Yingqian Zhang
Papers
2
Total Citations
41
H-Index
2
About
Yingqian Zhang is a leading researcher at the intersection of artificial intelligence, reinforcement learning, and operations research, with a focus on real-world industrial and societal applications. Her work addresses critical challenges in smart industry, asset management, and human-robot collaboration, bridging the gap between theoretical AI advances and practical deployment. Zhang’s most-cited paper, from the 14th International Conference on Agents and Artificial Intelligence (2022, 39 citations), tackles optimal maintenance planning using historical data and reinforcement learning—a domain where RL’s application remains underexplored despite its success in robotics and games. More recently, she has pioneered uncertainty-aware, fair, and efficient policies for collaborative human-robot order picking systems (2024), optimizing the allocation of human pickers to autonomous mobile robots (AMRs) in warehouses. This work demonstrates her commitment to designing AI systems that are not only performant but also equitable and robust to real-world variability. With a growing citation footprint and a focus on high-impact, applied problems, Zhang is shaping the future of intelligent automation in logistics, manufacturing, and beyond. Her research is essential reading for anyone interested in deploying RL and multi-agent systems in complex, human-centric environments.
Research Focus
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Top Papers
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