HELLO!
I am an assistant professor at McGill University in the Mathematics and Statistics department . I am a CIFAR AI Chair and I am an active member of the Montreal Machine Learning Optimization Group (MTL MLOpt) at MILA. Moreover I am the lead organizer of the OPTML Workshop for NeurIPS 2020. Previously, I was a research scientist at Google Brain, Montreal. You can view my CV here if you are interested in more details.
I received my Ph.D. from the Mathematics department at the University of Washington (2017) under Prof. Dmitriy Drusvyatskiy then I held a postdoctoral position in the Industrial and Systems Engineering at Lehigh University where I worked with Prof. Katya Scheinberg. I held an NSF postdoctoral fellowship (20182019) under Prof. Stephen Vavasis in the Combinatorics and Optimization Department at the University of Waterloo.
My research broadly focuses on designing and analyzing algorithms for largescale optimization problems, motivated by applications in data science. The techniques I use draw from a variety of fields including probability, complexity theory, and convex and nonsmooth analysis.
University of Washington, Lehigh University, University of Waterloo, McGill University, and MIlA have strong optimization groups which spans across many departments: Math, Stats, CSE, EE, and ISE. If you are interested in optimization talks at these places, check out the following seminars:
 Optimization for Machine Learning (OPT+ML) workshop at NeurIPS
 Montreal Machine Learning and Optimization (MTL MLOPT) at MILA
 Applied Mathematics at McGill University
 Trends in Optimization Seminar (TOPS/CORE) at University of Washington
 Institute for Foundations of Data Science at University of Washington/University of Wisconsin
 Machine Learning at Paul G. Allen School of Computer Science and Engineering, University of Washington
 COR@L at Lehigh University
 Combinatorics and Optimization at University of Waterloo
EMAIL: yumiko88(at)uw(dot)edu or yumiko88(at)u(dot)washington(dot)edu or courtney(dot)paquette(at)mcgill(dot)ca
OFFICE: BURN 913
RESEARCH
My research interests lie at the frontier of largescale continuous optimization. Nonconvexity, nonsmooth analysis, complexity bounds, and interactions with random matrix theory and highdimensional statistics appear throughout work. Modern applications of machine learning demand these advanced tools and motivate me to develop theoretical guarantees with an eye towards immediate practical value. My current research program is concerned with developing a coherent mathematical framework for analyzing averagecase (typical) complexity and exact dynamics of learning algorithms in the highdimensional setting.
You can view my CV here if you are interested in more details.
You can view my thesis titled: Structure and complexity in nonconvex and nonsmooth optimization.
PAPERS
 C. Paquette and E. Paquette. Dynamics of Stochastic Momentum Methods on Largescale, Quadratic Models. (2021) (to appear at NeurIPS 2021), arXiv pdf
 C. Paquette, K. Lee, F. Pedregosa, and E. Paquette. SGD in the Large: Averagecase Analysis, Asymptotics, and Stepsize Criticality. Proceedings of Thirty Fourth Conference on Learning Theory (COLT) (2021) no. 134, 35483626, pdf
 C. Paquette, B. van Merrienboer, F. Pedregosa, and E. Paquette. Halting time is predictable for large models: A Universality Property and Averagecase Analysis. (2020) (to appear in Found. Comput. Math.), arXiv pdf
 S. Baghal, C. Paquette, and SA Vavasis. A termination criterion for stochastic gradient for binary classification. (2020) (submitted), arXiv pdf
 C. Paquette and S. Vavasis. Potentialbased analyses of firstorder methods for constrained and composite optimization. (2019) (submitted), arXiv pdf
 C. Paquette and K. Scheinberg. A stochastic linesearch method with convergence rate. SIAM J. Optim. (30) (2020) no. 1, 349376, doi:10.1137/18M1216250, arXiv pdf
 D. Davis, D. Drusvyatskiy, K. MacPhee, and C. Paquette. Subgradient methods for sharp weakly convex functions. J. Optim. Theory Appl. (179) (2018) no. 3, 962982, doi:10.1007/s1095701813728, arXiv pdf
 D. Davis, D. Drusvyatskiy, and C. Paquette. The nonsmooth landscape of phase retrieval. IMA J. Numer. Anal. (40) (2020) no.4, 26522695, doi:10.1093/imanum/drz031, arXiv pdf
 C. Paquette, H. Lin, D. Drusvyatskiy, J. Mairal, and Z. Harchaoui. Acceleration for GradientBased NonConvex Optimization. 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2018), arXiv pdf
 D. Drusvyatskiy and C. Paquette. Efficiency of minimizing compositions of convex functions and smooth maps. Math. Program. 178 (2019), no. 12, Ser. A, 503558, doi:10.1007/s1010701813113, arXiv pdf
 D. Drusvyatskiy and C. Paquette. Variational analysis of spectral functions simplified. J. Convex Anal. 25(1), 2018. arXiv pdf
INVITED TALKS
I have given talks on the research above at the following conferences:

Halting Time is Predictable for Large Models: A Universality Property and Averagecase Analysis,
Operations Research Center Seminar, Sloan School of Management, Massachusetts Institute of Technology (MIT),
Boston,
MA
(February
2021,
upcoming)

