Prof. JI Xiaoqiang received his PhD from Columbia University in the United States. He is currently an assistant professor and Ph.D. supervisor at the School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen. He also serves as deputy director of the Guangdong Engineering Research Center for Embodied-AI Robotics, the expert committee member of Chine Simulation Federation Intelligent IOT Systems, and principal scientist of the ASEAN – China Artificial Intelligence Laboratory.

His research primarily focuses on intelligent control systems. He has presided over numerous scientific research and talent projects, including the Frontier Exploration Project on Data Science and Artificial Intelligence funded by the National Natural Science Foundation of China. To date, he has published more than 50 papers in top international journals and conferences such as IEEE Transactions on Automatic Control (TAC), Automatica, Journal of Field Robotics (JFR), IEEE/ASME Transactions on Mechatronics (T-Mech), IEEE Transactions on Automation Science and Engineering (T-ASE), IEEE Robotics and Automation Letters (RA-L), IEEE Conference on Decision and Control (CDC), and IEEE International Conference on Robotics and Automation (ICRA). Notably, he is one of the global pioneers in advancing learning control design for non-minimum phase systems. He acts as a reviewer for several top journals and conferences including IEEE-TAC, serves as an Associate Editor of MECC, and a Youth Editorial Board Member of Robot Learning. He has also held the position of Area Chair for international conferences such as the IROS, RCAR. Recently, he was awarded the CINT Outstanding Paper Award and the ISUI Best Paper Award.

The AI-driven Control and Decision Laboratory lead by Prof. JI is an interdisciplinary platform, which requires deep integration of basic sciences such as control theory, artificial intelligence, robotics, high-performance computing, big data, etc. It is committed to conducting research on basic theories and original innovations in the field of artificial intelligence and intelligent systems.

(*) denotes the corresponding author:

1. X. Ji*, S. Zhu, Y. Xu, and R. Longman. Lifted time stable inversion based feedforward control for linear non-minimum phase systems, Automatica, vol. 171, pp. 111979, 2025.
2. X. Ji, X.Zhang, S.Zhu, F.Deng and B.Zhu. Data-driven adaptive consensus control for heterogeneous nonlinear multi-agent systems using online reinforcement learning, Neurocomputing, Vol. 596, 127818, 2024.
3. J.Li, C.Zhao, X. Ji*, M.Li, G.Lu, Y.Xu, and D.Zhang. Multi-view instance attention fusion network for classification, Information Fusion, Vol. 101, 101974, 2024.
4. K.Xue, X.Ji*, D.Qu, Y.Peng and H.Qian*. Oboat: An agile omnidirectional robotic platform for unmanned surface vehicle tasks, IEEE/ASME Transactions on Mechatronics (T-Mech), vol.28, no.5, pp.2413-2424, Oct. 2023.
5. S. Zhu, Y. Wang, B. Zhu, and X. Ji*. Tracking error boundary of novel stable inversion based feedforward control for a class of non-minimum phase systems, CINT, vol 1714, Springer, 2023. [Excellent Paper Award]
6. R. Xu, X. Ji, C.Liu, J. Hou, H.Qian. Design and control of a wave-driven solar tracker, IEEE Transactions on Automation Science and Engineering (T-ASE), vol. 20, no. 2, pp.1007-1019, 2023.
7. K.Xue, J. Liu, N. Xiao, X. Ji*, and H. Qian*. A bio-inspired simultaneous surface and underwater risk assessment method based on stereo vision for USVs in nearshore clean waters, IEEE Robotics and Automation Letters (RA-L), 2022.
8. X. Ji, and R. Longman. Two new stable inverses of discrete time systems, Astrodynamics Specialist Conference, AAS/American Institute of Aeronautics and Astronautics (AIAA), vol. 171(1), 2020, pp. 4137-4143.
9. X. Ji, and R. Longman. The insensitivity of the iterative learning control inverse problem to initial run when stabilized by a new stable inverse. Modeling, Simulation and Optimization of Complex Process, Bock. et al., Springer, 2(4):257-275, 2020