In Nagpur, one of the hottest cities in the world, the battle with sweltering temperatures is seen everywhere.

At any given time, technology does two things to employment: It replaces traditional jobs, and it creates new lines of work. Machines replace farmers, but enable, say, aeronautical engineers to exist. So, if tech creates new jobs, who gets them? How well do they pay? How long do new jobs remain new, before they become just another common task any worker can do?

A new study of U.S. employment led by MIT labor economist David Autor sheds light on all these matters. In the postwar U.S., as Autor and his colleagues show in granular detail, new forms of work have tended to benefit college graduates under 30 more than anyone else. 

“We had never before seen exactly who is doing new work,” Autor says. “It’s done more by young and educated people, in urban settings.” 

The study also contains a powerful large-scale insight: A lot of innovation-based new work is driven by demand. Government-backed expansion of research and manufacturing in the 1940s, in response to World War II, accounted for a huge amount of new work, and new forms of expertise. 

“This says that wherever we make new investments, we end up getting new specializations,” Autor says. “If you create a large-scale activity, there’s always going to be an opportunity for new specialized knowledge that’s relevant for it. We thought that was exciting to see.” 

The paper, “What Makes New Work Different from More Work?” is forthcoming in the Annual Review of Economics. The authors are Autor; Caroline Chin, a doctoral student in MIT’s Department of Economics; Anna M. Salomons, a professor at Tilburg University’s Department of Economics and Utrecht University’s School of Economics; and Bryan Seegmiller PhD ’22, an assistant professor at Northwestern University’s Kellogg School of Management.

And yes, learning about new work, and the kinds of workers who obtain it, might be relevant to the spread of artificial intelligence — although, in Autor’s estimation, it is too soon to tell just how AI will affect the workplace.

“People are really worried that AI-based automation is going to erode specific tasks more rapidly,” Autor observes. “Eroding tasks is not the same thing as eroding jobs, since many jobs involve a lot of tasks. But we’re all saying: Where is the new work going to come from? It’s so important, and we know little about it. We don’t know what it will be, what it will look like, and who will be able to do it.”

“If everyone is an expert, then no one is an expert”

The four co-authors also collaborated on a previous major study of new work, published in 2024, which found that about six out of 10 jobs in the U.S. from 1940 to 2018 were in new specialties that had only developed broadly since 1940. The new study extends that line of research by looking more precisely at who fills the new lines of work. 

To do that, the researchers used U.S. Census Bureau data from 1940 through 1950, as well as the Census Bureau’s American Community Survey (ACS) data from 2011 to 2023. In the first case, because Census Bureau records become wholly public after about 70 years, the scholars could examine individual-level data about occupations, salaries, and more, and could track the same workers as they changed jobs between the 1940 and 1950 Census enumerations. 

Through a collaborative research arrangement with the U.S. Census Bureau, the authors also gained secure access to person-level ACS records. These data allowed them to analyze the earnings, education, and other demographic characteristics of workers in new occupational specialties — and to compare them with workers in longstanding ones.

New work, Autor observes, is always tied to new forms of expertise. At first, this expertise is scarce; over time, it may become more common. In any case, expertise is often linked to new forms of technology.

“It requires mastering some capability,” Autor says. “What makes labor valuable is not simply the ability to do stuff, but specialized knowledge. And that often differentiates high-paid work from low-paid work.” Moreover, he adds, “It has to be scarce. If everyone is an expert, then no one is an expert.”

By examining the census data, the scholars found that back in 1950, about 7 percent of employees had jobs in types of work that had emerged since 1930. More recently, about 18 percent of workers in the 2011-2023 period were in lines of work introduced since 1970. (That happens to be roughly the same portion of new jobs per decade, although Autor does not think this is a hard-and-fast trend.) 

In these time periods, new work has emerged more often in urban areas, with people under 30 benefitting more than any other age category. Getting a job in a line of new work seems to have a lasting effect: People employed in new work in 1940 were 2.5 times as likely to be in new work in 1950, compared to the general population. College graduates were 2.9 percentage points more likely than high school graduates to be engaged in new work. 

