My research is focused on the design and analysis of fast algorithms for machine learning, optimization and data science. 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, data science, theoretical computer science, and statistics.  I am looking forward to working with students at the PhD, masters, and undergraduate levels who are interested in the mathematical, computational, or statistical aspects of machine learning and data science.  I am currently teaching Statistical Methods for Data Science (DS502/MA 543), and I look forward to teaching both undergraduate and graduate courses in Mathematics and Data Science at WPI.