Halting Time is Predictable for Large Models: A Universality Property and Averagecase Analysis,
colloquium,
Operations
Research
and
Information
Engineering
(ORIE,
Cornell
University,
Ithaca,
NY
(February
2021)

Halting Time is Predictable for Large Models: A Universality Property and Averagecase Analysis,
Applied
Mathematics
Seminar,
McGill University
Montreal,
QC (January 2021)

Halting Time is Predictable for Large Models: A Universality Property and Averagecase Analysis,
Optimization
and
ML
Workshop,
Canadian Mathematical Society (CMS)
Montreal,
QC
(December
2020)
 Halting Time is Predictable for Large Models: A Universality Property and Averagecase Analysis, UW Machine Learning Seminar, Paul G. Allen School of Computer Science, University of Washington, Seattle, WA (November 2020)
 Halting Time is Predictable for Large Models: A Universality Property and Averagecase Analysis, Industrial Engineering, University of Pittsburgh, Pittsburgh, PA (November 2020)
 Halting Time is Predictable for Large Models: A Universality Property and Averagecase Analysis, Soup and Science, McGill University, Montreal, QC (September 2020)
 Halting Time is Predictable for Large Models: A Universality Property and Averagecase Analysis, Tutte Colloquium, Combinatorics and Optimization Department, University of Waterloo, Waterloo, ON (June 2020)
 Halting Time is Predictable for Large Models: A Universality Property and Averagecase Analysis, colloquium, Center for Artificial Intelligence Design (CAIDA), University of British Columbia, Vancouver, BC (June 2020)
 An adaptive line search method for stochastic optimization, Conference on Optimization, Fields Institute for Research in Mathematical Science, Toronto, ON (November 2019)
 Algorithms for stochastic nonconvex and nonsmooth optimization, St. Louis University Mathematics and Statistics Colloquium, St Louis, MO (November 2019)
 Stochastic Optimization: summer school talk , ADSI Summer School on Foundations of Data Science Seattle, WA (Aug. 2019); My notes can be found here
 Algorithms for stochastic problems lacking convexity or smoothness , Mathematics Colloquium, Ohio State University Columbus, OH (Feb. 2019); My slides can be found here
 Algorithms for stochastic problems lacking convexity or smoothness , Applied Mathematics Colloquium, Brown University Providence, RI (Feb. 2019); My slides can be found here
 Algorithms for stochastic problems lacking convexity or smoothness , Applied Mathematics Seminar, McGill University Montreal, QC (Feb. 2019); My slides can be found here
 Algorithms for stochastic problems lacking convexity or smoothness , Applied math and analysis seminar, Duke University Durham, NC (Jan. 2019); My slides can be found here
 Algorithms for stochastic problems lacking convexity or smoothness , Google Brain, Montreal Montreal, QC (Jan. 2019); My slides can be found here
 An adaptive line search method for stochastic optimization, Cornell ORIE's Young Researchers Workshop (2018), Ithaca, NY (Oct. 2018); My slides can be found here
 New analysis of adaptive stochastic optimization methods via supermartingales Part II: Convergence analysis for stochastic line search, Lehigh University DIMACS (2018), Bethlehem, PA (August. 2018); My slides can be found here
 Generic Acceleration Schema Beyond Convexity , INFORMS annual meeting (2017), Houston, TX (Oct. 2017); My slides can be found here
 Minimization of convex composite, Lehigh University Optimization Seminar, Bethlehem, PA (Sept 2017); My slides can be found here
 Proximal methods for minimizing convex compositions, SIAMoptimization, Vancouver, BC (May 2017)
 Catalyst for Gradientbased Nonconvex Optimization, InriaGrenoble Seminar, Grenoble (April 2017)
 Generic acceleration schema beyond convexity, Optimization and Statistical Learning, Les Houches (April 2017)
 Proximal methods for minimizing convex compositions, West Coast Optimization Meeting, University of British Columbia (September 2016); My slides can be found here
TEACHING
Current Course
 Math 315 (Ordinary Differential Equations) Website
 Math 597 (Topics course: Convex Analysis and Optimization) Website
Past Courses
I have taught the following courses:
 McGill University, Mathematics and Statistics Department
 Math 560 (graduate, instructor): Numerical Optimization, Spring 2021
 Math 315 (undergraduate, instructor): Ordinary Differential Equations, Fall 2020
 Lehigh University, Industrial and Systems Engineering
 ISE 417 (graduate, instructor): Nonlinear Optimization, Spring 2018
 University of Washington, Mathematics Department
 Math 125 BC/BD (undergraduate, TA): Calculus II Quiz Section, Winter 2017
 Math 307 E (undergraduate, instructor): Intro to Differential Equations, Winter 2016
 Math 124 CC (undergraduate, TA): Calculus 1, Autumn 2015
 Math 307 I (undergraduate, instructor): Intro to Differential Equations, Spring 2015
 Math 125 BA/BC (undergraduate, TA): Calculus 2, Winter 2015
 Math 307 K (undergraduate, instructor): Intro to Differential Equations, Autumn 2014
 Math 307 L (undergraduate, instructor): Intro to Differential Equations, Spring 2014