New work also has a wage premium, that is, better salaries on aggregate than in already-existing forms of work. Yet as the study shows, that wage premium also fades over time, as the particular expertise in many forms of new work becomes much more widely grasped. 

“The scarcity value erodes,” Autor says. “It becomes common knowledge. It itself gets automated. New work gets old.”

After all, Autor points out, driving a car was once a scarce form of expertise. For that matter, so was being able to use word-processing programs such as WordPerfect or Microsoft Word, well into the 1990s. After a while, though, being able to handle word-processing tools became the most elementary part of using a computer.

Back to AI for a minute

Studying who gets new jobs led the scholars to striking conclusions about how new work is created. Examining county-level data from the World War II era, when the federal government was backing new manufacturing in public-private partnerships throughout the U.S., the study shows that counties with new factories had more new work, and that 85 to 90 percent of new work from 1940 to 1950 was technology-driven. 

In this sense there was a great deal of demand-driven innovation at the time. Today, public discourse about innovation often focuses on the supply side, namely, the innovators and entrepreneurs trying to create new products. But the study shows that the demand side can significantly influence innovative activity. 

“Technology is not like, ‘Eureka!’ where it just happens,” Autor says. “Innovation is a purposive activity. And innovation is cumulative. If you get far enough, it will have its own momentum. But if you don’t, it’ll never get there.”

Which brings us back to AI, the topic so many people are focused on in 2026. Will AI create good new jobs, or will it take work away? Well, it likely depends how we implement it, Autor thinks. Consider the massive health care sector, where there could be a lot of types of tech-driven new work, if people are interested in creating jobs.

“There are different ways we could use AI in health care,” Autor says. “One is just to automate people’s jobs away. The other is to allow people with different levels of expertise to do different tasks. I would say the latter is more socially beneficial. But it’s not clear that is where the market will go.” 

On the other hand, maybe with government-driven demand in various forms, AI could get applied in ways that end up boosting health care-sector productivity, creating new jobs as a result. 

“More than half the dollars in health care in the U.S. are public dollars,” Autor observes. “We have a lot of leverage there, we can push things in that direction. There are different ways to use this.” 

This research was supported, in part, by the Hewlett Foundation, the Google Technology and Society Visiting Fellows Program, the NOMIS Foundation, the Schmidt Sciences AI2050 Fellowship, the Smith Richardson Foundation, the James M. and Cathleen D. Stone Foundation, and Instituut Gak.

Nature Climate Change, Published online: 21 May 2026; doi:10.1038/s41558-026-02634-9

Coal-based steel plants risk locking in 60 billion tonnes of CO2 by 2070, but most of these emissions can still be avoided at moderate cost. Steel need not be hard to decarbonize: policymakers must seize the narrow window to redirect investments towards cleaner alternatives this decade.

Nature Climate Change, Published online: 21 May 2026; doi:10.1038/s41558-026-02635-8

New steel capacity expansion is critical for the feasibility of climate targets, as plants operate for decades. Researchers estimate that while existing and planned plants could commit large emissions, strategic investments using climate finance can largely avert this.

MIT scientists Sven Dorkenwald and Whitney Henry have been named 2026 Searle Scholars, an award given annually to 15 exceptional early-career researchers in the fields of biomedical sciences and chemistry. Dorkenwald is an assistant professor of brain and cognitive sciences and an investigator at the McGovern Institute for Brain Research. Henry is the Robert A. Swanson (1969) Career Development Professor of Life Sciences and an intramural faculty member at the Koch Institute for Integrative Cancer Research.

In addition, MIT alumni Irene Kaplow ’10 and Jared Mayers PhD ’15 were also honored.

