Mark Vogelsberger: Simulating galaxy formation for clues to the universe

For all its brilliant complexity, the Milky Way is rather unremarkable as galaxies go. At least, that’s how Mark Vogelsberger sees it.

“Our galaxy has a couple features that might be a bit surprising, like the exact number of structures and satellites around it,” Vogelsberger muses. “But if you average over a lot of metrics, the Milky Way is actually a rather normal place.”

He should know. Vogelsberger, a newly tenured associate professor in MIT’s Department of Physics, has spent much of his career recreating the birth and evolution of hundreds of thousands of galaxies, starting from the very earliest moments of the universe on up to the present day. By harnessing the power of supercomputers all over the world, he has produced some of the most precise  theoretical models of galaxy formation, in mesmerizing detail.

His simulations of the universe have shown that galaxies can evolve into a menagerie of shapes, sizes, colors, and clusters, exhibiting a clear diversity in the galaxy population, which matches with what astronomers have observed in the actual universe. Using the simulations as a sort of computational movie reel, scientists can rewind the tape to study in detail the physical processes that underlie galaxy formation, as well as the distribution of dark matter throughout the universe.

At MIT, Vogelsberger is continuing to refine his simulations, pushing them farther back in time and over larger expanses of the universe, to get a picture of what early galaxies may have looked like. With these simuilations, he is helping astronomers determine what sort of structures next-generation telescopes might actually be able to see in the early universe.

One universe

Vogelsberger grew up in Hackenheim, a small village of about 2,000 residents in western Germany, where nearly every night was a perfect night for stargazing.

“There was very little light pollution, and there was literally a perfect sky,” he recalls.

When he was 10, Vogelsberger’s parents gave him a children’s book that included facts about the solar system, which he credits with sparking his early interest in astronomy. As a teenager, he and a friend set up a makeshift astronomy laboratory and taught themselves how to set up telescopes and build various instruments, one of which they designed to measure the magnetic field of different regions of the sun.

Germany’s university programs offered no astronomy degrees at the time, so he decided to pursue a diploma in computer science, an interest that he had developed in parallel with astronomy. He enrolled at the Kalrsruhe Institute of Technology for two semesters, then decided to pivot to a general physics diploma, which he completed at the University of Mainz.

He then headed to the University of Munich, where he learned to apply computer science techniques to questions of astronomy and astrophysics. His PhD work there, and at the Max Planck Institute for Astrophysics, involved simulating the detailed structure of dark matter and how it’s distributed at small scales across the universe.

The numerical simulations that he helped to develop showed that, at small scales comparable to the size of the Earth, dark matter can clump and move through the universe in “streams,” which the researchers were able to quantify for the first time through their simulations.

“I always enjoyed looking through a telescope as a hobby, but using a computer to do experiments with the whole universe was just a very exciting thing,” Vogelsberger says. “In astrophysics, we have only this one universe which we can observe. With a computer, we can create different universes, which we can check (with observations). That was very appealing to me.”

“Everything evolves”

In 2010, after earning a PhD in physics, Vogelsberger headed to Harvard University for a postdoc at the Center for Astrophysics. There, he redirected his research to visible matter, and to simulating the formation of galaxies through the universe.

He spent the bulk of his postdoc building what would eventually be Illustris — a highly detailed and realistic computer simulation of galaxy formation. The simulation starts by modeling the conditions of the early universe, around 400,000 years after the Big Bang. From there, Illustris simulates the expanding universe over its 13.8-billion-year evolution, exploring the ways in which gas and matter gravitate and condense to form stars, black holes, and galaxies.

“If you ran one of these simulations from beginning to end on a desktop computer it would take a couple thousand years,” Vogelsberger says. “So, we had to split this work among tens of thousands of computers to get to a reasonable run time of around six months.”

He and his colleagues ran the simulations on supercomputers in France, Germany, and the United States to reproduce the evolution of galaxies within a cubic volume of the universe measuring 350 million light years across — the largest simulation of the universe ever developed at the time.

The initial output from Illustris took the form of numbers. Vogelsberger went a step further to render those numbers into visual form, condensing the enormously complex computations into short, stunning videos of a rotating cube of the early expanding universe, sprouting seeds of swirling galaxies.

Vogelsberger and his colleagues published a paper in Nature in 2014, detailing the simulation’s output, along with its visualizations. Since then, he has received countless requests for the simulations, from scientists, media outlets, and planetariums, where the visualizations of galaxy formation have been projected onto domes in high definition. The simulations have even been commemorated in the form of a German postal stamp.

In 2013, Vogelsberger joined the physics faculty at MIT, where he remembers having initial doubts over whether he could keep up with the “top of the top.”

