Dynamic Learning and Control for Complex Population Systems and Networks

Wednesday, March 24, 2021 - 11:00am to 12:00pm

Systems commonly encountered in diverse scientific domains are complex, highly interconnected, and dynamic. These include the processes and mechanisms previously confined to biology, quantum science, social science, etc., which are increasingly studied and analyzed from a systems-theoretic viewpoint. The ability to decode the structural and dynamic information of such dynamical systems using observation (or measurement) data and the capability to precisely manipulate them are both essential steps toward enabling their safe and efficient deployment in critical applications.

In this talk, I will present some of the emerging learning and control problems associated with dynamic population systems and networks, including data-integrated methods for control synthesis, perturbation-based inference of nonlinear dynamic networks, and moment-based methods for ensemble control. In particular, I will present the bottlenecks associated with these challenging yet critical problems, motivating the need for a synergy between systems and control theory and techniques from artificial intelligence to build novel mathematically grounded tools that enable systematic solutions to these complex problems. In this context, in the first part of my talk, I will present some of the recent developments in solving inference problems for decoding the dynamics and the connectivity structure of nonlinear dynamical networks. Then, I will present model-agnostic data-integrated methods for solving optimal control problems associated with complex dynamic population systems such as neural networks and robotic systems.

Bio: Vignesh Narayanan (Member, IEEE) received the B.Tech. Electrical and Electronics Engineering degree from SASTRA University, Thanjavur, India, the M.Tech. degree with specialization in Control Systems from the National Institute of Technology Kurukshetra, Haryana, India, in 2012 and 2014, respectively, and the Ph.D. degree from the Missouri University of Science and Technology, Rolla, MO, USA, in 2017. He joined the Applied Mathematics Lab and Brain Dynamics and Control Research Group in the Dept. of Electrical and Systems Engineering at the Washington University in St. Louis, where he is currently working as a postdoctoral research associate. His current research interests include learning and adaptation in dynamic population systems, complex dynamic networks, reinforcement learning, and computational neuroscience.

Wednesday, March 24 at 11 am


Meeting ID: 955 9408 6334

Passcode: 1928