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About

Changjun Jiang is a leading researcher at the intersection of formal methods, artificial intelligence, and intelligent systems, with a particular focus on Petri net theory and its applications in human-computer interaction and reinforcement learning. His work is distinguished by the development of novel modeling frameworks that address critical challenges in complex, interactive environments. In his highly cited 2019 paper, Jiang introduced the Interactive-Control-Model for Human–Computer Interactive Systems based on Petri Nets (21 citations), providing a systematic strategy to mitigate errors in procedures involving both autonomous and semiautonomous robots. He further advanced this line of inquiry with the Variable Petri Nets approach (13 citations), enabling comprehensive modeling and analysis of mobile interactive systems. Demonstrating versatility, Jiang has also made significant contributions to AI-driven scheduling and decision-making. His 2023 work on Hybrid Residual Multiexpert Reinforcement Learning (17 citations) tackles the pressing real-world problem of high-density parking lot spatial scheduling within the metaverse context. Additionally, his research on offline reinforcement learning with uncertain action constraints (6 citations) addresses fundamental challenges in safe policy learning from static datasets, with direct implications for robotics and autonomous driving. Through these contributions, Jiang has established himself as a key figure in bridging formal verification with modern machine learning for reliable, interactive cyber-physical systems.