Pick-to-Learn for Systems and Control: Data-driven design with state-of-the art safety guarantees
Event details
| Date | 16.06.2025 |
| Hour | 15:00 › 16:00 |
| Speaker | Prof Dario Paccagnan, Associate Professor at the Department of Computing, Imperial College London, UK |
| Location | |
| Category | Conferences - Seminars |
| Event Language | English |
ABSTRACT: Data-driven methods have become powerful tools for tackling increasingly complex problems in Systems and Control. However, deploying these methods in real-world settings — especially safety-critical ones — requires rigorous safety and performance guarantees. This need has motivated much recent work at the interface of Statistical Learning and Control, aiming to integrate formal guarantees with data-driven design methods. However, many existing approaches achieve this only by sacrificing valuable data for testing/calibration or by restricting the design space, thus leading to suboptimal performances.
Against this backdrop, in this talk I will introduce Pick-to-Learn (P2L) for Systems and Control, a novel framework designed to equip any data-driven control method with state-of-the-art safety and performance guarantees. Crucially, P2L enables the use of all available data to jointly synthesize and certify the design, eliminating the need to set aside data for calibration or validation purposes.
I will then demonstrate how, as a result, P2L delivers designs and certificates that outperforms existing methods across a range of core problems including optimal control, reachability analysis, safe synthesis, and robust control.
BIOGRAPHY: Prof Dario Paccagnan is an Associate Professor at the Department of Computing, Imperial College London where he joined in the Fall 2020. Before that, he was a postdoctoral fellow at the University of California, Santa Barbara. He obtained his PhD from the Automatic Control Laboratory, ETH Zurich, Switzerland, in 2018. He received a B.Sc. and M.Sc. in Aerospace Engineering from the University of Padova, Italy, in 2011 and 2014, and a M.Sc. in Mathematical Modelling and Computation from the Technical University of Denmark in 2014; all with Honors. Prof. Dario Paccagnan's interests are at the interface of game theory, control theory, and learning theory with a focus on tackling societal-scale challenges. He was a finalist for the 2019 EECI best PhD thesis award and was recognized with the SNSF Early Postdoc Mobility Fellowship, the SNSF Doc Mobility Fellowship, and the ETH medal for his doctoral work. He is the recipient of the best student paper award (as advisor) at AISTAT 2025.
Against this backdrop, in this talk I will introduce Pick-to-Learn (P2L) for Systems and Control, a novel framework designed to equip any data-driven control method with state-of-the-art safety and performance guarantees. Crucially, P2L enables the use of all available data to jointly synthesize and certify the design, eliminating the need to set aside data for calibration or validation purposes.
I will then demonstrate how, as a result, P2L delivers designs and certificates that outperforms existing methods across a range of core problems including optimal control, reachability analysis, safe synthesis, and robust control.
BIOGRAPHY: Prof Dario Paccagnan is an Associate Professor at the Department of Computing, Imperial College London where he joined in the Fall 2020. Before that, he was a postdoctoral fellow at the University of California, Santa Barbara. He obtained his PhD from the Automatic Control Laboratory, ETH Zurich, Switzerland, in 2018. He received a B.Sc. and M.Sc. in Aerospace Engineering from the University of Padova, Italy, in 2011 and 2014, and a M.Sc. in Mathematical Modelling and Computation from the Technical University of Denmark in 2014; all with Honors. Prof. Dario Paccagnan's interests are at the interface of game theory, control theory, and learning theory with a focus on tackling societal-scale challenges. He was a finalist for the 2019 EECI best PhD thesis award and was recognized with the SNSF Early Postdoc Mobility Fellowship, the SNSF Doc Mobility Fellowship, and the ETH medal for his doctoral work. He is the recipient of the best student paper award (as advisor) at AISTAT 2025.
Practical information
- General public
- Free
Organizer
- Prof. Giancarlo Ferrari Trecate
Contact
- barbara.schenkel@epfl.ch