Dr. Mukhopadhyay works in both theoretical and applied side of Statistical data science. Over the past years, he has been developing a new and exciting discipline–“Nonparametric Data Science” for progressive unification of fundamental statistical learning tools. Under this new framework, significant number of statistical problems have been tackled to date, including network modeling, large-scale mode identification for discovery science, unified multiple testing, nonparametric copula dependence modeling, non-linear time series modeling, high-dimensional k-sample modeling, generalized empirical Bayes modeling, and nonparametric distributed learning for massive data. All of these results show how our general theory acts as an organizing principle for varieties of data analysis endeavors.