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About

Zhouchi Li is a researcher at the forefront of safe and resilient control for cyber-physical systems (CPS), with a focus on ensuring safety under sensor faults, attacks, and actuator failures. His work centers on the innovative application of Control Barrier Functions (CBFs) to guarantee that autonomous systems—from robotics to transportation and energy—remain within safe operational bounds even when compromised by adversaries or hardware faults. Li’s key contributions include developing frameworks that integrate CBFs with model-based reinforcement learning to provide provable safety guarantees during exploration under uncertain dynamics, and advancing robust control methods that mitigate the impact of biased sensor measurements and erroneous inputs. His most-cited paper (2020, 14 citations) addresses safe CPS under sensor faults and attacks, while his 2021 work (9 citations) bridges reinforcement learning and safety-critical control. Li has also pioneered resilient trajectory planning for autonomous systems in adversarial environments, a notable achievement that extends safety to dynamic, uncertain settings. With a growing body of work spanning 2019 to 2025, his research is shaping the next generation of secure, trustworthy autonomous systems.