition by multiplication

PhysicsGen turned just 24 human demonstrations into thousands of simulated ones, helping both digital and real-world robots reorient objects.

Yang and her colleagues first tested their pipeline in a virtual experiment where a floating robotic hand needed to rotate a block into a target position. The digital robot executed the task at a rate of 81 percent accuracy by training on PhysicGen’s massive dataset, a 60 percent improvement from a baseline that only learned from human demonstrations.

The researchers also found that PhysicsGen could improve how virtual robotic arms collaborate to manipulate objects. Their system created extra training data that helped two pairs of robots successfully accomplish tasks as much as 30 percent more often than a purely human-taught baseline.

In an experiment with a pair of real-world robotic arms, the researchers observed similar improvements as the machines teamed up to flip a large box into its designated position. When the robots deviated from the intended trajectory or mishandled the object, they were able to recover mid-task by referencing alternative trajectories from their library of instructional data.

Senior author Russ Tedrake, who is the Toyota Professor of Electrical Engineering and Computer Science, Aeronautics and Astronautics, and Mechanical Engineering at MIT, adds that this imitation-guided data generation technique combines the strengths of human demonstration with the power of robot motion planning algorithms.

“Even a single demonstration from a human can make the motion planning problem much easier,” says Tedrake, who is also a senior vice president of large behavior models at the Toyota Research Institute and CSAIL principal investigator. “In the future, perhaps the foundation models will be able to provide this information, and this type of data generation technique will provide a type of post-training recipe for that model.”

The future of PhysicsGen

Soon, PhysicsGen may be extended to a new frontier: diversifying the tasks a machine can execute.

“We’d like to use PhysicsGen to teach a robot to pour water when it’s only been trained to put away dishes, for example,” says Yang. “Our pipeline doesn’t just generate dynamically feasible motions for familiar tasks; it also has the potential of creating a diverse library of physical interactions that we believe can serve as building blocks for accomplishing entirely new tasks a human hasn’t demonstrated.”

Creating lots of widely applicable training data may eventually help build a foundation model for robots, though MIT researchers caution that this is a somewhat distant goal. The CSAIL-led team is investigating how PhysicsGen can harness vast, unstructured resources — like internet videos — as seeds for simulation. The goal: transform everyday visual content into rich, robot-ready data that could teach machines to perform tasks no one explicitly showed them.

Yang and her colleagues also aim to make PhysicsGen even more useful for robots with diverse shapes and configurations in the future. To make that happen, they plan to leverage datasets with demonstrations of real robots, capturing how robotic joints move instead of human ones.

The researchers also plan to incorporate reinforcement learning, where an AI system learns by trial and error, to make PhysicsGen expand its dataset beyond human-provided examples. They may augment their pipeline with advanced perception techniques to help a robot perceive and interpret their environment visually, allowing the machine to analyze and adapt to the complexities of the physical world.

For now, PhysicsGen shows how AI can help us teach different robots to manipulate objects within the same category, particularly rigid ones. The pipeline may soon help robots find the best ways to handle soft items (like fruits) and deformable ones (like clay), but those interactions aren’t easy to simulate yet.

Yang and Tedrake wrote the paper with two CSAIL colleagues: co-lead author and MIT PhD student Hyung Ju “Terry” Suh SM ’22 and MIT PhD student Bernhard Paus Græsdal. Robotics and AI Institute researchers Tong Zhao ’22, MEng ’23, Tarik Kelestemur, Jiuguang Wang, and Tao Pang PhD ’23 are also authors. Their work was supported by the Robotics and AI Institute and Amazon.

The researchers recently presented their work at the Robotics: Science and Systems conference.

In order to produce effective targeted therapies for cancer, scientists need to isolate the genetic and phenotypic characteristics of cancer cells, both within and across different tumors, because those differences impact how tumors respond to treatment.

Part of this work requires a deep understanding of the RNA or protein molecules each cancer cell expresses, where it is located in the tumor, and what it looks like under a microscope.

Traditionally, scientists have looked at one or more of these aspects separately, but now a new deep learning AI tool, CellLENS (Cell Local Environment and Neighborhood Scan), fuses all three domains together, using a combination of convolutional neural networks and graph neural networks to build a comprehensive digital profile for every single cell. This allows the system to group cells with similar biology — effectively separating even those that appear very similar in isolation, but behave differently depending on their surroundings.