“I realized very quickly that people have high expectations, but they also help you to achieve what you need to achieve, and the department is extremely supportive on all levels,” he says.

At MIT, he has continued to refine computer simulations for both galaxy formation and dark matter distribution. Recently, his group released Illustris TNG, a larger and more detailed simulation of galaxy formation. They are also working on a new simulation of radiation fields in the early universe, as well as exploring different models for dark matter.

“All these simulations start with a uniform universe — nothing but helium, hydrogen, and dark matter,” Vogelsberger says. “And when I watch how everything evolves to resemble something like our universe, it makes me wonder at how far we have gotten with our understanding of physics. Humankind has been around for a short period; nevertheless, we’ve been able to develop all these theories and technologies to be able to do something like this. It’s pretty amazing.”

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Arthur Mattuck, professor emeritus of mathematics, dies at 91

Arthur Paul Mattuck, emeritus professor of mathematics at MIT, passed away on Friday, Oct. 8, at the age of 91.

Mattuck came to MIT as a CLE Moore Instructor, a position he held from 1955 to 1957. He joined the faculty in 1958 and retired after 52 years of service in 2010. He continued to teach through fall 2019.

In his specialty, algebraic geometry, Mattuck contributed to the theory of curves and abelian varieties, and to the Riemann-Roch theorem. By the mid-1970s, he had graduated seven PhD students and had 18 mathematical descendants. Thereafter, he devoted his energy to an extensive review and revision of the mathematics undergraduate teaching program, so that the program could better serve the Institute.

Mattuck has long been recognized as one of the Institute's most effective and innovative lecturers. In the early 1960s, he chaired the department's Committee on First-Year Students and wrote the booklet, “Supplementary Problems for 18.01." In 1973, he received the Class of 1922 Professorship Chair, which was renewed continuously through June 1991. Its initial citation asserted that Mattuck's orientation to undergraduates' “likes and dislikes, and how they will react to different pedagogical styles, subject, contents and curriculum formats, has been responsible for significant educational innovations at MIT.”

Mattuck revised the department's first-year mathematics program, designing the calculus sequence 18.01,2 that has been widely influential and is still in use today. He introduced large-scale modular instruction for those first-year subjects, permitting self-pacing by the students, repeatable exams, grading conferences, and more. These innovations have been used, in modified forms, by many other universities. He ran workshops for calculus tutors, and developed the use of videotaping to improve the teaching of recitation sections, a tool that was soon adopted by other MIT departments. Mattuck led in developing the Undergraduate Mathematics Office (later the Academic Services Office), one of the first such offices at the Institute.

In 1981 Mattuck produced a second booklet, “The Torch or the Firehose: A Guide to Section Teaching,” a guide to recitation teaching at MIT. Written in an informal, good-humored style, it soon became a standard reference across the Institute and at other universities. A second edition was released in the mid-'90s. In 1998 Mattuck published the textbook “Introduction to Analysis” (reprinted in 2013). It provided a readable introduction to proofs for students of calculus advancing to real analysis.

Mattuck was department head from 1984 to 1989, and he chaired the department's Undergraduate Committee from 1972 to 1979 and again from 1982 to 1984. He served on a number of Institute committees, including the Policy Committee of the Division for Study and Research in Education, the Ad-Hoc Committee on Grading, and the first committee overseeing the first-year pass/fail grading policy that was implemented in the late 1960s. He also served two terms on the Council of the American Mathematical Society, and was made an AMS Fellow in its inaugural class of fellows in 2013.

Mattuck was born June 11, 1930, in Brooklyn, New York. He received an AB degree from Swarthmore College in 1951, and a PhD from Princeton University in 1954, studying under Emil Artin. He received a National Science Foundation postdoctoral fellowship that he took at Harvard University in 1954-55, and came to MIT as a CLE Moore Instructor in 1955. He was appointed lecturer in 1957-58, joined the faculty as assistant professor in 1958, was promoted to associate professor in 1961, and to professor in 1965.

In 1989, Mattuck received the MIT School of Science Teaching Prize for Excellence in Undergraduate Education. He was in the first group of faculty to be named a Margaret MacVicar Fellow, in 1992.

Beyond mathematics, Mattuck was an avid gardener. Many department members benefited from the bounty from the “victory garden” that he cultivated in the Fenway for decades. He was also a talented musician who performed on either cello or piano in the department's IAP Music Recital, essentially every year since its creation in the 1970s.

Mattuck is survived by his daughter Rosemary, her partner Jeffrey Broadman, and three nephews, Allan, Robin, and Martin. He was predeceased by his brother Richard Mattuck, a physicist.

A memorial service is being planned for a future date.

