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 at Stanford University, as a visiting Assistant Professor.
Over the course of the last decade, Dr. Mukhopadhyay has been engaged in developing the research program
SHARP STATISTICS, whose overarching goal is to develop a new general approach to statistical problem solving, enabling a deeper and more unified understanding of the mechanics of data analysis and the science of model building. Under this new framework, a significant number of statistical problems have been tackled to date, including: large-scale inference, decision-making under uncertainty, density sharpening, empirical Bayes modeling, causal inference, time series analysis, spectral graph analysis, conditional density modeling, high-dimensional k-sample modeling, copula dependency modeling, large-scale distributed learning, and beyond.
I’ve been lucky to have some wonderful students: Doug Fletcher (currently at U.S. Army Cyber Institute, West Point, NY), and 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.