Adam Oberman
About
Appointed Canada CIFAR AI Chair – 2020
Adam Oberman is a Canada CIFAR AI Chair at Mila and a professor in the Department of Mathematics and Statistics at McGill University, and director of the Applied Mathematics Laboratory at the Centre de Recherches Mathématiques.
His research focuses on mathematical approaches to machine learning. He is currently working on AI Safety, and self supervised learning, and AI for science. He has worked on generative modeling, algorithmic bias removal, stochastic optimization, adversarial robustness, among other areas.
Awards
- Simons Fellowship, Simons Foundation, 2017
- Monroe H. Martin Prize, Institute for Physical Science and Technology, 2010
- Early Career Award, CAIMS-PIMS, 2010
Relevant Publications
- Salvador, T., Cairns, S., Voleti, V., Marshall, N., & Oberman, A. (2022). FairCal: Fairness Calibration for Face Verification. International Conference on Learning Representations.
- Le Lan, C., Tu, S., Oberman, A., Agarwal, R., Bellemare, M.G. (2022). On the generalization of representations in reinforcement learning.
Finlay, C., Jacobsen, J. H., Nurbekyan, L., & Oberman, A. M. (2020). How to train your neural ODE: the world of Jacobian and kinetic regularization. arXiv, arXiv-2002.
Chaudhari, P., Oberman, A., Osher, S., Soatto, S., & Carlier, G. (2018). Deep relaxation: partial differential equations for optimizing deep neural networks. Research in the Mathematical Sciences, 5(3), 30.