Electrical Engineering and Computer Science


Faculty Candidate Seminar

Sequential Learning, Optimization and Control for Cyber-Physical Systems

Dileep Kalathil


Postdoctoral Researcher
University of California, Berkeley
 
Monday, March 27, 2017
09:00am - 10:30am
1017 DOW

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About the Event

Convergence of dramatic increase in the available data and processing power, enabled by ubiquitous sensing and computing capabilities is rapidly changing engineered systems. Cyber-Physical Systems (CPS) refers to such systems with tightly integrated computational, control and physical capabilities, like transportation networks, smart cities, autonomous vehicles, sensor networks, power grids and healthcare systems. However, designing and implementing CPS involve an array of complex and challenging tasks: learning and making inference from data, designing scalable optimization and control methods, and developing decentralized and adaptive decision making algorithms. In the first part of the talk, I will discuss an approach for simulation-based optimization and control of Markov Decision Process (MDP) models, in the context of CPS. Designing exact optimization and control of such systems may be intractable due to its complexity. I develop a class of algorithms called Empirical Dynamic Programming to overcome this difficulty, and provide provable non-asymptotic performance guarantees. In the second part of the talk, I will discuss a strategy for sequential learning and decision making for decentralized CPS. I will first introduce a multi-player multi-armed bandits framework for modeling this class of problems. I will then present a sequential learning and decision making algorithm to solve this problem and show that it achieves optimal performance. I will also briefly discuss my work on data-driven learning and control in the context of transportation CPS.

Biography

Dileep Kalathil is a postdoctoral scholar in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He received his PhD from University of Southern California (USC) in 2014 where he won the best PhD Dissertation Prize in the USC Department of Electrical Engineering. He received an M.Tech from IIT Madras where he won the award for the best academic performance in the EE department. His research interests include control theory, sequential learning, game theory and applied probability.

Additional Information

Contact: Linda Scovel

Email: lscovel@umich.edu

Sponsor(s): ECE

Open to: UM Only