Description
The use of data is becoming paramount in addressing modern design problems characterized by increasing complexity. In this talk, I will share my vision of key aspects of data-driven design: (i) how wise is it to use data indirectly by first seeking to identify a model of reality? (ii) how far can we go in providing reliability guarantees for data-driven design without any a priori knowledge of the characteristics of uncertainty? In the second part of the talk, I will introduce "Scenario Optimization", a body of data-driven optimization methods that offer some answers to the questions posed in the first part.
BIO: Marco C. Campi is Professor of Inductive Methods and Control at the University of Brescia, Italy. He has held visiting and teaching appointments with Australian, USA, Japanese, Indian, Turkish and various European universities, besides the NASA Langley Research Center. He has served the International Federation of Automatic Control (IFAC) as Chair for two Technical Committees and has been in diverse capacities on the Editorial Board of top-tier control and optimization journals. In 2008, he received the IEEE CSS George S. Axelby Award for his pioneering work on the Scenario Approach. He has delivered plenary and semi-plenary addresses at major conferences including CDC, MTNS, OPTIMIZATION and SYSID, and has been on the distinguished lecturer roster of the IEEE Control Systems Society for multiple terms. Marco C. Campi is a Fellow of IEEE and a Fellow of IFAC. His interests include: inductive methods, stochastic optimization, data-driven decision-making, and the fundamentals of probability theory.