Roger Grosse
About
Appointed Canada CIFAR AI Chair – 2017
Renewed Canada CIFAR AI Chair – 2023
Roger Grosse is a Canada CIFAR AI Chair at the Vector Institute and an Associate Professor at the Department of Computer Science at the University of Toronto.
As one of the founding members of the Vector Institute, Grosse’s work is yielding advances in understanding of what makes deep neural networks (DNNs) work, and how to fully optimize network architectures and algorithms so that they train faster, generalize better, can reveal the underlying structure of a problem and make more robust decisions. He has recently been working to use this understanding to monitor and mitigate potential catastrophic risks from advanced AI systems.
Awards
- Senior AI2050 Fellow, Schmidt Sciences, 2024
- Sloan Research Fellowship, 2021
- Ontario Early Researcher Award, 2018
- Connaught New Researcher Award, 2017
- NSERC Banting Postdoctoral Fellowship, 2015
- Best Student Paper, Conference on Uncertainty in AI, 2012
Relevant Publications
- Bae, J., Ng, N., Lo, A., Ghassemi, M., & Grosse, R. (2022). If influence functions are the answer, then what is the question? In Advances in Neural Information Processing Systems 35(pp. 17953-17967).
- Bae, J., Vicol, P., HaoChen, J. Z., Grosse, R. (2022). Amortized proximal optimization. In Advances in Neural Information Processing Systems 35(pp. 8982-8997).
- Vicol, P., Lorraine, J. P., Pedregosa, F., Duvenaud, D., Grosse R. (2022) On implicit bias in overparameterized bilevel optimization. In International Conference on Machine Learning (pp. 22234-22259). PMLR.
Burda, Y., Grosse, R., & Salakhutdinov, R. (2015). Importance weighted autoencoders.
Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2009). Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In Proceedings of the 26th annual international conference on machine learning (pp. 609-616).