Trambak Banerjee

I am an Assistant Professor of Analytics, Information and Operations Management (AIO) academic area at the University of Kansas School of Business. I completed my PhD in Business Administration (Statistics) from the Marshall School of Business at the University of Southern California. My research involves the development of rigorous statistical methods for analyzing modern high dimensional data when issues such as unobserved heterogeneity and noise accumulation in these datasets impede inferences using standard methods. The following are my current research interests:

• Nonparametric Empirical Bayes methods,
• Estimation and Prediction in Multivariate Mixed Models, and
• Statistical applications in Health, Consumer Behavior and Marketing.

I am interested in forging new collaborations at KU and exploring research ideas that have a common interest.

Education

• Doctor of Philosophy (PhD) University of Southern California - Marshall School of Business (2015 — 2020)
• Master of Science (MSc) University of Oxford (2013 — 2015)
• Master Of Statistics (MStat) - Biostatistics & Data Analysis Indian Statistical Institute, Kolkata (2004 — 2006)
• Bachelor Of Science (Honors) St. Xavier's College (Autonomous), Kolkata (2001 — 2004)

Companies

• Assistant Professor The University of Kansas School of Business (2020)
• Research Assistant USC Marshall School of Business (2015 — 2020)
• AVP - Customer Insights & Data Analytics Evalueserve (2011 — 2015)
• Lead Consultant FICO (2006 — 2011)

Publications and Preprints

• Nonparametric Empirical Bayes Estimation on Heterogeneous Data. Banerjee T, Fu L, James G and Sun W. (under review).
• Joint Modeling of Playing Time and Purchase Propensity in Massively Multiplayer Online Role Playing Games Using Crossed Random Effects. Banerjee T, Liu P, Mukherjee G, Dutta S and Che H. (under review).
• Rethinking gradient sparsification as total error minimization. Atal Narayan Sahu, Aritra Dutta, Ahmed M. Abdelmoniem, Trambak Banerjee, Marco Canini and Panos Kalnis. NeurIPS (2021).
• Improved Shrinkage Prediction under a Spiked Covariance Structure. Banerjee T, Mukherjee G and Paul D. Journal of Machine Learning Research (2021) 22(180):1−40. R-package: casp
• A General Framework for Empirical Bayes Estimation in Discrete Linear Exponential Family. Banerjee T, Liu Q, Mukherjee G and Sun W. Journal of Machine Learning Research (2021) 22(67):1−46.
• 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), Volume 14, no. 4, Pages 1777-1805.
• 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 (2020), Volume 115, no. 530, Pages 538-554. MATLAB-toolbox: cezij
• Best Paper award: 5th International Conference on Business Analytics and Intelligence, 2017 at IIMB. Adaptive Sparse Estimation with Side Information.
• Banerjee T, Mukherjee G and Sun W. Journal of the American Statistical Association (2020), Volume 115, no. 532, Pages 2053-2067. R-package: asus
• Distinguished Student Paper Award: 2019 ENAR Spring meeting. Runner-up: 2017 IISA annual conference student poster competition. 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.
• 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
• Mass Cytometric Analysis of HIV Entry, Replication, and Remodeling in Tissue CD4+ T Cells. Cell Reports (2017), Volume 20, Issue 4, 984 - 998.