The Role of Convexity in Data-Driven Decision-Making
Speaker: 鈥� Delft University of Technology, Netherlands
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Abstract: In this talk, we study a general class of data-driven decision-making problems and discuss different terminologies and research questions that emerge in this context. As a decision mechanism (the mapping from data to decisions), we introduce a broad class of data-driven optimization known as distributionally robust optimization. We then highlight three different aspects of computation, statistics, and real-time implementation of these problems, and elaborate on how convexity can help in each of these aspects. A particular focus will be given to real-time implementation, which is closely connected to the topic of Online Optimization.
Biography: Peyman Mohajerin Esfahani is an associate professor at the Delft Center for Systems and Control. He joined TU Delft in October 2016 as an assistant professor. Prior to that, 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. He currently serves as an associate editor of Operations Research, Transactions on Automatic Control, and Open Journal of Mathematical Optimization. He was one of the three finalists for the Young Researcher Prize in Continuous Optimization awarded by the Mathematical Optimization Society in 2016, and a recipient of the 2016 George S. Axelby Outstanding Paper Award from the IEEE Control Systems Society. He received the ERC Starting Grant and the INFORMS Frederick W. Lanchester Prize in 2020. He is the recipient of the 2022 European Control Award.
