Regular biography
Dong-Hwan Lee is a faculty member in the School of Electrical Engineering at KAIST. His research focuses on reinforcement learning theory and applications, control theory, machine learning, and artificial intelligence. His work explores the interplay among these disciplines, with particular emphasis on AI-based applications in robotics, self-driving cars, and large-scale language models. He is affiliated with the Department of Electrical Engineering at KAIST and can be contacted via email at donghwan@kaist.ac.kr or through his website at https://sites.google.com/site/donghwanleehome.
Scholar-generated biography
Donghwan Lee is a researcher at KAIST with expertise in decision making, control, and optimization. His work focuses on advanced control strategies for fuzzy systems, including stability analysis, stabilization conditions, and robust filtering techniques. He has contributed significantly to the development of nonquadratic stabilization methods, fuzzy Lyapunov functions, and relaxed LMI conditions for Takagi–Sugeno fuzzy systems. Additionally, he explores reinforcement learning frameworks for optimal control and distributed learning algorithms. His research also extends to visual SLAM and robust control for nonlinear systems, emphasizing practical applications in engineering and automation.