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

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

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

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

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

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

Overcoming the limits

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

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

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

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

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

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

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

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

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

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

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

Leveraging magnetism

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

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

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

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

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

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

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

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

Throughout campus and across the river in Boston and Brookline, MIT hosts a vibrant network of 43 fraternities and sororities, with more than 35 percent of undergraduate students belonging to one of these value-based communities. Each fraternity and sorority is a unique community that not only fosters leadership and builds lifelong friendships, but also takes its role in giving back seriously.

Keeping up a 143-year-long tradition of philanthropy, several fraternities and sororities raised funds for a variety of local charities this fall, including the Breast Cancer Research Foundation, Boston Area Rape Crisis Center, and Dignity Matters of Boston.

With donations still coming in, Liz Jason, associate dean of Fraternities, Sororities and Independent Living Groups (FSILG) at MIT, says, “Philanthropy is a defining tradition within our FSILG community; it’s where values become action. When chapters give back, they strengthen their bonds, uplift others, and demonstrate what it truly means to be part of MIT: using talent, passion, and collective effort to make a real difference.”

To raise money, the fraternities and sororities hosted a variety of fun, clever, and even unique events and challenges over the course of the fall semester.

Sorority Alpha Chi Omega held an event called Walk a Mile in Her Shoes, where participants donned heels for a relay race-style event to raise awareness of gender stereotypes, domestic violence, and sexual assault. They also held a bake sale at the event, with funds going to the Boston Area Rape Crisis Center.

The Interfraternity Council (IFC) hosted a Greek Carnival on Kresge Oval in October to benefit the Boston Area Rape Crisis Center and to raise awareness about sexual violence. They held a variety of games and activities, including a dunk tank, a bake sale, a tug-of-war competition, and other field-day games.

“In my own chapter, Delta Tau Delta, I’ve seen an interest in increasing our philanthropic efforts, and as a member of the IFC Executive Board, I realized we could take the initiative to reduce barriers to entry for all chapters through a single large fundraising event,” says senior Luc Gaitskell.

In mid-November, the MIT Panhellenic Association created an event in which members of the community donated clothing, and then Panhel used the clothing to set up a one-time thrift shop where community members could come buy second-hand clothes at discounted prices. All the money raised was donated to Dignity Matters.

“Service has always been at the heart of what MIT Panhel does,” says senior Sabrina Chen. “We chose to partner with Dignity Matters because their mission of helping individuals stay healthy and regain self-confidence resonates with our commitment to supporting women and advancing equity. Our thrift shop was a perfect way to raise money for the organization while encouraging affordable, sustainable fashion.”

Division of Student Life vice chancellor Suzy Nelson explains, “Our students are committed to a range of causes; their dedication reflects not only their generosity, but also the spirit of engaging the MIT community in giving back through philanthropy.”

Students interested in joining a fraternity, sorority, or an independent living group can find more information on the Division of Student Life website.

In February, President Sally Kornbluth announced the appointment of Professor Angela Koehler as faculty director of the MIT Health and Life Sciences Collaborative (MIT HEALS), with professors Iain Cheeseman and Katharina Ribbeck as associate directors. Since then, the leadership team has moved quickly to shape HEALS into an ambitious, community-wide platform for catalyzing research, translation, and education at MIT and beyond — at a moment when advances in computation, biology, and engineering are redefining what’s possible in health and the life sciences.

Rooted in MIT’s long-standing strengths in foundational discovery, convergence, and translational science, HEALS is designed to foster connections across disciplines — linking life scientists and engineers with clinicians, computational scientists, humanists, operations researchers, and designers. The initiative builds on a simple premise: that solving today’s most pressing challenges in health and life sciences requires bold thinking, deep collaboration, and sustained investment in people.

“HEALS is an opportunity to rethink how we support talent, unlock scientific ideas, and translate them into impact,” says Koehler, the Charles W. and Jennifer C. Johnson Professor in the Department of Biological Engineering and associate director of the Koch Institute for Integrative Cancer Research. “We’re building on MIT’s best traditions — convergence, experimentation, and entrepreneurship — while opening new channels for interdisciplinary research and community building.”

Koehler says her own path has been shaped by that same belief in convergence. Early collaborations between chemists, engineers, and clinicians convinced her that bringing diverse people together — what she calls “induced proximity” — can spark discoveries that wouldn’t emerge in isolation.

A culture of connection

Since stepping into their roles, the HEALS leadership team has focused on building a collaborative ecosystem that enables researchers to take on bold, interdisciplinary challenges in health and life sciences. Rather than creating a new center or department, their approach emphasizes connecting the MIT community across existing boundaries — disciplinary, institutional, and cultural.

“We want to fund science that wouldn’t otherwise happen — projects that bridge gaps, open new doors, and bring researchers together in ways that are genuinely constructive and collaborative,” says Iain Cheeseman, the Herman and Margaret Sokol Professor of Biology, core member of the Whitehead Institute for Biomedical Research, and associate head of the Department of Biology.

That vision is already taking shape through initiatives like the MIT HEALS seed grants, which support bold new collaborations between MIT principal investigators; the MIT–Mass General Brigham Seed Program, which supports joint research between investigators at MIT and clinicians at MGB; and the Biswas Postdoctoral Fellowship Program, designed to bring top early-career researchers to MIT to pursue cross-cutting work in areas such as computational biology, biomedical engineering, and therapeutic discovery.

