Subhadeep (Deep) Mukhopadhyay

Assistant Professor

  • Statistical Science
Office:Alter Hall 335

Dr. Mukhopadhyay works in both theoretical and applied side of Statistical data science. The ultimate goal of
his research is to develop a unified Statistical theory for data analysis that can lead to polyefficient and
versatile algorithms. Keeping the end in mind, he has launched 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: statistical spectral graph
analysis, large-scale mode identification for discovery science, unified multiple testing, nonparametric copula
dependence modelling, non-linear time series modelling, high-dimensional k-sample modelling, generalized
empirical Bayes modelling, and nonparametric distributed learning for massive data.