Professor Emeritus Jack Dennis, pioneering developer of dataflow models of computation, dies at 94

Jack Dennis, an influential MIT professor emeritus of computer science and engineering, died on March 14 at age 94. The original leader of the Computation Structures Group within the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), he pioneered the development of dataflow models of computation, and, subsequently, many novel principles of computer architecture inspired by dataflow models.

The second child of an engineer and a textile designer, Dennis showed early interest in both engineering and music, rewriting Gilbert and Sullivan lyrics with his parents and playing piano with the Norwalk Symphony Orchestra in Connecticut as a teen, while building a canoe at home with his father. As an undergraduate at MIT, he developed his wide array of interests further, joining the VI-A Cooperative Program in Electrical Engineering; working at the Air Force Cambridge Research Laboratories on projects in speech processing and novel radar systems; participating in the model railroad club; and joining the MIT Symphony Orchestra, where he met his first wife, Jane Hodgson ’55, SM ’56, PhD ’61. (The two later separated when she went to study medicine in Florida.) 

Dennis earned his BS (1953), MS (1954), and ScD (1958) from MIT before joining the then-Department of Electrical Engineering as a faculty member. He was promoted to full professor status in 1969. His doctoral thesis, entitled, “Mathematical Programming and electrical networks,” explored analogies between electric circuit theory and quadratic programming problems. Ideas he developed in that paper further crystallized in his 1964 paper, “Distributed solution of network programming problems,” which created an important early class of digital distributed optimization solvers.

In a 2003 piece that Dennis wrote for his undergraduate class’s 50th reunion, he remembered his earliest encounters with computers at the Institute: “I prepared programs written in assembly language on punched paper tape using Frieden 'Flexowriters,' and stood aside watching the myriad lights blink and flash while operator Mike Solamita fed the tapes [...] That was 1954. Fifty years later, much has changed: A room full of vacuum tubes has become a tiny chip with millions of transistors. A phenomenon once limited to research laboratories has become an industry producing commodity products that anyone can own and use beneficially.”

Dennis’ influence in steering that change was profound. As a collaborator with the teams behind both Project MAC and Multics, the earliest attempts to allow multiple users to work with a single computer seemingly simultaneously (i.e., a time-shared operating system), Dennis helped to specify the unique segment addressing and paging mechanisms that became a fundamental part of the General Electric Model 645 computer. His insights stemmed from a tendency to pay equal attention to both hard- and software when others considered themselves specialists in one or the other. 

“I formed the Computation Structures Group [within CSAIL] and focused on architectural concepts that could narrow the acknowledged gap between programming concepts and the organization of computer hardware,” Dennis explained in his 2003 recollection. “I found myself dismayed that people would consider themselves to be either hardware or software experts, but paid little heed to how joint advances in programming and architecture could lead to a synergistic outcome that might revolutionize computing practice.”

Dennis’ emphasis on synergy did not go unnoticed. Gerald Sussman, the Panasonic Professor of Electrical Engineering, points out “the relationship of [Dennis’] dataflow architecture to single-assignment programs, and thus to pure functional programs. This coupled the virtue of referential transparency in programming to the effective use of hardware parallelism. Dennis also pioneered the use of self-timed circuits in digital systems. The ideas from that work generalize to much of the work on highly distributed systems.” 

The Computation Structures Group attracted multiple scholars interested in developing asynchronous computing and dataflow architecture, many of whom became lifelong friends and collaborators. These included Peter Denning, with whom Dennis and Joseph Qualitz co-authored the textbook “Machines, Languages, and Computation” (1978); the late Arvind, who became faculty head of computer science for the Department of Electrical Engineering and Computer Science (EECS), and the late Guang R. Gao, who became distinguished professor of electrical and computer engineering at the University of Delaware. 

In recognition of his contributions to the Multics project, Dennis was elected fellow of the Institute of Electrical and Electronics Engineers (IEEE). Many additional honors would follow: He received the Association for Computing Machinery (ACM)/IEEE Eckert-Mauchly Award in 1984; was inducted as a fellow of the ACM (1994); was named to the National Academy of Engineering (2009); was elected to the (ACM) Special Interest Group on Operating Systems (SIGOPS) Hall of Fame (2012); and was awarded the IEEE John von Neumann Medal (2013). 

A successful researcher, Dennis was perhaps equally influential in the development of EECS’ curriculum, developing six subjects in areas of computer theory and systems: Theoretical Models for Computation; Computation Structures; Structure of Computer Systems; Semantic Theory for Computer Systems; Semantics of Parallel Computation; and Computer System Architecture (taught in collaboration with Arvind.) Several of the courses that Dennis developed continue to be taught, in updated form, to this day.

