We are primarily focused on Biostatistics with particular emphasis on statistical modeling and analysis of genomic data. We aim to develop novel statistical methods for genetic data analysis to better understand the genetic architecture of complex phenotypes. We have improvised and implemented Bayesian and classical statistics techniques, such as the Dirichlet process, mixture models, spike and slab priors, two-stage regressions, and high dimensional regressions (e.g., Lasso), measurement error theory, bootstrapping for high dimensional regressions, etc. Our lab is hosted by the Department of Mathematics at the Indian Institute of Technology (IIT) Hyderabad.

Main research facilities: rigorous training in applied statistics, cloud computing access to UK Biobank, and access to local computing servers.

News and update:

• A paper first authored by Arunabha has been published in Statistics in Medicine.

• A paper jointly first authored by Arunabha has been published in the Journal of the National Cancer Institute (JNCI).