The leadership team sees these programs not as endpoints, but as starting points for a broader shift in how MIT supports health and life sciences research.

For Cheeseman, whose lab is working to build on their fundamental discoveries on how human cells function to impact cancer treatment and rare human disease, HEALS represents a way to connect deep biological discovery with the translational insights emerging from MIT’s engineering and clinical communities. He puts it simply: “to me, this is deeply personal, recognizing the limitations that existed for my own work and hoping to unlock these possibilities for researchers across MIT.”

Training the next generation

Ribbeck, a biologist focused on mucus and microbial ecosystems, sees HEALS as a way to train scientists who are as comfortable discussing patient needs as they are conducting experiments at the bench. She emphasizes that preparing the next generation of researchers means equipping them with fluency in areas like clinical language, regulatory processes, and translational pathways — skills many current investigators lack. “Many PIs, although they do clinical research, may not have dedicated support for taking their findings to the next level — how to design a clinical trial, or what regulatory questions need to be addressed — reflecting a broader structural gap in translational training” she says.

A central focus for the HEALS leadership team is building new models for training researchers to move fluidly between disciplines, institutions, and methods of translation. Ribbeck and Koehler stress the importance of giving students and postdocs hands-on opportunities that connect research with real-world experience. That means expanding programs like the Undergraduate Research Opportunities Program (UROP), the Advanced UROP (SuperUROP), and the MIT New Engineering Education Transformation, and creating new ways for trainees to engage with industry, clinical partners, and entrepreneurship. They are learning at the intersection of engineering, biology, and medicine — and increasingly across disciplines that span economics, design, the social sciences, and the humanities, where students are already creating collaborations that do not yet have formal pathways. 

Koehler, drawing from her leadership at the Deshpande Center for Technological Innovation and the Koch Institute, notes that “if we invest in the people, the solutions to problems will naturally arise.” She envisions HEALS as a platform for induced proximity — not just of disciplines, but of people at different career stages, working together in environments that support both risk-taking and mentorship.

“For me, HEALS builds on what I’ve seen work at MIT — bringing people with different skill sets together to tackle challenges in life sciences and medicine,” she says. “It’s about putting community first and empowering the next generation to lead across disciplines.”

A platform for impact

Looking ahead, the HEALS leadership team envisions the collaborative as a durable platform for advancing health and life sciences at MIT. That includes launching flagship events, supporting high-risk, high-reward ideas, and developing partnerships across the biomedical ecosystem in Boston and beyond. ​​As they see it, MIT is uniquely positioned for this moment: More than three-quarters of the Institute’s faculty work in areas that touch health and life sciences, giving HEALS a rare opportunity to bring that breadth together in new configurations and amplify impact across disciplines.

From the earliest conversations, the leaders have heard a clear message from faculty across MIT — a strong appetite for deeper connection, for working across boundaries, and for tackling urgent societal challenges together. That shared sense of momentum is what gave rise to HEALS, and it now drives the team’s focus on building the structures that can support a community that wants to collaborate at scale.

“Faculty across MIT are already reaching out — looking to connect with clinics, collaborate on new challenges, and co-create solutions,” says Koehler. “That hunger for connection is why HEALS was created. Now we have to build the structures that support it.”

Cheeseman adds that this collaborative model is what makes MIT uniquely positioned to lead. “When you bring together people from different fields who are motivated by impact,” he says, “you create the conditions for discoveries that none of us could achieve alone.”

As language models (LMs) improve at tasks like image generation, trivia questions, and simple math, you might think that human-like reasoning is around the corner. In reality, they still trail us by a wide margin on complex tasks. Try playing Sudoku with one, for instance, where you fill in numbers one through nine in such a way that each appears only once across the columns, rows, and sections of a nine-by-nine grid. Your AI opponent will either fail to fill in boxes on its own or do so inefficiently, although it can verify if you’ve filled yours out correctly.

Whether an LM is trying to solve advanced puzzles, design molecules, or write math proofs, the system struggles to answer open-ended requests that have strict rules to follow. The model is better at telling users how to approach these challenges than attempting them itself. Moreover, hands-on problem-solving requires LMs to consider a wide range of options while following constraints. Small LMs can’t do this reliably on their own; large language models (LLMs) sometimes can, particularly if they’re optimized for reasoning tasks, but they take a while to respond, and they use a lot of computing power.

This predicament led researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) to develop a collaborative approach where an LLM does the planning, then divvies up the legwork of that strategy among smaller ones. Their method helps small LMs provide more accurate responses than leading LLMs like OpenAI’s GPT-4o, and approach the precision of top reasoning systems such as o1, while being more efficient than both. Their framework, called “Distributional Constraints by Inference Programming with Language Models” (or “DisCIPL”), has a large model steer smaller “follower” models toward precise responses when writing things like text blurbs, grocery lists with budgets, and travel itineraries.

The inner workings of DisCIPL are much like contracting a company for a particular job. You provide a “boss” model with a request, and it carefully considers how to go about doing that project. Then, the LLM relays these instructions and guidelines in a clear way to smaller models. It corrects follower LMs’ outputs where needed — for example, replacing one model’s phrasing that doesn’t fit in a poem with a better option from another.

