The country's disaster preparedness is in tatters as climate funding pledges from advanced countries and donor agencies fail to materialize.

The world is awash in data visualizations, from charts accompanying news stories on the economy to graphs tracking the weekly temperature to scatterplots showing relationships between baseball statistics.

At their core, data visualizations convey information, and everyone consumes that information differently. One person might scan the axes, while another may focus on an outlying data point or examine the magnitude of each colored bar.

But how do you consume that information if you can’t see it?

Making a data visualization accessible for blind and low-vision readers often involves writing a descriptive caption that captures some key points in a succinct paragraph.

“But that means blind and low-vision readers don’t get the ability to interpret the data for themselves. What if they had a different question about the data? Suddenly a simple caption doesn’t give them that. The core idea behind our group’s work in accessibility has been to maintain agency for blind and low-vision people,” says Arvind Satyanarayan, a newly tenured associate professor in the MIT Department of Electrical Engineering and Computer Science (EECS) and member of the Computer Science and Artificial Intelligence Laboratory (CSAIL).

Satyanarayan’s group has explored making data visualizations accessible for screen readers, which narrate content on a computer screen. His team created a hierarchical platform that allows screen reader users to explore various levels of detail in a visualization with their keyboard, drilling down from high-level information to individual data points.

Under the umbrella of human-computer interaction (HCI) research, Satyanarayan’s Visualization Group also develops programming languages and authoring tools for visualizations, studies the sociocultural elements of visualization design, and uses visualizations to analyze machine-learning models.

For Satyanarayan, HCI is about promoting human agency, whether that means enabling a blind reader to interpret data trends or ensuring designers still feel in control of AI-driven visualization systems.

“We really take a human-centered approach to data visualization,” he says.

An eye for technology

Satyanarayan found the field of data visualization almost by accident.

As a child growing up in India, Bahrain, and Abu Dhabi, his initial interest in science sprouted from his love for tinkering.

Satyanarayan recalls his father bringing home a laptop, which he loaded with simple games. The internet grew up along with him, and as a teenager he became heavily engaged in the popular blogging platform Movable Type.

A teacher at heart even as a teenager, Satyanarayan offered tutorials on how to use the platform and ran a contest for people to style their blog. Along the way, he taught himself the skills to develop plugins and extensions.

He enjoyed designing eye-catching and user-friendly blogs, laying the foundation for his studies in human-computer interaction.

When he arrived at the University of California at San Diego for college, he was interested enough in the HCI field to take an introductory class.

“I’d always been a student of history, and this intro class really appealed to me because it was more about the history of user interfaces, and tracing the provenance and development of the ideas behind them,” he says.

Almost as an afterthought, he spoke with the professor, Jim Hollan — a pioneer of the field. Even though he hadn’t thought much about research beforehand, Satyanarayan ended up spending the summer in Hollan’s lab, studying how people interact with wall-sized displays.

As he prepared to pursue graduate studies (Satyanarayan split his PhD between Stanford University and the University of Washington), he was unsure whether to focus on programming languages or HCI. When it came time to choose, the human-centered focus of HCI and the interdisciplinarity of data visualization drew him in.

“Data visualization is deeply technical, but it also draws from cognitive science, perceptual psychology, and visual arts and aesthetics, and then it also has a big stake in civic and social responsibility,” he says.

He saw how visualization plays a role in civic and social responsibility through his first project with his PhD advisor, Jeffrey Heer. Satyanarayan and his collaborators built a data visualization interface for journalists at newsrooms that couldn’t afford to hire data departments. That drag-and-drop tool allowed journalists to design the visualization and all the data storytelling they wanted to do around it.

That project seeded many elements that became his thesis, for which he studied new programming languages for visualization and developed interactive graphical systems on top of them.

After earning his PhD, Satyanarayan sought a faculty job and spent an exhausting interview season crisscrossing the country, participating in 15 interviews in only two months.

MIT was his very last stop.

“I remember being exhausted and on autopilot, thinking that this is not going well. But then, the first day of my interview at MIT was filled with some of the best conversations I had. People were so eager and interested in understanding my research and how it connected to theirs,” he says.

Charting a collaborative course

The collaborative nature of MIT remained important as he built his research group; one of the group’s first graduate students was pursuing a PhD in MIT’s program in History, Anthropology, and Science, Technology, and Society. They continue to work closely with faculty who study anthropology, topics in the humanities, and clinical machine learning.

With interdisciplinary collaborators, the Visualization Group has explored the sociotechnical implications of data visualizations. For instance, charts are frequently shared, disseminated, and discussed on social media, where they are stripped of their context.

“What happens as a result is they can become vectors for misinformation or misunderstanding. But that is not because they are poorly designed to begin with. We spent a lot of time unpacking those details,” Satyanarayan says.

His group is also studying tactile graphics, which are common in museums to help blind and low-vision individuals interact with exhibits. Often, making a tactile graphic boils down to 3D-printing a chart.

“But a chart was designed to be read with our eyes, and our eyes work very differently than our fingers. We are now drilling into what it means to design tactile-first visualizations,” he says.

Co-design is a driving principle behind all his group’s accessibility work. On many projects, they work closely with Daniel Hajas, a researcher at the University College of London who has been blind since the age of 16.

“That has been really important for us, to make sure as people who are not blind, that we are developing tools and platforms that are actually useful for blind and low-vision people,” he says.

His group is also studying the sociocultural implications of data visualization. For instance, during the height of the Covid-19 pandemic, data visualizations were often turned into memes and social artifacts that were used to support or contest data from experts.

“In reality, neither data nor visualizations are neutral. We’ve been thinking about the data you use to visualize, and the design choices behind specific visualizations, and what that is communicating besides insights about the data,” he says.

Visualizing a real-world impact

Interdisciplinarity is also a theme of Satyanarayan’s interactive data visualization class, which he co-teaches with faculty members Sarah Williams and Catherine D'Ignazio in the Department of Urban Studies and Planning; and Crystal Lee in Comparative Media Studies/Writing, with shared appointments in the School of Arts, Humanities, and Social Sciences and the MIT Schwarzman College of Computing.

