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David-Fleet

David Fleet

Appointment

Canada CIFAR AI Chair

Pan-Canadian AI Strategy

Connect

University of Toronto

Google Scholar

About

Appointed Canada CIFAR AI Chair – 2017

Renewed Canada CIFAR AI Chair – 2023

David Fleet is a Canada CIFAR AI Chair at the Vector Institute, a professor of computer science in the department of computer and mathematical sciences at the University of Toronto.

Fleet’s research interests span computer vision, image processing, visual neuroscience and machine learning with emphasis on mathematical foundations and algorithms for visual motion analysis, human post and motion estimation, models of human motion perception and stereopsis, generative models, and multi-view reconstruction for electron cryo-microscopy.

Awards

  • Highlight Paper Award, IEEE Conference on Computer Vision and Pattern Recognition, 2023
  • Outstanding Paper Award, Neural Information Processing Systems, 2022
  • Paper of the Year Award, Journal of Structural Biology, 2021
  • Koenderink Prize, 2010
  • Best Paper Award, British Machine Vision Conference (BMVC), 2009
  • Fellow, CIFAR (LMB), 2004-2019

Relevant Publications

  • Punjani, A., & Fleet, DJ. (2023). 3DFlex: Determining structure and motion of flexible proteins from cryo-EM. Nature Methods, 20(6), 860-870.
  • Azizi, S., Kornblith, S., Saharia, C., Norouzi, M. & Fleet, D.J. (2023) Synthetic data from diffusion models improves ImageNet classification. Transactions on Machine Learning Research.
  • Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., … & Norouzi, M. (2022). Photorealistic text-to-image diffusion models with deep language understanding. Neural Information Processing Systems.
  • Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, DJ., & Norouzi, M. (2022) Image super-resolution via iterative refinement. In IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (4), 4713-4726.
  • Punjani, A. & Fleet, D.J. (2021) 3D Variability Analysis: Directly resolving continuous flexibility and discrete heterogeneity from single particle cryo-EM images. Journal of Structural Biology, 213: 107702
  • Wang, J., Fleet, D.J. and Hertzmann, A. (2010) Optimizing walking controllers with uncertain user inputs and environments. ACM Transactions on Graphics 29(4), Article 73

Institution

University of Toronto

Vector Institute

Department

Computer and Mathematical Sciences

Education

  • PhD (Computer Science), University of Toronto
  • MSc (Computer Science), University of Toronto
  • BSc, (Honours Computer Science and Mathematics), Queen’s University

Country

Canada

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