The study, published recently in Nature Immunology, details the results of a collaboration between researchers from MIT, Harvard Medical School, Yale University, Stanford University, and University of Pennsylvania — an effort led by Bokai Zhu, an MIT postdoc and member of the Broad Institute of MIT and Harvard and the Ragon Institute of MGH, MIT, and Harvard.

Zhu explains the impact of this new tool: “Initially we would say, oh, I found a cell. This is called a T cell. Using the same dataset, by applying CellLENS, now I can say this is a T cell, and it is currently attacking a specific tumor boundary in a patient.

“I can use existing information to better define what a cell is, what is the subpopulation of that cell, what that cell is doing, and what is the potential functional readout of that cell. This method may be used to identify a new biomarker, which provides specific and detailed information about diseased cells, allowing for more targeted therapy development.”

This is a critical advance because current methodologies often miss critical molecular or contextual information — for example, immunotherapies may target cells that only exist at the boundary of a tumor, limiting efficacy. By using deep learning, the researchers can detect many different layers of information with CellLENS, including morphology and where the cell is spatially in a tissue.

When applied to samples from healthy tissue and several types of cancer, including lymphoma and liver cancer, CellLENS uncovered rare immune cell subtypes and revealed how their activity and location relate to disease processes — such as tumor infiltration or immune suppression.

These discoveries could help scientists better understand how the immune system interacts with tumors and pave the way for more precise cancer diagnostics and immunotherapies.

“I’m extremely excited by the potential of new AI tools, like CellLENS, to help us more holistically understand aberrant cellular behaviors within tissues,” says co-author Alex K. Shalek, the director of the Institute for Medical Engineering and Science (IMES), the J. W. Kieckhefer Professor in IMES and Chemistry, and an extramural member of the Koch Institute for Integrative Cancer Research at MIT, as well as an Institute member of the Broad Institute and a member of the Ragon Institute. “We can now measure a tremendous amount of information about individual cells and their tissue contexts with cutting-edge, multi-omic assays. Effectively leveraging that data to nominate new therapeutic leads is a critical step in developing improved interventions. When coupled with the right input data and careful downsteam validations, such tools promise to accelerate our ability to positively impact human health and wellness.”

Long article on the difficulty (impossibility?) of human spying in the age of ubiquitous digital surveillance.

For many years, health experts have been concerned about “food deserts,” places where residents lack good nutritional options. Now, an MIT-led study of three major global cities uses a new, granular method to examine the issue, and concludes that having fewer and less nutritional eating options nearby correlates with obesity and other health outcomes.

Rather than just mapping geographic areas, the researchers examined the dietary value of millions of food items on roughly 30,000 restaurant menus and derived a more precise assessment of the connection between neighborhoods and nutrition.

“We show that what is sold in a restaurant has a direct correlation to people’s health,” says MIT researcher Fabio Duarte, co-author of a newly published paper outlining the study’s results. “The food landscape matters.”

The open-access paper, “Data-driven nutritional assessment of urban food landscapes: insights from Boston, London, Dubai,” was published this week in Nature: Scientific Reports.

The co-authors are Michael Tufano, a PhD student at Wageningen University, in the Netherlands; Duarte, associate director of MIT’s Senseable City Lab, which uses data to study cities as dynamic systems; Martina Mazzarello, a postdoc at the Senseable City Lab; Javad Eshtiyagh, a research fellow at the Senseable City Lab; Carlo Ratti, professor of the practice and director of the Senseable City Lab; and Guido Camps, a senior researcher at Wageningen University.

Scanning the menu

To conduct the study, the researchers examined menus from Boston, Dubai, and London, in the summer of 2023, compiling a database of millions of items available through popular food-delivery platforms. The team then evaluated the food items as rated by the USDA’s FoodData Central database, an information bank with 375,000 kinds of food products listed. The study deployed two main metrics, the Meal Balance Index, and the Nutrient-Rich Foods Index.

The researchers examined about 222,000 menu items from over 2,000 restaurants in Boston, about 1.6 million menu items from roughly 9,000 restaurants in Dubai, and about 3.1 million menu items from about 18,000 restaurants in London. In Boston, about 71 percent of the items were in the USDA database; in Dubai and London, that figure was 42 percent and 56 percent, respectively.

