Jing，Gangshan, Professor of Chongqing University, China
Gangshan Jing received the Ph.D. degree in Control Theory and Control Engineering from Xidian University, Xi'an, China, in 2018. From 2016 to 2017, he was a research assistant in Department of Applied Mathematics, at Hong Kong Polytechnic University. From 2018 to 2019, he was a postdoctoral researcher in Department of Mechanical and Aerospace Engineering, at Ohio State University. From 2019-2021, he was a postdoctoral researcher in Department of Electrical and Computer Engineering, at North Carolina State University. Since 2021 Dec, he has been with the College of Automation, Chongqing University, Chongqing. His research interests include distributed control, optimization, and machine learning for network systems.
Contact Information
Email: jinggangshan@cqu.edu.cn
Address: Shazheng Street174#, Shapingba District,Chongqing, 400044,China
We are looking for postdocs to work together, on developing computationally efficient machine learning and control algorithms for large-scale network systems. Scholars with the background of systems control and machine learning are welcome! Feel free to contact me if you are interested. Detailed descriptions regarding the salary and benefits can be found on the following website:
https://rlsbj.cq.gov.cn/ywzl/zjrc/bsh/202005/t20200529_7523072.html
Education
Postdoc, North Carolina State University, 2019-2021.
Postdoc, Ohio State University, 2018-2019.
Ph.D., Xidian University, 2012-2018.
B.S., Ningxia University, 2008-2012.
Academic Experience
Professor, Dec. 2021- Present, School of Automation, Chongqing University, Chongqing, P. R. China
Postdoctoral Researcher, Sept. 2019 - Oct. 2021, Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, USA.
Postdoctoral Researcher, Oct. 2018 - Sept. 2019, Department of Mechanical and Aerospace Engineering, Ohio State University, Columbus, USA.
Research Assistant, Nov. 2017 - Jan. 2018, Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China.
Research Assistant, Nov. 2016 - May. 2017, Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China.
Research Interests
Multi-agent systems
Cyber-physical systems
Reinforcement learning
Formation Control
Network Localization
Distributed optimization
Publications
Journal Papers (selected)
G. Jing, H. Bai, J. George and A. Chakrabortty, “Model-free optimal control of linear multi-agent systems via decomposition and hierarchical approximation”, IEEE Transactions on Control of Network Systems, vol. 8, no. 3, pp. 1069-1081, 2021.
G. Jing, C. Wan, and R. Dai, “Angle-based sensor network localization,” IEEE Transactions on Automatic Control (full paper), doi: 10.1109/TAC.2021.3061980, 2021. (Full version: https://arxiv.org/abs/1912.01665).
G. Jing, G. Zhang, H. W. J. Lee, and L. Wang, “Angle-based shape determination theory of planar graphs with application to formation stabilization,” Automatica (Regular Paper), vol. 105, pp. 117-129, 2019. (Full version: https://arxiv.org/abs/ 1803.04276).
G. Jing, G. Zhang, H. W. J. Lee, and L. Wang, “Weak rigidity theory and its application to formation stabilization,” SIAM Journal on Control and Optimization, vol. 56, no. 3, pp. 2248-2273, 2018. (Arxiv version: http://arxiv.org/abs/1804.02795).
G. Jing, Y. Zheng, and L. Wang, “Consensus of multi-agent systems with distance-dependent communication networks,” IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 11, pp. 2712-2726, 2017.
Conference Papers (selected)
G. Jing, H. Bai, J. George and A. Chakrabortty, “Hierarchical reinforcement learning for optimal control of linear multi-agent systems: the homogeneous case”, IEEE Conference on Decision and Control, Austin, USA, Dec. 2021.
C. Wan, G. Jing, and R. Dai, “Local Shape-Preserving Formation Maneuver Control of Multi-agent Systems: From 2D to 3D”, IEEE Conference on Decision and Control, Austin, USA, Dec. 2021.
G. Jing, H. Bai, J. George and A. Chakrabortty, “Learning Distributed Stabilizing Controllers for Multi-Agent Systems”, IEEE American Control Conference, New Orleans, USA, May 2021.
G. Jing, H. Bai, J. George and A. Chakrabortty, “Model-free reinforcement learning of minimal-cost variance control,” IEEE Conference on Decision and Control, Jeju Island, Republic of Korea, Dec. 2020.
G. Jing, C. Wan, and R. Dai, “Angle fixability and angle-based network localization,” IEEE Conference on Decision and Control, pp. 7899-7904, Nice, France, Dec. 2019.