Ke Li

Ke Li is an Assistant Professor at Simon Fraser University's School of Computing Science in Vancouver, Canada. He holds a PhD from UC Berkeley (2019) and a BSc from the University of Toronto (2014). His research focuses on machine learning, computer vision, and algorithms, with contributions to generative models, optimization algorithms, and fast nearest neighbor search. He organizes the IAS Seminar Series on Theoretical Machine Learning and has advised multiple students in MSc/PhD programs.

Education:

• PhD in Computer Science, University of California, Berkeley, 2019
• Bachelor of Science (Honors) in Computer Science, University of Toronto, 2014

Research Interests: Li's work spans generative modeling (e.g., IMLE framework), learning-to-optimize frameworks, and fast nearest neighbor search algorithms that overcome dimensionality challenges. His research emphasizes foundational problems in machine learning, blending theoretical insights with practical applications.

Teaching: Li teaches graduate courses in machine learning and generative models. Recent courses include CMPT 726 (Machine Learning) and CMPT 983 (Special Topics in Generative Models).

Labs & Teams: His research involves the Tangent Lab and collaborations with institutions like the Institute for Advanced Study (IAS) and Stanford University.