I am a third year PhD candidate in Statistics co-advised by Prof. Gourab Mukherjee and Prof. Wenguang Sun in the department of Data Sciences and Operations at USC Marshall School of Business. I received MS degrees in Statistics and Mathematical Finance from the Indian Statistical Institute and University of Oxford, UK respectively, and a BS degree in Statistics from St. Xavier’s College, Kolkata.
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.
Research Interests: Empirical Bayes methods, Sparse estimation theory, Joint modeling of Longitudinal and Time-to-Event data, Computational Statistics
Publications / Articles in review
Theory and Methods
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. (under review)
A version of this paper won the Best Paper award at the 2017 BAI conference, IIMB
Feature Screening in Large Scale Cluster Analysis.
Banerjee T, Mukherjee G and Radchenko P.
Journal of Multivariate Analysis (2017), Volume 161, Pages 191-212
- Mass Cytometric Analysis of HIV Entry, Replication, and Remodeling in Tissue CD4+ T Cells. With Nadia Roan (UCSF Roan lab).
Cell Reports (2017), Volume 20, Issue 4, 984 - 998
Software and Datasets
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.
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.