The LLM communicates with its followers using a language they all understand — that is, a programming language for controlling LMs called “LLaMPPL.” Developed by MIT's Probabilistic Computing Project in 2023, this program allows users to encode specific rules that steer a model toward a desired result. For example, LLaMPPL can be used to produce error-free code by incorporating the rules of a particular language within its instructions. Directions like “write eight lines of poetry where each line has exactly eight words” are encoded in LLaMPPL, queuing smaller models to contribute to different parts of the answer.

MIT PhD student Gabriel Grand, who is the lead author on a paper presenting this work, says that DisCIPL allows LMs to guide each other toward the best responses, which improves their overall efficiency. “We’re working toward improving LMs’ inference efficiency, particularly on the many modern applications of these models that involve generating outputs subject to constraints,” adds Grand, who is also a CSAIL researcher. “Language models are consuming more energy as people use them more, which means we need models that can provide accurate answers while using minimal computing power.”

“It's really exciting to see new alternatives to standard language model inference,” says University of California at Berkeley Assistant Professor Alane Suhr, who wasn’t involved in the research. “This work invites new approaches to language modeling and LLMs that significantly reduce inference latency via parallelization, require significantly fewer parameters than current LLMs, and even improve task performance over standard serialized inference. The work also presents opportunities to explore transparency, interpretability, and controllability of model outputs, which is still a huge open problem in the deployment of these technologies.”

An underdog story

You may think that larger-scale LMs are “better” at complex prompts than smaller ones when it comes to accuracy and efficiency. DisCIPL suggests a surprising counterpoint for these tasks: If you can combine the strengths of smaller models instead, you may just see an efficiency bump with similar results.

The researchers note that, in theory, you can plug in dozens of LMs to work together in the DisCIPL framework, regardless of size. In writing and reasoning experiments, they went with GPT-4o as their “planner LM,” which is one of the models that helps ChatGPT generate responses. It brainstormed a plan for several “Llama-3.2-1B” models (smaller systems developed by Meta), in which those LMs filled in each word (or token) of the response.

This collective approach competed against three comparable ones: a follower-only baseline powered by Llama-3.2-1B, GPT-4o working on its own, and the industry-leading o1 reasoning system that helps ChatGPT figure out more complex questions, such as coding requests and math problems.

DisCIPL first presented an ability to write sentences and paragraphs that follow explicit rules. The models were given very specific prompts — for example, writing a sentence that has exactly 18 words, where the fourth word must be “Glasgow,” the eighth should be “in”, and the 11th must be “and.” The system was remarkably adept at handling this request, crafting coherent outputs while achieving accuracy and coherence similar to o1.

Faster, cheaper, better

This experiment also revealed that key components of DisCIPL were much cheaper than state-of-the-art systems. For instance, whereas existing reasoning models like OpenAI’s o1 perform reasoning in text, DisCIPL “reasons” by writing Python code, which is more compact. In practice, the researchers found that DisCIPL led to 40.1 percent shorter reasoning and 80.2 percent cost savings over o1.

DisCIPL’s efficiency gains stem partly from using small Llama models as followers, which are 1,000 to 10,000 times cheaper per token than comparable reasoning models. This means that DisCIPL is more “scalable” — the researchers were able to run dozens of Llama models in parallel for a fraction of the cost.

Those weren’t the only surprising findings, according to CSAIL researchers. Their system also performed well against o1 on real-world tasks, such as making ingredient lists, planning out a travel itinerary, and writing grant proposals with word limits. Meanwhile, GPT-4o struggled with these requests, and with writing tests, it often couldn’t place keywords in the correct parts of sentences. The follower-only baseline essentially finished in last place across the board, as it had difficulties with following instructions.

“Over the last several years, we’ve seen some impressive results from approaches that use language models to ‘auto-formalize’ problems in math and robotics by representing them with code,” says senior author Jacob Andreas, who is an MIT electrical engineering and computer science associate professor and CSAIL principal investigator. “What I find most exciting about this paper is the fact that we can now use LMs to auto-formalize text generation itself, enabling the same kinds of efficiency gains and guarantees that we’ve seen in these other domains.” 

In the future, the researchers plan on expanding this framework into a more fully-recursive approach, where you can use the same model as both the leader and followers. Grand adds that DisCIPL could be extended to mathematical reasoning tasks, where answers are harder to verify. They also intend to test the system on its ability to meet users’ fuzzy preferences, as opposed to following hard constraints, which can’t be outlined in code so explicitly. Thinking even bigger, the team hopes to use the largest possible models available, although they note that such experiments are computationally expensive.

Grand and Andreas wrote the paper alongside CSAIL principal investigator and MIT Professor Joshua Tenenbaum, as well as MIT Department of Brain and Cognitive Sciences Principal Research Scientist Vikash Mansinghka and Yale University Assistant Professor Alex Lew SM ’20 PhD ’25. CSAIL researchers presented the work at the Conference on Language Modeling in October and IVADO’s “Deploying Autonomous Agents: Lessons, Risks and Real-World Impact” workshop in November.

Their work was supported, in part, by the MIT Quest for Intelligence, Siegel Family Foundation, the MIT-IBM Watson AI Lab, a Sloan Research Fellowship, Intel, the Air Force Office of Scientific Research, the Defense Advanced Research Projects Agency, the Office of Naval Research, and the National Science Foundation.

Artificial intelligence can enhance decision-making and enable action with reduced risk and greater precision, making it a critical tool for national security. A new program offered jointly by the MIT departments of Mechanical Engineering (Course 2, MechE) and Electrical Engineering and Computer Science (Course 6, EECS) will provide breadth and depth in technical studies for naval officers, as well as a path for non-naval officers studying at MIT, to grow in their understanding of applied AI for naval and military applications.