Chosen by a scientific advisory board, Searle Scholars are considered among the most creative young researchers pursuing high-risk/high-reward research. The Searle Scholars Program is funded through the Searle Funds at The Chicago Community Trust and administered by Kinship Foundation. Each scholar will each receive $450,000 in flexible funding to support their work over the next three years.

Sven Dorkenwald

Sven Dorkenwald is a computational neuroscientist investigating the organizational principles of neuronal circuits. The synaptic connectivity of neurons, their connectome, is fundamental to how networks of neurons function. Dorkenwald develops computational and collaborative tools to map, analyze, and interpret synapse-resolution connectomes. His work has led to large connectomic reconstructions of the fruit fly brain and parts of mammalian brains. He uses these connectomes to investigate the architecture of neuronal circuits and how their structure supports complex computations.

“As I establish my new lab, the Searle Scholars Award will help us launch ambitious projects and set our long-term scientific direction,” says Dorkenwald. “I am deeply grateful for the support from the Kinship Foundation and look forward to interacting with this amazing cohort of Searle Scholars.”

Dorkenwald joined the faculty of MIT in 2026 as an assistant professor in the Department of Brain and Cognitive Sciences and an investigator at the McGovern Institute. He earned a BS in physics and an MS in computer engineering from the University of Heidelberg, followed by a PhD in computer science and neuroscience at Princeton University in 2023 under the mentorship of Sebastian Seung and Mala Murthy. Dorkenwald completed his postdoctoral training as a Shanahan Research Fellow at the Allen Institute and the University of Washington, while serving as a visiting faculty researcher at Google Research.

Whitney Henry

Whitney Henry investigates the potential of ferroptosis, an iron-dependent form of cell death, for developing novel therapies that target subpopulations of cancer cells that are highly metastatic, therapy-resistant, and therefore critical instigators of tumor relapse. Her research is focused on uncovering the molecular factors influencing ferroptosis susceptibility, investigating its effects on the tumor microenvironment, and developing innovative methods to manipulate ferroptosis resistance in living organisms, drawing from functional genomics, metabolomics, bioengineering, and a range of in vitro and in vivo models.

“I am incredibly grateful to the Kinship Foundation for supporting our research and giving us the freedom to ask bold, curiosity-driven scientific questions,” says Henry. “This support allows us to pursue ambitious ideas, take creative risks, and embark on new research directions.”

Henry joined the MIT faculty in 2024 as an assistant professor in the Department of Biology and a member of the Koch Institute, and is currently an HHMI Freeman Hrabowski Scholar. She received her bachelor's degree in biology with a minor in chemistry from Grambling State University and her PhD from Harvard University. Following her doctoral studies, she worked in the lab of Robert Weinberg at the Whitehead Institute for Biomedical Research and was supported by fellowships from the Jane Coffin Childs Memorial Fund for Medical Research and the Ludwig Center at MIT.

Alumni also honored

Irene Kaplow ’10, a graduate of the MIT Department of Mathematics, is an assistant professor in the Department of Biology and the Ray and Stephanie Lane Computational Biology Department at Carnegie Mellon University. Her selection as a Searle Scholar is for “deciphering transcriptional regulatory mechanisms underlying mammalian dietary phenotype evolution and their relationships to transcriptional regulatory responses to changes in diet.”

Jared Mayers PhD ’15, who earned his doctorate from the MIT Department of Biology, is an assistant professor at the Fred Hutchinson Cancer Center at the University of Washington. His selection as a Searle Scholar is for “a reverse-translational framework to decipher metabolic vulnerabilities of bacterial pathogens.”

In 2023, the Center for Computational Science and Engineering (CCSE), an academic unit in the MIT Schwarzman College of Computing, introduced a new standalone PhD degree program. This interdisciplinary PhD program blends both coursework and a thesis, enabling students to pursue research in cross-cutting methodological aspects of computational science and engineering.

