Eleven years ago, MIT Lincoln Laboratory staff member Robert Shin was challenged by a colleague to find a way to extend the resources of Beaver Works — a joint education and research center created by the MIT School of Engineering and the laboratory — to some of the best and brightest engineering students around the country. A year later, in summer 2016, the MIT Beaver Works Summer Institute (BWSI) was born. 

BWSI, which celebrated its 10th anniversary this summer, is a four-week program designed to offer high school students a transformational hands-on experience while teaching them skills to fuel their future studies and careers in science, technology, engineering, and mathematics (STEM).

"As I look back at the last 10 years of BWSI, I am humbled and grateful to be a part of this amazing journey," says Shin, who has served as the founding director and then the STEM program director for BWSI since 2016. "What started as an idea has evolved into something much bigger and more impactful than we ever imagined. Each year, our students have exceeded every expectation through their curiosity, hard work, willingness to accept challenges, and perseverance." 

Originally launched with a class of 46 students from 24 high schools across the country, the BWSI program has seen steady growth each year, both in the number of students enrolled and the total number of courses offered. In the first year of the program, BWSI offered just a single course — the Rapid Autonomous Complex Environment Competing Ackermann-Steering Robots (RACECAR) course — that challenged students to program vehicles to navigate a racetrack full of obstacles. A decade later, BWSI now offers 14 courses spanning fields such as artificial intelligence, data science, cybersecurity, quantum computing, and microelectronics to more than 440 students. 

"The nation needs innovators, problem-solvers, and leaders to tackle the national security challenges of tomorrow in a rapidly evolving world," says Melissa Choi, director of Lincoln Laboratory. "By bridging the gap between classroom theory and practical application, BWSI inspires the next generation of scientists and engineers and exemplifies the laboratory’s commitment as a federally funded research and development center to help develop the future STEM workforce." 

Beaver Works Operational Manager Lisa Kelley has been with the program from the start, for both the founding of Beaver Works in 2010 and the launch of BWSI in 2016. She recalls welcoming the first class of 46 students, adding that she never could have imagined the impact that BWSI would have a decade later. 

"Today, I look at the thousands of students who have come through our programs with immense pride and gratitude," says Kelley. "What makes me most proud is that we built something both exceptional and accessible, a program that challenges students at the highest level while expanding opportunity." 

Beyond the summer curriculum, BWSI has expanded throughout the years by offering eight-week outreach programs in the spring and fall that build technical foundations for high school students in grades 9–11 prior to entering the competitive summer program. One of these fall programs is "Girls Who Can," which focuses on engaging young girls with STEM. Additionally, in 2022 and 2023, BWSI launched national challenges that adapted the content of two of their courses (Build a CubeSat and Designing for Assistive Technology) into an eight-month team-based format with mentors. 

The BWSI curriculum has inspired collaborators to create similar programs in countries such as Greece, Japan, Canada, Mexico, Italy, and Spain, as well as parallel programs in schools and universities across the country. 

Beaver Works Manager Joel Grimm says that the growth of the program has continued to challenge and inspire him from year to year. "To me, Beaver Works and BWSI mean embracing change — saying 'yes' to something new and innovative," Grimm says. "I encourage students to step out of their comfort zone and see what’s possible, and I hope that more students understand that BWSI is a unique opportunity to take a risk and stretch beyond their expectations." 

After participating in BWSI, many students continue to engage with the program. One example is Diptanshu Sikdar, who participated in BWSI for two summers as a high school student before returning as a teaching assistant for the Quantum Software and Software Development courses. 

"BWSI transformed my curiosity about quantum computing into a strong foundation and introduced me to the beauty of algorithm design and computer science," says Sikdar, who went on to pursue a PhD in quantum science and credits his experience with BWSI for building connections that led to his current job at MITRE. "It was much more than a summer program for me; it was the point of origin for my quantum journey and the foundation for my growth as a student, researcher, teacher, and leader." 

It’s this type of transformational experience that Shin hopes BWSI students will continue to have well into the future. 

"I believe the last 10 years of BWSI were just the beginning," Shin says. "The BWSI program at MIT will continue to be impactful, and we see a future where all students interested in programs like BWSI will have an opportunity to participate and learn. Wouldn’t that be wonderful? It is not only possible, but we owe that to our next generation." 

To learn more about BWSI and how to support its mission, visit the BWSI website or email the administrator.

Justin.sh is reporting on a confidential Cellebrite document that has been leaked to him:

Cellebrite calls it Advanced Unlocks (AU from here on), not to be confused with the “advanced unlocking services” they’ve marketed for years. A press release from earlier this year confirms AU is a new product for 2026.

[…]

According to Cellebrite, AU:

• is a “next-generation AFU vector that is more resilient to Apple’s OS changes”
• works with Stolen Device Protection enabled, which tightens USB restrictions
• is enabled by a dedicated appliance with a stated range (!) of about 8 meters...

Dozens of surgical procedures require doctors to connect two pieces of tissue together in a delicate stitching process. It happens in triple heart bypass, C-sections, and surgeries to repair hernias and tendons. Today, those procedures are mostly done by surgeon-controlled robots or by hand, and the margin for error is small. Even small errors in suturing or stapling can lead to leaks with devastating consequences to patients.

SanaHeal wants to make connecting tissues more like using tape. The company has developed a bioadhesive technology that can rapidly stick to wet and uneven surfaces and form air- and fluid-tight seals to simplify surgical procedures. The company’s product, first developed at MIT, was inspired by sticky materials in nature like barnacles and spider webs.