“The potential for artificial intelligence is just starting to be fully realized. It’s a tool that dramatically improves speed, efficiency, and decision-making with countless applications,” says Commander Christopher MacLean, MIT associate professor of the practice in mechanical engineering, naval construction, and engineering. “AI is a force multiplier that can be used for data processing, decision support, unmanned and autonomous systems, cyber defense, logistics and supply chains, energy management, and many other fields.”

The program, called “2N6: Applied Artificial Intelligence Program for Naval Officers,” comprises a two-year master of science degree in mechanical engineering with an accompanying AI certificate awarded by the MIT Schwarzman College of Computing.

“The officers entering this program will learn from the world’s experts, and conduct cutting-edge relevant research, and will exit the program best prepared for their roles as leaders across the U.S. naval enterprise,” says MacLean.

The 2N6 curriculum is application focused, and the content is built to satisfy the U.S. Navy’s sub-specialty code for Applied Artificial Intelligence. Students will learn core AI concepts, as well as applications to special topics, such as decision-making for computational exercises; AI for manufacturing and design, with special emphasis on navy applications; and AI for marine autonomy of surface and underwater vehicles.

“The expanding influence of artificial intelligence is redefining our approach to problem-solving. AI holds the potential to address some of the most pressing issues in nearly every field,” says Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and Henry Ellis Warren Professor of Electrical Engineering and Computer Science. “I’m honored that the college can contribute to and support such a vital program that will equip our nation’s naval officers with the technical expertise they need for mission-relevant challenges.”

MIT has been a leading center of ship research and design for over a century, with work at the Institute today representing significant advancements in fluid mechanics and hydrodynamics, acoustics, offshore mechanics, marine robotics and sensors, and ocean sensing and forecasting. The 2N program will celebrate its 125th year at MIT in 2026.

“In MechE, we are embracing the use of AI to explore new frontiers in research and education, with deep grounding in the fundamentals, design, and scaling of physical systems,” says John Hart, the Class of 1922 Professor and head of MechE. “With the 2N6 program, we’re proud to be at the helm of such an important charge in training the next generation of leaders for the Navy.”

“Breakthroughs in artificial intelligence are reshaping society and advancing human decision-making and creativity,” says Asu Ozdaglar, deputy dean of the MIT Schwarzman College of Computing, head of EECS, and MathWorks Professor. “We are delighted to partner with the Department of Mechanical Engineering in launching this important collaboration with the U.S. Navy. The program will explore not only the forefront of AI advances, but also its effective application in Navy operations.”

2N6 was created following a visit to campus from Admiral Samuel Paparo, commander of the U.S. Indo-Pacific Command, with MIT Provost Anantha Chandrakasan, who was then dean of engineering and chief innovation and strategy officer.

“[Admiral Paparo] was given an overview of some of the cutting-edge work and research that MIT has done and is doing in the field of AI, [and was introduced to the 2N program],” says MacLean. “The admiral made the connection, envisioning an applied AI program similar to 2N.”

2N6 will run as a pilot program for at least two years. The program’s first cohort will comprise only U.S. Navy officers, with plans to expand more broadly.

“We are thrilled to build on the long-standing relationship between MIT and the U.S. Navy with this new program,” says Themis Sapsis, William I. Koch Professor in mechanical engineering and the director of the Center for Ocean Engineering at MIT. “It is specifically designed to train naval officers on the fundamentals and applications of AI, but also involve them in research that has direct impact to the Navy. We believe that 2N6 can model a new paradigm for advanced AI education focused more broadly on supporting national security.”

To our immune system, a potentially lifesaving gene therapy can look a lot like a dangerous infection. That’s because most genetic medicine uses viruses or double-stranded DNA to deliver genetic information to target cells. DNA in its traditional double helix form can lead to toxic immune stimulation and be difficult to package into cellular delivery vehicles. As a result, the reach of genetic medicine is limited today.

Kano Therapeutics is taking a different approach to genetic therapies. The company is developing gene-editing technologies using circular single-stranded DNA (cssDNA), a biomolecule that is less toxic than double stranded DNA and more stable than RNA, and could be delivered more efficiently to many parts of the body to treat genetic diseases, cancers, and more.

The company, which was founded by former MIT postdoc Floris Engelhardt, professor of biological engineering Mark Bathe, and John Vroom MBA ’22, is developing a platform for manufacturing cssDNA of customized lengths and sequences, which could deliver genetic material to fix or replace faulty genes.

“We can work with CRISPR and other gene-editing technologies,” Engelhardt says. “CRISPR finds a location in a genome, binds to it, and cuts at that location. That allows you to edit a gene or stop a gene from functioning. But what if you have a loss-of-function disease where you need to insert a new piece of genetic code? Our approach allows you to replace whole genes or add genetic information.”

Making DNA flexible

Around 2019, Bathe’s lab published research describing ways to engineer the sequence and length of cssDNA molecules, which have been used in labs for decades but have increasingly drawn interest for improving gene therapies. Several pharmaceutical companies immediately reached out.

“Single-stranded DNA is a little like messenger RNA, which can code for any protein in any cell, tumor, or organ,” Bathe says. “It fundamentally encodes for a protein, so it can be used across diseases, including rare diseases that may only affect a few people in the country.”

