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About

Shili Sheng is a leading researcher at the intersection of human-robot collaboration, brain-computer interfaces (BCIs), and safe autonomous decision-making. Her work spans two transformative domains: enabling intuitive robot control through neural signals and ensuring provably safe robot behavior in dynamic human environments. In her early career, Sheng pioneered SSVEP-based BCI systems that allow physically challenged individuals to command service robots for multiple tasks, integrating visual servo and navigation modules to translate brain activity into real-world robotic actions. More recently, she has become a driving force in trust-aware and safety-critical planning for human-robot teams. Her 2024 papers introduce groundbreaking frameworks that combine temporal logic specifications with human trust models for collaborative tasks, and leverage adaptive conformal prediction to guarantee safety in partially observable environments. Sheng’s work on safe POMDP online planning via shielding provides formal safety guarantees for robots operating among dynamic agents, a critical advancement for real-world deployment. With over 40 citations across her most influential publications, Sheng is shaping the future of assistive robotics and trustworthy autonomous systems, making her research essential reading for anyone working on human-centered AI and safe robot autonomy.