Portrait of Guillaume Lajoie

Guillaume Lajoie

Core Academic Member
Canada CIFAR AI Chair
Associate Professor, Université de Montréal, Department of Mathematics and Statistics
Visiting Researcher, Google
Research Topics
Computational Neuroscience
Deep Learning
Dynamical Systems
Optimization
Recurrent Neural Networks
Representation Learning

Biography

Guillaume Lajoie is an Associate professor in the Department of Mathematics and Statistics at Université de Montréal and a Core Academic Member of Mila – Quebec Artificial Intelligence Institute. He holds a Canada-CIFAR AI Research Chair, and a Canada Research Chair (CRC) in Neural Computation and Interfacing. He also holds a Health Research Scholar of Fonds de recherche du Québec.

Guillaume Lajoie was previously a postdoctoral fellow at the Max Planck Institute for Dynamics and Self-Organization in Germany and at the University of Washington’s Institute for Neuroengineering. He obtained his PhD from the Department of Applied Mathematics at the University of Washington (Seattle).

His research is positioned at the intersection of AI and Neuroscience where he develops tools to better understand mechanisms of intelligence common to both biological and artificial systems. His research group's contributions range from advances in multi-scale learning paradigms for large artificial systems, to applications in neurotechnology. Dr. Lajoie is actively involved in responsible AI development efforts, seeking to identify guidelines and best practices for use of AI in research and beyond.

Recent work has focused on the development of architectural inductive biases for information propagation in recurrent networks, as well as the development of algorithms and models for bidirectional brain-machine interface optimization.

Current Students

Independent visiting researcher
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PhD - Université de Montréal
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Postdoctorate - Université de Montréal
Co-supervisor :
PhD - Université de Montréal
Postdoctorate - Université de Montréal
Co-supervisor :
PhD - Université de Montréal
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PhD - Université de Montréal
Principal supervisor :
PhD - Université de Montréal
Master's Research - Polytechnique Montréal
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Master's Research - Polytechnique Montréal
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Collaborating researcher - Western Washington University (faculty; assistant prof))
Principal supervisor :
Master's Research - Université de Montréal
Co-supervisor :
Collaborating researcher - Université de Montréal
PhD - Université de Montréal
Co-supervisor :
PhD - Université de Montréal
Co-supervisor :
PhD - Université de Montréal
Co-supervisor :
PhD - Université de Montréal
Co-supervisor :
PhD - Université de Montréal
Principal supervisor :
Collaborating researcher - Université de Montréal
Postdoctorate - McGill University
Principal supervisor :
Collaborating Alumni - Université de Montréal
Master's Research - Université de Montréal
Principal supervisor :
PhD - Université de Montréal
Co-supervisor :
PhD - McGill University
Research Intern - Western Washington University
Co-supervisor :
PhD - Université de Montréal

Publications

Implicit Regularization in Deep Learning: A View from Function Space
Aristide Baratin
Thomas George
César Laurent
We approach the problem of implicit regularization in deep learning from a geometrical viewpoint. We highlight a possible regularization eff… (see more)ect induced by a dynamical alignment of the neural tangent features introduced by Jacot et al, along a small number of task-relevant directions. By extrapolating a new analysis of Rademacher complexity bounds in linear models, we propose and study a new heuristic complexity measure for neural networks which captures this phenomenon, in terms of sequences of tangent kernel classes along in the learning trajectories.
Implicit Regularization in Deep Learning: A View from Function Space
Aristide Baratin
Thomas George
César Laurent