The team then rated the nutritional value of the items appearing on menus, and correlated the food data with health-outcome data from Boston and London. In London, they found a clear correlation between neighborhood menu offerings and obesity, or the lack thereof; with a slightly less firm correlation in Boston. Areas with food options that include a lot of dietary fibers, sometimes along with fruits and vegetables, tend to have better health data.

In Dubai, the researchers did not have the same types of health data available but did observe a strong correlation between rental prices and the nutritional value of neighborhood-level food, suggesting that wealthier residents have better nourishment options.

“At the item level, when we have less nutritional food, we see more cases of obsesity,” Tufano says. “It’s true that not only do we have more fast food in poor neighborhoods, but the nutritional value is not the same.”

Re-mapping the food landscape

By conducting the study in this fashion, the scholars added a layer of analysis to past studies of food deserts. While past work has broken ground by identifying neighborhoods and areas lacking good food access, this research makes a more comprehensive assessment of what people consume. The research moves toward evaluating the complex mix of food available in any given area, which can be true even of areas with more limited options.

“We were not satisfied with this idea that if you only have fast food, it’s a food desert, but if you have a Whole Foods, it’s not,” Duarte says. “It’s not necessarily like that.”

For the Senseable City Lab researchers, the study is a new technique further enabling them to understand city dynamics and the effects of the urban environment on health. Past lab studies have often focused on issues such as urban mobility, while extending to matters such as mobility and air pollution, among other topics.

Being able to study food and health at the neighborhood level, though, is still another example of the ways that data-rich spheres of life can be studied in close detail.

“When we started working on cities and data, the data resolution was so low,” Ratti says. “Today the amount of data is so immense we see this great opportunity to look at cities and see the influence of the urban environment as a big determinant of health. We see this as one of the new frontiers of our lab. It’s amazing how we can now look at this very precisely in cities.”

From the very beginning, MIT Professor Mark Bear’s philosophy for the textbook “Neuroscience: Exploring the Brain” was to provide an accessible and exciting introduction to the field while still giving undergraduates a rigorous scientific foundation. In the 30 years since its first print printing in 1995, the treasured 975-page tome has gone on to become the leading introductory neuroscience textbook, reaching hundreds of thousands of students at hundreds of universities around the world.

“We strive to present the hard science without making the science hard,” says Bear, the Picower Professor in The Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences at MIT. The fifth edition of the textbook is out today from the publisher Jones & Bartlett Learning.

Bear says the book is conceived, written, and illustrated to instill students with the state of knowledge in the field without assuming prior sophistication in science. When he first started writing it in the late 1980s — in an effort soon joined by his co-authors and former Brown University colleagues Barry Connors and Michael Paradiso — there simply were no undergraduate neuroscience textbooks. Up until then, first as a graduate teaching assistant and then as a young professor, Bear taught Brown’s pioneering introductory neuroscience class with a spiral-bound stack of photocopied studies and other scrounged readings.

Don’t overwhelm

Because universities were only beginning to launch neuroscience classes and majors at the time, Bear recalls that it was hard to find a publisher. The demand was just too uncertain. With an unsure market, Bear says, the original publisher, Williams & Wilkins, wanted to keep costs down by printing only in black and white. But Bear and his co-authors insisted on color. Consistent with their philosophy for the book, they wanted students, even before they began reading, to be able to learn from attractive, high-quality illustrations.

“Rather than those that speak a thousand words, we wanted to create illustrations that each make a single point.” Bear says. “We don’t want to overwhelm students with a bunch of detail. If people want to know what’s in our book, just look at the pictures.”

Indeed, if the book had struck students as impenetrable and dull, Bear says, he and his co-authors would have squandered the advantage they had in presenting their subject: the inherently fascinating and exciting brain.

“Most good scientists are extremely enthusiastic about the science. It exciting. It’s fun. It turns them on,” Bear says. “We try to communicate the joy. We’re so lucky because the object of our affection is the brain.”

To help bring that joy and excitement across, another signature of the book throughout its 30-year-history has been the way it presents the process of discovery alongside the discoveries themselves, Bear says. While it’s instructive to provide students with the experimental evidence that supports the concepts they are learning, it would bog down the text to delineate the details of every experiment. Instead, Bear, Connors, and Paradiso have chosen to highlight the process of discovery via one-page guest essays by prominent neuroscientists who share their discovery stories personally. Each edition has featured about 25 such “Path of Discovery” essays, so more than 100 scientists have participated, including several Nobel Prize winners, such as the Picower Institute’s founding director, Susumu Tonegawa.

