· Mateo Díaz

Mateo Díaz\\(^2\) #

I will join the Department of Applied Mathematics and Statistics at Johns Hopkins University in the Fall of 2023 as an Assistant Professor. If you are planning to apply to our Ph.D. program and would like to work with me, feel free to include my name in your statement of purpose.

About #

I am Postdoctoral Scholar at Caltech hosted by Venkat Chandrasekaran and Joel Tropp. I obtained my PhD in Applied Mathematics from Cornell University advised by Damek Davis. Before Cornell, I completed a MSc in Mathematics and two BS in Mathematics, and Systems and Computing Engineering at Universidad de los Andes. There I was co-advised by Mauricio Junca and Mauricio Velasco. I spent the Fall of 2020 with the Algorithms and Optimization team at Google Research, hosted by Miles Lubin and David Applegate.

Research interests #

I am interested in the beautiful interplay between continuous optimization, geometry, and statistics and its applications to data science, machine learning and signal processing.

Contact #

email: < first_name > dd < at > caltech < dot > edu
office: Annenberg 305
mail: 1200 E. California Blvd., Mail Code 305-16, Pasadena, CA 91125

Publications #

In the pipeline #

Stochastic approximation with decision-dependent distributions: asymptotic normality and optimality
(with J. Cutler and Drusvyatskiy) Submitted, 2022. PDF
Clustering a Mixture of Gaussians with Unknown Covariance
(with D. Davis and K. Wang) Submitted, 2021. PDF
Infeasibility detection with primal-dual hybrid gradient for large-scale linear programming
(with D. Applegate, H. Lu, and M. Lubin) Submitted, 2021. PDF

Published #

Optimal Convergence Rates for the Proximal Bundle Method
(with B. Grimmer) SIAM Journal on Optimization, 2022. PDF
Escaping strict saddle points of the Moreau envelope in nonsmooth optimization
(with D. Davis and D. Drusvyatskiy) SIAM Journal on Optimization, 2022. PDF
Optimization of vaccination for COVID-19 in the midst of a pandemic
(with Q. Luo, R. Weightman, S. T. McQuade, E. Trélat, W. Barbour, D. Work, S. Samanaranayake, B. Piccoli) Networks and Heterogeneous Media, 2022. PDF
Practical Large-Scale Linear Programming using Primal-Dual Hybrid Gradient
(with D. Applegate, O. Hinder, H. Lu, M. Lubin, B. O’Donoghue, and W. Schudy) NeurIPS, 2021. PDF
Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence
(with V. Charisopoulos, Y. Chen, D. Davis, L. Ding, D. Drusvyatskiy) Foundations of Computational Mathematics, 2021. PDF
Efficient Clustering for Stretched Mixtures: Landscape and Optimality
(with K. Wang and Y. Yan) NeurIPS, 2020. PDF
Composite optimization for robust rank one bilinear sensing
(with V. Charisopoulos, D. Davis, and D. Drusvyatskiy) Information and Inference, 2020. PDF
Local angles and dimension estimation from data on manifolds
(with A. Quiroz, M. Velasco) Journal of Multivariate Analysis, 2019. PDF
The nonsmooth landscape of blind deconvolution
Workshop on Optimization for Machine Learning, 2019. PDF
Compressed sensing of data with known distribution
(with M. Junca, F. Rincón and M. Velasco) Applied and Computational Harmonic Analysis, 2018. PDF
In Search of Balance: The Challenge of Generating Balanced Latin Rectangles
(with C. Gomes, R. Le Bras) CPAIOR 2017. PDF