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Publication Topics
optimisation, power generation dispatch, power generation economics, distributed power generation, renewable energy sources, graph theory, power generation control, power grids, algebra, convex programming, demand side management, distributed algorithms, game theory, multi-agent systems, power markets, smart power grids, voltage regulators, Lyapunov methods, Newton method, battery powered vehicles, battery storage plants, concave programming, control engineering computing, control system synthesis, convergence
Chaojie Li received the B.Eng. degree in electronic science and technology and the M.Eng. degree in computer science from Chongqing University, Chongqing, China, in 2007 and 2011, respectively, and the Ph.D. degree from RMIT University, Melbourne, Australia, in 2017, where he was a Research Fellow for one and a half years. He is a Senior Algorithm Engineer with Alibaba Group. His current research interests include graph representation learning, distributed optimization and control in smart grid, neural networks, and their application.
optimisation, power generation dispatch, power generation economics, distributed power generation, renewable energy sources, graph theory, power generation control, power grids, algebra, convex programming, demand side management, distributed algorithms, game theory, multi-agent systems, power markets, smart power grids, voltage regulators, Lyapunov methods, Newton method, battery powered vehicles, battery storage plants, concave programming, control engineering computing, control system synthesis, convergence
Chaojie Li received the B.Eng. degree in electronic science and technology and the M.Eng. degree in computer science from Chongqing University, Chongqing, China, in 2007 and 2011, respectively, and the Ph.D. degree from RMIT University, Melbourne, Australia, in 2017, where he was a Research Fellow for one and a half years. He is a Senior Algorithm Engineer with Alibaba Group. His current research interests include graph representation learning, distributed optimization and control in smart grid, neural networks, and their application.
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IEEE Transactions on Automatic Controlno. 2 (2024): 936-951
IEEE Trans. Smart Gridno. 2 (2024): 1659-1669
DATABASES THEORY AND APPLICATIONS, ADC 2023 (2024): 183-196
IECON 2023- 49th Annual Conference of the IEEE Industrial Electronics Societypp.1-5, (2023)
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IEEE/CAA Journal of Automatica Sinicano. 12 (2023): 2179-2182
JOURNAL OF MACHINE LEARNING RESEARCH (2023)
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ICONIP (7)pp.303-314, (2023)
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