Chi-guhn Lee

Biography

Chi-Guhn Lee is a professor in the Department of Mechanical and Industrial Engineering at the University of Toronto. He received his Ph.D. in the area of Industrial & Operations Engineering from the University of Michigan, Ann Arbor, and joined the University of Toronto faculty in 2001. Prior to his Ph.D. studies, he spent over three years at Samsung SDS in Seoul, Korea, leading a project of re-usable OOP library for fast prototyping of system integration software. Professor Lee has done both theoretical and applied research in dynamic optimization under uncertainty. His theoretical works involve accelerated value iteration algorithm for Markov decision processes, progressive basis-function approximation for value function space, multi-variate Bayesian control chart optimization, and optimal learning using Multi-armed Bandit Model. His interest in application is diverse from supply chain optimization to financial engineering, to dynamic pricing and to healthcare optimization. In the past years, he and his team have actively adopted machine learning algorithms into their research portfolio. In particular, he is currently active in reinforcement learning, inverse reinforcement learning, and deep reinforcement learning.  Professor Lee holds positions as associate editor - Enterprise Information System and International Journal of Industrial Engineering - and serves as a member in a few editorial boards. Chi-Guhn Lee is a professor in the Department of Mechanical and Industrial Engineering at the University of Toronto. He received his Ph.D. in the area of Industrial & Operations Engineering from the University of Michigan, Ann Arbor, and joined the University of Toronto faculty in 2001. Prior to his Ph.D. studies, he spent over three years at Samsung SDS in Seoul, Korea, leading a project of re-usable OOP library for fast prototyping of system integration software. Professor Lee has done both theoretical and applied research in dynamic optimization under uncertainty. His theoretical works involve accelerated value iteration algorithm for Markov decision processes, progressive basis-function approximation for value function space, multi-variate Bayesian control chart optimization, and optimal learning using Multi-armed Bandit Model. His interest in application is diverse from supply chain optimization to financial engineering, to dynamic pricing and to healthcare optimization. In the past years, he and his team have actively adopted machine learning algorithms into their research portfolio. In particular, he is currently active in reinforcement learning, inverse reinforcement learning, and deep reinforcement learning.  Professor Lee holds positions as associate editor - Enterprise Information System and International Journal of Industrial Engineering - and serves as a member in a few editorial boards.

Research Interest

Operations Research