Maria Prandini, PhD

Politecnico di Milano, Italy
Full Professor and Chair of the Automation and Control Engineering Program

Addressing complexity in contemporary applications via data-driven and distributed optimization

Abstract

Motivated by applications in the energy domain, in this talk we shall discuss the key role of data-driven and distributed optimization for the operation of large-scale systems affected by uncertainty. In particular, we shall present recent results on stochastic optimal control with probabilistic input and state constraints and on decision making for multi-agent systems characterized by both discrete and continuous decision variables, together with their application to the integration of renewable energy resources in the electrical grid.

Biography of the speaker

Maria Prandini received the Ph.D. degree in Information Technology from the University of Brescia, Italy, in 1998. She was a postdoctoral researcher at the University of California at Berkeley from 1998 to ​2000.  In 2002, she joined Politecnico di Milano, where she is currently full professor and chair of the Automation and Control Engineering Study Program.

She was elected Fellow of the IEEE in 2020 and received the IEEE Control Systems Society (CSS) Distinguished Member award in 2018. In 2017, she was August-Wilhelm Scheer Visiting Professor and Honorary fellow of the TUM Institute for Advanced Studied. She was nominated Visiting Professor in Engineering at the University of Oxford for the triennium 2022-2025.

She has been active in the IEEE CSS, the International Federation of Automatic Control (IFAC), and the Association for Computing Machinery (ACM), contributing to their activities in different roles. She is IFAC President-elect for the triennium 2023-26. Previously, she was Vice-President for conference activities for IFAC (2020-23) and IEEE CSS (2016 and 2017), and a member of SIGBED Board of Directors (2019-21). Her research interests include stochastic hybrid systems, randomized algorithms, distributed and data-driven optimization, multi-agent systems, and the application of control theory to transportation and energy systems.