معرفی
Trambak Banerjee is an Assistant Professor in the Analytics, Information, and Operations academic area at the University of Kansas School of Business. His research focuses on developing rigorous statistical methods for analyzing modern high-dimensional data where issues such as unobserved heterogeneity and noise accumulation impede inferences using standard methods.
Education:
- Ph.D. in Business Administration (Statistics), Marshall School of Business, University of Southern California, 2020
- M.S. in Mathematical Finance, University of Oxford, 2015
- M.S. in Statistics, Indian Statistical Institute, Kolkata, 2006
- B.S. in Statistics, St. Xavier's College, University of Calcutta, 2004
Dr. Banerjee's research interests span multiple domains including Shrinkage Estimation, Empirical Bayes Prediction, High Dimensional Penalized Likelihood Methods, and statistical applications in Virology, Consumer Behavior, and Marketing. His industry experience in financial services has significantly influenced his research approach, often connecting methodological development to concrete applied problems. His work bridges theoretical statistics with practical applications in health, marketing, and finance.
His publication record demonstrates expertise in developing novel statistical frameworks for complex data problems, with a particular focus on nonparametric methods, empirical Bayes estimation, and testing procedures for heterogeneous data. His recent work includes significant contributions to statistical methods for single-cell virology, shrinkage prediction under complex covariance structures, and joint modeling approaches for user behavior in digital platforms.
Dr. Banerjee has developed several software packages to implement his methodological contributions, including truh for nonparametric two-sample testing under heterogeneity, cezij for constrained zero-inflated joint modeling, and several R packages for adaptive shrinkage and clustering procedures. His work combines theoretical rigor with practical implementation, making his methods accessible to researchers across multiple disciplines.
