​MEET STEFANIE JEGELKA
Stefanie is an X-Consortium Career Development Assistant Professor in the Department of EECS at MIT; she is also a member of the Computer Science and AI Lab, the Center for Statistics and an affiliate of IDSS and ORC. She obtained her PhD from ETH Zurich in collaboration with the Max Planck Institute for Intelligent Systems, and has also been a postdoctoral researcher at UC Berkeley. Stefanie's research interests span the theory and practice of algorithmic machine learning and optimization. She also explores applications of machine learning in computer vision, robotics, materials science and biology. She has received an NSF CAREER Award, a Google research award, the German Pattern Recognition Award and a Best Paper Award at the International Conference for Machine Learning (ICML). She has organized several workshops around optimization and combinatorial learning, and has been on the senior program committee for NIPS, ICML, AISTATS and UAI.
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​MEET RAIA HADSELL
Raia is a senior research scientist at DeepMind, has worked on deep learning and robotics problems for over 10 years. Her early research developed the notion of manifold learning using Siamese networks, which has been used extensively for invariant feature learning. After completing a PhD with Yann LeCun, which featured a self-supervised deep learning vision system for a mobile robot, her research continued at Carnegie Mellon’s Robotics Institute and SRI International, and in early 2014 she joined DeepMind in London to study artificial general intelligence. Her current research focuses on the challenge of continual learning for AI agents and robots.
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