Arnesh M. Sujanani
University of Waterloo. Department of Combinatorics and Optimization. Faculty of Mathematics.
Office: MC 5461
200 University Avenue West
Waterloo, Ontario
I am a postdoctoral fellow at University of Waterloo’s Department of Combinatorics and Optimization where I am advised by Henry Wolkowicz (24-26), Saeed Ghadimi (24-26), Walaa Moursi (24-26), and Stephen Vavasis (24-25). I am interested broadly in optimization for machine learning, continuous optimization, semidefinite programming, scientific computing, and numerical linear algebra. The main focus of my research is to develop scalable, fast, and parameter-free first order-methods for large-scale optimization problems, particularly those arising in machine learning and data science.
Previously, I received my PhD in Operations Research in Summer 2024 from Georgia Tech ISyE where I was advised by Renato D.C. Monteiro. I also received my M.S. in Mathematics in Spring 2024 from Georgia Tech and a B.S. in Applied and Computational Mathematics in Spring 2019 from University of Southern California.
news
| Apr 29, 2026 | The paper ``A low-rank augmented Lagrangian method for large-scale semidefinite programming based on a hybrid convex-nonconvex approach’’ has been published online in Mathematical Programming. |
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| Oct 14, 2025 | The paper ``cuHALLaR: A GPU Accelerated Low-Rank Augmented Lagrangian Method for Large-Scale Semidefinite Programming’’ has been submitted to Mathematical Programming Computation. |
| Sep 14, 2025 | The paper ``Asymptotically Fair and Truthful Allocation of Public Goods’’ has been accepted to Journal of Artificial Intelligence Research. |
| Jun 09, 2025 | The paper ``Efficient Parameter-Free Restarted Accelerated Gradient Methods for Convex and Strongly Convex Optimization’’ has been published online in Journal of Optimization Theory and Applications. layout: post date: 2025-09-14 07:59:00-0400 inline: true related_posts: false — The paper ``Asymptotically Fair and Truthful Allocation of Public Goods’’ has been accepted to Journal of Artificial Intelligence Research. |