In the popular course, students not only learn the technical skills to make data visualizations, but they also build final projects centered on an area of social importance. For the past two years, students have focused on the housing affordability crisis in the Boston area, in partnership with the Massachusetts Area Planning Council. The students enjoy the opportunity to make a real-world impact with their work, Satyanarayan says.

And he enjoys the course as much as they do.

“I love teaching. I really enjoy getting to interact with the students. Our students are so intellectually curious and committed. It reassures me that our future is in good hands,” he says.

One of Satyanarayan’s personal interests is running along the Charles River Esplanade in Boston, which he does almost every day. He also enjoys cooking, especially with ingredients he has never used before.

Satyanarayan and his wife, who met while they were graduate students at Stanford (her PhD is in microbiology), also delight in tending their plot in the Fenway Victory Gardens, which is overflowing with lilies, lavender, lilacs, peonies, and roses.

Their newest addition is a miniature poodle puppy named Fen, which they got when Satyanarayan earned tenure earlier this year.

Thinking toward the future of his research, Satyanarayan is keen to further explore how generative AI might effectively assist people in building visualizations, and its implications for human creativity.

“In the world of generative AI, this question of agency applies to all of us,” he says. “How do we make sure, for these AI-driven systems, that we haven’t lost the parts of the work we find most interesting?”

The Abdul Latif Jameel Water and Food Systems Lab (J-WAFS) announced that Daniela Giardina has been named the new J-WAFS executive director. Giardina stepped into the role at the start of the fall semester, replacing founding executive director Renee J. Robins ’83, who is retiring after leading the program since its launch in 2014.

“Daniela brings a deep background in water and food security, along with excellent management and leadership skills,” says Robins. “Since I first met her nearly 10 years ago, I have been impressed with her commitment to working on global water and food challenges through research and innovation. I am so happy to know that I will be leaving J-WAFS in her experienced and capable hands.”

A decade of impact

J-WAFS fuels research, innovation, and collaboration to solve global water and food systems challenges. The mission of J-WAFS is to ensure safe and resilient supplies of water and food to meet the local and global needs of a dramatically growing population on a rapidly changing planet. J-WAFS funding opportunities are open to researchers in every MIT department, lab, and center, spanning all disciplines. Supported research projects include those involving engineering, science, technology, business, social science, economics, architecture, urban planning, and more. J-WAFS research and related activities include early-stage projects, sponsored research, commercialization efforts, student activities and mentorship, events that convene local and global experts, and international-scale collaborations.

The global water, food, and climate emergency makes J-WAFS’ work both timely and urgent. J-WAFS-funded researchers are achieving tangible, real-time solutions and results. Since its inception, J-WAFS has distributed nearly $26 million in grants, fellowships, and awards to the MIT community, supporting roughly 10 percent of MIT’s faculty and 300 students, postdocs, and research staff from 40 MIT departments, labs, and centers. J-WAFS grants have also helped researchers launch 13 startups and receive over $25 million in follow-on funding.

Giardina joins J-WAFS at an exciting time in the program’s history; in the spring, J-WAFS celebrated 10 years of supporting water and food research at MIT. The milestone was commemorated at a special event attended by MIT leadership, researchers, students, staff, donors, and others in the J-WAFS community. As J-WAFS enters its second decade, interest and opportunities for water and food research continue to grow. “I am truly honored to join J-WAFS at such a pivotal moment,” Giardina says.

Putting research into real-world practice

Giardina has nearly two decades of experience working with nongovernmental organizations and research institutions on humanitarian and development projects. Her work has taken her to Africa, Latin America, the Caribbean, and Central and Southeast Asia, where she has focused on water and food security projects. She has conducted technical trainings and assessments, and managed projects from design to implementation, including monitoring and evaluation.

Giardina comes to MIT from Oxfam America, where she directed disaster risk reduction and climate resilience initiatives, working on approaches to strengthen local leadership, community-based disaster risk reduction, and anticipatory action. Her role at Oxfam required her to oversee multimillion-dollar initiatives, supervising international teams, managing complex donor portfolios, and ensuring rigorous monitoring across programs. She connected hands-on research with community-oriented implementation, for example, by partnering with MIT’s D-Lab to launch an innovation lab in rural El Salvador. Her experience will help guide J-WAFS as it pursues impactful research that will make a difference on the ground.

Beyond program delivery, Giardina has played a strategic leadership role in shaping Oxfam’s global disaster risk reduction strategy and representing the organization at high-level U.N. and academic forums. She is multilingual and adept at building partnerships across cultures, having worked with governments, funders, and community-based organizations to strengthen resilience and advance equitable access to water and food.

Giardina holds a PhD in sustainable development from the University of Brescia in Italy. She also holds a master’s degree in environmental engineering from the Politecnico of Milan in Italy and is a chartered engineer since 2005 (equivalent to a professional engineering license in the United States). She also serves as vice chair of the Boston Network for International Development, a nonprofit that connects and strengthens Boston’s global development community.

“I have seen first-hand how climate change, misuse of resources, and inequality are undermining water and food security around the globe,” says Giardina. “What particularly excites me about J-WAFS is its interdisciplinary approach in facilitating meaningful partnerships to solve many of these problems through research and innovation. I am eager to help expand J-WAFS’ impact by strengthening existing programs, developing new initiatives, and building strategic partnerships that translate MIT's groundbreaking research into real-world solutions,” she adds.

A legacy of leadership

Renee Robins will retire with over 23 years of service to MIT. Years before joining the staff, she graduated from MIT with dual bachelor’s degrees in both biology and humanities/anthropology. She then went on to earn a master’s degree in public policy from Carnegie Mellon University. In 1998, she came back to MIT to serve in various roles across campus, including with the Cambridge MIT Institute, the MIT Portugal Program, the Mexico City Program, the Program on Emerging Technologies, and the Technology and Policy Program. She also worked at the Harvard Graduate School of Education, where she managed a $15 million research program as it scaled from implementation in one public school district to 59 schools in seven districts across North Carolina.

