{"id":21547,"date":"2026-04-21T21:16:13","date_gmt":"2026-04-21T21:16:13","guid":{"rendered":"https:\/\/ideainthebox.com\/index.php\/2026\/04\/21\/ai-at-mit\/"},"modified":"2026-04-21T21:16:13","modified_gmt":"2026-04-21T21:16:13","slug":"ai-at-mit","status":"publish","type":"post","link":"https:\/\/ideainthebox.com\/index.php\/2026\/04\/21\/ai-at-mit\/","title":{"rendered":"AI at MIT"},"content":{"rendered":"
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At\u00a0MIT, AI has become so pervasive that you can almost find your way into it without meaning to. Take Sili Deng, an associate professor of mechanical engineering. Deng says she still doesn\u2019t know whether she\u2019d have gone all in on artificial intelligence had it not been for the covid pandemic. She had joined the faculty in 2019 and was in the process of setting up her lab to study combustion kinetics, emissions reduction, and flame synthesis of energy materials when covid hit, putting a halt to all lab renovations. Because she needed to start from scratch, she challenged herself and her postdocs to try machine learning \u201cand see, with the fundamental knowledge we have on the combustion side, what are the gaps that we think machine learning could [fill].\u201d Under her leadership, Deng\u2019s Energy and Nanotechnology Group used AI to develop a \u201cdigital twin\u201d that mirrors the performance of an energy\/flow device\u2014a digital replica of a physical system. Eventually, this model should be able to predict and control the workings of fuel combustion systems in real time.\u00a0<\/p>\n

Unlike Deng, who came to AI through the slings and arrows of outrageous fortune, Zachary Cordero, an associate professor of aero-astro, began using AI thanks to a colleague\u2019s expertise. In 2024 John Hart, head of the Department of Mechanical Engineering, suggested that Cordero, who develops novel materials and structures for emerging aerospace applications, meet with Faez Ahmed, an associate professor of mechanical engineering and an expert in machine learning and optimization for engineering design. Cordero says he hadn\u2019t previously been pursuing AI-related research: \u201cThis is all totally new to me.\u201d Working with Ahmed and other collaborators on a project sponsored by the US Defense Advanced Research Projects Agency (DARPA), Cordero developed an AI tool that can optimize the material composition of what\u2019s known as a blisk\u2014a bladed disk that\u2019s a key component in jet and rocket turbine engines. Their work aims to improve engine performance and longevity and could lead to more reliable reusable rocket engines for heavy-lift launch vehicles. Cordero says the AI system augmented human intuition\u2014even \u201con problems where it\u2019s almost impossible to have intuition.\u201d \u00a0<\/p>\n

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Professor Ju Li posits that if AI is given autonomy to do experiments, to try different things and fail and learn from that, it could evolve into something very similar to human intelligence.<\/strong><\/p>\n<\/blockquote>\n

Stories like these abound at MIT. In every department, in almost every lab on campus, AI technologies such as machine learning, large language models, and neural networks are transforming research\u2014turbocharging existing methods, opening previously unexplored or inaccessible pathways, and creating novel opportunities in drug development, computing, energy technologies, manufacturing, robotics, neuroscience, metallurgy, and even wildlife preservation. \u201cI cannot think of a single group meeting that we have where we\u2019re not talking about these tools,\u201d says Angela Koehler, the Charles W. and Jennifer C. Johnson Professor of Biological Engineering and faculty lead of the MIT Health and Life Sciences Collaborative (MIT HEALS). Her research group uses AI models to develop drug candidates designed to attach to molecular targets previously considered \u201cundruggable,\u201d such as transcription factors, RNA-binding proteins, or cytokines. \u201cI would say 90% of the thesis committees I\u2019m on involve a significant AI component,\u201d she says. \u201cAnd that definitely was not the case five years ago.\u201d<\/p>\n

\u201cArtificial intelligence is everywhere on campus,\u201d says Ian Waitz, MIT\u2019s vice president for research and the Jerome C. Hunsaker Professor of Aero-Astro. \u201cAny field with a tremendous amount of complexity will benefit from it. Life sciences. Materials science. Anyone who does any kind of image analysis uses these tools now. I don\u2019t know of a single research field here at MIT that hasn\u2019t been impacted by AI.\u201d<\/p>\n

AI isn\u2019t exactly new at MIT<\/h3>\n

Though Deng and Cordero may have come to it through happenstance or clever matchmaking, most developments in AI at MIT don\u2019t arise that way. Nor is the interest in it new. More than 70 years ago, in 1954, computer researcher Belmont G. Farley and physicist Wesley A. Clark ran the world\u2019s first computer simulation of a neural network at MIT. Interest in neural network technology\u2014now better known as deep learning\u2014waxed and waned over the next decades. Ju Li, PhD \u201900, the Carl Richard Soderberg Professor of Power Engineering (as well as a professor of nuclear science and engineering and materials science and engineering), remembers taking a course on neural networks during Independent Activities Period (IAP) in 1995, when he was a graduate student. \u201cIt was not a deep network\u2014just a few layers,\u201d recalls Li, who researches materials used in nuclear energy, batteries, electrolyzers, and energy-\u00adefficient computing. He characterizes it as essentially a regression tool that they used to fit curves.<\/p>\n

But over the past few years, activity in AI has exploded globally, fueled by powerful new models and an enormous increase in the computing power of chips; the resulting proliferation and evolution of data centers has in turn sparked more activity. Today, neural networks can have more than a thousand layers. Backed by massive investments in AI in both the public and private spheres, AI researchers have created a suite of tools that can scan almost immeasurable quantities and types of data; interface with sensors, robotics, and other mechanical devices; and communicate with human researchers in natural language.\u00a0<\/p>\n

