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Courtney Paquette

Appointment

  • Canada CIFAR AI Chair
  • Pan-Canadian AI Strategy

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Personal Page

Google Scholar

About

Courtney Paquette is a Canada CIFAR AI Chair at Mila, an assistant professor at the department of mathematics and statistics at McGill University, and a research scientist at Google Brain.

Paquette’s research focuses on designing and analyzing algorithms for large-scale optimization problems, motivated by applications in data science. Some of the techniques Paquette uses in her research include a variety of fields including probability, complexity theory, and convex and nonsmooth analysis.

Awards

  • Tanzi-Egerton Fellowship Award, 2016
  • Excellence in Teaching Award, University of Washington Mathematics Department, 2012

Relevant Publications

  • Paquette, C., Lee, K., Pedregosa, F., & Paquette, E. (2021). SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize Criticality.

  • Paquette, C., & Paquette, E. (2021). Dynamics of Stochastic Momentum Methods on Large-scale, Quadratic Models.

  • Davis, D., Drusvyatskiy, D., & Paquette, C. (2020). The nonsmooth landscape of phase retrieval. IMA Journal of Numerical Analysis, 40(4), 2652-2695.

  • Drusvyatskiy, D., & Paquette, C. (2019). Efficiency of minimizing compositions of convex functions and smooth maps. Mathematical Programming, 178(1), 503-558.

  • Paquette, C., Lin, H., Drusvyatskiy, D., Mairal, J., & Harchaoui, Z. (2018). Catalyst for gradient-based nonconvex optimization. In International Conference on Artificial Intelligence and Statistics (pp. 613-622). PMLR.

Institution

  • McGill University
  • Mila

Department

Mathematics and Statistics

Education

  • PhD (Mathematics), University of Washington

Country

  • Canada

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