SanaHeal’s technology has already gained regulatory approval to stop trauma bleeding, and next year it will enter human trials to help control bleeding during surgeries and for repair of various injured tissues.

The startup, which collaborates with larger companies for clinical trials and commercialization, believes those applications only scratch the surface of the technology’s potential.

“If someone is struggling to put something on biologic tissues, we can help,” SanaHeal founder and chief technology officer Hyunwoo Yuk SM ’16, PhD ’21 says. “The current way of doing this is by suturing through tissues, which is painful and difficult to do perfectly during surgery. Our solution is more like a Post-it note. It’s much less invasive and easier to use with surgical robots. Also, faster and more efficient surgical procedures have potential to help lower ever-increasing costs of surgeries that are significantly straining the health care system.”

A sticking problem

Yuk conducted his master’s and PhD in the lab of Xuanhe Zhao, MIT’s Uncas (1923) and Helen Whitaker Professor of Mechanical Engineering. Initially, his research focused on the mechanical properties of soft materials called hydrogels, but about halfway through the program, he pivoted to study their biomedical applications, in part motivated by a serious accident his family member was involved in.

“We were looking at how to make these hydrogels better adhesives, and because we are in Boston, we started working with doctors and medical schools in the surrounding area,” Yuk recalls.

Those conversations made him realize how often surgeons have to seal or attach tissues. That process usually involves staples or sutures, which can cause more damage to tissues, trigger immune responses, and lead to scar tissue or infections.

“These are often tiny tissues that sometimes require needles thinner than a human hair to be used during microsurgery,” Yuk explains. “They are very difficult surgeries, and they can lead to all kinds of complications.”

Yuk realized his hydrogels offered a better solution. The materials, which are made from polymers and water, are biocompatible, meaning they minimize immune responses and scar tissue buildup. Early versions of the technology acted like double-sided tape, absorbing water from wet tissues using polyacrylic acid — an absorbent material used in diapers — to adhere to them in a matter of seconds.

Yuk had planned to stay in academia after his PhD, but as he learned more about what it would take for the technology to reach patients, he decided to focus on leading that work.

“It’s very difficult for innovations to make it into the clinic and help patients if you’re in an academic research lab,” Yuk says. “We didn’t know anything about startups, so we used resources like MIT’s Deshpande Center, the Venture Mentoring Service, and MIT’s Technology Licensing Office in hope that there might be external interest in our technology. We quickly learned the regulatory hurdles presented too much risk at the early stages for potential partners, so we had to commercialize it ourselves.”

The founders received funding from the Deshpande Center to start developing the company. SanaHeal was officially formed in 2021.

Yuk and his collaborators initially focused on applying their technology to wounds to stop bleeding. But they soon shifted to focus on surgical applications, which are more numerous.

“It’s a major unmet need,” Yuk says. “We are giving surgeons more options for a host of complex procedures. They are really excited about this.”

Partnering for impact

SanaHeal provides its technology to larger medical device company partners, who are developing products for a wide range of applications including in open, laparoscopic, and endoscopic surgeries as well as in combination with existing devices for sutureless implantation in applications like hernia and nerve repairs.

“We have envisioned ourselves as a platform company from the beginning,” Yuk says. “We don’t have the resources to develop 10 products at once, so we partner with bigger companies to commercialize solutions.”

Yuk, who has been working on the technology for nearly a decade, admits the commercialization process has taken longer than he expected. But he believes the payoff — seeing a technology he first developed in a lab have an impact on patients — will make the journey worth it.

“My life goal as a researcher is to see the full lifecycle of a new technology, from the scientific discovery, to research, to understanding how it works, to engineering it, to working with subject matter experts, and finally developing a product that gets approved to help humans,” Yuk explains. “It’s rare, but it would be incredibly rewarding and would be a great story to tell to inspire my research colleagues.”

Nature Climate Change, Published online: 20 October 2026; doi:10.1038/s41558-026-02766-y

Clean hydrogen is a versatile decarbonization option, but given its energy-intensive production, high costs and limited near-term supply, uses with the greatest environmental and economic benefits must be prioritized. Analysis of ten end-use sectors identifies high-value applications and suggests that uniform supply-side subsidies risk misallocating clean hydrogen and public funds, highlighting the need for sector-specific demand-side policies.

Nature Climate Change, Published online: 20 October 2026; doi:10.1038/s41558-026-02767-x

Extreme southward swings of the South Indian convergence zone can lead to severe rainfall and flooding in southern Africa. Here the authors show that climate change leads to an increase in the frequency and intensity of such swings.

This post was co-written by Andi Cao, an EFF legal intern.

Automated license plate readers (ALPRs) are mass surveillance. They photograph every passing car and log where and when it was captured, building a searchable record of where nearly everyone drives. Cities should not buy them, and cities that already have them should shut them down.

Where these systems still operate, courts and state legislatures must reduce their harms by (among other things) requiring police to get a warrant before searching stored ALPR data. A warrant requirement would not make mass surveillance acceptable, but it would require police to justify a search to a judge before accessing records of people’s movements. Without this independent judicial check on police power, officers have been searching this data for improper reasons (many exposed by EFF) that violate our fundamental rights.

A warrant requires a judge to find probable cause and specificity—many warrantless police searches of ALPR data lack both. Officers have searched this data to profile Romani people, monitor protesters, investigate an abortion, find immigrants for deportation, track a man who complained about a deputy, stalk romantic partners, and even for no stated reason beyond “LMAO.” A warrant requirement would force law enforcement to justify these searches before accessing sensitive location records, not leave the people they target to discover and challenge the abuse after the harm is done.

