Short Biography

I received both my M.S. and Ph.D. in Information Technology from the the Politecnico di Milano, Italy, in 2000 and 2004, respectively. After graduating, I joined the Faculty of the Politecnico di Milano, where I currently holds a position of Associate Professor in the Automatic Control area at the Dipartimento di Elettronica, Informazione e Bioingegneria. During my career, I 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 I served for the EUCA Conference Editorial Board, while I'm 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. In 2024, I served as Tutorial Chair in the organizing committee of the 6th Learning for Dynamics and Control Conference (L4DC). I'm 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.

I'm the author/co-author of about 110 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. My research interests include data-driven optimization and decision-making, system identification, uncertainty quantification, and machine learning. With my co-authors I have 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.

 

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