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\"REGINA<\/p>\n
RACHEL WU VIA MIT NEWS OFFICE<\/div>\n<\/figure>\n<\/div>\n
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\u201cMany of the tools that we developed in the lab\u2014 they\u2019re very broadly used in the pharmaceutical industry. And they\u2019re really making significant impact.\u201d<\/strong><\/p>\n

Regina Barzilay<\/cite><\/p><\/blockquote>\n

Regina Barzilay has been working on AI since she came to MIT in 2003. Today, she\u2019s the School of Engineering Distinguished Professor for AI and Health and AI faculty lead of the MIT Abdul Latif Jameel Clinic for Machine Learning in Health. But she says that if anyone had told her even 10 years ago where the field would be now and what kinds of things she\u2019d be working on, she \u201cabsolutely\u201d wouldn\u2019t have believed it.<\/p>\n

AI applications for drug discovery and development, one of Barzilay\u2019s areas of expertise, have been particularly prolific and successful at MIT. Giovanni Traverso\u2019s lab, for example, has used AI to design nanoparticles that can deliver RNA vaccines and other therapies more efficiently than previous systems. Researchers at CSAIL (the Computer Science & Artificial Intelligence Laboratory, where Barzilay is a principal investigator) have used AI models to explain how a narrow-\u00adspectrum anti\u00adbiotic specifically targets harmful microbes in people with Crohn\u2019s disease. The Jameel Clinic has helped build models that can predict which flu vaccine will be most effective in a given year. \u201cMany of the tools that we developed in the lab\u2014they\u2019re very broadly used in the pharmaceutical industry,\u201d she explains. \u201cAnd they\u2019re really making significant impact.\u201d She says there\u2019s not even a question anymore about whether they make a difference. They\u2019ve become standard tools because they work every day.\u00a0<\/p>\n

One such tool is Boltz, an open-source AI model developed by a group at the Jameel Clinic and initially released in November 2024 as Boltz-1. Inspired by DeepMind\u2019s AlphaFold2\u2014a model that earned Demis Hassabis and John Jumper the 2024 Nobel Prize in chemistry\u2014Boltz-1 helps scientists predict the 3D structures of proteins and other biological molecules. The Jameel Clinic researchers soon followed up with Boltz-2, which in addition to predicting molecular structure can also predict affinity\u2014the strength with which a protein binds with a small molecule. Assays to measure affinity, a vital measure in drug development, are among the most importantperformed in biology and chemistry labs.\u00a0<\/p>\n

In October 2025, the Jameel Clinic released its latest iteration, BoltzGen\u2014a generative AI model capable of designing custom proteins that could bind with a wide range of biomolecular targets. Molecular binders already play important roles in fields including therapeutics, diagnostics, and biotechnology. BoltzGen is the first advanced, large-scale model that considers every single atom in the potential new protein and every atom in its target molecule, providing greater accuracy.\u00a0<\/p>\n

Hannes St\u00e4rk, the fourth-year PhD student at CSAIL who built BoltzGen, says the model works because it actually learns\u2014drawing inferences from the data it is trained with and then producing novel ideas inspired by that data. With machine learning, you want the model to generalize from the data you use to train it, says St\u00e4rk, who created BoltzGen over seven months, often working up to 12 hours a day. \u201cBecause otherwise,\u201d he says, \u201cyour solution is already in your training data.\u201d St\u00e4rk has also assembled a network of over 30 scientists both within and beyond MIT to explore the design and applications of molecular binders for use in drug development, metabolomics, and structural biology as well as in treating cancer, autoimmune diseases, and genetic diseases. \u201cIt\u2019s nice to have one model that can do all of this,\u201d he says. Training across all these areas also makes the model better at generalizing.<\/p>\n

Beyond drug discovery<\/h3>\n

As labs working in drug development continue to reap benefits from AI, other researchers across the Institute are busy applying existing AI tools or, more often, developing their own models for use in myriad disciplines and applications. A cross-\u00addisciplinary group involving the Department of Electrical Engineering and Computer Science (EECS), CSAIL, and Mass General Hospital has launched MultiverSeg, a tool that quickly annotates areas of interest in medical images and could help scientists develop new treatments and map disease progression. MIT researchers are also designing and running AI-directed automated laboratories to accelerate and refine the process of discovering new components for sustainable materials and solar panels. And Ahmed\u2019s MechE group is developing AI models to do such things as help automakers design high-performance vehicles or determine whether a large shipping vessel can be considered seaworthy. Ahmed also teaches a course titled AI and Machine Learning for Engineering Design. First offered in 2021, it attracts not only mechanical, civil, and environmental engineers but students from aero-astro, Sloan, and more.\u00a0<\/p>\n

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\"Sarah<\/p>\n
MIT TECHNOLOGY REVIEW<\/div>\n<\/figure>\n<\/div>\n
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\u201cThe goal is to tap into diverse types of raw data and turn that into \u201csomething that helps us understand what is putting species at risk.\u201d<\/strong><\/p>\n

Sara Beery<\/cite><\/p><\/blockquote>\n

Meanwhile, Priya Donti, an assistant professor of EECS and a PI at the Laboratory for Information & Decision Systems (LIDS), has developed AI-enabled optimization approaches to help schedule power generation resources on power grids. The machine-learning tools her group builds will help utility operators respond to many inevitable grid issues. \u201cThe big challenge is that on a power grid, you need to maintain this exact balance between the amount of power you\u2019re producing and putting into the grid and the amount that you\u2019re taking out on the other side,\u201d she explains. \u201cWhen you have a lot of variation from solar, wind, and other sources of power whose output varies based on the weather, you have to coordinate the grid much more tightly in order to maintain that balance.\u201d Information about the physics of how power grids work is embedded in Donti\u2019s AI model, so it functions and reacts much as a real grid would. \u00a0<\/p>\n