Dipak K. Dey

Bio

Prof. Dipak K. Dey is a Board of Trustees Distinguished Professor in the Department of Statistics at the University of Connecticut (UConn). A prominent statistician, he is most known for his pioneering work in Bayesian analysis, decision science, and model selection. With over 320 research articles published in reputable national and international journals, and over 10 books and edited volumes to his name, he has made a significant impact on the field of statistics and data science. Prof. Dey earned his Bachelor's and Master's degrees in Statistics from the Indian Statistical Institute and a Ph.D. in Statistics from Purdue University, under the supervision of Prof. Jim Berger. Before joining the UConn in 1985, Prof. Dey held academic positions at Stanford University, the University of Kentucky, and Texas Tech University, and has also held visiting appointments at several universities and institutions worldwide. He is a fellow of the American Association for the Advancement of Science, the American Statistical Association, the Institute of Mathematical Statistics, the International Society for Bayesian Analysis, and the International Statistical Institute, and has received numerous awards and honors for his work. Prof. Dey is a dedicated mentor to students and colleagues. He has supervised over 45 Ph.D. students and has collaborated with practically every colleague in his department in a career spanning more than 40 years, helping tenure-track faculty and Ph.D. students achieve their professional goals. His broad range of interest and expertise, combined with his devotion to his peers has been instrumental to many in the statistical community. One of his many awards was the Marth Award for mentorship at UConn. Prof. Dey has held multiple leadership positions. He was for fourteen years as department head and for five years as the Associate Dean for Research in the College of Liberal Arts and Sciences at UConn. He has been a highly effective leader, while maintaining an extremely active academic career. Among his many accomplishments in his leadership roles, Prof. Dey oversaw an expansion of the statistics department; he started a Biostatistics program, a partnership with UConn Health; he developed collaborative research program with various other schools, colleges and Institutes (e.g., CHIP, IMS, Center for Environmental Science); and he initiated corporate partnership with Pfizer, CIGNA and Travelers. Prof. Dey has served as an associate editor for several statistical journals, including the Journal of the American Statistical Association (1997-1999), the Journal of Statistical Planning and Inference (2001-2003), and is currently the editor- in- chief of Sankhya, series A and series B, official journal of Indian Statistical Institute, since 2016 which is the second oldest journal in Statistics in the world. Prof. Dey has a clear long-term vision for the field of statistics and data science based on his many years of experience as a researcher, mentor, teacher, and interdisciplinary collaborator. He believes that statistics should be introduced from an early age in schools in order to develop statistical thinking and to learn how to apply them in real-life situations. His goal for the profession is to make it broadly understood, much beyond STEM programs. At the college level, data science education must include statistics, mathematics, and computational skills in order to train students who plan to pursue a professional career as data scientists in industry, government, and academia.

Honors and Professional Activities

Editorial Service

Research

Books Published

1.      Practical Nonparametric and Semiparametric Bayesian Statistics (also at Amazon.com)
Springer-Verlag Lecture Notes Series, Volume 133,1998 (with P.Muller and D.Sinha)

2.      Generalized Linear Models: a Bayesian Perspective,
Marcel Dekker Inc., 2000 (with S.Ghosh and B.Mallick)

3.      A First Course in Linear Model Theory
Chapman and Hall,2001 (with N.Ravishanker) pdf file

4.      Handbook of Statistics, 25: Bayesian Thinking, Modeling and Computation
Elsevier (with C.R. Rao)

5.      Bayesian Statistics and Its Application, Anamaya, 2006 (with S.K. Upadhyay, U.Singh)

6.      Bayesian Bioinformatics. Chapman & Hall CRC (with S. Ghosh and B.K. Mallick), 2010.

7.      Frontiers of Statistical Decision Making and Bayesian Analysis. Springer (with M._H. Chen, P. Mueller, D. Sun and K. Ye). 2010.

8.      Essential Bayesian Models 1st Edition . Chapman & Hall/CRC Texts in Statistical Science ( C.R. Rao (Author), Dipak K. Dey (Author) ). 2011.

9.      Current Trends in Bayesian Methodology with Applications 1st Edition . Chapman & Hall/CRC Texts in Statistical Science ( Satyanshu K. Upadhyay (Editor), Umesh Singh (Editor), Dipak K. Dey (Editor) ). 2015.

10.      Extreme Value Modeling and Risk Analysis: Methods and Applications 1st Edition . Chapman & Hall/CRC Texts in Statistical Science ( Dipak K. Dey (Editor), Jun Yan (Editor) ). 2015.

11.      A First Course in Linear Model Theory (Chapman & Hall/CRC Texts in Statistical Science) 2nd Edition . Chapman & Hall/CRC Texts in Statistical Science ( Nalini Ravishanker (Author), Zhiyi Chi (Author), Dipak K. Dey (Author) ). 2021.

Teaching

  • Courses taught at University of Connecticut
    • Descriptive Statistics
    • Applied Regression Analysis
    • Design of Experiments
    • Statistical Methods
    • Intermediate Probability Theory
    • Statistical Decision Theory
    • Multivariate Analysis
    • Reliability and Statistical Quality Control
    • Theory of Estimation
    • Statistical Inference
    • Biostatistics
    • Bayes Theory
    • Longitudinal Data Analysis
    • Linear Models I
    • Linear Models II
    • Bayesian Data Analysis

 

Last updated: Saturday, June 10, 2022