MIT EECS Department

10/14/2026

Robert Gibson ’72, PhD ’79 spent his career at Los Alamos National Laboratory helping develop laser systems; but at home, he curated an unusual collection of vacuum tubes. His gift back to MIT: 300 of the tubes that made radio, television, long-distance telephone, radar, home audio, and even the earliest computers possible.

Robert Gibson ’72, PhD ’79, recently donated a permanent exhibit of nearly 300 tubes to MIT’s Research Laboratory of Electronics.Read more.

10/13/2026

Associate Professor Justin Solomon recently sat down with Bloomberg Odd Lots for a terrific discussion about AI's impact on advanced mathematics--starting with the very sensible question, "What exactly *is* advanced mathematics, anyway?"

10/13/2026

Marzyeh Ghassemi, a core faculty member in the MIT Institute for Medical Engineering and Science and a professor in MIT EECS, is conducting research to ensure that medical AI models do not learn biased rules or recommendations, that in the end, can harm minority populations.

She leads the Healthy ML Lab, which tackles the many novel technical opportunities for machine learning in health, and works to improve how AI is deployed for health and medicine. Lab members develop methods to detect failures, quantify uncertainty, reduce bias, and design safer ways for clinicians and patients to work with AI.

One of the projects Ghassemi is working on at the Healthy ML Lab is around integrating multi-modal clinical data, and using recurrent and convolution neural networks to predict when patients will need important interventions. While her work provides insights into model learning, the task is complicated by data sources that are sparse, noisy, heterogeneous, and with imbalanced outcomes.

“Health data really are unusually difficult: vital signs are measured continuously, laboratory tests are ordered irregularly, clinical notes appear at different times, some patients have genomics and environmental data. Multimodal models improve prediction, but we also need to examine what they learned before placing them in clinical workflows,” Ghassemi says.

https://imes.mit.edu/news-events/3-questions-finding-pulse-robust-ai-health

10/13/2026

MIT Spectrum connects friends and supporters of the Massachusetts Institute of Technology to MIT’s vision, impact, and exceptional community.

10/01/2026

Christina Delimitrou, the KDD Career Development Professor in Communications and Technology and an associate professor in the MIT EECS Department, received tenure in 2026.

She is also affiliated with CSAIL. Her research sits at the intersection of computer architecture and computer systems; specifically, she is one of the first systems researchers to apply machine learning techniques to design and management problems in the cloud.

Read more: https://news.mit.edu/2026/tenured-engineers-0615

10/01/2026

Early engineers deliberately incorporated randomness and probabilistic systems into AI, believing chaos was essential for machine intelligence and creativity. As modern AI has inherited these systems, Assistant Professor Benjamin Lindquist explains how it has become increasingly difficult to understand or control.

09/18/2026

Jacob Andreas, an associate professor in the MIT EECS Department, received tenure in 2026.

Andreas is affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL). His work is in natural language processing, and more broadly in AI. He aims to understand the computational foundations of language learning, and to build intelligent systems that can learn from human guidance.

Read more: https://news.mit.edu/2026/tenured-engineers-0615

09/17/2026

Today, we're reading this thoughtful longform piece on AI from Professor of the Practice Alfred Spector, together with Phi Beta Kappa CEO Frederick Lawrence. "We must harness the collective wisdom of the liberal arts (not just one subfield) and technology if we are to actually create a better world."

Our future depends on how successfully we fuse the liberal arts and technology

09/14/2026

Devavrat Shah, a professor in the MIT EECS Department and member of the Institute for Data, Systems, and Society (IDSS), is focused on designing AI systems that can handle second-by-second decision-making using limited computational resources.

In 2019, he co-founded an MIT spinoff company called Ikigai Labs, which built a foundation model that can take input from enterprise data from varied sources, continuously and at scale, so that it learns as it goes along by testing its predictions against real outcomes. The system is an extension of the kind of graphical models that are used by GPS devices to convert a sparse amount of data received from satellites into an accurate model of a position on the Earth’s surface.

While most AI models have been taught using text and images, this system takes tabular data as its input — structured data such as the familiar kind of row-and-column format used in spreadsheets. And then it provides the kind of real-time planning, on a vastly larger scale. “At some level,” Shah says, “digitizing these processes and being able to do predictions and constantly optimize is what leads to ultimately better business operations.”

https://www.eecs.mit.edu/helping-ai-models-to-meet-the-real-world/

09/14/2026

What if the future of sustainable construction looked less like traditional building and more like knitting? Assistant Professor Mariana Popescu is using a combination of computational design, digital fabrication, and industrial knitting machines to rethink concrete formworks.

What if the future of sustainable construction looked less like traditional building and more like knitting? Speaking with Building 32 host Karen Given, MIT CSAIL Assistant Professor Mariana Popescu explains how, using a combination of computational design, digital fabrication, and industrial knitti...