The new edition includes Path of Discovery essays by current Picower Institute Director Li-Huei Tsai and Picower Institute colleague Emery N. Brown. Tsai recounts her discovery that sensory stimulation of 40Hz rhythms in the brain can trigger a health-promoting response among many different cell types. Brown writes about how various biological cycles and rhythms in the brain and body, such as the circadian rhythms and brain waves, help organize our daily lives.

Immense impact

Jones & Bartlett reports that more than 470 colleges and universities in 48 U.S. states and the District of Columbia have used the fourth edition of the book. Various editions have also been translated into seven other languages, including Chinese, French, Portuguese, and Spanish. There are hundreds of reviews on Amazon.com with an average around 4.6 stars. One reviewer wrote about the fourth edition: “I never knew it was possible to love a textbook before!”

The reviews sometimes go beyond mere internet postings. Once, after Bear received an award in Brazil, he found himself swarmed at the podium by scores of students eager for him to sign their copies of the book. And earlier this year, when Bear needed surgery, the anesthesiologist was excited to meet him.

“The anesthesiologist was like, ‘Are you the Mark Bear who wrote the textbook?,’ and she was so excited, because she said, ‘This book changed my life,’” Bear recalls. “After I recovered, she showed up in the ICU for me to sign it. All of us authors have had this experience that there are people whose lives we’ve touched.”

While Bear is proud that so many students have benefited from the book, he also notes that teaching and textbook writing have benefited him as a scientist. They have helped him present his research more clearly, he says, and have given him a broad perspective on what’s truly important in the field.

“Experience teaching will influence the impact of your own science by making you more able to effectively communicate it.” Bear says. “And the teacher has a difficult job of surveying a field and saying, ‘I’ve got to capture the important advances and set aside the less-important stuff.’ It gives you a perspective that helps you to discriminate between more-important and less-important problems in your own research.”

Over the course of 30 years via their carefully crafted book, Bear, Connors, and Paradiso have lent that perspective to generations of students. And the next generation will start with today’s publishing of the new edition. 

Many planned solar and wind projects slated to go online by 2030 may still qualify for Biden-era credits under the new law.

The Georgia Republican said she spoke with EPA Administrator Lee Zeldin and will hold a hearing on weather-changing technology.

Lawmakers also will debate proposals related to abortion, hemp and redistricting when they convene July 21.

They say the youth-led lawsuit — which targets three of the president's energy-related executive orders — would cost money and jobs.

A budget deficit prompted state lawmakers to trim — but not eliminate — funding for a pilot program that will pay for air conditioning and insulation at three correctional facilities.

It’s yet another blow for the European Commission in its effort to get the anti-deforestation law up and running.

Monsoon season is now punctuated with intense flooding and dry spells, rather than sustained rain throughout.

People and governments are generally living in the past and haven’t embraced that extreme weather is now the norm, to say nothing about preparing for a nastier future.

Many leading suppliers have battery production hubs in China and Poland, where power systems remain heavily reliant on polluting fossil fuels, said a Greenpeace report.

Nature Climate Change, Published online: 11 July 2025; doi:10.1038/s41558-025-02362-6

Global datasets of surface temperature and sea surface temperature (SST) are routinely used in climate change studies. Here the authors show that while surface temperature datasets closely agree, four main SST datasets show substantial variation, with implications for their application.

The MIT Institute for Data, Systems, and Society (IDSS) announced the creation of a new endowed chair made possible by the generosity of IDSS professor post-tenure and “MIT lifer” Richard “Dick” Larson. Effective July 1, the fund provides a full professorship for senior IDSS faculty: the Distinguished Professorship in Data, Systems, and Society.

“As a faculty member, MIT has not only accepted but embraced my several mid-career changes of direction,” says Larson. “I have called five different academic departments my home, starting with Electrical Engineering (that is what it was called in the 1960s) and now finalized with the interdepartmental, interdisciplinary IDSS — Institute for Data, Systems and Society. Those beautiful three words  — data, systems, society — they represent my energy and commitment over the second half of my career. My gifted chair is an effort to keep alive those three words, with others following me doing research, teaching and mentoring centered around data, systems, society.”

