Speaker Prof. Fabio Pasqualetti
Department of Electrical Engineering and Computer Science, University of California, Irvine, United States
Date｜Time Feb 11 (Wednesday), 2026｜09:00
Zoom https://snu-ac-kr.zoom.us/my/jingyu.lee
Abstract
Autonomy, defined as a system’s ability to function reliably in dynamic, unpredictable, and contested environments, demands the adoption of cross-disciplinary approaches that go beyond the limitations and assumptions of traditionally siloed fields such as control systems, 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 systems, 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 the theory of partial differential equations and optimization can be used to design provably robust data-driven algorithms and characterize fundamental accuracy-robustness tradeoffs in open and closed-loop learning problems. Third, I will illustrate how theories from computational neuroscience can contribute to designing better performing and sustainable machine 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.
Biography
Fabio Pasqualetti is a Professor of Electrical Engineering and Computer Science at the University of California, Irvine. From 2013 to 2025, he served on the faculty of Mechanical Engineering at the University of California, Riverside. He completed a Doctor of Philosophy degree in Mechanical Engineering at the University of California, Santa Barbara, in 2012, a Laurea Magistrale degree (M.Sc. equivalent) in Automation Engineering at the University of Pisa, Italy, in 2007, and a Laurea degree (B.Sc. equivalent) in Computer Engineering at the University of Pisa, Italy, in 2004. His main research interests are in the areas of control and network systems, machine learning, and computational neuroscience. He is the recipient of the 2023 Antonio Ruberti Young Researcher Prize, the 2019 Young Investigator Research Award from the Air Force Office of Scientific Research, and the 2017 Young Investigator Award from the Army Research Office. His articles received the 2021 O. Hugo Schuck Best Paper Award, the 2020 Roberto Tempo Best CDC Paper Award, the 2020 Control Systems Letters Outstanding Paper Award, the 2019 ACC Best Student Paper Award, and the 2016 TCNS Outstanding Paper Award.