PhD candidate Emily Williams is poised to be the first graduate of the program. With a technical background in aerospace engineering and applied mathematics, her research interests include stochastic and generative modeling for multiscale chaotic systems. She earned a BS in aerospace engineering from the University of Illinois Urbana-Champaign and an MS in aeronautics and astronautics from MIT. She was awarded the Department of Energy Computational Science Graduate Fellowship, which funded her doctoral research. Here, she discusses her experience with the program and its impact on her career trajectory.

Q: What has been a highlight of the CCSE degree program?

A: I found the program curriculum to be extremely thoughtful and intentional. In particular, the program of study was constructed to cover many important areas of computational science and engineering research and education, from engineering and mathematical modeling to scientific and parallel computing. I found a lot of overlap with the DoE CSGF program of study, so I was given a lot of freedom to pursue very interesting technical electives that fit within CSE that I might not have been able to explore if I had been in a discipline-centric program.

Q: Why is this program so impactful, especially in the context of having a stand-alone PhD program?

A: I think a stand-alone PhD program helps to further establish the MIT CCSE as a leader in CSE research and education. The joint programs give graduate researchers more opportunity to learn and apply leading CSE methodologies to their disciplinary areas and primarily stay within their home department. For me, I’ve found that I’ve had more opportunities for collaboration, in potentially applying my methods to a wide range of different exciting applications. I think this theme of collaboration will continue to foster through those advancing through the standalone program in particular.

Q: What advice would you give to students considering this program?

A: I think my advice would be to keep an open mind. My interest in CSE was shaped by common threads in my education and research interests over the years that I didn’t think were connected at all. Through my fellowship and the standalone program, I felt like I was able to create my own path to my degree and take courses that excited me and fit within the CSE themes of our program of study.

A high-performance steel with MIT origins has come full circle. 

After proving its worth in Formula One and Baja 1000 race cars, the computationally designed material has now been incorporated into the 2026 electric race car built by the student-run MIT Motorsports team.

The MIT car is scheduled to race against cars from other universities in the Formula SAE Electric competition in June.

Designing materials

Gregory B. Olson, professor of the practice in the MIT Department of Materials Science and Engineering, founded the MIT Steel Research Group (SRG) in 1985 with the goal of using computers to accelerate the hunt for new materials by plumbing databases of those materials’ fundamental properties. It was the beginning of a new field — computational materials design — that would eventually lead to the Materials Genome Initiative, a national program announced by President Barack Obama in 2011.

In 1985, however, “nobody knew whether we could really do this,” says Olson. Olson and colleagues eventually showed that the approach worked, and around 1990 the Army Research Office funded an SRG project aimed at developing high-performance steels for the gears in helicopters. That work came to the attention of producers at “Infinite Voyage,” a science documentary that ran on the Public Broadcasting System.

“When “Infinite Voyage” came to see me about the helicopter gear steels,” Olson remembers, “we got into a discussion about my interest in race cars” and whether the steels might have an application there.

The answer was yes, and Olson found himself connecting with the Newman/Haas racing team that Michael and Mario Andretti were driving for. Newman/Haas was also featured in the “Infinite Voyage” program, so “my first discussion with their chief engineer was on live television,” says Olson, who is also affiliated with the MIT Materials Research Laboratory.

He and colleagues went on to design a novel gear steel that could withstand the extreme conditions associated with a race car. They did the work over a weekend. “The surface hardness was the same as for a conventional gear steel, but we gave it the core properties of an armor steel,” Olson says.

Introducing Ferrium C61

That steel, which became known as Ferrium C61, was commercialized through QuesTek Innovations, the materials-design company Olson co-founded. It became the company’s first product.

Although it was never used in Newman/Haas cars, QuesTek pitched it to Baja 1000 off-road racers.

“We particularly focused on the 1600 class of those racing dune buggies. They would go flying over a sand dune with the wheels spinning in the air. And when they land, there would be a tremendous jolt to the drive gears,” Olson says. The result: The racers’ gears made with conventional steel regularly failed.

“The average life for conventional drive gears was point-six race,” says Olson (meaning on average they lasted for only 60 percent of a race). “With Ferrium C61, we changed it from point-six to six races.” The gears could now complete an average six races before failing.

