Courtney Paquette
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.
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