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frank-wood2

Frank Wood

Appointment

  • Canada CIFAR AI Chair
  • Pan-Canadian AI Strategy

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University of British Columbia

Google Scholar

About

Frank Wood is a Canada CIFAR AI Chair at Mila and an associate professor of Computer Science at the University of British Columbia. 

Wood’s primary research areas include deep generative modeling, amortized inference, probabilistic programming, reinforcement learning, and applied probabilistic machine learning. His research interests range from the development of new probabilistic models and inference algorithms to real-world applications. Additionally, his research contributions include probabilistic programming systems, new models and inference algorithms, and novel applications of such models to problems in autonomous driving, computational neuroscience, vision, natural language processing, robotics, and reinforcement learning.

Relevant Publications

  • Masrani, V., Le, T. A., & Wood, F. (2019). The thermodynamic variational objective.

  • Beronov, B., Weilbach, C., Wood, F., & Campbell, T. Sequential Core-Set Monte Carlo.

  • Masrani, V., Brekelmans, R., Bui, T., Nielsen, F., Galstyan, A., Steeg, G. V., & Wood, F. (2021). q-Paths: Generalizing the Geometric Annealing Path using Power Means.

  • Warrington, A., Lavington, J. W., Scibior, A., Schmidt, M., & Wood, F. (2021, July). Robust asymmetric learning in pomdps. In International Conference on Machine Learning (pp. 11013-11023). PMLR.

  • Ścibior, A., Masrani, V., & Wood, F. (2021). Differentiable Particle Filtering without Modifying the Forward Pass.

Institution

  • Mila
  • University of British Columbia

Department

Computer Science

Education

  • PhD (Computer Science), Brown University
  • BS (Computer Science), Cornell University

Country

  • Canada

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