Dr. Deep Mukhopadhyay received his Ph.D. from Texas A&M University under the mentorship of Prof. Emanuel (Manny) Parzen. He previously held positions at Temple University, as an Assistant Professor, and Stanford University, as a visiting Assistant Professor.
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: machine learning, spectral graph analysis, large-scale mode identification for discovery science, unified large-scale inference, 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, thereby allowing us to connect different sub-fields of statistics using one universal language.
I’ve been lucky to have some wonderful students: Doug Fletcher (currently at U.S. Army Cyber Institute, West Point, NY), Kaijun Wang (currently a postdoctoral fellow at Fred Hutchinson, Seattle, WA). I’m always looking for motivated Ph.D. students who are excited about doing fundamental research in Statistics and Data Science. Drop me an email if you are interested.