What a Warrant Requires

Warrants are a check on police power. Before officers can search a person or their property, the Fourth Amendment requires them to convince a neutral judge, under oath, that probable cause exists, meaning a fair probability the search will turn up evidence of a crime. The warrant must also describe with particularity what will be searched, so police cannot go on a fishing expedition. Critically, the officer who wants to search is not the person who decides whether the search is justified.

That is why warrants are required for so many kinds of police surveillance. For example, police generally need a warrant to wiretap phone calls, to search the contents of a cell phone, and to obtain historical cell phone location records.

Stored ALPR data reveals sensitive information about the private details of our lives: where we go, when, and with whom. That’s why we believe police searches of stored ALPR data should also require a warrant. Earlier this month, a federal judge in Oklahoma agreed.

What Goes Wrong Without a Warrant: San Jose, California

San Jose demonstrates the harmful consequences of letting law enforcement search stored ALPR data without a warrant. EFF and the ACLU of Northern California represent SIREN and CAIR-California in a lawsuit challenging this practice.

The first problem is the sheer number of times that SJPD officers search stored ALPR data: 261,711 times between June 5, 2024, and June 17, 2025, about 692 searches a day. That scale is the result of having no warrant requirement, which would greatly reduce this kind of casual police surveillance.

Inevitably, officers abuse this unchecked power. In September, San Jose’s police chief disclosed an example from five months earlier: the department had fired an officer who used its ALPR system to track a woman who had accused his cousin of domestic violence, and then shared her location information with his cousin. The abusive search of stored ALPR data came to light only after the woman told police that her alleged assailant had implied he was tracking her.  A warrant requirement would have prevented this.

Of course, similar problems appear nationwide. For each example below, consider how things could have turned out differently if the officer had to put the reason for the search in a sworn warrant application and handed it to a judge.

Racial profiling of Romani people

More than 80 law enforcement agencies searched the Flock network using terms that unfairly stereotype Romani people, often without mentioning any crime. For example, the Grand Prairie (Texas) Police Department searched for “g*psy” six times using a Flock feature that identifies vehicles traveling together, in effect targeting a whole community without naming a crime.

No judge should sign that warrant. Ethnicity is not evidence of a crime, and a search aimed at an entire ethnic group is the opposite of particular: it targets people for who they are, not evidence that they committed a crime.

Surveillance of protesters

EFF found that more than 50 agencies ran hundreds of protest-related searches of ALPR data in 2025. For example, the Tempe (Arizona) Police Department ran three searches tied to the No Kings protest across 425 networks, with no identified crime.

This lacks probable cause and targets activity that is protected by the First Amendment.

Investigating an abortion

In 2025, a Texas sheriff’s office searched more than 83,000 Flock cameras, including cameras in Washington and Illinois, where abortion is legal, listing the reason as “had an abortion, search for female.” Records later obtained by EFF show deputies had opened a “death investigation” after the woman’s partner, who was later charged in connection with assaulting her, reported her self-managed abortion.

In a state where abortion is legal, a judge would not grant a warrant for a search like this.

Finding immigrants for deportation

San Francisco police let out-of-state agencies search the city’s ALPR data (which itself violates California law), and at least 19 of those searches were marked as related to ICE (though San Francisco is a sanctuary city). This problem is widespread. One investigation found more than 4,000 cases in which police departments around the country searched stored ALPR data on behalf of federal law enforcement, including for immigration investigations. Some agencies even connect their ALPR systems to an “Immigration Violator” hotlist populated by ICE, which includes plates tied to administrative warrants issued by ICE agents without any judge involved.

Here too, a judge in a state where police may not help enforce immigration law would not issue a warrant for these searches.

Retaliation against a critic

In May 2025, Navy veteran Napoleon Jones lawfully recorded a police traffic stop of another person in Sussex, Wisconsin. Jones alleges that a Waukesha County deputy retaliated by stopping and arresting him. He was released five hours later without citations or charges, and the deputy’s supervisor concluded the stop was illegal. Jones filed a complaint against the deputy the next day.

In the weeks that followed, officers reportedly ran more than 100 Flock searches against Jones, including some run by the deputy he had complained about. Deputies testified that a lieutenant ordered the searches in connection with Jones’s complaint.

No judge should authorize tracking a man for criticizing the police.

Stalking romantic partners

The Institute for Justice (IJ) has documented dozens of cases in which officers allegedly used ALPRs to track current, potential, and former romantic partners. For example, a Lufkin, Texas, officer was indicted on 100 felony counts after allegedly searching for his ex-girlfriend and people she knew more than 10,000 times. Likewise, a Milwaukee officer allegedly tracked a woman he was dating and her ex nearly 180 times over two months. IJ found that most of these cases surfaced only after victims reported the officer.

We hope that no officer would write “tracking the location of my ex-girlfriend” on a warrant application, and even if they did so, a judge would not authorize such a search.

Searches for “LMAO”

Sometimes officers do not even provide a real reason to search stored ALPR data. EFF found officers logging search reasons such as “LOL,” “LMAO,” “idk,” and random keyboard mashing. These entries are supposed to document why a search complies with an agency’s own use policies. A Goshen (Indiana) Police Department officer searched 6,474 networks, representing data from 82,413 cameras, with “idk” as the reason.

An officer who asked a judge for a warrant to track someone for “LMAO” would be laughed out of the courtroom.

The Solution

The best way to eliminate these abusive searches is to end ALPR mass surveillance. Where these systems remain, however, courts and state legislatures must require police to get a warrant before searching stored ALPR data. Warrants are the time-tested way to put an independent judicial check on police use of a dangerous surveillance technology. Officers must establish probable cause and specifically describe the proposed search before accessing the data, while existing exceptions for genuine emergencies still apply.