Following his retirement from teaching in 1987, he consulted on projects relating to parallel computer hardware and software for such varied groups as NASA Research Institute for Advanced Computer Science; Boeing Aerospace; McGill University; the Architecture Group of Carlstedt Elektronik in Gothenburg, Sweden; and Acorn Networks, Inc. His fruitful relationship with former student Guang Gao continued in the form of a lecture tour through China, as well as co-authorship of a book, “Dataflow Architecture,” currently in progress at MIT Press. 

A voracious lifelong learner, Dennis was fond of repeating a friend’s observation that “a scholar is just a book’s way of making another book.” In a full and active retirement, he still made room for music, trying his hand at composing; performing at Tanglewood as a tenor in Chorus Pro Musica; playing piano at the marriage of Guang Gao’s son Nick; and joining the chorus at the First Church in Belmont, Massachusetts, where his celebration of life (with concurrent livestreaming) will be held on Monday, June 8, at 2 p.m. 

Dennis is survived by his wife Therese Smith ’75; children David Hodgson Dennis of North Miami, Florida; Randall Dennis of Connecticut; and Galen Dennis, a resident of Australia. 

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Slice and dice

What if the Trojan horse had been pulled to pieces, revealing the ruse and fending off the invasion, just as it entered the gates of Troy?

That’s an apt description of a newly characterized bacterial defense system that chops up foreign DNA.

Bacteria and the viruses that infect them, bacteriophages — phages for short — are ceaselessly at odds, with bacteria developing methods to protect themselves against phages that are constantly striving to overcome those safeguards.

New research from the Department of Biology at MIT, recently published in Nature, describes a defense system that is integrated into the protective membrane that encapsulates bacteria. SNIPE, which stands for surface-associated nuclease inhibiting phage entry, contains a nuclease domain that cleaves genetic material, chopping the invading phage genome into harmless fragments before it can appropriate the host’s molecular machinery to make more phages. 

Daniel Saxton, a postdoc in the Laub Lab and the paper’s first author, was initially drawn to studying this bacterial defense system in E. coli, in part because it is highly unusual to have a nuclease that localizes to the membrane, as most nucleases are free-floating in the cytoplasm, the gelatinous fluid that fills the space inside cells.

“The other thing that caught my attention is that this is something we call a direct defense system, meaning that when a phage infects a cell, that cell will actually survive the attack,” Saxton says. “It’s hard to fend off a phage directly in a cell and survive — but this defense system can do it.” 

Light it up

For Saxton, the project came into focus during a fluorescence-based experiment in which viral genetic material would light up if it successfully penetrated the bacteria. 

“SNIPE was obliterating the phage DNA so fast that we couldn’t even see a fluorescent spot,” Saxton recalls. “I don’t think I’ve ever seen such an effective defense system before — you can barrage the bacteria with hundreds of phage per cell, but SNIPE is like god-tier protection.”

When the nuclease domain of SNIPE was mutated so it couldn’t chop up DNA, fluorescent spots appeared as usual, and the bacteria succumbed to the phage infection. 

Bacteria maintain tight control over all their defense systems, lest they be turned against their host. Some systems remain dormant until they flare up, for example, to halt all translation of all proteins in the cell, while others can distinguish between bacterial DNA and foreign, invading phage DNA. There were only two previously characterized mechanisms in the latter category before researchers uncovered SNIPE. 

“Right now, the phage field is at a really interesting spot where people are discovering phage defense systems at a breakneck pace,” Saxton says. 

Problems at the periphery

Saxton says they had to approach the work in a somewhat roundabout way because there are currently no published structures depicting all the steps of phage genome injection. Studying processes at the membrane is challenging: Membranes are dense and chaotic, and phage genome injection is a highly transient process, lasting only a few minutes. 

SNIPE seems to discern viral DNA by interacting with proteins the phage uses to tunnel through the bacteria’s protective membrane. This “subcellular localization,” according to Saxton, may also prevent SNIPE from inadvertently chopping up the bacteria’s own genetic material.

The model outlined in the paper is that one region of SNIPE binds to a bacterial membrane protein called ManYZ, while another region likely binds to the tape measure protein from the phage. 

The tape measure protein got its name because it determines the length of the phage tail — the part of the phage between the small, leglike protrusions and the bulbous head, which contains the phage’s genetic material. The researchers revealed that the phage’s tape measure protein enters the cytoplasm during injection, a phenomenon that had not been physically demonstrated before. 

There may also be other proteins or interactions involved. 

“If you shunt the phage genome injection through an alternate pathway that isn’t ManYZ, suddenly SNIPE doesn’t defend against the phage nearly as well,” Saxton says. “It’s unclear exactly how these proteins interact, but we do know that these two proteins are involved in this genome injection process.” 

Future directions

Saxton hopes that future work will expand our understanding of what occurs during phage genome injection and uncover the structures of the proteins involved, especially the tunnel complex in the membrane through which phages insert their genome.

Members of the Laub Lab are already collaborating with another lab to determine the structure of SNIPE. In the meantime, Saxton has been working on a new defense system in which molecular mimicry — bacterial proteins imitating phage proteins — may play a role. 

