The faculty members of the unit conduct both the methodological and applied research works. The topics broadly include:

Bayesian Statistics:

Semi-parametric Bayesian modelling, Bayesian variable selection, construction of Bayesian networks, Bayesian multiple hypothesis testing, Bayesian deep neural networks driven by recursive Gaussian processes, Bayesian spatio-temporal modelling.

Robust Statistics:

Robust variable screening and adaptive variable selection for regression in the context of ultra-high dimensional data, robust inference for binary longitudinal data, robust support vector machines, robust clustering methods, and robust estimation of directional mean.

Statistical Image Analysis:

Image restoration (denoising and deblurring) and registration using jump regression analysis nonparametric generalization of tree-based Machine Learning approaches. .

Statistical Pattern Recognition:

A wide variety of problems across many disciplines require classification or clustering techniques. There is a growing interface between statisticians and researchers in computer science, engineering among others that has led to development of very powerful nonparametric techniques. There is a group of scientists in the unit which is very active in this area.

Other areas include Survival Analysis, Nonparametric Statistics, High-Dimensional Statistics, Statistical Analysis, Circular Data Analysis, and Psychometrics.