Abstract
Can we allow humans to pick among different, yet reasonably similar, decisions? Are we able 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, do-as-I-say “optimal” directive?
In this seminar, we explore two complementary ways to render 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 method is stochastic and inspired by stochastic optimization with decision-dependent distributions.
Biographical Information
Andrea Simonetto obtained his PhD degree in system and control at Delft University of Technology, the Netherlands, in 2012. He then spent 3+1 years as a Post-Doctoral researcher. First with the Circuits and Systems Group at Delft University of Technology, and then, with the ICTEAM Institute at UCLouvain, Belgium. Subsequently, from February 2017 to August 2021, Andrea was a research staff member with the optimization and control group, then the AI and Quantum group at IBM Research Ireland. And, finally, in September 2021, Andrea became a research professor at ENSTA Paris, Institut Polytechnique de Paris.