Hi! I’m a first year PhD student in the CS Theory Group at Stanford. My interests broadly lie at the intersection of probability and theoretical computer science. Less broadly, I like thinking about Markov Chains, the complexity of statistical inference, and algorithmic applications of Wasserstein Gradient Flows.
Prior to grad school, I was a Pre-Doctoral Researcher at Google DeepMind. Even earlier, I was a dazed and confused undergrad at Indian Institute of Technology Kanpur, where I double majored in Electrical Engineering and Math.
Near-Optimal Streaming Heavy-Tailed Statistical Estimation with Clipped SGD
with Dheeraj Nagaraj, Soumyabrata Pal, Arun Suggala and Prateek Varshney
NeurIPS 2024 [Paper]
Provably Fast Finite-Particle Variants of SVGD via Virtual Particle Stochastic Approximation
with Dheeraj Nagaraj
Spotlight at NeurIPS 2023 [Paper]
Oral Presentation at OTML Workshop, NeurIPS 2023
Utilising the CLT Structure in Stochastic Gradient based Sampling : Improved Analysis and Faster Algorithms
with Dheeraj Nagaraj and Anant Raj
COLT 2023 [Paper]
Near Optimal Heteroscedastic Regression with Symbiotic Learning
with Dheeraj Baby, Dheeraj Nagaraj and Praneeth Netrapalli
COLT 2023 [Paper]
Sampling without Replacement Leads to Faster Rates in Finite-Sum Minimax Optimization
with Bernhard Schölkopf and Michael Muehlebach
NeurIPS 2022 [Paper]
Linear Time Streaming Algorithms for Heavy Tailed Statistics
MSR IISc Theory Seminar 2024 Slides
Sampling Through the Lens of Optimization : Recent Advances and Insights
MSR IISc Theory Seminar 2023, EPFL FLAIR Seminar 2024 Slides
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