Michael T. Laub, the Salvador E. Luria Professor of Biology and a Howard Hughes Medical Institute investigator, notes that one of the breakthrough experiments for demonstrating how SNIPE works came from a brainstorming session at a lab retreat.

“Daniel and I were kind of stuck with how to directly measure the effect of SNIPE during infection, but another postdoc in the lab, Ian Roney, who is a co-author on the paper, came up with a very clever idea that ultimately worked perfectly,” Laub recalls. “It’s a great example of how powerful internal collaborations can be in pushing our science forward.”

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Bridging space research and policy

While earning her dual master’s degrees in aeronautics and astronautics and public policy, Carissma McGee SM ’25 learned to navigate between two seemingly distinct worlds, bridging rigorous technical analysis and policy decisions.

As an undergraduate congressional intern and researcher, she saw a persistent gap in space policymaking. Policymakers often lacked technical expertise, while researchers were rarely involved in increasingly complex questions surrounding intellectual property and international collaboration in space.

Her work on intellectual property frameworks for space collaborations directly addresses that gap, combining expertise in gravitational microlensing and space telescope operations with policy analysis to tackle emerging governance challenges.

“I want to bring an expert level in science in the rooms where policy decisions are made,” says McGee, now a doctoral student in aeronautics and astronautics. “That perspective is critical for shaping the future of research and exploration.”

Likewise, she wants to bring her expertise in public policy into the lab.

“I enjoy being able to ask questions about intellectual property, territorial claims, knowledge transfer, or allocation of resources early on in a research project,” adds McGee.

McGee’s fascination with space started during her high school years in Delaware, when she first volunteered at a local observatory and then interned at the NASA Goddard Space Flight Center in Maryland.

Following high school, McGee attended Howard University. She was selected to participate in the Karsh STEM Scholars Program, a full-ride scholarship track for students committed to working continuously toward earning doctoral degrees. Howard, which holds an R1 research classification from the Carnegie Foundation, is in close proximity the Goddard Space Flight Center, as well as the American Astronomical Society and the D.C. Space Grant Consortium.

In 2020, after her first year at Howard, the Covid-19 pandemic sent McGee back to her hometown in Delaware. As it turned out, that gave her an opportunity to work with her local congresswoman, Lisa Blunt Rochester, then a U.S. representative. In addition to supporting the congresswoman’s constituents, she drafted dozens of letters related to STEM education and energy reform.

Working in government gave McGee an opportunity to use her voice to “advocate for astronomy and astrophysics with the American Astronomical Society, advocate for space sciences, and for science representation.”

As an undergraduate, McGee also conducted research linking computational physics and astronomy, working with both NASA’s Jet Propulsion Laboratory and Yale University’s Department of Astronomy. She also continued research begun in 2021 with the Harvard and Smithsonian Center for Astrophysics’ Black Hole Initiative, contributing to work associated with the Event Horizon Telescope.

When she visited MIT in 2023, McGee was struck by the Institute’s openness to interdisciplinary work and support of her interest in combining aeronautics and astronautics with policy.

Once at MIT, she started working in the Space, Telecommunications, Astronomy, and Radiation Laboratory (STAR Lab) with advisor Kerri Cahoy, professor of aeronautics and astronautics. McGee says she experienced a great deal of freedom to craft her own program.

“I was drawn to the lab’s work on satellite missions and CubeSats, and excited to discover that I could pursue exoplanet astrophysics research within this framework and that submitting a dual thesis or focusing on astrophysics applications was possible,” says McGee. “When I expressed interest in participating in the Technology [and] Policy Program for a dual thesis in a framework for space policy, my advisors encouraged me to explore how we could integrate these diverse interests into a path forward.”

In 2024, McGee was awarded a MathWorks Fellowship to pursue research associated with the Nancy Grace Roman Space Telescope and join a NASA mission.

“It was just amazing to join the exoplanet group at NASA,” she says. “I had a front-row seat to see how real researchers and workers navigate complex problems.”

McGee credits MathWorks with helping fellows to “be at the forefront of knowledge and shaping innovation.”

One of her proudest academic accomplishments is PyLIMASS, a software system she developed with collaborators at Louisiana State University, the Ohio State University, and NASA’s Goddard Space Flight Center. The tool enables more accurate mass and distance estimates in gravitational microlensing events, helping the Roman Space Telescope project meet its precision goals for studying exoplanets.

“To build software that didn’t previously exist — and to know it will be used for the Roman mission — is incredibly exciting,” McGee says.

In May 2025, McGee graduated with dual master’s degrees in aeronautics and astronautics and technology and policy. That same month, she presented her research at the American Astronomical Society meeting in Anchorage, Alaska, and at the Technology Management and Policy Conference in Portugal.