In late 2014, Robins joined J-WAFS as its founding executive director, playing a pivotal role in building it from the ground up and expanding the team to six full-time professionals. She worked closely with J-WAFS founding director Professor John H. Lienhard V to develop and implement funding initiatives, develop, and shepherd corporate-sponsored research partnerships, and mentor students in the Water Club and Food and Agriculture Club, as well as numerous other students. Throughout the years, Robins has inspired a diverse range of researchers to consider how their capabilities and expertise can be applied to water and food challenges. Perhaps most importantly, her leadership has helped cultivate a vibrant community, bringing together faculty, students, and research staff to be exposed to unfamiliar problems and new methodologies, to explore how their expertise might be applied, to learn from one another, and to collaborate.

At the J-WAFS 10th anniversary event in May, Robins noted, “it has been a true privilege to work alongside John Lienhard, our dedicated staff, and so many others. It’s been particularly rewarding to see the growth of an MIT network of water and food researchers that J-WAFS has nurtured, which grew out of those few individuals who saw themselves to be working in solitude on these critical challenges.”

Lienhard also spoke, thanking Robins by saying she “was my primary partner in building J-WAFS and [she is] a strong leader and strategic thinker.”

Not only is Robins a respected leader, she is also a dear friend to so many at MIT and beyond. In 2021, she was recognized for her outstanding leadership and commitment to J-WAFS and the Institute with an MIT Infinite Mile Award in the area of the Offices of the Provost and Vice President for Research.

Outside of MIT, Robins has served on the Board of Trustees for the International Honors Program — a comparative multi-site study abroad program, where she previously studied comparative culture and anthropology in seven countries around the world. Robins has also acted as an independent consultant, including work on program design and strategy around the launch of the Université Mohammed VI Polytechnique in Morocco.

Continuing the tradition of excellence

Giardina will report to J-WAFS director Rohit Karnik, the Abdul Latif Jameel Professor of Water and Food in the MIT Department of Mechanical Engineering. Karnik was named the director of J-WAFS in January, succeeding John Lienhard, who retired earlier this year.

As executive director, Giardina will be instrumental in driving J-WAFS’ mission and impact. She will work with Karnik to help shape J-WAFS’ programs, long-term strategy, and goals. She will also be responsible for supervising J-WAFS staff, managing grant administration, and overseeing and advising on financial decisions.

“I am very grateful to John and Renee, who have helped to establish J-WAFS as the Institute’s preeminent program for water and food research and significantly expanded MIT’s research efforts and impact in the water and food space,” says Karnik. “I am confident that with Daniela as executive director, J-WAFS will continue in the tradition of excellence that Renee and John put into place, as we move into the program’s second decade,” he notes.

Giardina adds, “I am inspired by the lab’s legacy of Renee Robins and Professor Lienhard, and I look forward to working with Professor Karnik and the J-WAFS staff.”

The first comprehensive map of mouse brain activity has been unveiled by a large international collaboration of neuroscientists. 

Researchers from the International Brain Laboratory (IBL), including MIT neuroscientist Ila Fiete, published their open-access findings today in two papers in Nature, revealing insights into how decision-making unfolds across the entire brain in mice at single-cell resolution. This brain-wide activity map challenges the traditional hierarchical view of information processing in the brain and shows that decision-making is distributed across many regions in a highly coordinated way.

“This is the first time anyone has produced a full, brain-wide map of the activity of single neurons during decision-making,” explains co-founder of IBL Alexandre Pouget. “The scale is unprecedented as we recorded from over half-a-million neurons across mice in 12 labs, covering 279 brain areas, which together represent 95 percent of the mouse brain volume. The decision-making activity, and particularly reward, lit up the brain like a Christmas tree,” adds Pouget, who is also a group leader at the University of Geneva in Switzerland.

Modeling decision-making

The brain map was made possible by a major international collaboration of neuroscientists from multiple universities, including MIT. Researchers across 12 labs used state-of-the-art silicon electrodes, called neuropixels probes, for simultaneous neural recordings to measure brain activity while mice were carrying out a decision-making task.

“Participating in the International Brain Laboratory has added new ways for our group to contribute to science,” says Fiete, who is also a professor of brain and cognitive sciences, an associate investigator at the McGovern Institute for Brain Research, and director of the K. Lisa Yang ICoN Center at MIT. “Our lab has helped standardize methods to analyze and generate robust conclusions from data. As computational neuroscientists interested in building models of how the brain works, access to brain-wide recordings is incredible: the traditional approach of recording from one or a few brain areas limited our ability to build and test theories, resulting in fragmented models. Now, we have the delightful but formidable task to make sense of how all parts of the brain coordinate to perform a behavior. Surprisingly, having a full view of the brain leads to simplifications in the models of decision-making,” says Fiete.

The labs collected data from mice performing a decision-making task with sensory, motor, and cognitive components. In the task, a mouse sits in front of a screen and a light appears on the left or right side. If the mouse then responds by moving a small wheel in the correct direction, it receives a reward.

In some trials, the light is so faint that the animal must guess which way to turn the wheel, for which it can use prior knowledge: the light tends to appear more frequently on one side for a number of trials, before the high-frequency side switches. Well-trained mice learn to use this information to help them make correct guesses. These challenging trials therefore allowed the researchers to study how prior expectations influence perception and decision-making.

Brain-wide results

The first paper, “A brain-wide map of neural activity during complex behaviour,” showed that decision-making signals are surprisingly distributed across the brain, not localized to specific regions. This adds brain-wide evidence to a growing number of studies that challenge the traditional hierarchical model of brain function, and emphasizes that there is constant communication across brain areas during decision-making, movement onset, and even reward. This means that neuroscientists will need to take a more holistic, brain-wide approach when studying complex behaviors in the future.

“The unprecedented breadth of our recordings pulls back the curtain on how the entire brain performs the whole arc of sensory processing, cognitive decision-making, and movement generation,” says Fiete. “Structuring a collaboration that collects a large standardized dataset which single labs could not assemble is a revolutionary new direction for systems neuroscience, initiating the field into the hyper-collaborative mode that has contributed to leaps forward in particle physics and human genetics. Beyond our own conclusions, the dataset and associated technologies, which were released much earlier as part of the IBL mission, have already become a massively used resource for the entire neuroscience community.”

