I am a final year Ph.D. Student at NYU Tandon School of Engineering, advised by Christopher Musco and part of the Algorithms and Foundations Group.
I research problems in mathematical computing from the perspective of theoretical computer science.
In the summer of 2022, I visited Michael Kapralov's group at EPFL and Haim Avron's group at TAU.
Links: Google Scholar, dblp, Github, Zoom Room
My recent publications have looked at:
Of course, I am interested in problems beyond these areas, and if you want to work with me on a problem, send me an email:
New preprint on arXiv: Algorithm-Agnostic Low-Rank Approximation of Operator Monotone Matrix Functions.
Paper accepted at SODA 2024: On the Unreasonable Effectiveness of Single Vector Krylov Methods for Low-Rank Approximation!
I organized a minisymposium on The Matrix-Vector Complexity of Linear Algebra at the first ever SIAM-NNP conference!
New preprint on arXiv: Hutchinson’s Estimator is Bad at Kronecker-Trace-Estimation.
New preprint on arXiv: On the Unreasonable Effectiveness of Single Vector Krylov Methods for Low-Rank Approximation.
I gave two talks at the NYU / UMass Quantum Linear Algebra reading group.
I gave a talk at the BIRS Perspectives on Matrix Computations about my new work on Krylov methods.
I presented Near-Linear Sample Complexity for Polynomial Regression at SODA 2023.
I gave a talk at the TCS Seminar at Purdue in early November to present my new research on the role of block size in Krylov Methods.
New paper accepted at SODA 2023: Near-Linear Sample Complexity for Polynomial Regression! I just gave a talk on it last week Friday at the Grad Student Seminar at CDS (at NYU).
I gave a talk at GAMM ANLA on the role of block size in Krylov Methods for low-rank approximation. A preprint will be available very soon, but until then you can check out my slides for a preview! Slides
I gave a talk at the SIAM Annual Meeting Minisymposium on Matrix Functions, Operator Functions, and Related Approximation Methods. Thanks to Heather, Andrew, and Ke for organizing!
I'm going be presenting Hutch++ this summer at HALG2022, with both a short talk and a poster.
I'm traveling this summer! I'm first in London for HALG2022. Then I'm spending June visiting Haim Avron at TAU, and July visiting Michael Kapralov at EPFL. If you're in the same place at the same time, drop me a line!
I recently organized a mini-conference for NYU CS Theory researchers to present their "Pandemic Papers" in-person. Thanks to everyone who showed up and made it a success! More details here
I'm honored to be awarded the Deborah Rosenthal, MD Award for Best Quals Examination in 2022, for my presentation Towards Optimal Spectral Sum Estimation in the Matrix-Vector Oracle Model.
I'm honored to be a ICLR 2022 Highlighted Reviewer.
|Code available on github Slides using TCS language Slides using Applied Math language
|Code available on github Landscape Poster Portrait Poster 4min Slides 12min Slides 25min Slides 35min Slides 1hr Slides
To date, I have presented every paper I published at the associated conference. This is a list of other talks or presentations I have given.
|Link to relevant paper here. My slides available here.
|Link to the original paper here. My slides available here.
I really enjoy teaching, and have been a TA for a few courses now:
Algorithmic Machine Learning and Data Science, New York University, Fall 2023
Responsible Data Science, New York University, Spring 2023
Algorithmic Machine Learning and Data Science, New York University, Fall 2020
Introduction to Machine Learning, New York University, Spring 2020
Introduction to the Analysis of Algorithms, Purdue University, Fall 2018
Service outside of reviewing:
Organizer for the Minisymposium "The Matrix-Vector Complexity of Linear Algebra" at SIAM-NNP 2023
Organizer for NYU TCS "Pandemic Presentations" Day
Organizer for NYU Tandon Theory Reading Group
Service as a reviewer:
IJCAI 2024 Reviewer
ICLR 2024 Reviewer
NeurIPS 2023 Reviewer
TMLR 2023 Reviewer
ICLR 2023 Reviewer
SODA 2023 External Reviewer
NeurIPS 2022 Reviewer
ICML 2022 Reviewer
STOC 2022 External Reviewer
ICLR 2022 Reviewer*
NeurIPS 2021 Reviewer*
ISIT 2017 External Reviewer
* Denotes Highlighted / Outstanding Reviewer