McGee remained at MIT to pursue her doctoral degree. Last fall, as an MIT BAMIT Community Advancement Program and Fund Fellow, she hosted a daylong conference for STEM students focused on how intellectual property frameworks shape technical fields.

McGee’s accomplishments and contributions have been celebrated with a number of honors recently. In 2026, she was named Miss Black Massachusetts United States, was recognized among MIT’s Graduate Students of Excellence, and received the MIT MLK Leadership Award in recognition of her service, integrity, and community impact.

Beyond her academic work, McGee is active across campus. She teaches Pilates with MIT Recreation, participates in the Graduate Women in Aerospace Engineering group, and serves as a graduate resident assistant in an undergraduate dorm on East Campus.

She credits the AeroAstro graduate community with keeping her momentum going.

“Even if we’re tired, there’s this powerful camaraderie among AeroAstro graduate students working together. Seeing my peers are pushing through similar research milestones and solve daunting problems motivates you to advance beyond the finish line to further developments in the field.”

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New technique makes AI models leaner and faster while they’re still learning

Training a large artificial intelligence model is expensive, not just in dollars, but in time, energy, and computational resources. Traditionally, obtaining a smaller, faster model either requires training a massive one first and then trimming it down, or training a small one from scratch and accepting weaker performance. 

Researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), Max Planck Institute for Intelligent Systems, European Laboratory for Learning and Intelligent Systems, ETH, and Liquid AI have now developed a new method that sidesteps this trade-off entirely, compressing models during training, rather than after.

The technique, called CompreSSM, targets a family of AI architectures known as state-space models, which power applications ranging from language processing to audio generation and robotics. By borrowing mathematical tools from control theory, the researchers can identify which parts of a model are pulling their weight and which are dead weight, before surgically removing the unnecessary components early in the training process.

"It's essentially a technique to make models grow smaller and faster as they are training," says Makram Chahine, a PhD student in electrical engineering and computer science, CSAIL affiliate, and lead author of the paper. "During learning, they're also getting rid of parts that are not useful to their development."

The key insight is that the relative importance of different components within these models stabilizes surprisingly early during training. Using a mathematical quantity called Hankel singular values, which measure how much each internal state contributes to the model's overall behavior, the team showed they can reliably rank which dimensions matter and which don't after only about 10 percent of the training process. Once those rankings are established, the less-important components can be safely discarded, and the remaining 90 percent of training proceeds at the speed of a much smaller model.

"What's exciting about this work is that it turns compression from an afterthought into part of the learning process itself,” says senior author Daniela Rus, MIT professor and director of CSAIL. “Instead of training a large model and then figuring out how to make it smaller, CompreSSM lets the model discover its own efficient structure as it learns. That's a fundamentally different way to think about building AI systems.”

The results are striking. On image classification benchmarks, compressed models maintained nearly the same accuracy as their full-sized counterparts while training up to 1.5 times faster. A compressed model reduced to roughly a quarter of its original state dimension achieved 85.7 percent accuracy on the CIFAR-10 benchmark, compared to just 81.8 percent for a model trained at that smaller size from scratch. On Mamba, one of the most widely used state-space architectures, the method achieved approximately 4x training speedups, compressing a 128-dimensional model down to around 12 dimensions while maintaining competitive performance.

"You get the performance of the larger model, because you capture most of the complex dynamics during the warm-up phase, then only keep the most-useful states," Chahine says. "The model is still able to perform at a higher level than training a small model from the start."

What makes CompreSSM distinct from existing approaches is its theoretical grounding. Conventional pruning methods train a full model and then strip away parameters after the fact, meaning you still pay the full computational cost of training the big model. Knowledge distillation, another popular technique, requires training a large "teacher" model to completion and then training a second, smaller "student" model on top of it, essentially doubling the training effort. CompreSSM avoids both of these costs by making informed compression decisions mid-stream.

The team benchmarked CompreSSM head-to-head against both alternatives. Compared to Hankel nuclear norm regularization, a recently proposed spectral technique for encouraging compact state-space models, CompreSSM was more than 40 times faster, while also achieving higher accuracy. The regularization approach slowed training by roughly 16 times because it required expensive eigenvalue computations at every single gradient step, and even then, the resulting models underperformed. Against knowledge distillation on CIFAR-10, CompressSM held a clear advantage for heavily compressed models: At smaller state dimensions, distilled models saw significant accuracy drops, while CompreSSM-compressed models maintained near-full performance. And because distillation requires a forward pass through both the teacher and student at every training step, even its smaller student models trained slower than the full-sized baseline.

The researchers proved mathematically that the importance of individual model states changes smoothly during training, thanks to an application of Weyl's theorem, and showed empirically that the relative rankings of those states remain stable. Together, these findings give practitioners confidence that dimensions identified as negligible early on won't suddenly become critical later.