QuesTek brought that data to meetings with different Formula One teams “to try to get C61 into other racing classes,” Olson says. 

Enter Red Bull, the British-licensed Formula One team. “The leading mechanical failure in Formula One racing is gearbox failures,” Olsen says. The gearbox houses the gearset, or collection of gears, in a car’s engine. “Once Red Bull adopted our steel for the gearset, they never had any gearbox failures, and they were world champions four times in the last decade.”

MIT Motorsports heard of this history and within the past year approached Olson about getting a sample of C61. “QuesTek had some stock available, and sold it at a high discount to the MIT team with, of course, instructions on how to heat-treat it,” Olson says. 

Because, of course, the students, who are mostly undergraduates, made the gears — and the car — themselves.

When it comes to keeping our texts, chats, and other digital messages safe from prying eyes, we have a powerful tool: end-to-end encryption. Used correctly, end-to-end encryption turns our conversations online into secret messages that can only be decoded by their intended recipients. In our latest EFFector newsletter, we're covering new developments in this tool, and how you can use it to prevent tech companies, governments, and other eavesdroppers from listening in.

JOIN OUR NEWSLETTER

For over 35 years, EFFector has been your guide to understanding the intersection of technology, civil liberties, and the law. This latest issue covers the shaky science backing social media bans, Canada's surveillance nightmare bill, and a victory for keeping private messages private.

Prefer to listen in? EFFector is now available on all major podcast platforms. This time, we're chatting with EFF Senior Security and Privacy Activist Thorin Klosowski on an important step forward for encrypted messaging—as well as a notable disappointment. You can find the episode and subscribe on your podcast platform of choice:

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Want to protect your private conversations? Sign up for EFF's EFFector newsletter for updates, ways to take action, and new merch drops. You can also fuel the fight for privacy and free speech online when you support EFF today!

Good report:

Executive Summary: Let’s say you wanted to make sure that your AI is secure. Can you just maximize the security and privacy benchmark and call it a day? Nope, because benchmarks don’t actually work for measuring AI capabilities (even when they are NOT emergent systemic properties like security). So let’s take a step back: how do you measure security in the first place? Good question. Over the last 30 years, security engineering for software evolved from black box penetration testing, through whitebox code analysis and architectural risk analysis to de facto process-driven standards like the Building Security In Maturity Model (BSIMM). Software had a very deep impact on business operations, and it appears that AI is going to have an even deeper impact. Will a software security-like measurement move work for AI? Probably. In the meantime we can make real progress in AI security by cleaning up our WHAT piles and managing risk by identifying and applying good assurance processes. (Spoiler alert: no matter what we do, we still don’t get a security meter for AI, so we need to be extra vigilant about security.)...

House Science Chair Brian Babin wants to ensure "rigorous oversight" is applied to companies that seek to alter the atmosphere.

Lone Star State officials are scrambling to get ready for projects that serve artificial intelligence, but a draft water outlook skips the subject.

Electric cars are expected to reach 30 percent of vehicle sales this year.

Insurers lack crucial detail about homes such as roof condition. In half of the homes surveyed by Moody's, insurance was mispriced.

Rep. Kathy Castor is still confident she can stay in Congress despite redistricting meant to push her out.

"It cannot be as bad as we thought, but it cannot be as good as we hoped,” said a climate researcher.

Urban camps are scaling back activities for children due to burdensome safety plan requirements, and rural camps are closing due to uncertainty.

The market for carbon credits has come under intense scrutiny in recent years after a series of projects were found to have exaggerated their climate benefits.

The startup said it was impossible to secure "conditions necessary to obtain the financing required" to proceed with the project.

Among all of the possible chemical compounds, it’s estimated that between 1020 and 1060 may hold potential as small-molecule drugs.

Evaluating each of those compounds experimentally would be far too time-consuming for chemists. So, in recent years, researchers have begun using artificial intelligence to help identify compounds that could make good drug candidates. 