Larson’s career has focused his operations research and systems expertise on a wide variety of problems, in both public and private sectors. His contributions span the fields of urban service systems (especially emergency response systems), disaster planning, pandemics, queueing, logistics, technology-enabled education, smart-energy houses, and workforce planning. His latest book, “Model Thinking for Everyday Life,” draws on decades of experience as a champion of STEM education at MIT and beyond, such as his leadership of MIT BLOSSOMS.

“Dick Larson has been making an impact at MIT for over half a century,” says IDSS Director Fotini Christia, the Ford International Professor in Political Science. “This gift extends his already considerable legacy and ensures his impact will continue to be felt for many years to come.”

Christia is pleased that IDSS and brain and cognitive science professor Alexander “Sasha” Rakhlin is the inaugural holder of the new professorship. The selection recognizes Rakhlin’s distinguished scholarly record, dedicated service to IDSS, excellence in teaching, and contributions to research in statistics and computation.

“Sasha’s analysis of neural network complexity, and his work developing tools for online prediction, are perfect examples of research which builds bridges across disciplines, and also connects different departments and units at MIT,” says Michale Fee, the Glen V. and Phyllis F. Dorflinger Professor of Neuroscience, and head of the Department of Brain and Cognitive Sciences. “It’s wonderful to see Sasha’s contributions recognized in this way, and I’m grateful to Dick Larson for supporting this vision.”

Rakhlin’s research is in machine learning, with an emphasis on statistics and computation. He is interested in formalizing the process of learning, in analyzing learning models, and in deriving and implementing emerging learning methods. A significant thrust of his research is in developing theoretical and algorithmic tools for online prediction, a learning framework where data arrives in a sequential fashion.

“I am honored to be the inaugural holder of the Distinguished Professorship in Data, Systems, and Society,” says Rakhlin. “Professor Larson’s commitment to education and service to MIT both serve as models to follow.”

A new security screener that people can simply walk past may soon be coming to an airport near you. Last year, U.S. airports nationwide began adopting HEXWAVE — a commercialized walkthrough security screening system based on microwave imaging technology developed at MIT Lincoln Laboratory — to satisfy a new Transportation Security Administration (TSA) mandate for enhanced employee screening to detect metallic and nonmetallic threats. The TSA is now in the process of evaluating HEXWAVE as a potential replacement of metal detectors to screen PreCheck passengers.

Typically, when you arrive at an airport security checkpoint line, you place your carry-on items on the conveyer belt, remove your shoes and any metallic items, and enter a body scanner. As you hold still for a few seconds with your feet spread apart and your arms extended over your head, the scanner creates a generic, featureless 3D body outline revealing any metallic or nonmetallic concealed weapons or other prohibited items.

Requiring individuals to stop, remove clothing and belongings, and pose for scans impedes traffic flow in airports and other highly populated venues, such as stadiums, shopping malls, mass transit stations, and schools. To enable more efficient screening of unstructured crowds and ensure public safety, the Department of Homeland Security (DHS) Science and Technology Directorate (S&T) sponsored Lincoln Laboratory to prototype a high-resolution imaging system capable of scanning people and their belongings as they walk by. This R&D effort was conducted as part of S&T's Surface Transportation Explosive Threat Detection Program, which aims to provide the surface-transportation end user-community (e.g., mass transit) with a layered and integrated capability to detect threat items at the speed of the traveling public.

The laboratory's prototype microwave imager, which consists of a set of antennas installed on flat panels, operates under the same fundamental principle as existing body scanners: low-energy radio waves (less powerful than those transmitted by a cellphone) are transmitted from antennas toward a person's body and reflect off skin and any hidden objects; the reflected waves return to the antennas and are processed by a computer to create an image, which security personnel then review to identify any potential concealed threats.

The novelty of the laboratory's invention lies in its ability to discreetly handle a constant stream of subjects in motion, measuring each subject very quickly (within tens of milliseconds) and reconstructing 3D microwave images of each subject at a video rate. To meet these challenging requirements, the laboratory team developed a cost-effective antenna array and efficient image-reconstruction algorithms. Compared to existing systems, the laboratory's 3D microwave imager runs 100 times faster using the same computing hardware. In 2017, the team demonstrated the prototype's ability to detect various simulated threat items at varying distances on a rail platform at the Massachusetts Bay Transit Authority (MBTA) Emergency Training Center in Boston.