The second paper, “Brain-wide representations of prior information,” showed that prior expectations — our beliefs about what is likely to happen based on our recent experience — are encoded throughout the brain. Surprisingly, these expectations are not only found in cognitive areas, but also brain areas that process sensory information and control actions. For example, expectations are even encoded in early sensory areas such as the thalamus, the brain’s first relay for visual input from the eye. This supports the view that the brain acts as a prediction machine, but with expectations encoded across multiple brain structures playing a central role in guiding behavior responses. These findings could have implications for understanding conditions such as schizophrenia and autism, which are thought to be caused by differences in the way expectations are updated in the brain.

“Much remains to be unpacked: If it is possible to find a signal in a brain area, does it mean that this area is generating the signal, or simply reflecting a signal generated somewhere else? How strongly is our perception of the world shaped by our expectations? Now we can generate some quantitative answers and begin the next phase experiments to learn about the origins of the expectation signals by intervening to modulate their activity,” says Fiete.

Looking ahead, the team at IBL plan to expand beyond their initial focus on decision-making to explore a broader range of neuroscience questions. With renewed funding in hand, IBL aims to expand its research scope and continue to support large-scale, standardized experiments.

New model of collaborative neuroscience

Officially launched in 2017, IBL introduced a new model of collaboration in neuroscience that uses a standardized set of tools and data processing pipelines shared across multiple labs, enabling the collection of massive datasets while ensuring data alignment and reproducibility. This approach to democratize and accelerate science draws inspiration from large-scale collaborations in physics and biology, such as CERN and the Human Genome Project.

All data from these studies, along with detailed specifications of the tools and protocols used for data collection, are openly accessible to the global scientific community for further analysis and research. Summaries of these resources can be viewed and downloaded on the IBL website under the sections: Data, Tools, Protocols.

This research was supported by grants from Wellcome, the Simons Foundation, the National Institutes of Health, the National Science Foundation, the Gatsby Charitable Foundation, and by the Max Planck Society and the Humboldt Foundation.

3D printing has come a long way since its invention in 1983 by Chuck Hull, who pioneered stereolithography, a technique that solidifies liquid resin into solid objects using ultraviolet lasers. Over the decades, 3D printers have evolved from experimental curiosities into tools capable of producing everything from custom prosthetics to complex food designs, architectural models, and even functioning human organs. 

But as the technology matures, its environmental footprint has become increasingly difficult to set aside. The vast majority of consumer and industrial 3D printing still relies on petroleum-based plastic filament. And while “greener” alternatives made from biodegradable or recycled materials exist, they come with a serious trade-off: they’re often not as strong. These eco-friendly filaments tend to become brittle under stress, making them ill-suited for structural applications or load-bearing parts — exactly where strength matters most.

This trade-off between sustainability and mechanical performance prompted researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Hasso Plattner Institute to ask: Is it possible to build objects that are mostly eco-friendly, but still strong where it counts?

Their answer is SustainaPrint, a new software and hardware toolkit designed to help users strategically combine strong and weak filaments to get the best of both worlds. Instead of printing an entire object with high-performance plastic, the system analyzes a model through finite element analysis simulations, predicts where the object is most likely to experience stress, and then reinforces just those zones with stronger material. The rest of the part can be printed using greener, weaker filament, reducing plastic use while preserving structural integrity.

“Our hope is that SustainaPrint can be used in industrial and distributed manufacturing settings one day, where local material stocks may vary in quality and composition,” says MIT PhD student and CSAIL researcher Maxine Perroni-Scharf, who is a lead author on a paper presenting the project. “In these contexts, the testing toolkit could help ensure the reliability of available filaments, while the software’s reinforcement strategy could reduce overall material consumption without sacrificing function.” 

For their experiments, the team used Polymaker’s PolyTerra PLA as the eco-friendly filament, and standard or Tough PLA from Ultimaker for reinforcement. They used a 20 percent reinforcement threshold to show that even a small amount of strong plastic goes a long way. Using this ratio, SustainaPrint was able to recover up to 70 percent of the strength of an object printed entirely with high-performance plastic.

They printed dozens of objects, from simple mechanical shapes like rings and beams to more functional household items such as headphone stands, wall hooks, and plant pots. Each object was printed three ways: once using only eco-friendly filament, once using only strong PLA, and once with the hybrid SustainaPrint configuration. The printed parts were then mechanically tested by pulling, bending, or otherwise breaking them to measure how much force each configuration could withstand. 

In many cases, the hybrid prints held up nearly as well as the full-strength versions. For example, in one test involving a dome-like shape, the hybrid version outperformed the version printed entirely in Tough PLA. The team believes this may be due to the reinforced version’s ability to distribute stress more evenly, avoiding the brittle failure sometimes caused by excessive stiffness.

“This indicates that in certain geometries and loading conditions, mixing materials strategically may actually outperform a single homogenous material,” says Perroni-Scharf. “It’s a reminder that real-world mechanical behavior is full of complexity, especially in 3D printing, where interlayer adhesion and tool path decisions can affect performance in unexpected ways.”

A lean, green, eco-friendly printing machine

SustainaPrint starts off by letting a user upload their 3D model into a custom interface. By selecting fixed regions and areas where forces will be applied, the software then uses an approach called “Finite Element Analysis” to simulate how the object will deform under stress. It then creates a map showing pressure distribution inside the structure, highlighting areas under compression or tension, and applies heuristics to segment the object into two categories: those that need reinforcement, and those that don’t.

Recognizing the need for accessible and low-cost testing, the team also developed a DIY testing toolkit to help users assess strength before printing. The kit has a 3D-printable device with modules for measuring both tensile and flexural strength. Users can pair the device with common items like pull-up bars or digital scales to get rough, but reliable performance metrics. The team benchmarked their results against manufacturer data and found that their measurements consistently fell within one standard deviation, even for filaments that had undergone multiple recycling cycles.

Although the current system is designed for dual-extrusion printers, the researchers believe that with some manual filament swapping and calibration, it could be adapted for single-extruder setups, too. In current form, the system simplifies the modeling process by allowing just one force and one fixed boundary per simulation. While this covers a wide range of common use cases, the team sees future work expanding the software to support more complex and dynamic loading conditions. The team also sees potential in using AI to infer the object’s intended use based on its geometry, which could allow for fully automated stress modeling without manual input of forces or boundaries.

3D for free

The researchers plan to release SustainaPrint open-source, making both the software and testing toolkit available for public use and modification. Another initiative they aspire to bring to life in the future: education. “In a classroom, SustainaPrint isn’t just a tool, it’s a way to teach students about material science, structural engineering, and sustainable design, all in one project,” says Perroni-Scharf. “It turns these abstract concepts into something tangible.”

As 3D printing becomes more embedded in how we manufacture and prototype everything from consumer goods to emergency equipment, sustainability concerns will only grow. With tools like SustainaPrint, those concerns no longer need to come at the expense of performance. Instead, they can become part of the design process: built into the very geometry of the things we make.

Co-author Patrick Baudisch, who is a professor at the Hasso Plattner Institute, adds that “the project addresses a key question: What is the point of collecting material for the purpose of recycling, when there is no plan to actually ever use that material? Maxine presents the missing link between the theoretical/abstract idea of 3D printing material recycling and what it actually takes to make this idea relevant.”

Perroni-Scharf and Baudisch wrote the paper with CSAIL research assistant Jennifer Xiao; MIT Department of Electrical Engineering and Computer Science master’s student Cole Paulin ’24; master’s student Ray Wang SM ’25 and PhD student Ticha Sethapakdi SM ’19 (both CSAIL members); Hasso Plattner Institute PhD student Muhammad Abdullah; and Associate Professor Stefanie Mueller, lead of the Human-Computer Interaction Engineering Group at CSAIL.

The researchers’ work was supported by a Designing for Sustainability Grant from the Designing for Sustainability MIT-HPI Research Program. Their work will be presented at the ACM Symposium on User Interface Software and Technology in September.

EFF urges California state lawmakers to pass S.B. 524, authored by Sen. Jesse Arreguín. This bill is an important first step in regaining control over police using generative AI to write their narrative police reports. 

This bill does several important things: It mandates that police reports written by AI include disclaimers on every page or within the body of the text that make it clear that this report was written in part or in total by a computer. It also says that any reports written by AI must retain their first draft. That way, it should be easier for defense attorneys, judges, police supervisors, or any other auditing entity to see which portions of the final report were written by AI and which parts were written by the officer. Further, the bill requires officers to sign and verify that they read the report and its facts are correct. And it bans AI vendors from selling or sharing the information a police agency provided to the AI.

These common-sense, first-step reforms are important: watchdogs are struggling to figure out where and how AI is being used in a police context. In fact, a popular AI police report writing tool, Axon’s Draft One, would be out of compliance with this bill, which would require them to redesign their tool to make it more transparent. 

This bill is an important first step in regaining control over police using generative AI to write their narrative police reports. 

Draft One takes audio from an officer’s body-worn camera, and uses AI  to turn that dialogue into a narrative police report. Because independent researchers have been unable to test it, there are important questions about how the system handles things like sarcasm, out of context comments, or interactions with members of the public that speak languages other than English. Another major concern is Draft One’s inability to keep track of which parts of a report were written by people and which parts were written by AI. By design, their product does not retain different iterations of the draft—making it easy for an officer to say, “I didn’t lie in my police report, the AI wrote that part.” 

All lawmakers should pass regulations of AI written police reports. This technology could be nearly everywhere, and soon. Axon is a top supplier of body-worn cameras in the United States, which means they have a massive ready-made customer base. Through the bundling of products, AI-written police reports could be at a vast percentage of police departments. 

AI-written police reports are unproven in terms of their accuracy, and their overall effects on the criminal justice system. Vendors still have a long way to go to prove this technology can be transparent and auditable. While it would not solve all of the many problems of AI encroaching on the criminal justice system, S.B. 524 is a good first step to rein in an unaccountable piece of technology. 

We urge California lawmakers to pass S.B. 524. 

In 1992 EFF presented our very first awards recognizing key leaders and organizations advancing innovation and championing civil liberties and human rights online. Now in 2025 we're continuing to celebrate the accomplishments of people working toward a better future for everyone with the EFF Awards!

All are invited to attend the EFF Awards on Wednesday, September 10 at the San Francisco Design Center. Whether you're an activist, an EFF supporter, a student interested in cyberlaw, or someone who wants to munch on a strolling dinner with other likeminded individuals, anyone can enjoy the ceremony!

REGISTER TODAY!

GENERAL ADMISSION: $55 | CURRENT EFF MEMBERS: $45 | STUDENTS: $35

If you're not able to make it, we'll also be hosting a livestream of the event on Friday, September 12 at 12:00 PM PT. The event will also be recorded, and posted to YouTube and the Internet Archive after the livestream.

We are honored to present the three winners of this year's EFF Awards: Just Futures Law, Erie Meyer, and Software Freedom Law Center, India. But, before we kick off the ceremony next week, let's take a closer look at each of the honorees. This time—Erie Meyer, winner of the EFF Award for Protecting Americans' Data:

Erie Meyer is a Senior Fellow at the Vanderbilt Policy Accelerator where she focuses on the intersection of technology, artificial intelligence, and regulation, and a Senior Fellow at the Georgetown Law Institute for Technology Law & Policy. Since January 20, Meyer has helped organize former government technologists to stand up for the privacy and integrity of governmental systems that hold Americans’ data. In addition to organizing others, she filed a declaration in federal court in February warning that 12 years of critical records could be irretrievably lost in the CFPB’s purge by the Trump Administration’s Department of Government Efficiency. In April, she filed a declaration in another case warning about using private-sector AI on government information. That same month, she testified to the House Oversight Subcommittee on Cybersecurity, Information Technology, and Government Innovation that DOGE is centralizing access to some of the most sensitive data the government holds—Social Security records, disability claims, even data tied to national security—without a clear plan or proper oversight, warning that “DOGE is burning the house down and calling it a renovation.” 

