alt text  Trambak Banerjee
PhD candidate in Statistics

Email: trambakb[at]
Address: USC Marshall School of Business
308 Bridge Hall
3670 Trousdale Parkway
Los Angeles, CA 90089-0809

I completed my PhD in Business Administration (Statistics) in May 2020 from the Marshall School of Business at the University of Southern California. In Fall 2020, I will be joining the School of Business at the University of Kansas as a tenure-track Assistant Professor - Business Analytics. Prior to joining USC, I have served as a quantitative modeler at FICO and Evalueserve where I have developed statistical models for credit scoring and for estimating banking regulatory capital pertaining to Credit and Operational risk events.

PhD Thesis: Shrinkage Methods for Big and Complex Data Analysis (Advisor: Gourab Mukherjee. Committee: Gareth James, Jinchi Lv, Wenguang Sun, Shantanu Datta)

Current Research Interests: Shrinkage Estimation and Empirical Bayes Prediction, High Dimensional Penalized Likelihood Methods, Statistical applications in Virology, Consumer Behavior and Marketing.

Publications & Articles in review

  1. Improved Shrinkage Prediction under a Spiked Covariance Structure.
    Banerjee T, Mukherjee G and Paul D. (under review)
    R-package: casp

  2. A General Framework for Empirical Bayes Estimation in Discrete Linear Exponential Family.
    Banerjee T, Liu Q, Mukherjee G and Sun W. (under review)

  3. A Nearest-Neighbor Based Nonparametric Test for Viral Remodeling in Heterogeneous Single-Cell Proteomic Data.
    Banerjee T, Bhattacharya B and Mukherjee G.
    Annals of Applied Statistics (2020) (to appear)

  4. Adaptive Sparse Estimation with Side Information.
    Banerjee T, Mukherjee G and Sun W.
    Journal of the American Statistical Association (2019)
    R-package: asus
    Distinguished Student Paper Award: 2019 ENAR Spring meeting
    Runner-up: 2017 IISA annual conference student poster competition

  5. A Large-scale Constrained Joint Modeling Approach For Predicting User Activity, Engagement And Churn With Application To Freemium Mobile Games.
    Banerjee T, Mukherjee G, Dutta S and Ghosh P.
    Journal of the American Statistical Association (2019)
    MATLAB-toolbox: cezij
    Best Paper award: 5th International Conference on Business Analytics and Intelligence, 2017 at IIMB

  6. Discussion of CARS: covariate assisted ranking and screening for large-scale two-sample inference by Cai, Sun and Wang.
    Banerjee T and Mukherjee G.
    Journal of the Royal Statistical Society, Series B (2019), Volume 81, Pages 223-224

  7. Feature Screening in Large Scale Cluster Analysis.
    Banerjee T, Mukherjee G and Radchenko P.
    Journal of Multivariate Analysis (2017), Volume 161, Pages 191-212
    R-package: fusionclust

  8. Mass Cytometric Analysis of HIV Entry, Replication, and Remodeling in Tissue CD4+ T Cells.
    Cell Reports (2017), Volume 20, Issue 4, 984 - 998


  1. asus - An R package that implements the ASUS (Adpative SURE thresholding with Side Information) procedure for estimating a high-dimensional sparse parameter when along with the primary data we can also gather side information from secondary data sources.

  2. fusionclust - An R package for clustering and feature screening in large scale problems. In particular, fusionclust provides the Big Merge Tracker (BMT) and COSCI algorithms for convex clustering and feature screening using an ℓ1 fusion penalty.

  3. casp - An R package for Coordinate-wise Adaptive Shrinkage Prediction in a high-dimensional non-exchangeable hierarchical Gaussian model with unknown location as well as unknown spiked covariance structure (in progress).

  4. cezij - A MATLAB toolbox for simultaneous and hierarchical selection of fixed and random effects in high-dimensional penalized generalized linear mixed models.


  1. Summer 2018 - Instructor for undergraduate Applied Business Statistics (BUAD 310g).

  2. Fall 2018 - Teaching Assistant for graduate Regression and Generalized Linear Models (GSBA 604).