Ke Li

I am an Assistant Professor at Simon Fraser University in beautiful Vancouver, Canada. I was formerly a Member of the Institute for Advanced Study (IAS) in Princeton, and received my Ph.D. from UC Berkeley, where I was advised by Jitendra Malik, and my bachelor's in computer science from the University of Toronto. My research interests are in machine learning, computer vision and algorithms. I can be reached by e-mail at keli [at] sfu [dot] ca. While at the IAS, I organized the IAS Seminar Series on Theoretical Machine Learning with Sanjeev Arora - check out past seminars here and on Twitter.

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Prospective MSc/PhD Students: I will be taking on a few new students this year. If you are interested in working with me, please fill out this form. Due to the volume of emails I receive, I am unfortunately unable to respond to every email; however, I review submissions through the form regularly and will reach out to selected students.
Prospective SFU Undergraduate/MPCS Students: If you are interested in working on a research or capstone project on AI or related areas, please fill out this form.

For a quick introduction to my research, see the following talk videos:

IAS Workshop on Theory of Deep Learning (Video) (Slides): this is on generative modelling and nearest neighbour search and is aimed at machine learning researchers
CMU ML/Duolingo Seminar (Video) (Slides): this is an extended version of the above (with more details on nearest neighbour search) and is aimed at machine learning graduate students
CIFAR Deep Learning and Reinforcement Learning Summer School (Video) (Slides): this is on generative modelling and is aimed at a broader audience in the style of a tutorial
IAS Special Year Seminar (Video): this is on meta-learning and is aimed at machine learning researchers

Research Directions

I am interested in tackling fundamental problems that cannot be solved using a straightforward application of conventional techniques. Below are the major areas that I contributed to:


Selected Papers

Generative Modelling

Neural Rendering

Learning to Optimize

Fast Nearest Neighbour Search

Instance Segmentation

Other Topics


CMPT 726: Machine Learning (Spring 2023)
CMPT 983 G200: Generative Models (Fall 2022)
CMPT 726: Machine Learning (Spring 2022)
CMPT 983 G200: Generative Models (Fall 2021)
CMPT 726: Machine Learning (Spring 2021)
CS 189: Introduction to Machine Learning (Summer 2018)


Regression Done Right Overcoming Mode Collapse and the Curse of Dimensionality (Extended Version) No More Mode Collapse Implicit Maximum Likelihood Estimation Tutorial on Implicit Generative Models Fast k-Nearest Neighbour Search via Dynamic Continuous Indexing Meta-Learning: Why It's Hard and What We Can Do Learning to Optimize Meta-Learning

Professional Service

Seminars: Workshops: Journals: Conferences: