Understanding uncertainty in a data-rich world: the scenario approach perspective
Events

Understanding uncertainty in a data-rich world: the scenario approach perspective

SEPTEMBER 08, 2025

Bar chart that displays data in a visual and informative way.

September 8, 2025 | 11:15 a.m.
Department of Electronics, Information and Bioengineering - Politecnico di Milano
"Emilio Gatti" Conference Room (Building 20)

Speaker: Licio Romao (Technical University of Denmark)

Contacts: Prof. Simone Garatti | simone.garatti@polimi.it

Abstract

Scenario optimisation has gained significant attention in the control community due to its ability to provide distribution-free guarantees on the optimal solution of convex (and, more recently, non-convex) optimisation problems. At the core of the approach lies the construction of so-called scenario programs, where constraints are enforced for each available sample. Fully supported convex problems constitute the class of scenario programs for which tight bounds on the violation probability are available. In the first part of the talk, I will present recent developments on the sampling-and-discarding scheme for scenario programs and introduce a tight bound on the violation probability for a natural removal algorithm. These developments build on the connection between a-priori scenario programs and compression learning theory. In the second part of the talk, I will build on these results to establish connections between the scenario approach and conformal prediction. We will also show how conformal prediction arguments can be exploited to recover one of the first results concerning the expectation of the violation probability.


Short Bio

Licio Romao is an Assistant Professor in the Department of Wind and Energy Systems at the Technical University of Denmark (DTU). Previously, he held postdoctoral positions in the Computer Science Department at the University of Oxford and in the Department of Aeronautics and Astronautics at Stanford University. He obtained his PhD from the University of Oxford and was awarded the IET Control & Automation Doctoral Dissertation Prize. Other awards include the 2022 Distinguished Paper Award (AAAI) and the 2024 Hybrid Systems TC Outstanding Student Paper Prize, both as co-author. His research interests lie in applying mathematical engineering methods to reason about stochastic uncertainty in sequential decision-making and feedback control problems. Current research efforts focus on certifying decisions and enhancing the security of power systems in the context of the ongoing energy transition.