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3 Questions: Blending computing with other disciplines at MIT

The demand for computing-related training is at an all-time high. At MIT, there has been a remarkable tide of interest in computer science programs, with heavy enrollment from students studying everything from economics to life sciences eager to learn how computational techniques and methodologies can be used and applied within their primary field.

Launched in 2020, the Common Ground for Computing Education was created through the MIT Stephen A. Schwarzman College of Computing to meet the growing need for enhanced curricula that connect computer science and artificial intelligence with different domains. In order to advance this mission, the Common Ground is bringing experts across MIT together and facilitating collaborations among multiple departments to develop new classes and approaches that blend computing topics with other disciplines.

Dan Huttenlocher, dean of the MIT Schwarzman College of Computing, and the chairs of the Common Ground Standing Committee — Jeff Grossman, head of the Department of Materials Science and Engineering and the Morton and Claire Goulder and Family Professor of Environmental Systems; and Asu Ozdaglar, deputy dean of academics for the MIT Schwarzman College of Computing, head of the Department of Electrical Engineering and Computer Science, and the MathWorks Professor of Electrical Engineering and Computer Science — discuss here the objectives of the Common Ground, pilot subjects that are underway, and ways they’re engaging faculty to create new curricula for MIT’s class of “computing bilinguals.”

Q: What are the objectives of the Common Ground and how does it fit into the mission of the MIT Schwarzman College of Computing?

Huttenlocher: One of the core components of the college mission is to educate students who are fluent in both the “language” of computing and that of other disciplines. Machine learning classes, for example, attract a lot of students outside of electrical engineering and computer science (EECS) majors. These students are interested in machine learning for modeling within the context of their fields of interest, rather than inner workings of machine learning itself as taught in Course 6. So, we need new approaches to how we develop computing curricula in order to provide students with a thorough grounding in computing that is relevant to their interests, to not just enable them to use computational tools, but understand conceptually how they can be developed and applied in their primary field, whether it be science, engineering, humanities, business, or design.

The core goals of the Common Ground are to infuse computing education throughout MIT in a coordinated manner, as well as to serve as a platform for multi-departmental collaborations. All classes and curricula developed through the Common Ground are intended to be created and offered jointly by multiple academic departments to meet ‘common’ needs. We’re bringing the forefront of rapidly-changing computer science and artificial intelligence fields together with the problems and methods of other disciplines, so the process has to be collaborative. As much as computing is changing thinking in the disciplines, the disciplines are changing the way people develop new computing approaches. It can’t be a stand-alone effort — otherwise it won’t work.

Q: How is the Common Ground facilitating collaborations and engaging faculty across MIT to develop new curricula?

Grossman: The Common Ground Standing Committee was formed to oversee the activities of the Common Ground and is charged with evaluating how best to support and advance program objectives. There are 29 members on the committee — all are faculty experts in various computing areas, and they represent 18 academic departments across all five MIT schools and the college. The structure of the committee very much aligns with the mission of the Common Ground in that it draws from all parts of the Institute. Members are organized into subcommittees currently centered on three primary focus areas: fundamentals of computational science and engineering; fundamentals of programming/computational thinking; and machine learning, data science, and algorithms. The subcommittees, with extensive input from departments, framed prototypes for what Common Ground subjects would look like in each area, and a number of classes have already been piloted to date.

It has been wonderful working with colleagues from different departments. The level of commitment that everyone on the committee has put into this effort has truly been amazing to see, and I share their enthusiasm for pursuing opportunities in computing education.

Q: Can you tell us more about the subjects that are already underway?

Ozdaglar: So far, we have four offerings for students to choose from: in the fall, there’s Linear Algebra and Optimization with the Department of Mathematics and EECS, and Programming Skills and Computational Thinking in-Context with the Experimental Study Group and EECS; Modeling with Machine Learning: From Algorithms to Applications in the spring, with disciplinary modules developed by multiple engineering departments and MIT Supply Chain Management; and Introduction to Computational Science and Engineering during both semesters, which is a collaboration between the Department of Aeronautics and Astronautics and the Department of Mathematics. 

We have had students from a range of majors take these classes, including mechanical engineering, physics, chemical engineering, economics, and management, among others. The response has been very positive. It is very exciting to see MIT students having access to these unique offerings. Our goal is to enable them to frame disciplinary problems using a rich computational framework, which is one of the objectives of the Common Ground.

We are planning to expand Common Ground offerings in the years to come and welcome ideas for new subjects. Some ideas that we currently have in the works include classes on causal inference, creative programming, and data visualization with communication. In addition, this fall, we put out a call for proposals to develop new subjects. We invited instructors from all across the campus to submit ideas for pilot computing classes that are useful across a range of areas and support the educational mission of individual departments. The selected proposals will receive seed funding from the Common Ground to assist in the design, development, and staffing of new, broadly-applicable computing subjects and revision of existing subjects in alignment with the Common Ground’s objectives. We are looking explicitly to facilitate opportunities in which multiple departments would benefit from coordinated teaching.

