Title:Learning to Control Dynamic Systems: Stability and Robustness Bio: Zhong-Ping JIANG received the M.Sc. degree in statistics from the University of Paris XI, France, in 1989, and the Ph.D. degree in automatic control and mathematics from the Ecole des Mines de Paris (now, called ParisTech-Mines), France, in 1993, under the direction of Prof. Laurent Praly. Currently, he is a Professor of Electrical and Computer Engineering at the Tandon School of Engineering, New York University. His main research interests include stability theory, robust/adaptive/distributed nonlinear control, robust adaptive dynamic programming, reinforcement learning and their applications to information, mechanical and biological systems. In these fields, he has written five books and is author/co-author of over 450 peer-reviewed journal and conference papers. Dr. Jiang has served as Deputy Editor-in-Chief, Senior Editor and Associate Editor for numerous journals. Prof. Jiang is a Fellow of the IEEE, a Fellow of the IFAC, a Fellow of the CAA and is among the Clarivate Analytics Highly Cited Researchers. Abstract: This talk looks at problems at the interface of machine learning and automatic control that are motivated by open challenges arising from cyber-physical systems and computational neuroscience. In particular, it will introduce a new design paradigm, called “Robust Adaptive Dynamic Programming (RADP)”, that is fundamentally different from traditional control theory. In the classical paradigm, controllers are often designed for a given class of dynamical control systems; it is a model-based design. In the RADP paradigm, controllers are learned online using real-time input-output data collected along the trajectories of the control system in question. An entanglement of techniques from reinforcement learning and model-based control theory is advocated to find a sequence of suboptimal controllers that will approximate the optimal solution as learning steps increase. Rigorous stability and robustness analysis can be derived for the closed-loop system with real-time learning-based controllers. The effectiveness of RADP as a new framework for data-driven nonlinear control design is demonstrated via its applications to electric power systems, autonomous vehicles, and biological motor control.