Shuo Yang

Shanghai Jiao Tong University, University of Pennsylvania

Papers

Total Citations

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About

Shuo Yang is a researcher working at the intersection of robotics, formal methods, and multi-agent systems, with a focus on safe and intelligent autonomous systems. His work addresses some of the most pressing challenges in modern robotics: how to plan robot behaviors that are not only optimal and goal-directed, but also provably secure and safe. His most notable contribution, "Secure-by-Construction Optimal Path Planning for Linear Temporal Logic Tasks" (2020), demonstrates his expertise in applying formal specification languages to robot motion planning. By integrating Linear Temporal Logic with security guarantees against passive eavesdroppers, Yang bridges the gap between formal verification and practical autonomous systems — a contribution that has earned 19 citations and influenced subsequent work in privacy-aware robotics. More recently, his research has evolved toward the complexities of multi-agent environments. His 2024 paper on adaptive safety using Control Barrier Functions tackles the notoriously difficult problem of maintaining safety guarantees when agents have incomplete information about one another — a critical challenge for real-world deployment of autonomous systems. Taken together, Yang's work reflects a rigorous and forward-thinking research agenda, combining formal methods, control theory, and machine learning to build robots that are simultaneously capable, secure, and safe.