The talk will start at 9:00 AM Pacific Time on Wednesday, January 27, 2021. To join the seminar, you may use the Zoom link below or watch the livestream on YouTube.
Zoom:
https://caltech.zoom.us/j/88148459434YouTube:
https://youtu.be/QW0I10a2T54Title: Learning for Safety-Critical Control in Dynamical Systems
Abstract: This talk describes ongoing research at Caltech on integrating learning into the design of safety-critical controllers for dynamical systems. To achieve control-theoretic safety guarantees while using powerful function classes such as deep neural networks, we must carefully integrate conventional control principles with learning into unified frameworks. I will focus primarily on two paradigms: integration in dynamics modeling and integration at the policy/controller design. A special emphasis will be placed on methods that both admit relevant safety guarantees and are practical to deploy.
Bio: Yisong Yue is a professor of Computing and Mathematical Sciences at the California Institute of Technology. He was previously a research scientist at Disney Research. Before that, he was a postdoctoral researcher in the Machine Learning Department and the iLab at Carnegie Mellon University. He received a Ph.D. from Cornell University and a B.S. from the University of Illinois at Urbana-Champaign.
Yisong's research interests are centered around machine learning, and in particular getting theory to work in practice. To that end, his research agenda spans both fundamental and applied pursuits. In the past, his research has been applied to information retrieval, recommender systems, text classification, learning from rich user interfaces, analyzing implicit human feedback, data-driven animation, behavior analysis, sports analytics, experiment design for science, protein engineering, program synthesis, learning-accelerated optimization, robotics, and adaptive planning & allocation problems.
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