I am broadly interested in probability theory, stochastic processes, and their applications in machine learning. A focus area of my research is the design and analysis of MCMC algorithms. My Ph.D. advisor is Dr. David A. Levin.

During my Ph.D., I have analyzed several sampling algorithms. I have a keen interest in designing randomized algorithms that converge faster than the state of the art, especially for state spaces that grow exponentially.

Currently, I am excited to collaborate with Dr. Dheeraj M. Nagaraj at Google DeepMind and Dr. Anant Raj at Indian Institute of Science on a problem at the intersection of sampling and optimization in the Wasserstein space.