I am broadly interested in probability theory, stochastic processes, and their applications in machine learning and AI. My PhD advisor is Dr. David A. Levin

A focus area of my research is the design and analysis of MCMC algorithms. During my PhD, I have analyzed non-reversible and Hamiltonian Monte-Carlo Markov chains. 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. I like using elegant coupling techniques, Fourier-transform based methods for the analysis of rate of convergence of these algorithms.

Currently, I am excited to collaborate with Dr. Daniel Lowd on applying probabilistic methods in adversarial machine learning.