I am a Ph.D. student in the Computer Science department at Cornell University, where I have the great fortune to be advised by Prof. Karthik Sridharan and Prof. Robert D. Kleinberg. I am interested in theoretical aspects of Machine Learning and Computer Science. Recently, I have been working on understanding non-convex learning from a stochastic optimization viewpoint. I am also interested in theoretical aspects of reinforcement learning (RL).
I completed my undergraduate from Indian Institute of Techonology Kanpur (IIT Kanpur), India in 2016 and, after that, spent a year at Google Research as a part of the Brain Residency program (now called AI Residency).
SGD: The role of Implicit Regularization, Batch-size and Multiple Epochs
with Satyen Kale and Karthik Sridharan.
Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations
with Christoph Dann, Yishay Mansour, Mehryar Mohri, and Karthik Sridharan.
NeurIPS 2021. (Spotlight)
Remember What You Want to Forget: Algorithms for Machine Unlearning
with Jayadev Acharya, Gautam Kamath, and Ananda Theertha Suresh.
NeurIPS 2021. Short version at TPDP 2021 - Theory and Practice of Differential Privacy.
Neural Active Learning with Performance Guarantees
with Pranjal Awasthi, Christoph Dann, Claudio Gentile, and Zhilei Wang.
Second-Order Information in Non-Convex Stochastic Optimization: Power and Limitations
with Yossi Arjevani, Yair Carmon, John C Duchi, Dylan J Foster and Karthik Sridharan
COLT 2020. Honorable mention for best talk award at NYAS ML symposium 2020.
The Complexity of Making the Gradient Small in Stochastic Convex Optimization
with Dylan Foster, Ohad Shamir, Nathan Srebro, Karthik Sridharan and Blake Woodworth
COLT 2019. Best Student Paper Award.
Uniform Convergence of Gradients for Non-Convex Learning and Optimization
with Dylan Foster and Karthik Sridharan
NeurIPS 2018. Short version at ICML 2018 Nonconvex Optimization workshop.
A Brief Study of in-domain Transfer and Learning from Fewer Samples using a Few Simple Priors
with Marc Pickett and James Davidson
ICML 2017 workshop: Picky Learners - Choosing Alternative Ways to Process Data.
Awarded the second best paper prize among the workshop submissions.