One of those researchers is MIT Associate Professor Connor Coley PhD ’19, the Class of 1957 Career Development Associate Professor with shared appointments in the departments of Chemical Engineering and Electrical Engineering and Computer Science and the MIT Schwarzman College of Computing. His research straddles the line between chemical engineering and computer science, as he develops and deploys computational models to analyze vast numbers of possible chemical compounds, design new compounds, and predict reaction pathways that could generate those compounds. 

“It’s a very general approach that could be applied to any application of organic molecules, but the primary application that we think about is small-molecule drug discovery,” he says.

The intersection of AI and science

Coley’s interest in science runs in the family. In fact, he says, his family includes more scientists than non-scientists, including his father, a radiologist; his mother, who earned a degree in molecular biophysics and biochemistry before going to the MIT Sloan School of Management; and his grandmother, a math professor.

As a high school student in Dublin, Ohio, Coley participated in Science Olympiad competitions and graduated from high school at the age of 16. He then headed to Caltech, where he chose chemical engineering as a major because it offered a way to combine his interests in science and math.

During his undergraduate years, he also pursued an interest in computer science, working in a structural biology lab using the Fortran programming language to help solve the crystal structure of proteins. After graduating from Caltech, he decided to keep going in chemical engineering and came to MIT in 2014 to start a PhD.

Advised by professors Klavs Jensen and William Green, Coley worked on ways to optimize automated chemical reactions. His work focused on combining machine learning and cheminformatics — the application of computation methods to analyze chemical data — to plan reaction pathways that could make new drug molecules. He also worked on designing hardware that could be used to perform those reactions automatically. 

Part of that work was done through a DARPA-funded program called Make-It, which was focused on using machine learning and data science to improve the synthesis of medicines and other useful compounds from simple building blocks.

“That was my real entry point into thinking about cheminformatics, thinking about machine learning, and thinking about how we can use models to understand how different chemicals can be made and what reactions are possible,” Coley says.

Coley began applying for faculty jobs while still a graduate student, and accepted an offer from MIT at age 25. He received a mix of advice for and against taking a job at the same school where he went to graduate school, and eventually decided that a position at MIT was too enticing to turn down.

“MIT is a very special place in terms of the resources and the fluidity across departments. MIT seemed to be doing a really good job supporting the intersection of AI and science, and it was a vibrant ecosystem to stay in,” he says. “The caliber of students, the enthusiasm of the students, and just the incredible strength of collaborations definitely outweighed any potential concerns of staying in the same place.”

Chemistry intuition

Coley deferred the faculty position for one year to do a postdoc at the Broad Institute, where he sought more experience in chemical biology and drug discovery. There, he worked on ways to identify small molecules, from billions of candidates in DNA-encoded libraries, that might have binding interactions with mutated proteins associated with diseases.

After returning to MIT in 2020, he built his lab group with the mission of deploying AI not only to synthesize existing compounds with therapeutic potential, but also to design new molecules with desirable properties and new ways to make them. Over the past few years, his lab has developed a variety of computational approaches to tackle those goals. 

“We try to think about how to best pair a challenge in chemistry with a potential computational solution. And often that pairing motivates the development of new methods,” Coley says. One model his lab has developed, known as ShEPhERD, was trained to evaluate potential new drug molecules based on how they will interact with target proteins, based on the drug molecules’ three-dimensional shapes. This model is now being used by pharmaceutical companies to help them discover new drugs.

“We’re trying to give more of a medicinal chemistry intuition to the generative model, so the model is aware of the right criteria and considerations,” Coley says.

In another project, Coley’s lab developed a generative AI model called FlowER, which can be used to predict the reaction products that will result from combining different chemical inputs. 

In designing that model, the researchers built in an understanding of fundamental physical principles, such as the law of conservation of mass. They also compelled the model to consider the feasibility of the intermediate steps that need to take place on the pathway from reactants to products. These constraints, the researchers found, improved the accuracy of the model’s predictions.

