Simone Garatti was born in Brescia, Italy, in 1976. He received both his M.S. (cum laude) and Ph.D. (cum laude) in Information Technology from the Politecnico di Milano, Italy, in 2000 and 2004, respectively. After graduating, he joined the Faculty of the Politecnico di Milano, where he currently holds a position of Associate Professor in the Automatic Control area at the Dipartimento di Elettronica, Informazione e Bioingegneria. During his career, he also was visiting scholar at some prestigious foreign universities, like the University of California San Diego (UCSD) (as winner of a fellowship for the short-term mobility of researchers from the National Research Council of Italy (CNR)), the Massachusetts Institute of Technology (MIT), and the University of Oxford. From 2013 to 2019 he served for the EUCA Conference Editorial Board, while he is currently member of the IEEE-CSS Conference Editorial Board and Associate Editor of the International Journal of Adaptive Control and Signal Processing and of the Machine Learning and Knowledge Extraction journal. He is also member of the IFAC Technical Committee on Modeling, Identification and Signal Processing, of the IEEE-CSS Technical Committee on Computational Aspects of Control System Design, and of the IEEE-CSS Technical Committee on System Identification and Adaptive Control.  Simone Garatti is the author/co-author of about ninety contributions in international journals, international books, and proceedings of international conferences and of the book "Introduction to the Scenario Approach" published by SIAM in 2018. His research interests include data-driven optimization and decision-making, system identification, uncertainty quantification, and machine learning. Simone Garatti (jointly with Marco Campi) has pioneered the theory of the scenario approach, a unitary framework to make designs where the effect of uncertainty is controlled by knowledge drawn from past experience that has marked significant advances in systems and control design, stochastic and uncertain optimization, and machine learning.