Featuring Yisong Yue, California Institute of Technology

Abstract: This talk describes ongoing research at Caltech on integrating learning into the design of reliable controllers for dynamical systems. For example, to achieve certifiable control-theoretic guarantees while using powerful function classes such as deep neural networks, we must carefully integrate conventional control & planning principles with learning into unified frameworks.  A special emphasis will be placed on methods that both admit relevant behavioral guarantees and are practical to deploy.  These methods are demonstrated in a variety of applications, including smooth broadcasting of sports games, agile aerial flight while dealing with perturbations and boundary conditions, and fast planning in resource-limited safety-critical settings such as Mars rover navigation.

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 recently spent a 2-year sabbatical in the autonomous driving industry.  Yisong is also the Senior Program Chair of the ICLR 2024 (International Conference on Learning Representations).  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, from novel learning-theoretic frameworks all the way to deployment in autonomous driving on public roads. His work has been recognized with multiple paper awards and nominations, including in robotics, computer vision, sports analytics, machine learning for health, and information retrieval.