Engelhardt had also worked on cssDNA as a PhD student in Munich. She met Bathe at a conference.

“We were considering collaborating on research,” Engelhardt recalls. “Then Mark heard I was finishing my PhD and said, ‘Wait a minute. Instead of collaborating, I should hire you.’”

Within 48 hours of submitting her PhD thesis, Engelhardt received an email asking her to apply to Bathe’s lab as a postdoc. She was drawn to the position because she would be focusing on research that had the potential to help patients.

“MIT is very good at creating industry-focused postdocs,” Engelhardt says. “I was inspired by the idea of doing postdoc work with the goal of spinning out a company, as opposed to doing solely academic-focused research.”

Bathe and Engelhardt learned from members of the pharmaceutical industry how single-stranded DNA could help overcome limitations in gene and cell therapies. Although CRISPR-based treatments have recently been approved for a few genetic diseases, CRISPR’s effectiveness has been limited by its potential toxicity and inefficient delivery to specific sites in the body. Also, those treatments can only be administered once because CRISPR often gets labeled as foreign by our immune systems and rejected from the body.

Engelhardt began exploring MIT’s resources to help commercialize her research. She met Vroom through an online “founder speed dating” event at MIT. She also received support from the Venture Mentoring Service, took classes at MIT’s Sloan School of Management, and worked with MIT’s Industrial Liaison Program. Early on, Bathe suggested Engelhardt work with MIT’s Technology License Office, something she says she tells every founder to do the moment they start thinking about commercializing their research.

In 2021, Kano won the $20,000 first place prize at the MIT Sloan Healthcare Innovation Prize (SHIP) to commercialize a new way to design and manufacture single-stranded DNA. Kano uses fermentation to produce its cssDNA less expensively than approaches based on chemical DNA synthesis.

“No one had the ability to access this type of genetic material, and so a lot of our work was around creating the highest-quality, economically scalable process to allow circular single-stranded DNA to be commercially viable,” Engelhardt says.

Engelhardt and Vroom began meeting with investors as soon as Engelhardt finished her postdoc work in 2021. The founders worked to raise money over the next year while Vroom finished his MBA.

Today, Kano’s circular ssDNA can be used to insert entire genes, up to 10,000 nucleotides long, into the body. Kano is planning to partner with pharmaceutical companies to make their gene therapies more targeted and potent. For instance, pharmaceutical partners could use Kano’s platform to join the CD19 and CD20 genes, which are expressed in certain tumor cells, and stipulate that only if both genes bind to a cell receptor do they enter that cell’s genome and make edits.

Overall, Engelhardt says working with circular single-stranded DNA makes Kano’s approach more flexible than platforms like CRISPR.

“We realized working with pharmaceutical companies early on in my postdoc there was a lack of design understanding because of the lack of access to these molecules,” Engelhardt says. “When it comes to gene or cell therapies, people just think of the gene itself, not the flanking sequences or anything else that goes around the gene. Now that the DNA isn’t stuck in a double helix all the time, I can create small, three-dimensional structures — think loops or hairpins — that work, for example, as a binding protein that pulls it into the nucleus. That unlocks a completely new path for DNA because it makes it engineerable — not only on a structural level but also a sequence level.”

Partnering for impact

To facilitate more partnerships, Kano is signing agreements with partners that give it a smaller percentage of eventual drug royalties but allow it to work with many companies at the same time. In a recent collaboration with Merck KGaA, Kano combined its circular cssDNA platform with the company’s lipid nanoparticles solutions for delivering gene therapies. Kano is also in discussions with other large pharmaceutical companies to jointly bring cancer drugs into the clinic over the next two years.

“That’s exciting because we’ll be implementing our DNA into partners’ drug system, so when they file their new drug and dose their first patients, our DNA is going to be the therapeutic information carrier for efficacy,” Engelhardt says. “As a first-time founder, this is where you want to go. We talk about patient impact all the time, and this is how we’re going to get it.”

Kano is also developing the first databank mapping cssDNA designs to activity, to speed up the development of new treatments.

“Right now, there is no understanding of how to design DNA for these therapies,” Engelhardt says. “Everyone who wants to differentiate needs to come up with a new editing tool, a new delivery tool, and there’s no connecting company that can enable those areas of expertise. When partners come to us, we can say, ‘The gene sequence is all yours.’ But often it’s not just about the sequence. It’s also about the promoter or flanking sequence that allows you to insert your DNA into the genome, or that makes DNA package well into your delivery nanoparticle. At Kano, we’re building the best knowledgebase to use DNA material to treat diseases.”

The UK Parliament convened earlier this week to debate a petition signed by almost 2.9 million people calling for an end to the government’s plans to roll out a national digital ID. Ahead of that debate, EFF and 12 other civil society organizations wrote to politicians in the country urging MPs to reject the Labour government’s newly announced digital ID proposal.

The UK’s Prime Minister Keir Starmer pitched the scheme as a way to “cut the faff” in proving people’s identities by creating a virtual ID on personal devices with information like names, date of birth, nationality, photo, and residency status to verify their right to live and work in the country. 

But the case for digital identification has not been made. 

As we detail in our joint briefing, the proposal follows a troubling global trend: governments introducing expansive digital identity systems that are structurally incompatible with a rights-respecting democracy. The UK’s plan raises six interconnected concerns:

1. Mission creep
2. Infringements on privacy rights
3. Serious security risks
4. Reliance on inaccurate and unproven technologies
5. Discrimination and exclusion
6. The deepening of entrenched power imbalances between the state and the public.