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Avoiding shortcut solutions in artificial intelligence

If your Uber driver takes a shortcut, you might get to your destination faster. But if a machine learning model takes a shortcut, it might fail in unexpected ways.

In machine learning, a shortcut solution occurs when the model relies on a simple characteristic of a dataset to make a decision, rather than learning the true essence of the data, which can lead to inaccurate predictions. For example, a model might learn to identify images of cows by focusing on the green grass that appears in the photos, rather than the more complex shapes and patterns of the cows.  

A new study by researchers at MIT explores the problem of shortcuts in a popular machine-learning method and proposes a solution that can prevent shortcuts by forcing the model to use more data in its decision-making.

By removing the simpler characteristics the model is focusing on, the researchers force it to focus on more complex features of the data that it hadn’t been considering. Then, by asking the model to solve the same task two ways — once using those simpler features, and then also using the complex features it has now learned to identify — they reduce the tendency for shortcut solutions and boost the performance of the model.

One potential application of this work is to enhance the effectiveness of machine learning models that are used to identify disease in medical images. Shortcut solutions in this context could lead to false diagnoses and have dangerous implications for patients.

“It is still difficult to tell why deep networks make the decisions that they do, and in particular, which parts of the data these networks choose to focus upon when making a decision. If we can understand how shortcuts work in further detail, we can go even farther to answer some of the fundamental but very practical questions that are really important to people who are trying to deploy these networks,” says Joshua Robinson, a PhD student in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and lead author of the paper.

Robinson wrote the paper with his advisors, senior author Suvrit Sra, the Esther and Harold E. Edgerton Career Development Associate Professor in the Department of Electrical Engineering and Computer Science (EECS) and a core member of the Institute for Data, Systems, and Society (IDSS) and the Laboratory for Information and Decision Systems; and Stefanie Jegelka, the X-Consortium Career Development Associate Professor in EECS and a member of CSAIL and IDSS; as well as University of Pittsburgh assistant professor Kayhan Batmanghelich and PhD students Li Sun and Ke Yu. The research will be presented at the Conference on Neural Information Processing Systems in December. 

The long road to understanding shortcuts

The researchers focused their study on contrastive learning, which is a powerful form of self-supervised machine learning. In self-supervised machine learning, a model is trained using raw data that do not have label descriptions from humans. It can therefore be used successfully for a larger variety of data.

A self-supervised learning model learns useful representations of data, which are used as inputs for different tasks, like image classification. But if the model takes shortcuts and fails to capture important information, these tasks won’t be able to use that information either.

For example, if a self-supervised learning model is trained to classify pneumonia in X-rays from a number of hospitals, but it learns to make predictions based on a tag that identifies the hospital the scan came from (because some hospitals have more pneumonia cases than others), the model won’t perform well when it is given data from a new hospital.     

For contrastive learning models, an encoder algorithm is trained to discriminate between pairs of similar inputs and pairs of dissimilar inputs. This process encodes rich and complex data, like images, in a way that the contrastive learning model can interpret.

The researchers tested contrastive learning encoders with a series of images and found that, during this training procedure, they also fall prey to shortcut solutions. The encoders tend to focus on the simplest features of an image to decide which pairs of inputs are similar and which are dissimilar. Ideally, the encoder should focus on all the useful characteristics of the data when making a decision, Jegelka says.

So, the team made it harder to tell the difference between the similar and dissimilar pairs, and found that this changes which features the encoder will look at to make a decision.

“If you make the task of discriminating between similar and dissimilar items harder and harder, then your system is forced to learn more meaningful information in the data, because without learning that it cannot solve the task,” she says.

But increasing this difficulty resulted in a tradeoff — the encoder got better at focusing on some features of the data but became worse at focusing on others. It almost seemed to forget the simpler features, Robinson says.

To avoid this tradeoff, the researchers asked the encoder to discriminate between the pairs the same way it had originally, using the simpler features, and also after the researchers removed the information it had already learned. Solving the task both ways simultaneously caused the encoder to improve across all features.

Their method, called implicit feature modification, adaptively modifies samples to remove the simpler features the encoder is using to discriminate between the pairs. The technique does not rely on human input, which is important because real-world data sets can have hundreds of different features that could combine in complex ways, Sra explains.

From cars to COPD

The researchers ran one test of this method using images of vehicles. They used implicit feature modification to adjust the color, orientation, and vehicle type to make it harder for the encoder to discriminate between similar and dissimilar pairs of images. The encoder improved its accuracy across all three features — texture, shape, and color — simultaneously.

