CISE/Hariri Institute Distinguished Seminar: Francesco Bullo, University of California, Santa Barbara

Talk Title: Contraction Theory for Optimization, Control and Neural Networks

This talk surveys recent advances on contraction theory for dynamical systems, as a robust, computationally-friendly and modular stability theory. Starting from basic notions, I will present classic and novel theoretical properties and examples of contracting dynamics. As first application I will discuss contractivity methods for optimization-based control, including gradient controllers for online feedback optimization and safety filters based on control barrier functions. Second, I will discuss the contractivity properties of recurrent neural networks and biologically-plausible computing. We describe recent work on neural circuits for sparse reconstruction and policy composition.

Francesco Bullo is a distinguished professor and Mosher endowed chair of Mechanical Engineering at the University of California, Santa Barbara. He was previously with the University of Padova (Laurea degree, Italy), the California Institute of Technology (Ph.D. degree), and the University of Illinois at Urbana-Champaign. His research interests include contraction theory, mathematical neuroscience, and neural networks. He is the author or coauthor of Geometric Control of Mechanical Systems (Springer, 2004), Distributed Control of Robotic Networks (Princeton, 2009), Lectures on Network Systems (KDP, 2024), and Contraction Theory for Dynamical Systems (KDP, 2026). He served as IEEE CSS President and SIAG CST Chair. He is a Fellow of ASME, IEEE, IFAC, NetSci, and SIAM.

Faculty Host: John Baillieul

Student Host: Onur Okuducu