February 28, 2025, Webb Hall 1100

Data-driven and learning-based methods have attracted considerable attention in recent years both for the analysis of dynamical systems and for control design. While there are many interesting and exciting results in this direction, our understanding of fundamental limitations of learning for control is lagging. This talk will focus on the question of when learning can be hard or impossible in the context of dynamical systems and control. In the first part of the talk, I will discuss a new observation on immersions and how it reveals some potential limitations in learning Koopman embeddings. In the second part of the talk, I will show what makes it hard to learn to stabilize linear systems from a sample-complexity perspective. While these results might seem negative, I will conclude the talk with some thoughts on how they can inspire interesting future directions.

Necmiye Ozay is the Chen-Luan Family Faculty Development Professor of Electrical and Computer Engineering, and an associate professor of Electrical Engineering and Computer Science and of Robotics at the University of Michigan, Ann Arbor. She received her PhD in Electrical Engineering from Northeastern University in 2010. After a postdoctoral position at Caltech in Computing and Mathematical Sciences, she joined Michigan in 2013. Her research interests include dynamical systems, control, optimization, and formal methods with applications in learning-enabled cyber-physical systems, system identification, verification and validation, and safe autonomy. She received the 1938E Award and a Henry Russel Award from the University of Michigan for her contributions to teaching and research. She received five young investigator awards, including NSF CAREER Award. She is also the recipient of the 2021 Antonio Ruberti Young Researcher Prize from the IEEE Control Systems Society for her fundamental contributions to the control and identification of hybrid and cyber-physical systems.