TALK    [MERL Seminar Series 2024] Na Li presents talk titled Close the Loop: From Data to Actions in Complex Systems

Date released: April 10, 2024

  •  TALK    [MERL Seminar Series 2024] Na Li presents talk titled Close the Loop: From Data to Actions in Complex Systems
    (Learn more about the MERL Seminar Series.)
  • Date & Time:

    Wednesday, April 10, 2024; 12:00 PM

  • Abstract:

    The explosive growth of machine learning and data-driven methodologies have revolutionized numerous fields. Yet, translating 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 requirements such as verifiability, robustness, and safety. In this talk, we bridge this gap by introducing innovative 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 achieve efficiency, safety, and robustness with provable performance.

  • Speaker:

    Na Li
    Harvard University

    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, and Google Brain. 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 has been an associate editor for IEEE Transactions on Automatic Control, Systems & Control Letters, IEEE Control Systems Letters, and served on the organizing committee for a few conferences. She received the NSF career award (2016), AFSOR Young Investigator Award (2017), ONR Young Investigator Award(2019), Donald P. Eckman Award (2019), McDonald Mentoring Award (2020), the IFAC Manfred Thoma Medal (2023), along with some other awards.

  • MERL Host:

    Yebin Wang

  • Research Areas:

    Control, Dynamical Systems, Machine Learning