These protections must also cover real-time tracking. An ALPR “hotlist” is a list of license plates that trigger an alert when a camera spots a matching plate. Officers can stalk potential romantic partners by putting them on a hotlist, as a Florida deputy allegedly did with a woman he met on the set of a TV show. Thus, state legislators should require officers to get a warrant before putting a person on an ALPR hotlist (as well as a warrant to search stored ALPR data).

Related Cases: SIREN and CAIR-CA v. San Jose

From its beginning in 2015, the premise of MIT’s Aging Brain Initiative (ABI) has been that neurodegenerative diseases such as Alzheimer’s are complex, “system-wide” breakdowns that will require advances of many kinds from many sources to address. The six talks at the ABI’s symposium “Innovations for the Aging Brain” on Sept. 25 illustrated what that’s come to mean at MIT, as scientists and engineers described their latest work to improve health for the aging brain.

“The ABI is unique because it brings together researchers from a diverse array of fields,” said ABI Director Li-Huei Tsai, Picower Professor in the the Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences (BCS). “Today’s symposium showcases MIT’s collective brilliance across neuroscience, engineering, chemistry, and beyond.”

One of the talks, for instance, featured a team effort including Giovanni Traverso, the Karl Van Tassel (1925) Professor of Mechanical Engineering and a physician at Brigham and Women’s Hospital, who has joined forces with Tsai and other collaborators to develop an artificial intelligence system that could improve personalized Alzheimer’s care by matching patients to the best prevention interventions for them. The system integrates data from clinical trials, patient lifestyle histories, and multi-omics data (e.g., genomics, proteomics) to predict an individual’s Alzheimer’s progression and likely response to interventions. 

Speaking alongside project leads Arvid Gollwitzer, an engineer-scientist in the Broad Institute of MIT and Harvard, and MIT-Novo Nordisk AI Fellow Adrián Noriega de la Colina, Traverso described how the team engineered an AI model, called FINGERS-7B, to integrate these multiple highly diverse data sources into its training, thereby amassing 8 trillion “tokens” to inform its predictions.

“Every individual is very unique in their biology, and that links to how we respond to those interventions,” said Noriega. Not only could AI predictions help tailor individual prevention measures, he added, but also it could inform clinical trial designs by better pinpointing the most appropriate populations to target with a prospective drug or other treatment.

Picower Professor Earl K. Miller focused on prevention of a different problem for the aging brain: cognitive side effects from exposure to too much general anesthesia. Noting high rates of postoperative delirium in older adults after major surgery, Miller described his research with ABI founding member and Institute Professor Emery N. Brown, a Picower Institute and BCS colleague, to develop a closed-loop system that uses brain wave-based measurements of unconsciousness to optimize anesthesia dosing. Miller and Brown’s labs have shown that regardless of their molecular means of action, multiple anesthesia drugs induce unconsciousness in the same way: by shifting brainwaves out of phase with each other, disrupting the coordination of communication across the brain’s cortex that sustains cognition and consciousness. Informed by such brain wave “signatures” of unconsciousness, the system could help anesthesiologists to safely tailor dosing during surgery.

In her talk, associate professor of mechanical engineering Ritu Raman, the Eugene Bell Career Development Chair of Tissue Engineering, described how she’s approaching the study of neuromuscular diseases, including amyotrophic lateral sclerosis (ALS) and muscular dytrophies, by developing advanced lab models of muscle tissue that also integrate nerves and vasculature. For instance, for ALS, her lab has been able to make the cultures from induced pluripotent stem cells derived from patients. The lab models give her team an opportunity to examine neuromuscular junctions in detail and to model how they change in response to potentially therapeutic drugs.

In many neurodegenerative diseases associated with aging, such as Alzheimer’s and Parkinson’s, a major pathology is misfolding and aggregation of intrinsically disordered proteins, such as amyloid-beta or alpha-synuclein. In her talk, William R (1964) and Daniel L. (1995) Young Career Development Assistant Professor of Chemistry Oleta Johnson described her lab’s work to prevent aggregation before it starts. In pursuit of that strategy, she said her lab’s research is focused on developing the chemical knowledge and means to identify proteins at issue, detect that they are moving into a problematic state, and then control their conformation.

Eugene McDermott Professor in the Brain Sciences and Human Behavior Alan Jasanoff, a member of the BCS and Biological Engineering departments as well as the McGovern Institute for Brain Research, described a very different application of chemistry to aging brain research. He described how his lab develops novel chemical probes to make molecules in the brain visible in magnetic resonance imaging. These include the neuromodulatory enzyme cholinesterase, which is the target of many of the few drugs approved to treat Alzheimer’s, and also lipids, which several brain cell types mishandle in Alzheimer’s, Tsai and others have shown. His lab has also developed technology to genetically encode a novel probe in brain cells to improve MRI visualization of brain activity.

And while many researchers focus on the prominent problems that proteins or lipids present in neurodegenerative disease, Novartis Professor of Chemistry Laura Kiessling discussed a less-appreciated but still important component of cellular function: sugars called glycans that line the surface of cells. In particular, researchers have linked a glycan called heparan sulfate to Alzheimer’s disease. Kiessling’s lab, working with ABI founding member Ed Boyden, Y. Eva Tan Professor of Neurotechnology, is working to characterize and understand glycans. Her lab, for instance, has shown that glycans of different sequences organize on cell membranes so that they can bind and recruit specific proteins to distinct regions on cells.

