Portrait of Derek Nowrouzezahrai

Derek Nowrouzezahrai

Core Academic Member
derek@mila.quebec
Canada CIFAR AI Chair
Associate Professor, McGill University, Department of Electrical and Computer Engineering
Research Topics
Computational Photography
Computer Vision
Deep Learning
Dynamical Systems
Generative Models
Reinforcement Learning
Representation Learning
Google Scholar

Biography

Derek Nowrouzezahrai is a full professor at McGill University, where he directs the Centre for Intelligent Machines and co-directs the Graphics Lab.

He is also a Canada CIFAR AI Chair and holds the Ubisoft–Mila research Chair, Scaling Game Worlds with Responsible AI.

Nowrouzezahrai’s research tackles the simulation of various physical phenomena, such as the dynamics of moving objects and the simulation of lighting for realistic image synthesis, which have applications in virtual reality, video games, fluid simulation and control, digital manufacturing, computationally augmented optics and geometry processing. He is also interested in the development of differentiable simulators of these dynamical systems and their applications to inverse problems in robotics and vision.

This work relies fundamentally on developing high performance and sample efficient (Markov chain) Monte Carlo-based methods, high-order statistics and computational methods for complex multi-dimensional integration problems, differentiable physics-based simulators and numerical methods for dynamical systems, and on applying machine learning to 3D, visual and interactive media.

Current Students

Daniel Bairamian
PhD - McGill University
Paul Barde
Collaborating researcher - McGill University
Co-supervisor :
Chris Pal
paul.b.barde@gmail.com
Website
Github
Google Scholar
Chris Emezue
Master's Research - Université de Montréal
Principal supervisor :
Chris Pal
Website
Github
Google Scholar
Daniel Kong
PhD - McGill University
Github
Ken Ming Lee
Master's Research - McGill University
Website
Github
Hossam Mohamed S M Ahmed
PhD - McGill University
Github
Somjit Nath
PhD - McGill University
Principal supervisor :
Samira Ebrahimi Kahou
Website
Google Scholar
Yasaman Sabbagh Ziarani
PhD - McGill University
Yuanyuan Tao
PhD - McGill University
Mattie Tesfaldet
PhD - McGill University
Co-supervisor :
Chris Pal
Website
Github
Google Scholar
Junha Yoo
Master's Research - McGill University

Publications

Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization
Paul Barde
Julien Roy
Wonseok Jeon
Joelle Pineau
Chris Pal
Derek Nowrouzezahrai
Adversarial imitation learning alternates between learning a discriminator -- which tells apart expert's demonstrations from generated ones … (see more)-- and a generator's policy to produce trajectories that can fool this discriminator. This alternated optimization is known to be delicate in practice since it compounds unstable adversarial training with brittle and sample-inefficient reinforcement learning. We propose to remove the burden of the policy optimization steps by leveraging a novel discriminator formulation. Specifically, our discriminator is explicitly conditioned on two policies: the one from the previous generator's iteration and a learnable policy. When optimized, this discriminator directly learns the optimal generator's policy. Consequently, our discriminator's update solves the generator's optimization problem for free: learning a policy that imitates the expert does not require an additional optimization loop. This formulation effectively cuts by half the implementation and computational burden of adversarial imitation learning algorithms by removing the reinforcement learning phase altogether. We show on a variety of tasks that our simpler approach is competitive to prevalent imitation learning methods.
arxiv.org