Digital ID schemes don’t simply verify who you are—they redefine who can access services and what those services look like. They become a gatekeeper to essential societal infrastructure, enabling governments and state agencies to close doors as easily as they open them. And they disproportionately harm those already at society’s margins, including people seeking asylum and undocumented communities, who already face heightened surveillance and risk.

Even the strongest recommended safeguards cannot resolve the core problem: a mandatory digital ID scheme that shifts power dramatically away from individuals and toward the state. No one should be coerced—technically or socially—into a digital system in order to participate fully in public life. And at a time when almost 3 million people in the UK have called on politicians to reject this proposal, the government must listen to people and say no to digital ID.

Read our civil society briefing in full here.

Governments and companies constantly face decisions about how to allocate finite amounts of money to clean energy technologies that can make a difference to the world’s climate, its economies, and to society as a whole. The process is inherently uncertain, but research has been shown to help predict which technologies will be most successful. Using data-driven bases for such decisions can have a significant impact on allowing more informed decisions that produce the desired results.

The role of these predictive tools, and the areas where further research is needed, are addressed in a perspective article published Nov. 24 in Nature Energy, by professor Jessika Trancik of MIT’s Sociotechnical Systems Research Center and Institute of Data, Systems, and Society and 13 co-authors from institutions around the world.

She and her co-authors span engineering and social science and share “a common interest in understanding how to best use data and models to inform decisions that influence how technology evolves,” Trancik says. They are interested in “analyzing many evolving technologies — rather than focusing on developing only one particular technology — to understand which ones can deliver.” Their paper is aimed at companies and governments, as well as researchers. “Increasingly, companies have as much agency as governments over these technology portfolio decisions,” she says, “although government policy can still do a lot because it can provide a sort of signal across the market.”

The study looked at three stages of the process, starting with forecasting the actual technological changes that are likely to play important roles in coming years, then looking at how those changes could affect economic, social, and environmental conditions, and finally, how to apply these insights into the actual decision-making processes as they occur.

Forecasting usually falls into two categories, either data-driven or expert-driven, or a combination of those. That provides an estimate of how technologies may be improving, as well as an estimate of the uncertainties in those predictions. Then in the next step, a variety of models are applied that are “very wide ranging,” Trancik says, “different models that cover energy systems, transportation systems, electricity, and also integrated assessment models that look at the impact of technology on the environment and on the economy.”

And then, the third step is “finding structured ways to use the information from predictive models to interact with people that may be using that information to inform their decision-making process,” she says. “In all three of these steps, how you need to recognize the vast uncertainty and tease out the predictive aspects. How you deal with uncertainty is really important.”

In the implementation of these decisions, “people may have different objectives, or they may have the same objective but different beliefs about how to get there. And so, part of the research is bringing in this quantitative analysis, these research results, into that process,” Trancik says. And a very important aspect of that third step, she adds, is “recognizing that it’s not just about presenting the model results and saying, ‘here you go, this is the right answer.’ Rather, you have to bring people into the process of designing the studies and interacting with the modeling results.”

She adds that “the role of research is to provide information to, in this case, the decision-making processes. It’s not the role of the researchers to push for one outcome or another, in terms of balancing the trade-offs,” such as between economic, environmental, and social equity concerns. It’s about providing information, not just for the decision-makers themselves, but also for the public who may influence those decisions. “I do think it’s relevant for the public to think about this, and to think about the agency that actually they could have over how technology is evolving.”

In the study, the team highlighted priorities for further research that needs to be done. Those priorities, Trancik says, include “streamlining and validating models, and also streamlining data collection,” because these days “we often have more data than we need, just tons of data,” and yet “there’s often a scarcity of data in certain key areas like technology performance and evolution. How technologies evolve is just so important in influencing our daily lives, yet it’s hard sometimes to access good representative data on what’s actually happening with this technology.” But she sees opportunities for concerted efforts to assemble large, comprehensive data on technology from publicly available sources.

Trancik points out that many models are developed to represent some real-world process, and “it’s very important to test how well that model does against reality,” for example by using the model to “predict” some event whose outcome is already known and then “seeing how far off you are.” That’s easier to do with a more streamlined model, she says.

“It’s tempting to develop a model that includes many, many parameters and lots of different detail. But often what you need to do is only include detail that’s relevant for the particular question you’re asking, and that allows you to make your model simpler.” Sometimes that means you can simplify the decision down to just solving an equation, and other times, “you need to simulate things, but you can still validate the model against real-world data that you have.”

“The scale of energy and climate problems mean there is much more to do,” says Gregory Nemet, faculty chair in business and regulation at the University of Wisconsin at Madison, who was a co-author of the paper. He adds, “while we can’t accurately forecast individual technologies on their own, a variety of methods have been developed that in conjunction can enable decision-makers to make public dollars go much further, and enhance the likelihood that future investments create strong public benefits.”

This work is perhaps particularly relevant now, Trancik says, in helping to address global challenges including climate change and meeting energy demand, which were in focus at the global climate conference COP 30 that just took place in Brazil. “I think with big societal challenges like climate change, always a key question is, ‘how do you make progress with limited time and limited financial resources?’” This research, she stresses, “is all about that. It’s about using data, using knowledge that’s out there, expertise that’s out there, drawing out the relevant parts of all of that, to allow people and society to be more deliberate and successful about how they’re making decisions about investing in technology.”