We're excited to celebrate Erie Meyer and the other EFF Award winners in person in San Francisco on September 10! We hope that you'll join us there.

Thank you to Fastly, DuckDuckGo, Corellium, and No Starch Press for their year-round support of EFF's mission.

Want to show your team’s support for EFF? Sponsorships ensure we can continue hosting events like this to build community among digital rights supporters. Please visit eff.org/thanks or contact tierney@eff.org for more information on corporate giving and sponsorships.

EFF is dedicated to a harassment-free experience for everyone, and all participants are encouraged to view our full Event Expectations.

Questions? Email us at events@eff.org.

​​In an era of pervasive online surveillance, organizations have an important role to play in protecting their communities’ privacy. Millions of people browse the web on computers provided by their schools, libraries, and employers. By default, popular browsers on these computers leave people exposed to hidden trackers.

Organizations can enhance privacy and security on their devices by installing Privacy Badger, EFF’s free, open source browser extension that automatically blocks trackers. Privacy Badger is already used by millions to fight online surveillance and take back control of their data.

Why Should Organizations Install Privacy Badger on Managed Devices?

Protect People from Online Surveillance

Most websites contain hidden trackers that let advertisers, data brokers, and Big Tech companies monitor people’s browsing activity. This surveillance has serious consequences: it fuels scams, government spying, predatory advertising, and surveillance pricing. 

By installing Privacy Badger on managed devices, organizations can protect entire communities from these harms. Most people don’t realize the risks of browsing the web unprotected. Organizations can step in to make online privacy available to everyone, not just the people who know they need it. 

Ad Blocking is a Cybersecurity Best Practice

Privacy Badger helps reduce cybersecurity threats by blocking ads that track you (unfortunately, that’s most ads these days). Targeted ads aren’t just a privacy nightmare. They can also be a vehicle for malware and phishing attacks. Cybercriminals have tricked legitimate ad networks into distributing malware, a tactic known as malvertising.

The risks are serious enough that the U.S. Cybersecurity and Infrastructure Security Agency (CISA) recommends federal agencies deploy ad-blocking software. The NSA, CIA, and other intelligence agencies already follow this guidance. These agencies are using advertising systems to surveil others, but blocking ads for their own employees. 

All organizations, not just spy agencies, should make ad blocking part of their security strategy.

A Tracker Blocker You Can Trust

Four million users already trust Privacy Badger, which has been recommended by The New York Times' Wirecutter, Consumer Reports, and The Washington Post.

Trust is crucial when choosing an ad-blocking or tracker-blocking extension because they require high levels of browser permissions. Unfortunately, not all extensions deserve that trust. Avast’s “privacy” extension was caught collecting and selling users’ browsing data to third parties—the very practice it claimed to prevent.

Privacy Badger is different. EFF released it over a decade ago, and the extension has been open-source—meaning other developers and researchers can inspect its code—that entire time. Built by a nonprofit with a 35-year history fighting for user rights, organizations can trust that Privacy Badger works for its users, not for profit. 

Which organizations should deploy Privacy Badger?

All of them! Installing Privacy Badger on managed devices improves privacy and security across an organization. That said, Privacy Badger is most beneficial for two types of organizations: libraries and schools. Both can better serve their communities by safeguarding the computers they provide.

Libraries

The American Library Association (ALA) already recommends installing Privacy Badger on public computers to block third-party tracking. Librarians have a long history of defending privacy. The ALA’s guidance is a natural extension of that legacy for the digital age. While librarians protect the privacy of books people check out, Privacy Badger protects the privacy of websites they visit on library computers. 

Millions of Americans depend on libraries for internet access. That makes libraries uniquely positioned to promote equitable access to private browsing. With Privacy Badger, libraries can ensure that safe and private browsing is the default for anyone using their computers. 

Libraries also play a key role in promoting safe internet use through their digital literacy trainings. By including Privacy Badger in these trainings, librarians can teach patrons about a simple, free tool that protects their privacy and security online.

Schools

Schools should protect their students’ from online surveillance by installing Privacy Badger on computers they provide. Parents are rightfully worried about their children’s privacy online, with a Pew survey showing 85% worry about advertisers using data about what kids do online to target ads. Deploying Privacy Badger is a concrete step schools can take to address these concerns. 

By blocking online trackers, schools can protect students from manipulative ads and limit the personal data fueling social media algorithms. Privacy Badger can even block tracking in Ed Tech products that schools require students to use. Alarmingly, a Human Rights Watch analysis of Ed Tech products found that 89% shared children’s personal data with advertisers or other companies.

Instead of deploying invasive student monitoring tools, schools should keep students safe by keeping their data safe. Students deserve to learn without being tracked, profiled, and targeted online. Privacy Badger can help make that happen.

How can organizations deploy Privacy Badger on managed devices?

System administrators can deploy and configure Privacy Badger on managed devices by setting up an enterprise policy. Chrome, Firefox, and Edge provide instructions for automatically installing extensions organization-wide. You’ll be able to configure certain Privacy Badger settings for all devices. For example, you can specify websites where Privacy Badger is disabled or prevent Privacy Badger’s welcome page from popping up on computers that get reset after every session. 

We recommend educating users about the addition of Privacy Badger and what it does. Since some websites deeply embed tracking, privacy protections can occasionally break website functionality. For example, a video might not play or a comments section might not appear. If this happens, users should know that they can easily turn off Privacy Badger on any website. Just open the Privacy Badger popup and click “Disable for this site.” 

Don't hesitate to reach out if you're interested in deploying Privacy Badger at scale. Our team is here to help you protect your community's privacy. And if you're already deploying Privacy Badger across your organization, we'd love to hear how it’s going! 

