Adam Oberman is a Professor in the Department of Mathematics and Statistics at McGill University, and director of the Applied Mathematics Laboratory at the Centre de Recherches Mathematiques. He has held visiting positions at UCLA. Before coming to McGill in 2012, he was tenured at Simon Fraser university, and a postdoc at University of Texas, Austin. He was a student at the University of Toronto (undergraduate) and University of Chicago (graduate).
His research focusses on mathematical approaches to machine learning: optimization (including stochastic gradient descent), regularization approaches (including averaged models and gradient regularization), and robust models, including adversarially trained models. He teaches a theory course on machine learning, including generalization theory, and a scientific computing course with a focus on high dimensional methods.