“Thinking about those intermediate steps, the mechanisms involved, and how the reaction evolves is something that chemists do very naturally. It’s how chemistry is taught, but it’s not something that models inherently think about,” Coley says. “We’ve spent a lot of time thinking about how to make sure that our machine-learning models are grounded in an understanding of reaction mechanisms, in the same way an expert chemist would be.”

Students in his lab also work on many different areas related to the optimization of chemical reactions, including computer-aided structure elucidation, laboratory automation, and optimal experimental design.

“Through these many different research threads, we hope to advance the frontier of AI in chemistry,” Coley says.

Justin Solomon, associate professor in the MIT Department of Electrical Engineering and Computer Science (EECS), has been appointed associate dean of engineering education in the MIT School of Engineering, effective July 1.

In this new role, Solomon will focus on advancing innovation in engineering education across the school. He will help shape new pedagogical approaches in the context of an AI-enabled world and will explore experiential, hands-on, and other modes of learning. Working closely with academic departments, Solomon will serve as a thought partner in integrating AI into curricula and will help facilitate interdisciplinary and shared teaching opportunities across departments and other schools. He will also play a key role in helping the school implement relevant recommendations from the Committee on AI Use in Teaching, Learning, and Research Training. 

Solomon will explore opportunities to build industry collaborations, including new models for internships and industry-engaged learning on campus. Collaborating with department heads and the School of Engineering leadership team, he will also support faculty in designing new courses and evolving existing programs to meet emerging opportunities in engineering.

“Justin’s interdisciplinary approach will be especially valuable as we continue to evolve engineering education to meet new opportunities and challenges. His extensive experience applying AI across a wide range of domains will help each academic department thoughtfully integrate AI and new educational models into their curricula,” says Paula T. Hammond, dean of the School of Engineering and Institute Professor. “I look forward to the vision and perspective he will bring to the school’s leadership team.”

A dedicated educator, Solomon has played a central role in shaping computing education at MIT. He is a key contributor to the Common Ground for Computing, where he co-teaches the core class 6.C01 (Modeling with Machine Learning: From Algorithms to Applications) with Regina Barzilay, the Delta Electronics Professor in the MIT Department of Electrical Engineering and Computer Science and affiliate faculty member at the Institute for Medical Engineering and Science. Within EECS, he teaches 6.7350 (Numerical Algorithms for Computing and Machine Learning) as well as 6.8410 (Shape Analysis). He is also the founder of the Summer Geometry Initiative, a six-week program that introduces students to geometry processing through intensive training, collaboration, and research experiences.

Solomon’s dedication to teaching and helping students has been honored with various awards, including the EECS Outstanding Educator Award and the Burgess (1952) and Elizabeth Jamieson Prize for Excellence in Teaching. He is the author of “Numerical Algorithms,” a textbook that presents a modern approach to numerical analysis for computer science students.

Solomon is a principal investigator at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), where he leads the Geometric Data Processing Group. His research sits at the intersection of geometry and computation, with applications spanning computer graphics, autonomous navigation, political redistricting, physical simulation, 3D modeling, and medical imaging. He is also a core faculty member of the MIT-IBM Watson AI Lab, contributing to research that advances the foundations and applications of artificial intelligence.

His scholarly contributions have been recognized with numerous distinctions, including the 2023 Harold E. Edgerton Faculty Achievement Award for exceptional contributions in teaching, research, and service. In 2025, he was named a Schmidt Polymath, supporting interdisciplinary research across areas such as acoustics and climate that rely on large-scale simulation of physical systems.

Solomon joined the MIT faculty in 2016. He previously held an NSF Mathematical Sciences Postdoctoral Research Fellowship in Princeton University’s Program in Applied and Computational Mathematics. He earned his bachelor’s, master’s, and doctoral degrees from Stanford University. While studying at Stanford, he also worked as a research assistant at Pixar Animation Studios.