“We are really excited about the fact that we’ve observed this now for the first time and have a toolkit to study other diseases,” she said. Understanding glycan binding could even give scientists a means to target different cells and cellular processes.

Following the six talks, more than a dozen trainees at a poster session presented even more aging brain-related projects from around the Institute, further highlighting MIT’s diversity of research in the area, something the ABI exists to encourage.

From massive AI datasets to the sugars on individual cells, the symposium highlighted how MIT scientists are pushing the frontiers of science and engineering to improve understanding of, and interventions for, the health of the aging brain.

Despite millions of years of evolution, the development of the human fetus remains a complex process that is vulnerable to genetic, environmental, and metabolic disruptions. These disruptions could result in developmental abnormalities, starting with one of the first organs to begin developing in the fetus that continues to develop after birth: the brain.

In a routine prenatal ultrasound, the sonographer will flag possible brain abnormalities for additional magnetic resonance imaging (MRI). But imperfect imaging can result in missed or incorrect diagnoses. 

To assist with the early detection and treatment of these abnormalities, researchers at MIT, Boston Children’s Hospital, and Inria have developed and trained a deep learning model called E(3)-Pose that helps image the fetus’s head while it’s in motion to capture a high-resolution image that the radiologist can examine. A paper about the work was presented at the 2026 European Conference on Computer Vision. 

“One of the problems with having the baby move is that the slices are all over the place,” says first author Ramya Muthukrishnan. Muthukrishnan is an MIT PhD student in the lab of Sunlin (1966) and Priscilla Chou Professor of Electrical Engineering and Computer Science Polina Golland, who is a principal investigator at the MIT Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic) and the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL).

By slices, Muthukrishnan is referring to a series of 2D images taken during a fetal MRI scan. These 2D planes stacked together effectively make up one high-resolution, 3D image of the fetus’s head. But if the head shifts during imaging, each 2D image “slice” could end up being taken from a different angle, resulting in gaps in the final scan and unusual slice orientation. These problems may cause the physician reviewing the MRI to miss or incorrectly diagnose a pathology in the fetal brain.

“There are very fine details that you need a very good volumetric picture of the brain to see,” explains Muthukrishnan. “If you have coverage gaps, you can’t see critical regions, and you need to be able to see well to detect abnormal development.”

What makes E(3)-Pose particularly good at tracking fetal motion is its focus on physics. Rather than simply training the model to learn physics from the data, the very principles of physics are baked into the neural network: left-right symmetry of the fetal head is embedded in the model architecture, making it easier for the model to accurately identify the brain’s orientation. 

When Muthukrishnan began working on the project at the start of her PhD, she had no background in physics, let alone MRI physics. Most of her experience was in computer science. “I was surprised when I had to go and implement this on the scanner,” she recalls. “I was not prepared for how many times I would crash the scanner because putting things on hardware and getting it to work on hardware is a different beast than training a model to run on data on a GPU.” 

Despite the challenges, Muthukrishnan persisted. She remembers shadowing a radiologist who performed fetal MRI scans at Boston Children’s Hospital at the start of her PhD and observing the shared frustration of the patient and radiologist: the pregnant mother felt uncomfortable after spending an hour in an MRI, only to quit the scan halfway; the radiologist was unable to procure a full, high-quality MRI to monitor the development of the fetus.

“Seeing frustration about it helped me decide what I wanted to do with my PhD, because it felt like this was a pain point,” Muthukrishnan says.

Fortunately, she had help: Co-author Borjan Gagoski, an assistant professor of radiology at Harvard Medical School and a researcher at Boston Children's Hospital, developed a sequence that acquires additional MRI scans to assist with training E(3)-Pose and worked with Muthukrishnan to help troubleshoot the scanner whenever it crashed. Co-author and Boston Children’s Research Assistant Aryn Lee recruited the pregnant volunteers and assisted with acquiring each of the MRI scans. Senior authors who helped guide the project include Elfar Adalsteinsson, MIT professor of electrical engineering and computer science; Ellen Grant, the Käthe Beutler Professor of Pediatrics and professor of radiology at Harvard Medical School; and Golland. Benjamin Billot, a researcher at Inria and a former postdoc in Golland’s group, helped Muthukrishnan get started with the initial method he developed.

“Ramya’s work elegantly combines geometric concepts like equivariance and symmetry with the power of deep learning to change how MRI scanners acquire the images, and to ultimately help the physicians detect and treat problems early in development. It’s an impressive research project that is deep and fundamental in how it models image acquisition, with clear clinical impact,” says Golland.

Grant, who directs the Fetal Neonatal Neuroimaging and Developmental Science Center (FNNDSC) at Boston Children’s, adds that Muthukrishnan’s work “will automate fetal brain MRI, replacing today's slow, manual process with faster and more comfortable scans for mothers, while also providing higher-quality images for radiologists to interpret.”

E(3)-Pose’s applications could extend beyond motion tracking for fetal MRIs. It could assist with imaging other hard-to-image anatomy, and even autonomous driving applications, where limited sensors need to capture noisy, 3D data in order to track 3D objects.

For now, Muthukrishnan hopes to improve E(3)-Pose so that it can help with imaging second-trimester or younger fetuses, as the model is primarily trained on third trimester fetuses. Second-trimester fetuses are “so small, they move around a lot more because there’s more space in the uterus,” Muthukrishnan says. “They’re more important to focus on because the earlier you detect brain abnormalities, the easier it is to intervene.” 

This work is supported by the MIT Jameel Clinic, the MIT Takeda Program, the Chou Family Transformative Research Fund, the National Institute of Biomedical Imaging and Bioengineering, and the Eunice Kennedy Shriver National Institute of Child Health and Human Development.