As with other areas such as epidemiology, where the power of analytical forecasting may be more widely appreciated, she says, “in other areas of technology as well, there’s a lot we can do to anticipate where things are going, how technology is evolving at the global or at the national scale … There are these macro-level trends that you can steer in certain directions, that we actually have more agency over as a society than we might recognize.”

The study included researchers in Massachusetts, Wisconsin, Colorado, Maryland, Maine, California, Austria, Norway, Mexico, Finland, Italy, the U.K., and the Netherlands. 

President Tharman Shanmugaratnam of the Republic of Singapore visited MIT on Tuesday, meeting campus leaders while receiving the Miriam Pozen Prize and delivering a lecture on fiscal policy at the MIT Sloan School of Management.

“We really have to re-orient fiscal policy and develop new fiscal compacts,” said Tharman in his remarks, referring to the budget policy challenges countries face at a time of expanding government debt.

His talk, “The Compacts We Need: Fiscal Choices and Risk-sharing for Sustained Prosperity,” was delivered before a capacity audience of students, faculty, administrators, and staff at MIT’s Samberg Center.

Tharman is a trained economist who for many years ran Singapore’s central bank and has become a notable presence in global policymaking circles. Presenting a crisp summary of global trends, he observed that debt levels in major economies are at or beyond levels once regarded as unsustainable.

“There is no realistic solution to putting government debts back on a sustainable path other than having to make major adjustments to taxes and spending,” he said. However, he emphasized that his remarks were distinctly not “a call for austerity.” Instead, as he outlined, well-considered public investment can reduce the need for additional spending and thus be fiscally sound over time.

For instance, he noted, sound policy approaches can reduce individuals’ health care needs by better providing the conditions in which people stay healthy. Lowering some of these individual burdens and investing in community-building policies can help society both fiscally and by enhancing social solidarity.

“The challenge is to make these adjustments while re-fashioning fiscal policy so that people can see the adjustments — they can see the value in government spending that their taxes are contributing to — and to make adjustments in a way that doesn’t reduce growth,” Tharman said. “You do need growth for solidarity.”

In this sense, he proposed, “We need new fiscal compacts, new retirement compacts, and new global compacts to address the risks that are posed in the minds of individuals, as well as the largest risks” in society. Countries are vulnerable to a variety of shocks, he noted, calling climate change the “defining challenge of our time.” And yet, he added, for all of this, sensible policymaking can encourage people, creating more support for public-minded governance.

“It is that sharing of hopes and aspirations that is at the heart of true solidarity, not the sharing of fears,” Tharman concluded.

Before the lecture, Tharman was greeted by MIT Provost Anantha Chandrakasan, who presented him with a small gift from the MIT Glass Lab, and MIT Sloan Dean Richard Locke. Locke then made welcoming remarks at the event, praising Tharman’s “remarkable leadership in international financial policy, among other things.” After the lecture, Tharman also met with a group of MIT students from Singapore.

The Miriam Pozen Prize is awarded every two years by the MIT Golub Center for Finance and Policy, part of MIT Sloan. The prize, which recognizes extraordinary contributions to financial policy, was created to draw attention to the important research on financial policy conducted at the Golub Center, whose mission is to support research and educational initiatives related to governments’ roles as financial institutions and as regulators of the global financial system. It is named for the mother of MIT Sloan Senior Lecturer Robert C. Pozen, who is also the former executive chairman of MFS Investment Management, and a former vice chairman of Fidelity Investments and president of Fidelity Management and Research Company.

In introductory remarks. Robert Pozen said he was “deeply honored” to present the prize, adding, “It’s very unusual to have someone who is both a brilliant economist and an effective political leader, and that combination is exactly what we’re trying to honor and recognize.”

The previous recipients of the award are Mario Draghi PhD ’77, the former prime minister of Italy and president of the European Central Bank; and the late Stanley Fischer PhD ’69, an influential MIT economist who later became governor of the Bank of Israel, and then vice-chairman of the U.S. Federal Reserve. Draghi received the honor in 2023, and Fischer in 2021.

Tharman was first elected to his current office in 2023. In Singapore, he previously served as, among other roles, deputy prime minister, minister for finance, minister for education, and chairman of the Monetary Authority of Singapore.

Tharman holds a BA in economics from the London School of Economics, an MA in economics from the University of Cambridge, and an MPA from the Harvard Kennedy School at Harvard University.

MIT and Singapore have developed a sustained and productive relationship in research and education over the last quarter-century. The Singapore-MIT Alliance for Research and Technology (SMART), formally launched in 2007, is MIT’s first research center located outside of the United States, featuring work in several interdisciplinary areas of innovation.

The MIT-Singapore program also provides MIT students with research, work, and educational opportunities in Singapore. Additionally, MIT Institute Professor Emeritus Thomas Magnanti, who was present at Tuesday’s event, was the founding president of the Singapore University of Technology and Design, in 2009.

Tuesday’s event also had introductory remarks from Deborah J. Lucas, Sloan Distinguished Professor of Finance at MIT Sloan and director of the MIT Golub Center for Finance and Policy; Peter Fischer, Golub Distinguished Senior Fellow at MIT Sloan and a former under secretary in the U.S. Treasury Department; and Robert C. Merton, School of Managament Distinguished Professor of Finance at MIT Sloan.