To see if the method would stand up to more complex data, the researchers also tested it with samples from a medical image database of chronic obstructive pulmonary disease (COPD). Again, the method led to simultaneous improvements across all features they evaluated.

While this work takes some important steps forward in understanding the causes of shortcut solutions and working to solve them, the researchers say that continuing to refine these methods and applying them to other types of self-supervised learning will be key to future advancements.

“This ties into some of the biggest questions about deep learning systems, like ‘Why do they fail?’ and ‘Can we know in advance the situations where your model will fail?’ There is still a lot farther to go if you want to understand shortcut learning in its full generality,” Robinson says.

This research is supported by the National Science Foundation, National Institutes of Health, and the Pennsylvania Department of Health’s SAP SE Commonwealth Universal Research Enhancement (CURE) program.

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School of Science appoints 11 faculty members to named professorships

The School of Science has announced that 11 faculty members have been appointed to named professorships. These positions offer additional support to professors to advance their research and develop their careers.

Andrew Babbin was named a Cecil and Ida Green Career Development Professor. A marine biogeochemist, Babbin studies the processes that return fixed nitrogen in the ocean back to nitrogen gas, exploring marine nitrogen’s control on life in the ocean and its effects on climate. His research sheds light on the ocean’s potential to sustain life and store carbon. Babbin earned a BS degree from Columbia University in 2008 and a PhD from Princeton University in 2014. He came to MIT in 2014 as a postdoc in the Department of Civil and Environmental Engineering before joining the Department of Earth, Atmospheric and Planetary Sciences in January 2017.

Gloria Choi was selected as the Mark Hyman Jr. Career Development Professor. Choi, an associate professor in the Department of Brain and Cognitive Sciences and an investigator with the Picower Institute, examines the interaction of the immune system with the brain and the effects of that interaction on neurodevelopment, behavior, and mood. She also studies how social behaviors are regulated according to sensory stimuli, context, internal state, and physiological status, and how these factors modulate neural circuit function via a combinatorial code of classic neuromodulators and immune-derived cytokines. She received her bachelor’s degree from the University of California at Berkeley, and her PhD from Caltech, where she studied with David Anderson. She was a postdoc in the laboratory of Richard Axel at Columbia University. Choi joined the MIT faculty as an assistant professor in 2013.

Arlene Fiore joined MIT as the inaugural Peter H. Stone and Paola Malanotte Stone Professor in Earth, Atmospheric and Planetary Sciences in July 2021. Her research encompasses air pollution, chemistry-climate connections, trends and variability in atmospheric constituents, and biosphere-atmosphere interactions. Fiore’s group investigates regional meteorology and climate feedbacks due to aerosols versus greenhouse gases, future air pollution responses to climate change, as well as the factors controlling the oxidizing capacity of the atmosphere. After earning a bachelor’s degree and PhD from Harvard University, Fiore held a research scientist position at the Geophysical Fluid Dynamics Laboratory and was appointed as an associate professor with tenure at Columbia University in 2011.

Peter H. Fisher is now the Thomas A. Frank (1977) Professor of Physics. His interests include the detection of dark matter, development of new particle detectors, compact energy supplies, and wireless energy transmission. Currently serving as the head of the Department of Physics, Fisher also holds appointments in the Institute for Soldier Nanotechnologies, the Laboratory for Nuclear Science, and the Kavli Institute. He is involved in CERN’s Alpha Magnetic Spectrometer experiment to make high-precision measurements of cosmic rays and the development of new ideas for dark matter. After receiving a BS in engineering physics from the University of California at Berkeley in 1983 and a PhD in nuclear physics from Caltech in 1988, Fisher was at the Johns Hopkins University from 1989 to 1994 and joined MIT in 1994.

Danna Freedman has been named the Frederick George Keyes Professor of Chemistry. Freedman leverages inorganic chemistry to solve problems in physics. Her research focuses on creating spin-based quantum bits and synthesizing new emergent materials. Freedman received her bachelor’s degree from Harvard University and her PhD from the University of California at Berkeley, then conducted postdoctoral research at MIT before joining the faculty at Northwestern University as an assistant professor in 2012, where she was promoted to associate professor in 2018 and full professor in 2020. Freedman returned to MIT’s Department of Chemistry in 2021.

