I am broadly interested in probability theory, stochastic processes, and their applications in diverse fields, such as theoretical computer science and machine learning. My Ph.D. advisor is David A. Levin.

A focus area of my research is the analysis of the rate of convergence of ergodic Markov chains. During my Ph.D., I have analyzed non-reversible and Hamiltonian Monte-Carlo Markov chains. I have a keen interest in designing Markov chains that converge faster than the state of the art, especially for state spaces that grow exponentially. I like using elegant coupling techniques, Fourier-transform based methods for the analysis of rate of convergence of these algorithms.

On the applications front, I am interested in connections between Markov Chain Monte Carlo (MCMC) methods and reinforcement learning, optimization. I am also eager to explore analysis of other mathematical and statistical methods used in data science.