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About

Kangfeng Ye is a formal methods researcher specializing in probabilistic modelling, automated verification, and domain-specific languages for robotics. His work sits at the intersection of software engineering, robotics, and formal verification, with a particular focus on developing rigorous mathematical foundations for safety-critical systems. Ye is best known for his contributions to RoboChart, a timed and probabilistic domain-specific language within the RoboStar framework, which enables automated verification of robotic systems through model checking and theorem proving. His most cited work, "Probabilistic Modelling and Verification Using RoboChart and PRISM" (2021, 26 citations), extended RoboChart to explicitly handle uncertainty — a defining challenge in real-world robotics. Complementary contributions include formally verified animation using interaction trees and probabilistic semantics that ground the language in mathematically sound foundations. Beyond robotics, Ye has advanced the theory of probabilistic unifying relations to reason about both epistemic and aleatoric uncertainty, bridging probabilistic programming with theorem proving. His applied work includes safety assurance for agricultural robots, demonstrating real-world relevance. With over 60 citations across his publications, Ye's research offers both theoretical depth and practical tools for engineers building verifiably safe autonomous systems.