The method also comes with a pragmatic safety net. If a compression step causes an unexpected performance drop, practitioners can revert to a previously saved checkpoint. "It gives people control over how much they're willing to pay in terms of performance, rather than having to define a less-intuitive energy threshold," Chahine explains.

There are some practical boundaries to the technique. CompreSSM works best on models that exhibit a strong correlation between the internal state dimension and overall performance, a property that varies across tasks and architectures. The method is particularly effective on multi-input, multi-output (MIMO) models, where the relationship between state size and expressivity is strongest. For per-channel, single-input, single-output architectures, the gains are more modest, since those models are less sensitive to state dimension changes in the first place.

The theory applies most cleanly to linear time-invariant systems, although the team has developed extensions for the increasingly popular input-dependent, time-varying architectures. And because the family of state-space models extends to architectures like linear attention, a growing area of interest as an alternative to traditional transformers, the potential scope of application is broad.

Chahine and his collaborators see the work as a stepping stone. The team has already demonstrated an extension to linear time-varying systems like Mamba, and future directions include pushing CompreSSM further into matrix-valued dynamical systems used in linear attention mechanisms, which would bring the technique closer to the transformer architectures that underpin most of today's largest AI systems.

"This had to be the first step, because this is where the theory is neat and the approach can stay principled," Chahine says. "It's the stepping stone to then extend to other architectures that people are using in industry today."

"The work of Chahine and his colleagues provides an intriguing, theoretically grounded perspective on compression for modern state-space models (SSMs)," says Antonio Orvieto, ELLIS Institute Tübingen principal investigator and MPI for Intelligent Systems independent group leader, who wasn't involved in the research. "The method provides evidence that the state dimension of these models can be effectively reduced during training and that a control-theoretic perspective can successfully guide this procedure. The work opens new avenues for future research, and the proposed algorithm has the potential to become a standard approach when pre-training large SSM-based models."

The work, which was accepted as a conference paper at the International Conference on Learning Representations 2026, will be presented later this month. It was supported, in part, by the Max Planck ETH Center for Learning Systems, the Hector Foundation, Boeing, and the U.S. Office of Naval Research.

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The flawed fundamentals of failing banks

Bank runs are dramatic: Picture Depression-era footage of customers lined up, trying to get their deposits back. Or recall Lehmann Brothers emptying out in 2008 or Silicon Valley Bank collapsing in 2023.

But what causes these runs in the first place? One viewpoint is that something of a self-fulfilling prophecy is involved. Panic spreads, and suddenly many customers are seeking their money back, until an otherwise solid institution is run into the ground.

That is not exactly Emil Verner’s position, however. Verner, an MIT economist, has been studying bank failures empirically for years and now has a different perspective. Verner and his collaborators have produced extensive evidence suggesting that when banks fail, it is usually because they are in a fundamentally shaky position. A bank run generally finishes off an already flawed business rather than upending a viable one.

“What we essentially find is that banks that fail are almost always very weak, and are in trouble,” says Verner, who is the Jerome and Dorothy Lemelson Professor of Management and Financial Economics at the MIT Sloan School of Management. “Most banks that have been subject to runs have been pretty insolvent. Runs are more the final spasm that brings down weak banks, rather than the causes of indiscriminate failures.”

This conclusion has plenty of policy relevance for the banking sector and follows a lengthy analysis of historical data. In one forthcoming paper, in the Quarterly Journal of Economics, Verner and two colleagues reviewed U.S. bank data from 1863 to 2024, concluding that “the primary cause of bank failures and banking crises is almost always and everywhere a deterioration of bank fundamentals.” In a 2021 paper in the same journal, Verner and two other colleagues studied banking data from 46 countries covering 1870-2016, and found that declining bank fundamentals usually preceded runs. And currently, Verner is working to make more historical U.S. bank data publicly available to scholars.

Seen in this light, sure, bank runs are damaging, but bank failures likely have more to do with bad portfolios, poor risk management, and minimal assets in reserve, rather than sentiment-driven client behavior.

“From the idea that bank crises are really about sudden runs on bank debt, we’re moving to thinking that runs are one symptom of crisis that runs deeper,” Verner says. “For most people, we’re saying something reasonable, refining our knowledge, and just shifting the emphasis,” Verner says.

For his research and teaching, Verner received tenure at MIT last year.

Landing in a “great place”

Verner is a native of Denmark who also lived in the U.S. for several years while growing up. Around the time he was finishing school, the U.S. housing market imploded, taking some financial institutions with it.

“Everything came crashing down,” Verner said. “I got obsessed with understanding it.”

As an undergraduate, he studied economics at the University of Copenhagen. After three years, Verner was unconvinced the discipline had fully explained financial crises. He decided to keep studying economics in graduate school, and was accepted into the PhD program at Princeton University.

Along the way, Verner became a historically minded economist, digging into data and cases from past decades to shed light on larger patterns about crises and bank insolvency.

