Kshiteej Sheth

I am a second 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.

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.


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

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

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

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

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