Adam M. Oberman

Associate Academic Member

Canada CIFAR AI Chair

Full Professor, McGill University, Department of Mathematics and Statistics

Research Topics

AI Safety

Deep Learning

Generative Models

Machine Learning Theory

Representation Learning

Biography

I am a professor at McGill University, in the Department of Mathematics and Statistics. My research revolves around the application of advanced mathematical techniques to the field of deep learning. My primary areas of expertise include generative modelling, stochastic optimization methods, fairness/bias removal in computer vision, and generalization in reinforcement learning.

Before joining McGill in 2012, I held a tenured faculty position at Simon Fraser University and completed a postdoctoral fellowship at the University of Texas, Austin. I obtained my undergraduate education at the University of Toronto and pursued graduate studies at the University of Chicago. I have also held visiting positions at the University of California, Los Angeles (UCLA) and at the National Institute for Research in Digital Science and Technology (INRIA) in Paris.

My early research encompassed the fields of partial differential equations and scientific computing, where I made significant contributions to areas like numerical optimal transportation, geometric PDEs and stochastic control problems.

I teach two comprehensive theory courses on machine learning, covering topics such as statistical learning theory and kernel theory.

For prospective graduate students interested in working with me, please apply to both Mila – Quebec Artificial Intelligence Institute and the Department of Mathematics and Statistics at McGill. Alternatively, applicants may consider co-supervision opportunities with advisors from the computer science program at McGill or Université de Montréal.