“I’ve always thought history was extremely fascinating in itself,” Verner says. And while history may not repeat, he notes, it is “a really valuable tool. It helps you think through what could happen, what are similar scenarios, and how agents acted when facing similar constraints and incentives in the past.”

For studying financial crises in particular, he adds, history helps in multiple ways. Crises are rare, so historical cases add data. Changes over time, like more financial regulations and more complex investment tools, provide different settings to examine the same cause-and-effect issues. “History is a useful laboratory to study these questions,” Verner says.

After earning his PhD from Princeton, Verner went on the job market and landed his faculty position at MIT Sloan. Many aspects of Institute life — the classroom experience, the collegiality, the campus — have strongly resonated with him.

“MIT is a great place,” Verner says simply. “Great colleagues, great students.”

Focused on fundamentals

Over the last decade, Verner has published papers on numerous topics in addition to banking crises. As an outgrowth of his doctoral work, for instance, he published innovative papers examining the dampening effect that household debt has on economic growth in many countries. He also co-authored the lead paper in an issue of the American Economic Review last year examining the way German hyperinflation after World War I reallocated wealth to large business with substantial debt, leading them to grow faster.

Still, the main focus of Verner’s work right now is on banking crises and bank failures — including their causes. In a 2024 paper looking at private lending in 117 countries since 1940, Verner and economist Karsten Müller showed that financial crises are often preceded by credit booms in what scholars call the “non-tradeable” sector of the economy. That includes industries such as retail or construction, which do not produce easily tradeable goods. Firms in the non-tradeable sector tend to rely more heavily on loans secured by real estate; during real estate booms, such firms use high valuations to borrow more, and they become more vulnerable to crashes — which helps explain why bank portfolios, in turn, can crater as well.

In recent years, in the process of studying these topics, Verner has helped expand the domain of known U.S. historical data in the field. Working with economists Sergio Correa and Stephan Luck, Verner has helped apply large language models to historical newspaper collections, unearthing information about 3,421 runs on individual banks from 1863 to 1934; they are making that data freely available to other scholars.

This topic has important policy implications. If runs are a contagion bringing down worthy banks, then one solution is to provide banks with more liquidity to get through the crisis — something that has indeed been tried in the U.S. However, if bank failures are more based in fundamentals about risk and not keeping enough capital on hand, more systemic policy options about best practices might be logical. At a minimum, substantive new research can help alter the contents of those discussions.

“When banks fail, it’s usually because these banks have taken a lot of risk and have big losses,” Verner says. “It’s rarely unjustified. So that means these types of liquidity interventions alone are not enough to stop a crisis.”

The expansive research Verner has helped conduct includes a number of specific indicators that fundamentals are a big factor in failure. For instance, examining how infrequently banks recover their all assets shows how shaky their foundations are.

“The recovery rate on assets is informative about how solvent a bank was,” Verner says. “This is where I think we’ve contributed something new.” Some economists in the past have cited particular examples of struggling banks making depositors whole, but those are exceptions, not the rule. “Sometimes people argue this or that bank was actually solvent because depositors ended up getting all their money back, and that might be true of one bank, but on aggregate it’s not the case,” Verner says.

Overall, Verner intends to keep following the facts, digging up more evidence, and seeing where it leads.

“While there is this notion that liquidity problems can arise pretty much out of nowhere, I think we are changing that emphasis by showing that financial crises happen basically because banks become insolvent,” Verner underscores. “And then the bank run is that final dramatic spasm — which slightly shifts how we teach and talk about it, and perhaps think about the policy response.”

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Desirée Plata appointed associate dean of engineering

Desirée Plata, the School of Engineering Distinguished Climate and Energy Professor in the MIT Department of Civil and Environmental Engineering, has been named associate dean of engineering, effective July 1.

In her new role, Plata will focus on fostering early-stage research initiatives across the school’s faculty and on strengthening entrepreneurial and innovation efforts. She will also support the school’s Technical Leadership and Communication (TLC) Programs, including: the Gordon Engineering Leadership Program, the Daniel J. Riccio Graduate Engineering Leadership Program, the School of Engineering Communication Lab, and the Undergraduate Practice Opportunities Program.

Plata will join Associate Dean Hamsa Balakrishnan, who continues to lead faculty searches, fellowships, and outreach programs. Together, the two associate deans will serve on key leadership groups including Engineering Council and the Dean’s Advisory Council to shape the school’s strategic priorities.

“Desirée’s leadership, scholarship, and commitment to excellence have already had a meaningful impact on the MIT community, and I look forward to the perspective and energy she will bring to this role,” says Paula T. Hammond, dean of the School of Engineering and Institute Professor in the Department of Chemical Engineering.