"The goal of our work is to provide security staff with more effective tools to protect public spaces. To that end, microwave imaging technology can quickly and unobtrusively provide visibility of items carried into a venue," says William Moulder, who led the technology's development at Lincoln Laboratory.

In 2018, the security company Liberty Defense licensed the imaging technology and entered into a cooperative research and development agreement (CRADA) with Lincoln Laboratory. Transitioning technology to industry for commercialization is part of the laboratory's role as a federally funded research and development center, and CRADAs provide a mechanism for such transition to happen. Through the CRADA, Liberty Defense maintained Lincoln Laboratory's core image-reconstruction intellectual property and made the technology enhancements required for commercialization, including an entirely new hardware architecture, radio frequency (RF) antenna modules, and a transceiver system that meets Federal Communications Commission waveform and RF performance requirements for indoor and outdoor operation. The co-organizational team facilitating the transition of the technology was recognized by the Federal Laboratory Consortium for Technology Transfer with a 2019 Excellence in Technology Transfer Award for the Northeast region.

By 2021, Liberty Defense had prototyped a walk-through security screening system, HEXWAVE. That same year, through the TSA's On-Person Screening Capability Program, Liberty Defense received a contract award to demonstrate HEXWAVE's enhanced threat-detection and high-throughput capabilities for screening aviation workers. Following successful testing of HEXWAVE at sports complexes, entertainment arenas, and shopping centers, both nationally and internationally, Liberty Defense began offering the product for sale.

"HEXWAVE is a great example of how federally funded R&D can be successfully transitioned to industry to meet real-world security needs," says Asha Rajagopal, the laboratory's chief technology transfer officer. "By working with Liberty Defense, we helped accelerate the delivery of a critical capability into the hands of those protecting public spaces."

In 2023, TSA began testing HEXWAVE as a potential replacement of metal detectors used to screen passengers in TSA PreCheck lanes. Airports across the United States started deploying HEXWAVE in 2024 to meet the TSA's employee screening mandate by the April 2026 deadline. Liberty Defense notes various other markets for HEXWAVE; the first units for commercial applications were delivered to Los Alamos National Laboratory in 2023, and the technology has since been deployed at other national labs, correctional facilities, government buildings, and courthouses.

"Liberty was extremely fortunate to license the technology from MIT Lincoln Laboratory," says Bill Frain, CEO of Liberty Defense. "From the outset, they've been a true partner — bringing not only deep innovation and technical expertise, but also a clear vision for commercial deployment. Together, we've successfully brought next-generation technology to market to help protect people in public spaces."

This post was co-authored by EFF legal intern Noam Shemtov.

We are in a constant dialogue with Internet search engines, ranging from the mundane to the confessional. We ask search engines everything: What movies are playing (and which are worth seeing)? Where’s the nearest clinic (and how do I get there)? Who’s running in the sheriff’s race (and what are their views)? These online queries can give insight into our private details and innermost thoughts, but police increasingly access them without adhering to longstanding limits on government investigative power.

A Virginia appeals court is poised to review such a request in a case called Commonwealth v. Clements. In Clements, police sought evidence under a “reverse-keyword warrant,” a novel court order that compels search engines like Google to hand over information about every person who has looked up a word or phrase online. While the trial judge correctly recognized the privacy interest in our Internet queries, he overlooked the other wide-ranging harms that keyword warrants enable and upheld the search.

But as EFF and the ACLU explained in our amicus brief on appeal, reverse keyword warrants simply cannot be conducted in a lawful way. They invert privacy protections, threaten free speech and inquiry, and fundamentally conflict with the principles underlying the Fourth Amendment and its analog in the Virginia Constitution. The court of appeals now has a chance to say so and protect the rights of Internet users well beyond state lines.

To comply with a keyword warrant, a search engine has to trawl through its entire database of user queries to pinpoint the accounts or devices that made a responsive search. For a dominant service like Google, that means billions of records. Such a wide dragnet will predictably pull in people with no plausible connection to a crime under investigation if their searches happened to include keywords police are interested in.

Critically, investigators seldom have a suspect in mind when they seek a reverse-keyword warrant. That isn’t surprising. True to their name, these searches “work in reverse” from the traditional investigative process. What makes them so useful is precisely their ability to identify Internet users on the sole basis of what they searched online. But what makes a search technique convenient to the government does not always make it constitutional. Quite the opposite: the constitution is anathema to inherently suspicionless dragnets.