Michel Goemans has been named the RSA Professor of Mathematics. Goemans has been head of the Department of Mathematics since July 1, 2018, following a year as interim head. He received his undergraduate degree in applied mathematics from Université Catholique de Louvain in 1987 and completed his PhD at MIT in 1990. He has been on the faculty since 1992, receiving tenure in 1999, and held the Leighton Family Professorship from 2007 to 2017. The RSA cryptosystem is the brainchild of Ron Rivest, Adi Shamir, and Len Adleman, whose fruitful collaboration spanned the Laboratory for Computer Science — today the Computer Science and Artificial Intelligence Laboratory (CSAIL) — and the Department of Mathematics. Goemans is also a member of the Theory of Computation Group of CSAIL, and recently joined the Computing Council of the MIT Schwarzman College of Computing. Goemans’ research interests include combinatorics, optimization and algorithms. In particular, his pioneering use of semidefinite optimization and other techniques for designing approximation algorithms for hard combinatorial optimization problems has been rewarded with several awards, such as the Fulkerson, Farkas and Dantzig prizes.

Or Hen was named the Class of 1956 Career Development Associate Professor of Physics. He investigates quantum chromodynamic effects in the nuclear medium and the interplay between partonic and nucleonic degrees of freedom in nuclei. Specifically, Hen utilizes high-energy scattering of electron, neutrino, photon, proton, and ion off atomic nuclei to study short-range correlations: temporal fluctuations of high-density, high-momentum, nucleon clusters in nuclei with important implications for nuclear, particle, atomic, and astrophysics. He received his undergraduate degree in physics and computer engineering from the Hebrew University and earned his PhD in experimental physics at Tel-Aviv University. Hen was an MIT Pappalardo Fellow in Physics from 2015 to 2017 before joining the physics faculty in July 2017.

Brett McGuire is now the Class of 1943 Career Development Assistant Professor of Chemistry. He uses the tools of physical chemistry, molecular spectroscopy, and observational astrophysics to understand how the chemical ingredients for life evolve with and help shape the formation of stars and planets. His group aims to detect more new molecules in space and to better understand their significance, advancing the field of astrochemistry. McGuire obtained a bachelor’s degree from the University of Illinois at Urbana-Champaign in 2009, a master’s degree from Emory University in 2011, and a PhD from Caltech in 2015. McGuire joined the Department of Chemistry in 2020.

Iain W. Stewart has been selected for the Otto (1939) and Jane Morningstar Professorship in Science. Stewart is a professor of physics and the director of the Center for Theoretical Physics. His research interests involve theoretical nuclear and particle physics. In particular, he focuses upon the development and application of effective field theories to answer fundamental questions about interactions between elementary particles. Stewart earned a bachelor’s degree in physics and mathematics and a master’s degree in physics from the University of Manitoba in Canada. He then received his PhD from Caltech in 1999. Stewart joined the physics faculty at MIT in 2003, was promoted to associate professor with tenure in 2009, and became a full professor in 2013.

Ankur Moitra, a theoretical computer scientist, is now the Norbert Wiener Professor of Mathematics. The aim of his work is to bridge the gap between theoretical computer science and machine learning by developing algorithms with provable guarantees and foundations for reasoning about their behavior. Moitra received his bachelor’s degree in electrical and computer engineering from Cornell University in 2007 and his master’s degree and PhD from MIT in computer science in 2009 and 2011, respectively, then spent two years as a fellow at the Institute for Advanced Study and Princeton University. Moitra returned to MIT in 2013 as a professor in applied mathematics and a principal investigator in CSAIL.

Seychelle M. Vos has been named a Robert A. Swanson (1969) Career Development Professor of Life Sciences. Vos examines the interplay of genome organization and gene expression to gain insight into how the organization of a cell affects what it becomes. Vos’ lab examines these pieces at a molecular scale using varied approaches from single-particle cryo-electron microscopy to X-ray crystallography, biochemistry to genetics. This work can help to build a biological understanding of diseases such as developmental disorders or cancers. She received her BS in genetics in 2008 from the University of Georgia and her PhD in molecular and cell biology in 2013 from the University of California at Berkeley. Vos joined the Department of Biology in 2019.

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A new way to generate light using pre-existing defects in semiconductors

Researchers from the Low Energy Electronic Systems (LEES) interdisciplinary research group at the Singapore-MIT Alliance for Research and Technology (SMART), MIT’s research enterprise in Singapore, together with collaborators at MIT, National University of Singapore (NUS), and Nanyang Technological University (NTU), have discovered a new method of generating long-wavelength (red, orange, and yellow) light using intrinsic defects in semiconducting materials, with potential applications as direct light emitters in commercial light sources and displays. This technology would be an improvement on current methods, which use phosphors, for instance, to convert one color of light to another.

A type of group-III element nitride-based light-emitting diode (LED), indium gallium nitride (InGaN) LEDs were first fabricated over two decades ago in the 1990s, and have since evolved to become ever-smaller while growing increasingly powerful, efficient, and durable. Today, InGaN LEDs can be found across myriad industrial and consumer use cases, including signals and optical communication and data storage — and are critical in high-demand consumer applications such as solid state lighting, television sets, laptops, mobile devices, and augmented- and virtual-reality solutions.

