Tuesday, February 1, 2022
Title: “From Data to Decisions in Dynamic Environments”
Speaker: Peyman Mohajerin Esfahani, Associate Professor, Delft Center for Systems and Control
A broad spectrum of applications, such as power systems operation, manufacturing, energy management, 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 (data-driven) counterparts of these programs and study a probabilistic bridge between these solutions and the original ones. The discussion will be motivated by applications concerning the security of power systems and autonomous vehicles. In the last part of the talk, we shift our attention to computational aspects of the dynamic programming (DP) operation, a general framework for sequential decision-making tasks. We will discuss how an interesting analogy between the convex conjugate operator and the Fourier transform can reduce the typical time complexity of the DP operation from O(XU) to O(X + U) where X and U denote the size of the discrete state and input spaces, respectively.
Peyman Mohajerin Esfahani is an associate professor at the Delft Center for Systems and Control, and a co-director of the Delft-AI Energy Lab 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 BSc and MSc degrees from 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 one of the three finalists for the Young Researcher Prize in Continuous Optimization awarded by the Mathematical Optimization Society in 2016, and was a recipient of the 2016 George S. Axelby Outstanding Paper Award from the IEEE Control Systems Society. He also received the ERC Starting Grant and the INFORMS Frederick W. Lanchester Prize in 2020