Building better batteries, faster
PhD student Pablo Leon uses machine learning to expedite research on novel battery materials, while helping newer students navigate graduate school.
PhD student Pablo Leon uses machine learning to expedite research on novel battery materials, while helping newer students navigate graduate school.
Mary Ellen Zurko pioneered user-centered security in the 1990s. Now she’s using those insights to help the nation thwart influence operations.
Engineers 3D print materials with networks of sensors directly incorporated.
The faculty members will work together to advance the cross-cutting initiative of the MIT Schwarzman College of Computing.
Hailing from a small town in Italy, Matteo Bucci is determined to address some of the unknowns plaguing fundamental science.
Researchers use machine learning to automatically solve, explain, and generate university-level math problems at a human level.
Researchers train a machine-learning model to monitor and adjust the 3D printing process to correct errors in real-time.
Engineers working on “analog deep learning” have found a way to propel protons through solids at unprecedented speeds.
Neuroscience professor and Science Hub investigator Ted Adelson explains how simulating the sense of touch with a camera can make robots smarter.
“Interpretability methods” seek to shed light on how machine-learning models make predictions, but researchers say to proceed with caution.
Methods that make a machine-learning model’s predictions more accurate overall can reduce accuracy for underrepresented subgroups. A new approach can help.
International award supports early-career scientists and engineers as they pursue interdisciplinary works.
Professor of electrical engineering and computer science will receive additional support to advance his research and career.
Researchers have made strides toward machine-learning models that can help doctors more efficiently find information in a patient’s health record.
A geometric deep-learning model is faster and more accurate than state-of-the-art computational models, reducing the chances and costs of drug trial failures.