Hello! I am a fourth-year Ph.D. student in CS at NYU, where I am affiliated with 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.
I am interested in developing theoretically sound and practical algorithms for strategic data collection and efficient data sampling in machine learning. This includes topics like active learning, efficient exploration, optimal design, decision making.
Questions interesting to me:
Agnostic Active Learning of Single Index Models with
Linear Sample Complexity
†Aarshvi Gajjar, †Wai Ming Tai, †Xingyu Xu, Chinmay Hegde, Christopher Musco and Yi Li
COLT, 2024
Associated poster for minisymposium on Scientific ML for Scarce Data, SIAM MDS24
Preliminary version: Adaptive Experimental Design and Active Learning Workshop, NeurIPS, 2023
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
† indicates equal contribution.
Contact: [first-name]@nyu.edu.
Website adapted from Gregory Gundersen