Make Private Browsing the Default at Your Organization

Schools, libraries, and other organizations can make private browsing the norm by deploying Privacy Badger on devices they manage. If you work at an organization with managed devices, talk to your IT team about Privacy Badger. You can help strengthen the security and privacy of your entire organization while joining the fight against online surveillance.

Anthropic reports on a Claude user:

We recently disrupted a sophisticated cybercriminal that used Claude Code to commit large-scale theft and extortion of personal data. The actor targeted at least 17 distinct organizations, including in healthcare, the emergency services, and government and religious institutions. Rather than encrypt the stolen information with traditional ransomware, the actor threatened to expose the data publicly in order to attempt to extort victims into paying ransoms that sometimes exceeded $500,000.

The actor used AI to what we believe is an unprecedented degree. Claude Code was used to automate reconnaissance, harvesting victims’ credentials, and penetrating networks. Claude was allowed to make both tactical and strategic decisions, such as deciding which data to exfiltrate, and how to craft psychologically targeted extortion demands. Claude analyzed the exfiltrated financial data to determine appropriate ransom amounts, and generated visually alarming ransom notes that were displayed on victim machines...

A new crypto venture whose largest shareholders include President Donald Trump’s sons disclosed to investors that rising temperatures “pose a threat” to its operations, in contrast to the family’s rejections of climate change.

The scientific organization is funding the review as the Trump administration attempts to revoke the endangerment finding.

Advocates hope the decision will encourage U.S. pension funds to scrutinize their own ties to the global asset manager.

A judge stopped state officials from being able to impose penalties of up to $10,000 for violating the Republican-led measure.

Developing countries mostly will have to rely on their own budgets or help from other nations to prepare for climate impacts, a report says.

Air regulators offered guidance ahead of a climate disclosure law for corporations with at least $500 million in global revenue.

The country also said the EU should set up an independent body to act as a central bank for the carbon market to safeguard its integrity.

Secretary-General António Guterres hailed the nation commemorating the 50th anniversary of its independence from Australia.

The project was inspired by Francis’ 2015 encyclical “Laudato Si” (Praised Be), which cast care for the planet as an existential moral concern.

In the past few years, governments across the world have rolled out different digital identification options, and now there are efforts encouraging online companies to implement identity and age verification requirements with digital ID in mind. This blog is the second in a short series that explains digital ID and the pending use case of age verification. Upcoming posts will evaluate what real protections we can implement with current digital ID frameworks and discuss how better privacy and controls can keep people safer online.

Digital identity encompasses various aspects of an individual's identity that are presented and verified through either the internet or in person. This could mean a digital credential issued by a certification body or a mobile driver’s license provisioned to someone’s mobile wallet. They can be presented in plain text on a device, as a scannable QR code, or through tapping your device to something called a Near Field Communication (NFC) reader. There are other ways to present credential information that is a little more privacy preserving, but in practice those three methods are how we are seeing digital ID being used today.

Advocates of digital ID often use a framework they call the "Triangle of Trust." This is usually presented as a triangle of exchange between the holder of an ID—those who use a phone or wallet application to access a service; the issuer of an ID—this is normally a government entity, like the state Departments of Motor Vehicles in the U.S, or a banking system; and the verifier of an ID—the entity that wants to confirm your identity, such as law enforcement, a university, a government benefits office, a porn site, or an online retailer.

This triangle implies that the issuer and verifier—for example, the government who provides the ID and the website checking your age—never need to talk to one another. This theoretically avoids the tracking and surveillance threats that arise by preventing your ID, by design, from phoning home every time you verify your ID with another party.

But it also makes a lot of questionable assumptions, such as:

1) the verifier will only ever ask for a limited amount of information. 

2) the verifier won’t store information it collects.

3) the verifier is always trustworthy. 

The third assumption is especially problematic. How do you trust that the verifier will protect your most personal information and not use, store, or sell it beyond what you have consented to? Any of the following could be verifiers:

• Law enforcement when doing a traffic stop and verifying your ID as valid.
• A government benefits office that requires ID verification to sign up for social security benefits.
• A porn site in a state or country which requires age verification or identity verification before allowing access.
• An online retailer selling products like alcohol or tobacco.

Looking at the triangle again, this isn’t quite an equal exchange. Your personal ID like a driver’s license or government ID is both one of the most centralized and sensitive documents you have—you can’t control how it is issued or create your own, having to go through your government to obtain one. This relationship will always be imbalanced. But we have to make sure digital ID does not exacerbate these imbalances.

The effort to answer the questions of how to prevent verifier abuse is ongoing. But instead of working on the harms that these systems cause, the push for this technology is being fast-tracked by governments around the world scrambling to solve what they see as a crisis of online harms by mandating age verification. And current implementations of the Triangle of Trust have already proven disastrous.

One key example of the speed of implementation outpacing proper protections is the Digital Credential API. Initially launched by Google and now supported by Apple, this rollout allows for mass, unfettered verification by apps and websites to use the API to request information from your digital ID. The introduction of this technology to people’s devices came with no limits or checks on what information verifiers can seek—incentivizing verifiers to over-ask for ID information beyond the question of whether a holder is over a certain age, simply because they can. 

Digital Credential API also incentivizes for a variety of websites to ask for ID information that aren’t required and did not commonly do so previously. For example, food delivery services, medical services, and gaming sites, and literally anyone else interested in being a verifier, may become one tomorrow with digital ID and the Digital Credential API. This is both an erosion of personal privacy, as well as a pathway into further surveillance. There must be established limitations and scope, including:

• verifiers establishing who they are and what they plan to ask from holders. There should also be an established plan for transparency on verifiers and their data retention policies.
• ways to identify and report abusive verifiers, as well as real consequences, like revoking or blocking a verifier from requesting IDs in the future.
• unlinkable presentations that do not allow for verifier and issuer collusion. As well as no data shared between verifiers you attest to. Preventing tracking of your movements in person or online every time you attest your age.

A further point of concern arises in cases of abuse or deception. A malicious verifier can send a request with no limiting mechanisms or checks and the user who rejects the request could be  fully blocked from the website or application. There must be provisions that ensure people have access to vital services that will require age verification from visitors.

