Johns Hopkins and Amazon collaborate to explore transformative power of AI

/ April 12, 2022

Johns Hopkins University and Amazon are teaming up to harness the power of artificial intelligence to transform the way humans interact online and with the world. The new JHU + Amazon Initiative for Interactive AI, housed in the Johns Hopkins Whiting School of Engineering, will leverage the university’s world-class expertise in

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Bloomberg Distinguished Professor Rama Chellappa elected to American Institute for Medical and Biological Engineering’s College of Fellows.

/ February 11, 2022

MINDS would like to congratulate Rama Chellappa, Bloomberg Distinguished Professor in the departments of Electrical and Computer Engineering and Biomedical Engineering and chief scientist at the Johns Hopkins Institute for Assured Autonomy, on his election to the American Institute for Medical and Biological Engineering’s College of Fellows. Election to the AIMBE

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2021 Theorinet Retreat

/ September 23, 2021

Recordings: September 20th, 2021: Session 1: Overparameterization September 20th, 2021 Session 2: Optimization and robustness September 21st, 2021: Session 1: Learning on graphs September 21st, 2021: Session 2: Limits of learning

MINDS Director René Vidal works to ensure the safety of autonomous systems

/ August 27, 2021

Using a $7.5 million, five-year grant from the U.S. Department of Defense, a multi-university team that includes Johns Hopkins engineers is tackling one of today’s most complex and important technological challenges: How to ensure the safety of autonomous systems, from self-driving cars and aerial delivery drones to robotic surgical assistants. “The

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Associate Professor Vladimir Braverman receives Silver Best Paper Award at ICML Workshop

/ August 27, 2021

Congratulations to MINDS Associate Professor Vladimir Braverman, whose paper “Adversarial Robustness of Streaming Algorithms through Importance Sampling” has won a Silver Best Paper Award at the 2021 ICML Workshop, “A Blessing in Disguise: The Prospects and Perils of Adversarial Machine Learning.” More information can be found here.