Andrea Simonetto

Department of Applied Mathematics, ENSTA Paris, Institut Polytechnique de Paris, France

Talk: Flexible Optimization for Cyber-Physical and Human Systems

We study how to construct optimization problems whose outcomes are sets of feasible, close-to-optimal decisions for human users to pick from, instead of a single, hardly explainable “optimal” directive. In particular, we explore two complementary ways to render convex optimization problems stemming from cyber-physical applications “flexible”. In doing so, the optimization outcome is a trade-off between engineering best and flexibility for the users to decide to do something slightly different. The first method is based on robust optimization and convex reformulations. The second one is stochastic and inspired by stochastic optimization with decision-dependent distributions.

Bio: Andrea Simonetto obtained his PhD degree in systems and control from Delft University of Technology, The Netherlands in 2012. He has spent 3+1 years as a postdoctoral researcher in Delft and at the Université catholique de Louvain, Belgium, and subsequently he was a research staff member at IBM Research Ireland, in the AI and Quantum team. Since September 2021, he has been an associate professor in optimization in the applied mathematics department of ENSTA Paris, Institut Polytechnique de Paris, France. His interests span numerical optimization and control for a broad range of applications: smart grids, transportation, health, and quantum computing.