Exciting news for Safari users: Privacy Badger is now available on Safari for macOS! You’ll need Safari version 26.5 or higher to use it.

Whether you want to protect yourself from data brokers or just don’t want Big Tech profiting off your browsing data, Privacy Badger is here to help. More than 4 million people already use EFF’s free tracker-blocking extension to take a stand against online surveillance. Safari users can now join them and EFF in the fight for a better web.  

Install Privacy Badger

Support the Fight Against Online Tracking

Safari support has been one of Privacy Badger’s most requested features for years. Thanks to recent improvements in Safari's extension support, we’ve finally made it happen for Safari version 26.5 and higher. Privacy Badger is now available on all major desktop browsers. And before you ask, yes, we will look into supporting Safari for iOS once we've ironed out any issues on macOS.

Why Install Privacy Badger on Safari? 

Most websites contain invisible tracking code that lets companies collect, share, and profit from your personal data. Some of those companies are data brokers that sell your information to anyone willing to pay. Creepy ads are just the tip of the iceberg when it comes to the harms of online tracking: Once your information enters the data broker ecosystem, it’s nearly impossible to know who buys it or what they’re doing with it. Privacy Badger blocks online trackers to prevent your browsing data from being used against you.  

Privacy Badger goes beyond Safari’s built-in privacy features to provide additional protection against online tracking. In its default browsing mode, Safari limits the information that trackers can access but still allows them to load. Privacy Badger blocks trackers from loading in the first place, preventing them from tracking you using techniques that bypass Safari’s protections. 

Privacy Badger works well by itself, and will continue to enhance your privacy when installed along side an ad blocker or with Safari’s Advanced settings to block trackers in all windows. Since Privacy Badger automatically learns to block trackers, Privacy Badger will block trackers that other tools don’t know about or choose not to block. 

For example, Privacy Badger blocks tracking from widgets like embedded tweets, comments sections, and video players. These widgets can share your data even if you never click on them. Privacy Badger prevents these trackers from loading and replaces them with click-to-activate placeholders. 

Beyond tracker blocking, Privacy Badger automatically sends the Global Privacy Control (GPC) signal to tell websites not to sell or share your personal information. The GPC signal is already legally binding in a growing number of states, including California, Colorado, New Jersey, and Connecticut.  

All these features (and more) work together to help keep your browsing activity private on Safari. 

Try It Out and Spread the Word 

If you use Safari on macOS, give Privacy Badger a try. You’ll install Privacy Badger through the App Store on your macOS device. Installing extensions is a little more complicated on Safari than other browsers, so follow Apple’s directions here if you’re having trouble.  

This is our first release of Privacy Badger for Safari, and we will keep improving it based on your feedback. If you run into any problems, let us know by emailing extension-devs@eff.org, or by opening an issue on GitHub. And if blocking harmful trackers causes issues on a website you visit, you can disable Privacy Badger for that specific site while keeping it enabled and protecting you everywhere else. You can also let us know about broken sites by using the “Report broken site” button found next to the “Disable for this site” button in Privacy Badger’s popup window. 

When you use Privacy Badger, you join EFF and millions of other users in the fight for a better web. Help spread the word by telling your friends and family who use Safari that they can use Privacy Badger too!  

Install Privacy Badger 

Support the Fight Against Online Tracking 

Free Tracker Blocker Used by Millions Will Protect Even More Users' Privacy

SAN FRANCISCO—Privacy Badger, the award-winning tracker blocker made by the member-supported digital rights nonprofit Electronic Frontier Foundation (EFF), is now available for Safari on macOS.  

Safari users can download the free browser extension today and join the more than 4 million people using Privacy Badger to take a stand against data brokers and online surveillance. Users will need Safari version 26.5 or higher to use it.

Ranked as one of the best ad tracker blockers by The Wirecutter, Consumer Reports, PC Mag and more, Privacy Badger is different from other blocking extensions. Created by EFF to stop companies from spying on you online, Privacy Badger automatically learns to block hidden trackers on the web, along with additional privacy-protecting features. 

"Most websites contain invisible tracking code that lets companies collect, share, and profit from your personal data," said Lena Cohen, Staff Technologist at EFF. "Creepy ads are just the tip of the iceberg when it comes to the harms of online tracking: Once your information enters the data broker ecosystem, it’s nearly impossible to know who buys it or what they’re doing with it. Safari support has long been one of Privacy Badger’s most requested features and EFF is excited to be able to make this important tool available to Safari users to fight back for a better web." 

On Safari, Privacy Badger goes beyond the browser’s built-in privacy features to provide additional protection against online tracking. In its default browsing mode, Safari limits the information that trackers can access, but it still allows them to load. Privacy Badger blocks online trackers from loading in the first place, preventing them from tracking you using techniques that bypass Safari’s protections. 

With today's launch, Privacy Badger is now available on all major desktop browsers. 

As the leading nonprofit defending online civil liberties, EFF is committed to fighting mass surveillance in the courts, through activism, and by making free, privacy-protecting tools like Privacy Badger. Now, even more people can join the movement against online tracking, by installing Privacy Badger themselves, telling their friends and family who use Safari about it, or by supporting Privacy Badger today. 

