Kshiteej Sheth

I am a third year CS PhD student at EPFL working with Prof. Michael Kapralov. I am broadly interested in algorithms for massive datasets, with a focus on randomized algorithms for large scale numerical linear algebra, high-dimensional data analysis and optimization. I will be spending the Fall of 2024 as an Applied Science intern at Amazon Luxembourg working on large scale network optimization.

Previously, I worked with Prof. Ola Svensson during my MSc on clustering problems. Before that I worked with Prof. Anirban Dasgupta and Prof. Dinesh Garg (IBM Research, Bengaluru) on randomized linear algebra. I also spent a summer at Caltech on a SURF fellowship working with Dr. Ashish Mahabal on deep learning for astronomy.

You can reach me at firstname dot lastname at epfl dot ch.

Publications

Improved Algorithms for Kernel Matrix-Vector Multiplication.
Piotr Indyk, Michael Kapralov, KS, Tal Wagner.
ICML 2024 workshop on Long Context Foundation Models.

Sublinear Time Low-Rank Approximation of Toeplitz Matrices.
Cameron Musco and KS.
SODA 2024.
[arxiv].

Toeplitz Low-Rank Approximation with Sublinear Query Complexity.
Michael Kapralov, Hannah Lawrence, Mikhail Makarov, Cameron Musco and KS.
SODA 2023.
[arxiv].

Towards Non-Uniform k-Center with Constant types of Radii.
Xinrui Jia, Lars Rohwedder, KS and Ola Svensson.
SOSA 2022.
[arxiv].

Fair Colorful k-Center Clustering.
Xinrui Jia, KS and Ola Svensson.
Math. Programming 2021.
Preliminary version in IPCO 2020.
[arxiv][talk].

Improved linear embeddings via Lagrange duality.
KS, Dinesh Garg and Anirban Dasgupta.
Machine Learning, 2019.
[paper].

Deep-learnt classification of light curves.
Ashish Mahabal, KS, Fabian Gieseke, Akshay Pai, S George Djorgovski, Andrew J Drake and Matthew J Graham.
IEEE SSCI 2017.
[arxiv].

Teaching

Service