Government's efforts to tackle verifiers potentially abusing digital ID requests haven’t come to fruition yet. For example, the EU Commission recently launched its age verification “mini app” ahead of the EU ID wallet for 2026. The mini app will not have a registry for verifiers, as EU regulators had promised and then withdrew. Without verifier accountability, the wallet cannot tell if a request is legitimate. As a result, verifiers and issuers will demand verification from the people who want to use online services, but those same people are unable to insist on verification and accountability from the other sides of the triangle. 

While digital ID gets pushed as the solution to the problem of uploading IDs to each site users access, the security and privacy on them varies based on implementation. But when privacy is involved, regulators must make room for negotiation. There should be more thoughtful and protective measures for holders interacting with more and more potential verifiers over time. Otherwise digital ID solutions will just exacerbate existing harms and inequalities, rather than improving internet accessibility and information access for all.

Many attempts have been made to harness the power of new artificial intelligence and large language models (LLMs) to try to predict the outcomes of new chemical reactions. These have had limited success, in part because until now they have not been grounded in an understanding of fundamental physical principles, such as the laws of conservation of mass. Now, a team of researchers at MIT has come up with a way of incorporating these physical constraints on a reaction prediction model, and thus greatly improving the accuracy and reliability of its outputs.

The new work was reported Aug. 20 in the journal Nature, in a paper by recent postdoc Joonyoung Joung (now an assistant professor at Kookmin University, South Korea); former software engineer Mun Hong Fong (now at Duke University); chemical engineering graduate student Nicholas Casetti; postdoc Jordan Liles; physics undergraduate student Ne Dassanayake; and senior author Connor Coley, who is the Class of 1957 Career Development Professor in the MIT departments of Chemical Engineering and Electrical Engineering and Computer Science.

“The prediction of reaction outcomes is a very important task,” Joung explains. For example, if you want to make a new drug, “you need to know how to make it. So, this requires us to know what product is likely” to result from a given set of chemical inputs to a reaction. But most previous efforts to carry out such predictions look only at a set of inputs and a set of outputs, without looking at the intermediate steps or considering the constraints of ensuring that no mass is gained or lost in the process, which is not possible in actual reactions.

Joung points out that while large language models such as ChatGPT have been very successful in many areas of research, these models do not provide a way to limit their outputs to physically realistic possibilities, such as by requiring them to adhere to conservation of mass. These models use computational “tokens,” which in this case represent individual atoms, but “if you don’t conserve the tokens, the LLM model starts to make new atoms, or deletes atoms in the reaction.” Instead of being grounded in real scientific understanding, “this is kind of like alchemy,” he says. While many attempts at reaction prediction only look at the final products, “we want to track all the chemicals, and how the chemicals are transformed” throughout the reaction process from start to end, he says.

In order to address the problem, the team made use of a method developed back in the 1970s by chemist Ivar Ugi, which uses a bond-electron matrix to represent the electrons in a reaction. They used this system as the basis for their new program, called FlowER (Flow matching for Electron Redistribution), which allows them to explicitly keep track of all the electrons in the reaction to ensure that none are spuriously added or deleted in the process.

The system uses a matrix to represent the electrons in a reaction, and uses nonzero values to represent bonds or lone electron pairs and zeros to represent a lack thereof. “That helps us to conserve both atoms and electrons at the same time,” says Fong. This representation, he says, was one of the key elements to including mass conservation in their prediction system.

The system they developed is still at an early stage, Coley says. “The system as it stands is a demonstration — a proof of concept that this generative approach of flow matching is very well suited to the task of chemical reaction prediction.” While the team is excited about this promising approach, he says, “we’re aware that it does have specific limitations as far as the breadth of different chemistries that it’s seen.” Although the model was trained using data on more than a million chemical reactions, obtained from a U.S. Patent Office database, those data do not include certain metals and some kinds of catalytic reactions, he says.

“We’re incredibly excited about the fact that we can get such reliable predictions of chemical mechanisms” from the existing system, he says. “It conserves mass, it conserves electrons, but we certainly acknowledge that there’s a lot more expansion and robustness to work on in the coming years as well.”

But even in its present form, which is being made freely available through the online platform GitHub, “we think it will make accurate predictions and be helpful as a tool for assessing reactivity and mapping out reaction pathways,” Coley says. “If we’re looking toward the future of really advancing the state of the art of mechanistic understanding and helping to invent new reactions, we’re not quite there. But we hope this will be a steppingstone toward that.”

“It’s all open source,” says Fong. “The models, the data, all of them are up there,” including a previous dataset developed by Joung that exhaustively lists the mechanistic steps of known reactions. “I think we are one of the pioneering groups making this dataset, and making it available open-source, and making this usable for everyone,” he says.

The FlowER model matches or outperforms existing approaches in finding standard mechanistic pathways, the team says, and makes it possible to generalize to previously unseen reaction types. They say the model could potentially be relevant for predicting reactions for medicinal chemistry, materials discovery, combustion, atmospheric chemistry, and electrochemical systems.

In their comparisons with existing reaction prediction systems, Coley says, “using the architecture choices that we’ve made, we get this massive increase in validity and conservation, and we get a matching or a little bit better accuracy in terms of performance.”

He adds that “what’s unique about our approach is that while we are using these textbook understandings of mechanisms to generate this dataset, we’re anchoring the reactants and products of the overall reaction in experimentally validated data from the patent literature.” They are inferring the underlying mechanisms, he says, rather than just making them up. “We’re imputing them from experimental data, and that’s not something that has been done and shared at this kind of scale before.”

The next step, he says, is “we are quite interested in expanding the model’s understanding of metals and catalytic cycles. We’ve just scratched the surface in this first paper,” and most of the reactions included so far don’t include metals or catalysts, “so that’s a direction we’re quite interested in.”

In the long term, he says, “a lot of the excitement is in using this kind of system to help discover new complex reactions and help elucidate new mechanisms. I think that the long-term potential impact is big, but this is of course just a first step.”

The work was supported by the Machine Learning for Pharmaceutical Discovery and Synthesis consortium and the National Science Foundation.