My statistical research interests lie in developing new, and tailoring existing statistical methods to efficiently process and analyze high-dimensional genomic data. In particular, my areas of interest include applications of dimension reduction, resampling methods, supervised and unsupervised classification, and regression techniques applied to the data arising from cancer studies. I am interested in methods for all stages of high-throughput data lifecycle: starting from preprocessing and ending with the integrated inference across different studies and data types. My latest interests include analyzing nextgen sequencing and metabolomics data. My research work in statistics and bioinformatics is largely inspired by collaborations with both basic scientists and clinicians.