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Department of EECS names new chair recipients
The new chairs became effective July 1.
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A technique for more effective multipurpose robots
With generative AI models, researchers combined robotics data from different sources to help robots learn better.
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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.
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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.
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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.
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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…
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A new methodology simulates counterfactual, time-varying, and dynamic treatment strategies, allowing doctors to choose the best course of action.
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Physics and the machine-learning “black box”
In 2.C01, George Barbastathis demonstrates how mechanical engineers can use their knowledge of physical systems to keep algorithms in check and develop more accurate predictions.
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Nonsense can make sense to machine-learning models
Deep-learning methods confidently recognize images that are nonsense, a potential problem for medical and autonomous-driving decisions.