For more on Privacy Badger for Safari on macOS: https://www.eff.org/deeplinks/2026/10/announcing-privacy-badger-safari-macos

For more on the dangers of online tracking: https://www.eff.org/deeplinks/2025/03/online-tracking-out-control-privacy-badger-can-help-you-fight-back 

For more on how Privacy Badger works: https://privacybadger.org/ 

Contact: press@eff.org

Tags: Privacy BadgerAd trackersSurveillance Advertising

Interesting research:

Abstract: Vision-language models (VLMs) can solve visual CAPTCHAs without task-specific training, but the agents built on them approach every challenge from scratch. For such an agent, the hundredth instance of a familiar puzzle costs as much time and compute as the first. Specialized detectors invert the trade-off, answering in milliseconds but only for categories they were trained on. Neither improves with exposure. We study what changes when a solver improves with use. Our system pairs a fine-tuned YOLOv8 detector with an open-weight VLM behind a confidence-based router, and runs entirely from screenshots and operating-system input events, with no browser automation or DOM access. It reaches 85.4% overall and 84.2% macro accuracy across 16 classes, exceeding either component alone. Every answer VLM produces also serves as a training label, so the detector absorbs categories it was never trained for, typically after one or two encounters and without human annotation. The same loop also repairs it. A CAPTCHA operator can perturb images against the publicly released detector and drive its accuracy to 0%, but the perturbations leave VLM untouched, and its labels let the detector recover. Under a year-long simulated arms race in which the CAPTCHA operator re-crafts its perturbations each month, the solver recovers every round, and a cheap ~70%-accurate open-weight teacher hardens it as effectively as a perfect oracle. Visual CAPTCHA defenses that assume a failing bot stays failing therefore understate how quickly an adaptive solver returns...

Nature Climate Change, Published online: 19 October 2026; doi:10.1038/s41558-026-02770-2

A resilient society must prepare not only for common harms but also for uncommon vulnerabilities, such as rare disease where the consequences leave no room for failure. Ensuring that people with rare diseases have reliable access to essential care is crucial for fairness and safety in a changing climate.

In principle, artificial intelligence could make finding the right material for an application as simple as ordering dinner: Scientists would tell a large language model what they want, and it would serve them a perfectly designed crystal for the job.

Unfortunately, that approach doesn’t work well yet, and the problem comes down to data: Most materials consist of crystals, which are 3D periodic arrangements of atoms whose structures are stored in specialized formats, not the text streams that today’s large language models are trained on. The experimental literature is also heavily biased toward a few popular classes of crystals, making AI outputs skew toward those materials.

Now MIT researchers have created a multimodal AI model that tackles these problems. The model combines 3D atomic structures with natural language descriptions to create rich linguistic descriptions about the structures from experimental research papers and first-principles computational databases. The unified description outlines not just the chemical composition and structure, but also its properties, processing considerations, and potential applications, helping AI learn more about what makes materials impactful.

In a new paper in Nature Communications, the researchers showed their system could identify better material designs than models trained on text streams alone by allowing the model to “see” the atomic arrangements and bonding patterns. For example, when the researchers asked the model to identify materials suited for solid-state lithium-ion battery electrolytes, its top results included materials that had been independently identified as promising candidates. The researchers also showed their model learned geometric representations of material structures in ways text-only models could not.

“Structure is really at the heart of the materials science field,” says Ju Li, the Carl Richard Soderberg Professor in Power Engineering in the Department of Materials Science and Engineering (DMSE). “If you understand the structure, you can infer quite a lot about the properties and performance of materials. We wanted to build the connection between a material’s atomic structure and its properties and processing for the model.”

Joining Li on a paper about the model are Yang Jeong Park, first author and former MIT postdoc; Mayank Kumaran, a recent University of Illinois Urbana-Champaign computer engineering graduate; Chia-Wei Hsu SM ’26; and Elsa Olivetti, the Jerry McAfee (1940) Professor in Engineering in DMSE.

Getting models to see in 3D

Materials scientists are always on the hunt for new crystals that will lead to the products of the future. Over the last decade, AI has reshaped that process, proposing and designing new crystalline structures, which have highly ordered atomic structures and make up more than 90 percent of solid materials on Earth. Most of those models treat solid crystalline materials as 3D graphs.

The rise of large language models has given researchers another tool for innovation. But existing large language models rely heavily on text from published papers and books, limiting their knowledge of the physical world, since many potential crystal structures are stored in computational databases. Text-based models also lack understanding of visual geometry.

To overcome those challenges, the researchers decided to create a vision-language model for crystalline materials.

They trained a language model to create textual descriptions of crystal structures using an approach known as contrastive learning, in which models compare similar and dissimilar examples of structures and textual descriptions to achieve a unified understanding. This is similar to aligning the visual and language regions of the brain.

Following training, the researchers compared the predictions of two versions of the model: one trained on a dataset containing the AI-generated descriptions and the other trained on a dataset containing text from papers only. They found that the model trained on AI-generated descriptions successfully learned how to align geometric structures with text descriptions while the other did not.

“We wanted to create an AI model that works broadly across materials,” Li says. “That’s why we decided to synthesize narratives rather than rely solely on existing papers. We found when we used that narrative dataset, the models are better at predicting the function.”

Accelerating materials science

The researchers then showed their model could be given a natural language query describing a property and identify crystalline materials with that property, outperforming a baseline.

“There are sometimes these step changes within materials science around algorithmic integration toward useful tools, which the community uses to further advance the field,” Olivetti says. “This approach helps us see more deeply into three-dimensional structures and link structures to property and processing. I hope this can be one of those step changes.”

Li compares the model to AlphaFold, an AI model that links protein structures with function that has helped advance biological research and drug development.

The researchers believe their new approach can work with many crystalline materials, and they hope other material scientists will use it to accelerate their work.

“Being able to see a three-dimensional crystal and how its atoms are connected is super foundational,” Li says. “I think this will be useful to anyone who practices materials science.”