The Fourth Amendment forbids “exploratory rummaging”—in fact, it was drafted in direct response to British colonial soldiers’ practice of indiscriminately searching people’s homes and papers for evidence of their opposition to the Crown. To secure a lawful warrant, police must have a specific basis to believe evidence will be found in a given location. They must also describe that location in some detail and say what evidence they expect to find there. It’s hard to think of a less specific description than “all the Internet searches in the world” or a weaker hunch than “whoever committed the crime probably looked up search term x.” Because those airy assertions are all law enforcement can martial in support of keyword warrants, they are “tantamount to high-tech versions of the reviled ‘general warrants’ that first gave rise to the . . . Fourth Amendment” and Virginia’s even stronger search-and-seizure provision.

What’s more, since keyword warrants compel search engine companies to hand over records about anyone anywhere who looked up a particular search term within a given timeframe, they effectively make a suspect out of every person whose online activity falls within the warrant’s sweep. As one court has said about related geofences, this approach “invert[s] probable cause” and “cannot stand.”

Keyword warrants’ fatal flaws are even more drastic considering that privacy rights apply with special force to searches of items—like diaries, booklists, and Internet search queries—that reflect a person’s free thought and expression. As both law and lived experience affirm, the Internet is “the most important place[] . . . for the exchange of views.” Using it—and using keyword searches to navigate the practical infinity of its contents—is “indispensable to participation in modern society.” We shouldn’t have to engage in that core endeavor with the fear that our searches will incriminate us, subject to police officers’ discretion about what keywords are worthy of suspicion. That outcome would predictably chill people from accessing information about sensitive and important topics like reproductive health, public safety, or events in the news that could be relevant to a criminal investigation.

The Virginia Court of Appeals now has the opportunity in Clements to protect privacy and speech rights by affirming that keyword warrants can’t be reconciled with constitutional protections guaranteed at the federal or state level. We hope it does so.

In addition to the typical rigors of MIT classes, Terrascope Subject 2.00C/1.016/EC.746 (Design for Complex Environmental Issues) poses some unusual hurdles for students to navigate: collaborating across time zones, bridging different cultural and institutional experiences, and trying to do hands-on work over Zoom. That’s because the class includes students from not only MIT, but also Diné College in Tsaile, Arizona, within the Navajo Nation, and the University of Puerto Rico-Ponce (UPRP).

Despite being thousands of miles apart, students work in teams to tackle a real-world problem for a client, based on the Terrascope theme for the year. “Understanding how to collaborate over long distances with people who are not like themselves will be an important item in many of these students’ toolbelts going forward, in some cases just as much as — or more than — any particular design technique,” says Ari Epstein, Terrascope associate director and senior lecturer. Over the past several years, Epstein has taught the class along with Joel Grimm of MIT Beaver Works and Libby Hsu of MIT D-Lab, as well instructors from the two collaborating institutions. Undergraduate teaching fellows from all three schools are also key members of the instructional staff.

Since the partnership began three years ago (initially with Diné College, with the addition of UPRP two years ago), the class themes have included food security and sustainable agriculture in Navajo Nation; access to reliable electrical power in Puerto Rico; and this year, increasing museum visitors’ engagement with artworks depicting mining and landscape alteration in Nevada.

Each team — which includes students from all three colleges — meets with clients online early in the term to understand their needs; then, through an iterative process, teams work on designing prototypes. During MIT’s spring break, teams travel to meet with the clients onsite to get feedback and continue to refine their prototypes. At the end of the term, students present their final products to the clients, an expert panel, and their communities at a hybrid showcase event held simultaneously on all three campuses.

Free-range design engineering

“I really loved the class,” says Graciela Leon, a second-year mechanical engineering major who took the subject in 2024. “It was not at all what I was expecting,” she adds. While the learning objectives on the syllabus are fairly traditional — using an iterative engineering design process, developing teamwork skills, and deepening communication skills, to name a few — the approach is not. “Terrascope is just kind of like throwing you into a real-world problem … it feels a lot more like you are being trusted with this actual challenge,” Leon says.

The 2024 challenge was to find a way to help the clients, Puerto Rican senior citizens, turn on gasoline-powered generators when the electrical power grid fails; some of them struggle with the pull cords necessary to start the generators. The students were tasked with designing solutions to make starting the generators easier.

