I am a third year Ph.D. student in the Algorithms and Foundations Group at NYU. My advisors are Christopher Musco and Chinmay Hegde.
I completed MS from UMass with Cameron Musco as my advisor. Prior to that, I worked as a Strat at Goldman Sachs and obtained my undergraduate degree from IIIT Hyderabad.
My research centers around algorithms for data-limited problems. Specifically, questions that interest me are:
All author names are in alphabetical order, unless denoted by †.
† Improved Bounds for Agnostic Active Learning of Single Index Models
Aarshvi Gajjar , Xingyu Xu, Chinmay Hegde and Christopher Musco
Under review, short version at RealML Workshop NeurIPS, 2023
Active Learning for Single Neuron Models with Lipschitz
Non-Linearities
Aarshvi Gajjar, Chinmay Hegde and Christopher Musco
AISTATS, 2023, short version Spotlight at DLDE Workshop, NeurIPS 2022
Subspace Embeddings under Nonlinear Transformations
Aarshvi Gajjar, Cameron Musco
ALT (Conference on Algorithmic Learning Theory), 2021
Contact. [firstname] 'at' nyu 'dot' edu