Network distributionally robust optimization: Applications in generation planning and distributed optimization

Abstract: This talk focuses on stochastic optimization problems defined over a network and explores data-driven distributionally robust (DR) solution methods to solve it. Specifically, we will look at chance-constrained optimization that finds application in the generation planning problem and the expectation minimization problem that is motivated by distributed optimization and federated learning. The DR formulations of the problem have attractive statistical guarantees but pose computational difficulties. The talk will provide algorithms to handle these challenges, paying special attention to the large-scale nature of the problem and the fact that the data about the uncertainty cannot be aggregated at one single location in the network. We will end the talk with future challenges and research directions.

Bio: Ashish Cherukuri is an Assistant Professor in Engineering and Technology Institute Groningen, University of Groningen, Netherlands. Previously, he was a Postdoctoral Researcher at the Automatic Control Laboratory, ETH Zurich, Switzerland. He received the B.Tech degree from the Indian Institute of Technology, Delhi, India, in 2008, the M.Sc degree from ETH, Zürich, Switzerland, in 2010, and the Ph.D. degree from the University of California, San Diego, CA, USA, in 2017. His research interests include distributed optimization, game theory, optimization under uncertainty with applications in power and transportation networks.