Ever-growing demand for such electronic devices has driven over two decades of research into achieving higher optical output, reliability, longevity, and versatility from semiconductors — leading to the need for LEDs that can emit different colors of light. Traditionally, InGaN material has been used in modern LEDs to generate purple and blue light, with aluminium gallium indium phosphide (AlGaInP) — a different type of semiconductor — used to generate red, orange, and yellow light. This is due to InGaN’s poor performance in the red and amber spectrum caused by a reduction in efficiency as a result of higher levels of indium required. 

In addition, such InGaN LEDs with considerably high indium concentrations remain difficult to manufacture using conventional semiconductor structures. As such, the realization of fully solid-state white-light-emitting devices — which require all three primary colors of light — remains an unattained goal.

Addressing these challenges, SMART researchers have laid out their findings in a paper titled “Light-Emitting V-Pits: An Alternative Approach toward Luminescent Indium-Rich InGaN Quantum Dots,” recently published in the journal ACS Photonics. In their paper, the researchers describe a practical method to fabricate InGaN quantum dots with significantly higher indium concentration by using preexisting defects in InGaN materials. 

In this process, the coalescence of so-called V-pits, which result from naturally existing dislocations in the material, directly forms indium-rich quantum dots, small islands of material that emit longer-wavelength Growing these structures on conventional silicon substrates further eliminates the need for patterning or unconventional substrates. The researchers also conducted high spatially resolved compositional mapping of the InGaN quantum dots, providing the first visual confirmation of their morphology.

In addition to the formation of quantum dots, the nucleation of stacking faults — another intrinsic crystal defect — further contributes to emissions of longer wavelengths. 

Jing-Yang Chung, SMART graduate student and lead author of the paper, says, “For years, researchers in the field have attempted to tackle the various challenges presented by inherent defects in InGaN quantum-well structures. In a novel approach, we instead engineered a nano-pit defect to achieve a platform for direct InGaN quantum dot growth. As a result, our work demonstrates the viability of using silicon substrates for new indium-rich structures which, along with addressing current challenges in the low efficiencies of long-wavelength InGaN light emitters, also alleviate the issue of expensive substrates.”

In this way, SMART’s discovery represents a significant step forward in overcoming InGaN’s reduced efficiency when producing red, orange and yellow light. In turn, this work could be instrumental in developing future micro LED arrays consisting of a single material.

Silvija Gradečak, co-author and principal investigator at LEES, adds, “Our discovery also has implications for the environment. For instance, this breakthrough could lead to a more rapid phasing out of non-solid-state lighting sources — such as incandescent bulbs — and even the current phosphor-coated blue InGaN LEDs with a fully solid-state color-mixing solution, in turn leading to a significant reduction in global energy consumption.” 

“Our work could also have broader implications for the semiconductor and electronics industry, as the new method described here follows standard industry manufacturing procedures and can be widely adopted and implemented at scale,” says SMART CEO and LEES Lead Principal Investigator Eugene Fitzgerald. “On a more macro level, apart from the potential ecological benefits that could result from InGaN-driven energy savings, our discovery will also contribute to the field’s continued research into and development of new efficient InGaN structures.”

The research is carried out by SMART and supported by the National Research Foundation (NRF) Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) program. For this paper, the LED structures were grown using SMART’s unique facilities and know-how, structural studies were conducted at NUS using state-of-the-art, atomically-resolved electron microscopes, while nanoscale optical studies were conducted at MIT and NTU.

LEES is creating new integrated circuit technologies that result in increased functionality, lower power consumption and higher performance for electronic systems. These integrated circuits of the future will impact applications in wireless communications, power electronics, LED lighting, and displays. LEES has a vertically-integrated research team possessing expertise in materials, devices, and circuits, comprising multiple individuals with professional experience within the semiconductor industry. This ensures that the research is targeted to meet the needs of the semiconductor industry both within Singapore and globally.

SMART was established by MIT and the NRF in 2007. SMART is the first entity in CREATE developed by NRF and serves as an intellectual and innovation hub for research interactions between MIT and Singapore, undertaking cutting-edge research projects in areas of interest to both. SMART currently comprises an Innovation Center and five interdisciplinary research groups: Antimicrobial Resistance, Critical Analytics for Manufacturing Personalized-Medicine, Disruptive & Sustainable Technologies for Agricultural Precision, Future Urban Mobility, and LEES.

