Yangyang Qian is currently an Assistant Professor with the Division of Industrial Data Science in the School of Data Science, Lingnan University, Hong Kong. He received the B.Eng. degree in automation from the Xi’an University of Posts and Telecommunications, Xi’an, China, in 2012, the M.Eng. degree in control science and engineering from the Harbin Institute of Technology, Shenzhen, China, in 2015, and the Ph.D. degree from the Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, in 2019. He was a Postdoctoral Research Associate with the Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA, and then with the Charles L. Brown Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, USA, from 2020 to 2025. His primary research interests include distributed cooperative control, multi-agent systems, battery systems, and microgrids. Dr. Qian has served as an Associate Editor for Transactions of the Institute of Measurement and Control (TIMC), International Conference on Unmanned Aircraft Systems (ICUAS), and IEEE International Conference on Control & Automation (ICCA).

• Cooperative Control
• Multi-Agent Systems
• Battery Energy Storage Systems
• Microgrids
• Reinforcement Learning

We are seeking highly motivated Ph.D. Students, Research Assistants, and Postdoctoral Researchers to join our dynamic research group at Lingnan University. Our research areas include

1. Multi-Agent Systems and Control
   By leveraging model-based, data-driven, or learning-based approaches, we design and analyze distributed cooperative control strategies for multi-agent systems, with applications in mobile robots, autonomous vehicles (e.g., unmanned aerial vehicles), and other related fields.
2. Distributed Control of Microgrids
   Our work focuses on developing advanced distributed control solutions to improve the reliability, efficiency, and resilience of microgrids under diverse cyber-physical constraints.
3. Distributed Management of Battery Energy Storage Systems
   We develop distributed control and optimization algorithms for networked battery energy storage systems, enabling multiple battery units to operate efficiently under time-varying loads and uncertain environments.

Ideal candidates should have a strong background in control systems, power and energy systems, robotic systems, or related fields. If you are eager to contribute to innovative research, we would like to hear from you! Please send your CV and any supporting materials (e.g., academic transcripts) to [email protected].