Terrascope instructors teach fundamental skills such as iterative design spirals and scrum workflow frameworks, but they also give students ample freedom to follow their ideas. Leon admits she was a bit frustrated at first, because she wasn’t sure what she was supposed to be doing. “I wanted to be building things and thought, ‘Wow, I have to do all these other things, I have to write some kind of client profile and understand my client’s needs.’ I was just like, ‘Hand me a drill! I want to design something!’”

When he took the class last year, Uziel Rodriguez-Andujar was also thrown off initially by the independence teams had. Now a second-year UPRP student in mechanical engineering, he’s accustomed to lecture-based classes. “What I found so interesting is the way [they] teach the class, which is, ‘You make your own project, and we need you to find a solution to this. How it will look, and when you have it — that’s up to you,’” he says.

Clearing hurdles

Teaching the course on three different campuses introduces a number of challenges for students and instructors to overcome — among them, operating in three different time zones, overcoming language barriers, navigating different cultural and institutional norms, communicating effectively, and designing and building prototypes over Zoom.

“The culture span is huge,” explains Epstein. “There are different ways of speaking, different ways of listening, and each organization has different resources.”

First-year MIT student EJ Rodriguez found that one of the biggest obstacles was trying to convey ideas to teammates clearly. He took the class this year, when the theme revolved around the environmental impacts of lithium mining. The client, the Nevada Museum of Art, wanted to find ways to engage visitors with its artwork collection related to mining-related landscape changes.

Rodriguez and his team designed a pendulum with a light affixed to it that illuminates a painting by a Native American artist. When the pendulum swings, it changes how the visitor experiences the artwork. The team built parts for the pendulum on different campuses, and they reached a point where they realized their pieces were incompatible. “We had different visions of what we wanted for the project, and different vocabulary we were using to describe our ideas. Sometimes there would be a misunderstanding … It required a lot of honesty from each campus to be like, ‘OK, I thought we were doing exactly this,’ and obviously in a really respectful way.”

It’s not uncommon for students at Diné College and UPRP to experience an initial hurdle that their MIT peers do not. Epstein notes, “There’s a tendency for some folks outside MIT to see MIT students as these brilliant people that they don’t belong in the same room with.” But the other students soon realize not only that they can hold their own intellectually, but also that their backgrounds and experiences are incredibly valuable. “Their life experiences actually put them way ahead of many MIT students in some ways, when you think about design and fabrication, like repairing farm equipment or rebuilding transmissions,” he adds.

That’s how Cauy Bia felt when he took the class in 2024. Currently a first-year graduate student in biology at Diné College, Bia questioned whether he’d be on par with the MIT students. “I’ve grown up on a farm, and we do a lot of building, a lot of calculations, a lot of hands-on stuff. But going into this, I was sweating it so hard [wondering], ‘Am I smart enough to work with these students?’ And then, at the end of the day, that was never an issue,” he says.

The value of reflection

Every two weeks, Terrascope students write personal reflections about their experiences in the class, which helps them appreciate their academic and personal development. “I really felt that I had undergone a process that made me grow as an engineer,” says Leon. “I understood the importance of people and engineering more, including teamwork, working with clients, and de-centering the project away from what I wanted to build and design.”

When Bia began the semester, he says, he was more of a “make-or-break-type person” and tended to see things in black and white. “But working with all three campuses, it kind of opened up my thought process so I can assess more ideas, more voices and opinions. And I can get broader perspectives and get bigger ideas from that point,” he says. It was also a powerful experience culturally for him, particularly “drawing parallels between Navajo history, Navajo culture, and seeing the similarities between that and Puerto Rican culture, seeing how close we are as two nations.”

Rodriguez-Andujar gained an appreciation for the “constant struggle between simplicity and complexity” in engineering. “You have all these engineers trying to over-engineer everything,” he says. “And after you get your client feedback [halfway through the semester], it turns out, ‘Oh, that doesn’t work for me. I’m sorry — you have to scale it down like a hundred times and make it a lot simpler.’”

For instructors, the students’ reflections are invaluable as they strive to make improvements every year. In many ways, you might say the class is an iterative design spiral, too. “The past three years have themselves been prototypes,” Epstein says, “and all of the instructional staff are looking forward to continuing these exciting partnerships.”