I obtained my PhD in Applied Mathematics from Cornell University, where I was advised by Damek Davis. Before going to Cornell I completed my MSc in Mathematics and two BS in Mathematics and Systems and Computing Engineering at Los Andes University. There I was co-advised by Mauricio Junca and Mauricio Velasco.
Broadly speaking, I am interested in the beautiful interplay between continuous optimization, geometry, and statistics and its applications to data science, machine learning and signal processing. In case you would like to take a look, here is my CV.
2021-06-17 Damek Davis, Dmitriy Drusvyatskiy, and I just uploaded a new paper called Escaping strict saddle points of the Moreau envelope in nonsmooth optimization.
2021-06-09 New paper entitled Practical Large-Scale Linear Programming using Primal-Dual Hybrid Gradient in collaboration with David Applegate, Oliver Hinder, Haihao Lu, Miles Lubin, Brendan O'Donoghue, and Warren Schudy.
2021-05-17 Ben Grimmer and I just uploaded a new paper entitled Optimal Convergence Rates for the Proximal Bundle Method.
2021-02-09 David Applegate, Haihao Lu, Miles Lubin, and I just uploaded a new paper entitled Infeasibility detection with primal-dual hybrid gradient for large-scale linear programming.
2020-11-05 Our paper on Low-rank matrix recovery with composite optimization has been accepted for publication in Foundations of Computational Mathematics.
2020-09-25 Our paper on Efficient Clustering for Stretched Mixtures: Landscape and Optimality was accepted for publication in NeurIPS 2020.
2020-08-31 I started an internship at Google research in
NYC! my house.
2020-07-30 Our paper on
2020-03-22 Kaizheng Wang, Yuling Yan, and I just uploaded a new paper entitled Efficient Clustering for Stretched Mixtures: Landscape and Optimality.
2020-02-03 Katya Scheinberg and I are organizing the Optimization Seminar. The topic this semester is Robustness.