New research demonstrates how AI models can be tested to ensure they don’t cause harm by revealing anonymized patient health data.
A faster problem-solving tool that guarantees feasibility
The FSNet system, developed at MIT, could help power grid operators rapidly find feasible solutions for optimizing the flow of electricity.
Helping scientists run complex data analyses without writing code
Co-founded by an EECS alumnus, Watershed Bio offers researchers who aren’t software engineers a way to run large-scale analyses to accelerate biology.
Department of EECS names new chair recipients
The new chairs became effective July 1.
A technique for more effective multipurpose robots
With generative AI models, researchers combined robotics data from different sources to help robots learn better.
New method uses crowdsourced feedback to help train robots
Human Guided Exploration (HuGE) enables AI agents to learn quickly with some help from humans, even if the humans make mistakes.
Unpacking the “black box” to build better AI models
Stefanie Jegelka seeks to understand how machine-learning models behave, to help researchers build more robust models for applications in biology, computer vision, optimization, and more.
Learning on the edge
A new technique enables AI models to continually learn from new data on intelligent edge devices like smartphones and sensors, reducing energy costs and privacy risks.
Department of EECS announces 2022 promotions
The Department of EECS is proud to announce the following promotions and hire: To Associate Professor with tenure Guy Bresler is being promoted to Associate Professor with tenure, effective July 1, 2022. Bresler…
A new methodology simulates counterfactual, time-varying, and dynamic treatment strategies, allowing doctors to choose the best course of action.