David Duvenaud
Appointment
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
About
Appointed Canada CIFAR AI Chair – 2021
David Duvenaud is a Canada CIFAR AI Chair at the Vector Institute and an assistant professor in the department of computer science and statistical sciences at the University of Toronto. He is also a founding member of the Vector Institute and the co-founder of Fable Therapuetics, a machine learning-based drug discovery company.
Duvenaud’s research focuses on AGI governance, evaluation, and mitigating catastrophic risks from future systems.
Awards
- Ontario Early Researcher Award, 2022
- Sloan Research Fellowship, 2022
- Distinguished Paper Award, International Conference on Functional Programming, 2022
- Outstanding Paper Honorable Mention, International Conference on Machine Learning, 2022
- Best Paper Award, Neural Information Processing Systems Conference (NIPSC), 2018
Relevant Publications
- Xu, W., Chen, R.T.Q., Li, X. & Duvenaud, D. (2022). Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations. In Proceedings of The 25th International Conference on Artificial Intelligence and Statistics. 151:721-738.
- Lorraine, J., Acuna, D., Vicol, P., & Duvenaud, D. (2022). Complex momentum for optimization in games. In Proceedings of The 25th International Conference on Artificial Intelligence and Statistics. 151:7742-7765.
- Grathwohl, W., Swersky, K., Hashemi, M., Duvenaud, D. & Maddison, C. (2021). Oops I Took A Gradient: Scalable Sampling for Discrete Distributions. In Proceedings of the 38th International Conference on Machine Learning 139:3831-3841.
Li, X., Chen, R. T. Q., Wong, T.-K. L., & Duvenaud, D. (2020). Scalable gradients for stochastic differential equations. In Artificial intelligence and statistics.
Chang, C.-H., Creager, E., Goldenberg, A., & Duvenaud, D. (2019). Explaining image classifiers by adaptive dropout and generative in-filling. In International conference on learning representations.