Close the Loop: From Data to Actions in Intelligent Physical Systems
Professor Li Na
Electrical Engineering and Applied Mathematics
Harvard University, USA
Date: 23 January 2026, Friday
Time: 10.30am - 11.30am
Venue: Lecture Theatre 25 (SS1-B2-01)
Host: Prof Xie Lihua
Abstract
The explosive growth of machine learning and data-driven methodologies have revolutionized numerous fields. Yet, the translation of these successes to the domain of dynamical physical systems remains a significant challenge, hindered by the complex and often unpredictable nature of such environments. Closing the loop from data to actions in these systems faces many difficulties, stemming from the need for sample efficiency and computational feasibility amidst intricate dynamics, along with many other requirement such as verifiability, robustness, and safety. We will discuss how to bridge this gap by introducing innovative reinforcement learning approaches that harness representation-based methods, domain knowledge, and the physical structures of systems. We present a comprehensive framework that integrates these components to develop reinforcement learning and control strategies that are not only tailored for the complexities of physical systems but also achieves efficiency, safety, and robustness with provable performance.
Biography
Na Li is a Winokur Family Professor of Electrical Engineering and Applied Mathematics at Harvard University. She received her Bachelor's degree in Mathematics from Zhejiang University in 2007 and Ph.D. degree in Control and Dynamical systems from California Institute of Technology in 2013. She was a postdoctoral associate at the Massachusetts Institute of Technology 2013-2014. She has held a variety of short-term visiting appointments including the Simons Institute for the Theory of Computing, MIT, Google Brain, and MERL. Her research lies in the control, learning, and optimization of networked systems, including theory development, algorithm design, and applications to real-world cyber-physical societal system. She is an IEEE member and a senior editor of IEEE Transactions on Control of Network Systems. She was an associate editor for IEEE Transactions on Automatic Control, Systems & Control Letters, IEEE Control Systems Letters and also served on the organizing committee for a few conferences and workshops such as IEEE CDC, AMC E-energy, and NSF workshop on Reinforcement Learning. She received the NSF career award, AFSOR Young Investigator Award, ONR Young Investigator Award, Donald P. Eckman Award, McDonald Mentoring Award, IFAC Distinguished Lecture, IFAC Manfred Thoma Medal, Ruberti Young Researcher Prize, along with other awards.