BS Mathematics, and Engineering Sciences-Electrical, Yale University 2011
PhD Applied Mathematics, Massachusetts Institute of Technology (MIT) 2016
I design and analyze optimization and Markov Chain Monte Carlo sampling algorithms, with provable runtime, robustness, and privacy guarantees for applications in Machine Learning, Data Science, and Statistics. In doing so, I aim to introduce new mathematical tools from physics and geometry to the design and analysis of optimization and sampling algorithms used in ML.
I am especially interested in Markov chain and stochastic gradient-based optimization and sampling algorithms. These algorithms are used to rapidly explore a high-dimensional or non-convex function or probability distribution in applications such as deep learning and Bayesian statistics. I am also very interested in the application of these algorithms to problems in machine learning, differential privacy, data science, theoretical computer science, and statistics.