Portrait of David Meger

David Meger

Associate Academic Member
Associate Professor, McGill University, School of Computer Science
Research Topics
Computer Vision
Reinforcement Learning

Biography

David Meger is an associate professor at McGill University’s School of Computer Science.

He co-directs the Mobile Robotics Lab within the Centre for Intelligent Machines, one of Canada's largest and longest-running robotics research groups. He was the general chair of Canada’s first joint CS-CAN conference in 2023.

Meger's research contributions include visually guided robots powered by active vision and learning, deep reinforcement learning models that are widely cited and used by researchers and industry worldwide, and field robotics that allow for autonomous deployment underwater and on land.

Current Students

Julie Alhosh
Master's Research - McGill University
Website
Github
Google Scholar
Rambod Azimi
Collaborating researcher - McGill University
Principal supervisor :
Samira Ebrahimi Kahou
Website
Github
Google Scholar
William Bonilla
PhD - McGill University
Github
Valliappan Chidambaram Adaikkappan
PhD - McGill University
Github
Google Scholar
Wesley Chung
PhD - McGill University
Co-supervisor :
Doina Precup
Farnoosh Faraji
PhD - McGill University
Co-supervisor :
Gregory Dudek
Github
Google Scholar
Anas Houssaini
Master's Research - McGill University
Co-supervisor :
Hsiu-Chin Lin
Github
Zina Kamel
Master's Research - McGill University
Co-supervisor :
Hsiu-Chin Lin
Sahand Rezaei-Shoshtari
PhD - McGill University
Principal supervisor :
Doina Precup
Website
Github
Google Scholar
Arian Sargazi
PhD - McGill University
Steven Wang
Master's Research - McGill University
Harley Wiltzer
PhD - McGill University
Co-supervisor :
Marc Gendron-Bellemare
Website
Github
Google Scholar
Hanna Yurchyk
PhD - McGill University

Publications

Tractable Representations for Convergent Approximation of Distributional HJB Equations
Julie Alhosh
Harley Wiltzer
David Meger
2025-03-07
ArXiv (preprint)
arxiv.org
arxiv.org
Fairness in Reinforcement Learning with Bisimulation Metrics
Sahand Rezaei-Shoshtari
Hanna Yurchyk
Scott Fujimoto
Doina Precup
David Meger
Ensuring long-term fairness is crucial when developing automated decision making systems, specifically in dynamic and sequential environment… (see more)s. By maximizing their reward without consideration of fairness, AI agents can introduce disparities in their treatment of groups or individuals. In this paper, we establish the connection between bisimulation metrics and group fairness in reinforcement learning. We propose a novel approach that leverages bisimulation metrics to learn reward functions and observation dynamics, ensuring that learners treat groups fairly while reflecting the original problem. We demonstrate the effectiveness of our method in addressing disparities in sequential decision making problems through empirical evaluation on a standard fairness benchmark consisting of lending and college admission scenarios.
2024-12-22
ArXiv (preprint)
doi.org
arxiv.org
Shedding Light on Large Generative Networks: Estimating Epistemic Uncertainty in Diffusion Models
Lucas Berry
Axel Brando
David Meger
Generative diffusion models, notable for their large parameter count (exceeding 100 million) and operation within high-dimensional image spa… (see more)ces, pose significant challenges for traditional uncertainty estimation methods due to computational demands. In this work, we introduce an innovative framework, Diffusion Ensembles for Capturing Uncertainty (DECU), designed for estimating epistemic uncertainty for diffusion models. The DECU framework introduces a novel method that efficiently trains ensembles of conditional diffusion models by incorporating a static set of pre-trained parameters, drastically reducing the computational burden and the number of parameters that require training. Additionally, DECU employs Pairwise-Distance Estimators (PaiDEs) to accurately measure epistemic uncertainty by evaluating the mutual information between model outputs and weights in high-dimensional spaces. The effectiveness of this framework is demonstrated through experiments on the ImageNet dataset, highlighting its capability to capture epistemic uncertainty, specifically in under-sampled image classes.
2024-06-05
ArXiv (preprint)
doi.org
arxiv.org