ECE Seminar: Data-Driven and Brain-Inspired Autonomous Systems

Data-Driven and Brain-Inspired Autonomous Systems

Fabio Pasqualetti, University of California, Riverside

Thursday, January 30th at 3:00pm

DL514 or Zoom (https://yale.zoom.us/j/93111960700)

Hosted by: Professor Morse

Abstract:

Autonomy, defined as a system's ability to function reliably in dynamic, unpredictable, and contested environments, requires adopting cross-disciplinary approaches that go beyond the limitations and assumptions of traditionally siloed fields such as control theory, machine learning, and robotics. Truly innovative solutions can—and often should—draw inspiration from seemingly distant disciplines, such as neuroscience, which studies the most autonomous, resilient, and capable system we know: the human brain.

In this talk, I will discuss how methods from control theory, machine learning, and neuroscience are integrated in our research toward achieving autonomy, presenting a series of specific results. First, I will show how data and machine learning techniques can be used to solve classic control problems, such as the Linear Quadratic Gaussian control problem, more efficiently and robustly, offering new insights into the performance-robustness tradeoff in control design. Second, I will explain how tools from partial differential equations and optimization theory can be used to design provably robust data-driven algorithms and to characterize fundamental accuracy-robustness tradeoffs in open and closed-loop learning problems. Third, I will illustrate how computational neuroscience theories can contribute to developing better performing and more sustainable learning and decision-making algorithms, and how control and network theories can, in turn, offer novel pathways to better understand and treat the human brain. Finally, I will outline ongoing and future research directions.

Bio:

Fabio Pasqualetti is a Professor of Mechanical Engineering at the University of California, Riverside. He received his Ph.D. in Mechanical Engineering from the University of California, Santa Barbara in 2012. Prior to that, he completed both his Laurea Magistrale (M.S. equivalent) in Automation Engineering and Laurea (B.S. equivalent) in Computer Engineering at the University of Pisa, Italy, in 2007 and 2004 respectively. His research focuses on control and network systems, machine learning, and computational neuroscience. He is the recipient of the Antonio Ruberti Young Researcher Prize (2023), the Young Investigator Research Award from the Air Force Office of Scientific Research (2019), and the Young Investigator Award from the Army Research Office (2017). His articles received the O. Hugo Schuck Best Paper Award (2021), the Roberto Tempo Best CDC Paper Award (2020), the Control Systems Letters Outstanding Paper Award (2020), the ACC Best Student Paper Award (2019), and the IEEE Transactions on Control of Network Systems Outstanding Paper Award (2016).