In her comments, Lucas said that Tharman “personifies the qualities the award was created to honor,” while Fischer cited his emphasis on “the betterment of humankind.”

Merton praised Tharman’s “deep commitment for advancing financial policy in a way that serves both national and global arenas.” He added: “You have always believed that policy is not just about numbers, but about people. And that sound financial [policies] serve the many, not just the few.”

The promise of personal AI assistants rests on a dangerous assumption: that we can trust systems we haven’t made trustworthy. We can’t. And today’s versions are failing us in predictable ways: pushing us to do things against our own best interests, gaslighting us with doubt about things we are or that we know, and being unable to distinguish between who we are and who we have been. They struggle with incomplete, inaccurate, and partial context: with no standard way to move toward accuracy, no mechanism to correct sources of error, and no accountability when wrong information leads to bad decisions...

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Let’s say an environmental scientist is studying whether exposure to air pollution is associated with lower birth weights in a particular county.

They might train a machine-learning model to estimate the magnitude of this association, since machine-learning methods are especially good at learning complex relationships.

Standard machine-learning methods excel at making predictions and sometimes provide uncertainties, like confidence intervals, for these predictions. However, they generally don’t provide estimates or confidence intervals when determining whether two variables are related. Other methods have been developed specifically to address this association problem and provide confidence intervals. But, in spatial settings, MIT researchers found these confidence intervals can be completely off the mark.

When variables like air pollution levels or precipitation change across different locations, common methods for generating confidence intervals may claim a high level of confidence when, in fact, the estimation completely failed to capture the actual value. These faulty confidence intervals can mislead the user into trusting a model that failed.

After identifying this shortfall, the researchers developed a new method designed to generate valid confidence intervals for problems involving data that vary across space. In simulations and experiments with real data, their method was the only technique that consistently generated accurate confidence intervals.

This work could help researchers in fields like environmental science, economics, and epidemiology better understand when to trust the results of certain experiments.

“There are so many problems where people are interested in understanding phenomena over space, like weather or forest management. We’ve shown that, for this broad class of problems, there are more appropriate methods that can get us better performance, a better understanding of what is going on, and results that are more trustworthy,” says Tamara Broderick, an associate professor in MIT’s Department of Electrical Engineering and Computer Science (EECS), a member of the Laboratory for Information and Decision Systems (LIDS) and the Institute for Data, Systems, and Society, an affiliate of the Computer Science and Artificial Intelligence Laboratory (CSAIL), and senior author of this study.

Broderick is joined on the paper by co-lead authors David R. Burt, a postdoc, and Renato Berlinghieri, an EECS graduate student; and Stephen Bates an assistant professor in EECS and member of LIDS. The research was recently presented at the Conference on Neural Information Processing Systems.

Invalid assumptions

Spatial association involves studying how a variable and a certain outcome are related over a geographic area. For instance, one might want to study how tree cover in the United States relates to elevation.

To solve this type of problem, a scientist could gather observational data from many locations and use it to estimate the association at a different location where they do not have data.

The MIT researchers realized that, in this case, existing methods often generate confidence intervals that are completely wrong. A model might say it is 95 percent confident its estimation captures the true relationship between tree cover and elevation, when it didn’t capture that relationship at all.

After exploring this problem, the researchers determined that the assumptions these confidence interval methods rely on don’t hold up when data vary spatially.

Assumptions are like rules that must be followed to ensure results of a statistical analysis are valid. Common methods for generating confidence intervals operate under various assumptions.

First, they assume that the source data, which is the observational data one gathered to train the model, is independent and identically distributed. This assumption implies that the chance of including one location in the data has no bearing on whether another is included. But, for example, U.S. Environmental Protection Agency (EPA) air sensors are placed with other air sensor locations in mind.

Second, existing methods often assume that the model is perfectly correct, but this assumption is never true in practice. Finally, they assume the source data are similar to the target data where one wants to estimate.

But in spatial settings, the source data can be fundamentally different from the target data because the target data are in a different location than where the source data were gathered.

For instance, a scientist might use data from EPA pollution monitors to train a machine-learning model that can predict health outcomes in a rural area where there are no monitors. But the EPA pollution monitors are likely placed in urban areas, where there is more traffic and heavy industry, so the air quality data will be much different than the air quality data in the rural area.

In this case, estimates of association using the urban data suffer from bias because the target data are systematically different from the source data.

A smooth solution

The new method for generating confidence intervals explicitly accounts for this potential bias.

Instead of assuming the source and target data are similar, the researchers assume the data vary smoothly over space.

For instance, with fine particulate air pollution, one wouldn’t expect the pollution level on one city block to be starkly different than the pollution level on the next city block. Instead, pollution levels would smoothly taper off as one moves away from a pollution source.

“For these types of problems, this spatial smoothness assumption is more appropriate. It is a better match for what is actually going on in the data,” Broderick says.

When they compared their method to other common techniques, they found it was the only one that could consistently produce reliable confidence intervals for spatial analyses. In addition, their method remains reliable even when the observational data are distorted by random errors.

In the future, the researchers want to apply this analysis to different types of variables and explore other applications where it could provide more reliable results.

This research was funded, in part, by an MIT Social and Ethical Responsibilities of Computing (SERC) seed grant, the Office of Naval Research, Generali, Microsoft, and the National Science Foundation (NSF).