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Differences in T cells’ functional states determine resistance to cancer therapy

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer in humans. Some patients with NSCLC receive a therapy called immune checkpoint blockade (ICB) that helps kill cancer cells by reinvigorating a subset of immune cells called T cells, which are “exhausted” and have stopped working. However, only about 35 percent of NSCLC patients respond to ICB therapy. Stefani Spranger’s lab at the MIT Department of Biology explores the mechanisms behind this resistance, with the goal of inspiring new therapies to better treat NSCLC patients. In a new study published on Oct. 29 in Science Immunology, a team led by Spranger lab postdoc Brendan Horton revealed what causes T cells to be non-responsive to ICB — and suggests a possible solution.

Scientists have long thought that the conditions within a tumor were responsible for determining when T cells stop working and become exhausted after being overstimulated or working for too long to fight a tumor. That’s why physicians prescribe ICB to treat cancer — ICB can invigorate the exhausted T cells within a tumor. However, Horton’s new experiments show that some ICB-resistant T cells stop working before they even enter the tumor. These T cells are not actually exhausted, but rather they become dysfunctional due to changes in gene expression that arise early during the activation of a T cell, which occurs in lymph nodes. Once activated, T cells differentiate into certain functional states, which are distinguishable by their unique gene expression patterns.

The notion that the dysfunctional state that leads to ICB resistance arises before T cells enter the tumor is quite novel, says Spranger, the Howard S. and Linda B. Stern Career Development Professor, a member of the Koch Institute for Integrative Cancer Research, and the study’s senior author.

“We show that this state is actually a preset condition, and that the T cells are already non-responsive to therapy before they enter the tumor,” she says. As a result, she explains, ICB therapies that work by reinvigorating exhausted T cells within the tumor are less likely to be effective. This suggests that combining ICB with other forms of immunotherapy that target T cells differently might be a more effective approach to help the immune system combat this subset of lung cancer.

In order to determine why some tumors are resistant to ICB, Horton and the research team studied T cells in murine models of NSCLC. The researchers sequenced messenger RNA from the responsive and non-responsive T cells in order to identify any differences between the T cells. Supported in part by the Koch Institute Frontier Research Program, they used a technique called Seq-Well, developed in the lab of fellow Koch Institute member J. Christopher Love, the Raymond A. (1921) and Helen E. St. Laurent Professor of Chemical Engineering and a co-author of the study. The technique allows for the rapid gene expression profiling of single cells, which permitted Spranger and Horton to get a very granular look at the gene expression patterns of the T cells they were studying.

Seq-Well revealed distinct patterns of gene expression between the responsive and non-responsive T cells. These differences, which are determined when the T cells assume their specialized functional states, may be the underlying cause of ICB resistance.

Now that Horton and his colleagues had a possible explanation for why some T cells did not respond to ICB, they decided to see if they could help the ICB-resistant T cells kill the tumor cells. When analyzing the gene expression patterns of the non-responsive T cells, the researchers had noticed that these T cells had a lower expression of receptors for certain cytokines, small proteins that control immune system activity. To counteract this, the researchers treated lung tumors in murine models with extra cytokines. As a result, the previously non-responsive T cells were then able to fight the tumors — meaning that the cytokine therapy prevented, and potentially even reversed, the dysfunctionality.

Administering cytokine therapy to human patients is not currently safe, because cytokines can cause serious side effects as well as a reaction called a “cytokine storm,” which can produce severe fevers, inflammation, fatigue, and nausea. However, there are ongoing efforts to figure out how to safely administer cytokines to specific tumors. In the future, Spranger and Horton suspect that cytokine therapy could be used in combination with ICB.

“This is potentially something that could be translated into a therapeutic that could increase the therapy response rate in non-small cell lung cancer,” Horton says.

Spranger agrees that this work will help researchers develop more innovative cancer therapies, especially because researchers have historically focused on T cell exhaustion rather than the earlier role that T cell functional states might play in cancer.

“If T cells are rendered dysfunctional early on, ICB is not going to be effective, and we need to think outside the box,” she says. “There's more evidence, and other labs are now showing this as well, that the functional state of the T cell actually matters quite substantially in cancer therapies.” To Spranger, this means that cytokine therapy “might be a therapeutic avenue” for NSCLC patients beyond ICB.

Jeffrey Bluestone, the A.W. and Mary Margaret Clausen Distinguished Professor of Metabolism and Endocrinology at the University of California-San Francisco, who was not involved with the paper, agrees. “The study provides a potential opportunity to ‘rescue’ immunity in the NSCLC non-responder patients with appropriate combination therapies,” he says.

This research was funded by the Pew-Stewart Scholars for Cancer Research, the Ludwig Center for Molecular Oncology, the Koch Institute Frontier Research Program through the Kathy and Curt Mable Cancer Research Fund, and the National Cancer Institute.

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