From Data to Decisions in Large Scale Dynamical Systems

A broad spectrum of applications, such as power systems operation, manufacturing, anomaly detection and security, can be viewed as decision-making problems in uncertain and dynamic environments. This class of problems is naturally formulated in an optimization framework whose exact solution is often intractable. In this seminar, we consider tractable randomized counterparts of these programs. We establish a probabilistic bridge between the proposed random solutions and the original ones from both feasibility and performance perspectives. We also discuss how the presented results lead to a novel design paradigm along with a scalable methodology for the problem of fault detection, which is applicable to high dimensional nonlinear dynamical systems. The discussion will be motivated by a cyber-physical attack emanating from the vulnerabilities introduced by the interaction between IT infrastructure and power systems.

Bio: Peyman Mohajerin Esfahani is an assistant professor in the Delft Center for Systems and Control at the Delft University of Technology. Prior to joining TU Delft, he held several research appointments at EPFL, ETH Zurich, and MIT between 2014 and 2016. He received the B.Sc. and M.Sc. degrees in Sharif University of Technology, Iran, and the PhD degree from ETH Zurich. His research interests include theoretical and practical aspects of decision-making problems in uncertain and dynamic environments, with applications to control and security of large-scale and distributed systems.

He was selected for the Spark Award by ETH Zurich for the twenty best inventions of the year in 2012, and received the SNSF Postdoc Mobility fellowship in 2015. He was one of the three finalists for the Young Researcher Prize in Continuous Optimization awarded by the Mathematical Optimization Society in 2016. He was a recipient of the 2016 George S. Axelby Outstanding Paper Award from the IEEE Control Systems Society.