I am a fourth-year Ph.D. student at NYU, where I am a part of the Theoretical Computer Science Group. My advisors are Christopher Musco and Chinmay Hegde.
Previously, I completed my masters from UMass Amherst, where I did my thesis with Cameron Musco. I also worked as a Strat at Goldman Sachs and earned my B.Tech. from IIIT Hyderabad.
My research centers on randomized methods for sample and compute efficient learning. Some questions that interest me are:
Active Learning for Single Neuron Models with Lipschitz
Non-Linearities
Aarshvi Gajjar, Chinmay Hegde and Christopher Musco
AISTATS, 2023
Preliminary version: selected as Spotlight at DLDE Workshop , NeurIPS 2022
Subspace Embeddings under Nonlinear Transformations
Aarshvi Gajjar, Cameron Musco
ALT, 2021
Authors are listed alphabetically, except for those marked with †, indicating equal contribution.
Contact. [firstname]@nyu.edu
Website adapted from Gregory Gundersen