Plata’s research centers on the sustainable design of industrial processes and materials through environmental chemistry, with an emphasis on clean energy technologies. She develops ways to make industrial processes more environmentally sustainable, incorporating environmental objectives into the design phase of processes and materials. Her work spans nanomaterials and carbon-based materials for pollution reduction, as well as advanced methods for environmental cleanup and energy conversion.  Plata directs MIT’s Parsons Laboratory, which conducts interdisciplinary research on natural systems and human adaptation to environmental change.

Plata is a leader on campus and beyond in climate and sustainability initiatives. She serves as director of the MIT Climate and Sustainability Consortium (MCSC), an industry–academia collaboration launched to accelerate solutions for global climate challenges. She founded and directs the MIT Methane Network, a multi-institution effort to cut global methane emissions within this decade. Plata also co-directs the National Institute of Environmental Health Sciences MIT Superfund Research Program, which focuses on strategies to protect communities concerned about hazardous chemicals, pollutants, and other contaminants in their environment.

Beyond academia, Plata has co-founded two climate and energy startups, Nth Cycle and Moxair. Nth Cycle is redefining metal refining and the domestic battery supply chain. Earlier this month, the company signed a $1.1 billion off-take agreement to help establish a secure and circular technology for battery minerals.

Her company Moxair specializes in advanced approaches for low-level methane monitoring and destruction. In 2026, with support from the U.S. Department of Energy and collaboration with MIT, Moxair will build and demonstrate a first-of-a-kind dilute methane oxidation technology to tackle methane emissions using transition metal catalysts.

As an educator, Plata has helped develop programs that enhance research experience for students and postdocs. She played a pivotal role in the founding of the MIT Postdoctoral Fellowship Program for Engineering Excellence, serving on its faculty steering committee, overseeing admissions, and leading both the academic track and entrepreneurship track. She also helped design the MCSC Climate and Sustainability Scholars Program, a yearlong program open to juniors and seniors across MIT.

Plata earned a BS in chemistry from Union College in 2003 and a PhD in the joint MIT-Woods Hole Oceanographic Institution program in oceanography and applied ocean science in 2009. After completing her doctorate, she held faculty positions at Mount Holyoke College, Duke University, and Yale University. While at Yale, she served as associate director of research at the university’s Center for Green Chemistry and Green Engineering. In 2018, Plata joined MIT’s faculty in the Department of Civil and Environmental Engineering.

Her work as a scholar and educator has earned numerous awards and honors. She received MIT’s Harold E. Edgerton Faculty Achievement Award in 2020, recognizing her excellence in research, teaching, and service. She has also been honored with an NSF CAREER Award and the Odebrecht Award for Sustainable Development. Plata is a fellow of the American Chemical Society and was a Young Investigator Sustainability Fellow at Caltech.

Plata is a two-time National Academy of Engineering Frontiers of Engineering Fellow and a two-time National Academy of Sciences Kavli Frontiers of Science Fellow. Her dedication to mentoring was recognized with MIT’s Junior Bose Award for Excellence in Teaching and the Frank Perkins Graduate Advising Award.

de MIT News https://ift.tt/7jCTmnJ

Physicists zero in on the mass of the fundamental W boson particle

When fundamental particles are heavier or lighter than expected, physicists’ understanding of the universe can tip into the unknown. A particle that is just beyond its predicted mass can unravel scientists’ assumptions about the forces that make up all of matter and space. But now, a new precision measurement has reset the balance and confirmed scientists’ theories, at least for one of the universe’s core building blocks.

In a paper appearing today in the journal Nature, an international team including MIT physicists reports a new, ultraprecise measurement of the mass of the W boson.

The W boson is one of two elementary particles that embody the weak force, which is one of the four fundamental forces of nature. The weak force enables certain particles to change identities, such as from protons to neutrons and vice versa. This morphing is what drives radioactive decay, as well as nuclear fusion, which powers the sun.

Now, scientists have determined the mass of the W boson by analyzing more than 1 billion proton-colliding events produced by the Large Hadron Collider (LHC) at CERN (the European Organization for Nuclear Research) in Switzerland. The LHC accelerates protons toward each other at close to the speed of light. When they collide, two protons can produce a W boson, among a shower of other particles.

Catching a W boson is nearly impossible, as it decays almost immediately into two types of particles, one of which, a neutrino, is so elusive that it cannot be detected. Scientists are left to measure the other particle, known as a muon, and model how it might add up to the total mass of its parent, the W boson. In the new study, scientists used the Compact Muon Solenoid (CMS) experiment, a particle detector at the LHC that precisely tracks muons and other particles produced in the aftermath of proton collisions.

From billions of proton-proton collisions, the team identified 100 million events that produced a W boson decaying to a muon and a neutrino. For each of these events, they carried out detailed analyses to narrow in on a precise mass measurement. In the end, they determined that the W boson has a mass of 80360.2 ± 9.9 megaelectron volts (MeV). This new mass is in line with predictions of the Standard Model, which is physicists’ best rulebook for describing the fundamental particles and forces of nature.