The work was supported, in part, by the U.S. Defense Threat Reduction Agency through the University Research Alliance; the U.S. Defense Advanced Research Projects Agency; and the National Research Foundation of Korea.

New research shows that squid lived all around the globe even before the Cretaceous-Paleogene mass extinction event.

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

Blog moderation policy.

Kendall Square in Cambridge, Massachusetts, is often cited as the most innovative square mile in the world. But when the Kendall Square Association gathered local stakeholders for its 18th Annual Meeting Thursday, the conversation focused on innovation primarily in the context of community.

The event, which took place at the nonprofit startup incubator The Engine, attracted people from across Cambridge, including business leaders, elected officials, local residents, and students, for a night of local food and conversation about the advance of technologies like artificial intelligence and what they mean for society.

“How can we make AI not an enemy of work, but a tool for workers to expand their capabilities and build new expertise or do new things?” asked MIT Institute Professor Daron Acemoglu in a keynote talk. “The world is at a point in time where we need new innovations and new adaptations to growing challenges.”

Acemoglu’s keynote was one of several presentations at the event, which also featured music from the Kendall Square Orchestra, a vote on new KSA board members, and awards for organizations making a positive impact through innovation.

KSA is a nonprofit made up of over 150 organizations across Kendall Square, from major companies to universities, research organizations, and the independent shops and restaurants that give Kendall Square its distinct character.

“People come to Kendall Square to make the world better,” said KSA Executive Director Beth O’Neill Maloney in her opening remarks. “People come here to improve the human condition, people come here to solve big problems, and people come here to innovate for good.”

Acemoglu, who shared the Nobel Prize in economics in 2024 for his work studying how institutions affect prosperity, spoke on the subject of his new book, “What Happened to Liberal Democracy?”

Acemoglu says liberal democracy is under siege as trust in liberal institutions decline, political polarization rises, and people lose faith in elected officials. He acknowledged that the shared prosperity liberal democracy promised has not always been delivered, especially over the last 50 years, but insisted it is the best path forward and in need of renewed support.

“I believe liberal democratic institutions, together with the norms that support them, are one of the greatest human achievements,” Acemoglu said. “It would be unthinkable for people who lived 300 years ago that we could live in societies consisting of hundreds of millions of people with diverse interests, different views, doing different things, and people still participate in politics and reach compromises, and we have state institutions that, by and large, actually provide services and do things for the people, and that we build opportunities for prosperity from which most people benefit while social freedoms are largely upheld.”

Cambridge Mayor Sumbul Siddiqui also spoke at the event. Siddiqui began her talk by telling the story of Cambridge residents working with biotech labs in the 1970s to create a groundbreaking policy on recombinant DNA research, which helped turn Kendall Square into the life science hub it is today.

“All these years later I think that story captures what makes Kendall Square so special: It’s our willingness to work together even when we face big challenges,” Siddiqui said. “It isn’t just the universities or labs, it’s also the culture of collaboration among nonprofits, governments, and residents.”

Broad Institute Chief Communications Officer Lee McGuire started his talk by reviewing a 2009 report on what made Kendall Square such a powerful driver of innovation, noting that factors like talent density, education, and a collective voice remain strong in the area today.

“This report was brilliant in that it captures so many of the special things that matter for a place like Kendall,” McGuire said. “It is the job of the Kendall Square Association and all of us to help protect those things, make sure we name them, understand them, and see them as essential elements of making sure this community stays what it is no matter what happens. As long as there’s a good idea, this is the place that’s going to get that idea moving and scale it up to change the world.”

This is a problem:

Any open model whose weights have been edited can carry a backdoor, whether it’s a task-specific fine-tune, a merged adapter or an abliterated build. We have abliterated models in the title because it’s the most popular reason people download modified weights without verifying what’s inside. Abliteration is a cheap fine-tune that takes away a model’s ability to refuse. You find the direction in the weights that carries “no” and subtract it. The model then answers things a safety-tuned version would turn down. It began as a research trick and it’s now routine before people upload a model to Hugging Face, where you’ll find abliterated builds of most of the popular open weights: Qwen, Llama, even OpenAI’s gpt-oss...

Nature Climate Change, Published online: 16 October 2026; doi:10.1038/s41558-026-02792-w

In a year containing summits from the three United Nations environmental frameworks, we consider how cooperation across the Conferences of the Parties (COPs) can facilitate implementation and impact. Co-timed with the start of the biodiversity COP, we highlight the reciprocal relationship between climate change and biodiversity.

Nature Climate Change, Published online: 16 October 2026; doi:10.1038/s41558-026-02778-8

This year, all three major Rio Conventions — on biodiversity, climate and desertification — convene their respective Conference of the Parties (COP). In advance of the biodiversity COP17, Astrid Schomaker, the executive secretary of the Convention on Biological Diversity, discusses the irrefutable need to integrate biodiversity conservation into broader environmental action to ensure local and global security and prosperity in a rapidly changing world.

Nature Climate Change, Published online: 16 October 2026; doi:10.1038/s41558-026-02775-x

Climate change will further increase the frequency and intensity of extreme weather events. Subjective experience and attribution of extreme weather events to climate change, rather than objective exposure alone, are associated with greater climate-relevant attention and choices. Political affiliation influences attention and behaviour irrespective of subjective attribution and objective exposure.

Nature Climate Change, Published online: 16 October 2026; doi:10.1038/s41558-026-02765-z

Mechanisms behind climate decision-making remain unclear. This study shows that political affiliation and subjective attribution shape attention allocation and climate-related choices, with subjective attribution moderating the effects of political affiliation and objective weather exposure.