CGSW 9.0 Plenary Speaker: Na Li, Harvard University

Date: January 27, 2022
Time: 10:00am-10:30am
Location: 8 Saint Mary’s Street, PHO 906

Scalable Distributed Control and Learning of Networked Dynamical Systems

Recent radical evolution in distributed sensing, computation, communication, and actuation has fostered the emergence of cyber-physical network systems. Regardless of the specific application, one central goal is to shape the network’s collective behavior through the design of admissible local decisionmaking algorithms. This is nontrivial due to various challenges, especially the system’s high complexity and yet limited local information. In this talk, I will present our recent progress in formally advancing the systematic design of distributed coordination in network systems via harnessing special properties of the underlying problems and systems.

Na Li is a Gordon McKay professor in Electrical Engineering and Applied Mathematics at Harvard University.  She received her Bachelor 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 Massachusetts Institute of Technology 2013-2014.  She has hold a variety of shorter term visiting appointments including  the Simons Institute for the Theory of Computing, MIT, and Google Brain. Her research lies in 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 NSF career award (2016), AFSOR Young Investigator Award (2017), ONR Young Investigator Award(2019),  Donald P. Eckman Award (2019), McDonald Mentoring Award (2020), the Manfred Thoma Medal (2023), along with some other awards.

See the full CGSW 9.0 Agenda here.