The precision of the new measurement is on par with a previous measurement made in 2022 by the Collider Detector at Fermilab (CDF). That measurement took physicists by surprise, as it was significantly heavier than what the Standard Model predicted, and therefore raised the possibility of “new physics,” such as particles and forces that have yet to be discovered.

Because the new CMS measurement is just as precise as the CDF result and agrees with the Standard Model along with a number of other experiments, it is more likely that physicists are on solid ground in terms of how they understand the W boson.

“It’s just a huge relief, to be honest,” says Kenneth Long, a lead author of the study, who is a senior postdoc in MIT’s Laboratory for Nuclear Science. “This new measurement is a strong confirmation that we can trust the Standard Model.”

The study is authored by more than 3,000 members of CERN’s CMS Collaboration. The core group who worked on the new measurement includes about 30 scientists from 10 institutions, led by a team at MIT that includes Long; Tianyu Justin Yang PhD ’24; David Walter and Jan Eysermans, who are both MIT postdocs in physics; Guillelmo Gomez-Ceballos, a principal research scientist in the Particle Physics Collaboration; Josh Bendavid, a former research scientist; and Christoph Paus, a professor of physics at MIT and principal investigator with the Particle Physics Collaboration.

Piecing together

The W boson was first discovered in 1983 and is predicted to be the fourth heaviest among all the fundamental particles. Multiple experiments have aimed to narrow in on the particle’s mass, with varying degrees of precision. For the most part, these experiments have produced measurements that agree with the Standard Model’s predictions. The 2022 measurement by Fermilab’s CDF experiment is the one significant outlier. It also happens to be the most precise experiment to date.

“If you take the CDF measurement at face value, you would say there must be physics beyond the Standard Model,” says co-author Christoph Paus. “And of course that was the big mystery.”

Paus and his colleagues sought to either support or refute the CDF’s findings by making an independent measurement, with an experiment that matches CDF’s precision. Their new W boson mass measurement is a product of 10 years’ worth of work, both to analyze actual particle collision events and to simulate all the scenarios that could produce those events.

For their new study, the physicists analyzed proton collision events that were produced at the LHC in 2016. When it is running, the particle collider generates proton collisions at a furious rate of about one every 25 nanoseconds. The team analyzed a portion of the LHC’s 2016 dataset that encompasses billions of proton-proton collisions. Among these, they identified about 100 million events that produced a very short-lived W boson.

“A particle like the W boson exists for a teeny tiny moment — something like 10^-24 seconds — before decaying to two particles, one of which is a neutrino that can’t be measured directly,” Long explains. “That’s the tricky part: You have to measure the other particle — a muon — really well, and be able to piece things together with only one piece of the puzzle.”

Gathering momentum

When a muon is produced from the decay of a W boson, it carries half of the W boson’s mass, which is converted into momentum that carries the muon away from the original collision. Due to the strong magnetic field inside the CMS detector, the electrically charged muon follows a path whose curvature is a function of its momentum. Scientists’ challenge is to track the muon’s path and every interaction it may have with other particles and its surroundings, in order to estimate its initial momentum.

The muon’s momentum is also influenced by the momentum of the W boson before it decays. Decoding the impact of the W boson’s motion from the effects of its mass presented a major challenge. To infer the W boson mass, the team first carried out simulations of every scenario they could think of that a muon might experience after a proton-proton collision in the chaotic environment of the particle collider. In all, the team produced 4 billion such simulated events described by state-of-the-art theoretical calculations. The simulations encoded diverse hypotheses about how the muon momentum is affected by the physical features of the CMS detector, as well as uncertainties in the predictions that govern W boson production in LHC collisions.

The researchers compared their simulations with data from the 2016 LHC run. For every proton-proton collision event that occurs in the collider, scientists can use the CMS detector at CERN’s LHC to precisely measure the energy and momentum of resulting particles such as muons. The team analyzed CMS measurements of muons that were produced from over 100 million W boson events. They then overlaid this data onto their simulations of the muon momentum, which they then converted to a new mass for the W boson.

That mass — 80360.2 ± 9.9 megaelectron volts — is significantly lighter than the CDF experiment’s measurement. What’s more, the new estimate is within the range of what the Standard Model predicts for the W boson’s mass, bolstering physicists’ confidence in the Standard Model and its descriptions of the major particles and forces of nature.

“With the combination of our really precise result and other experiments that line up with the Standard Model’s predictions, I think that most people would place their bets on the Standard Model,” Long says. “Though I do think people should continue doing this measurement. We are not done.”

“We want to add more data, make our analysis techniques more precise, and basically squeeze the lemon a little harder. There is always some juice left,” Paus adds. “With a better look, then we can say for certain whether we truly understand this one fundamental building block.”

This work was supported, in part, by multiple funding agencies, including the U.S. Department of Energy, and the SubMIT computing facility, sponsored by the MIT Department of Physics. 

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