My research is highly inter-disciplinary and collaborative including biological, computational and statistical components. At the core of my research is to computationally identify and characterize species-specific orphan genes in the human genome. I am interested in understanding the evolutionary origins, functions, and regulation of these orphan genes, particularly in diseases like cancer. This necessitates developing new statistical models and computational tools for the analysis of big heterogeneous RNA-Seq datasets. To enable my research, I have developed MetaOmGraph, a public java tool for interactive visualization and exploratory analysis of big omics datasets and pyrpipe, a python package allowing easy integration of RNA-Seq tools into a reproducible pipeline. Currently, I am developing and applying deep generative learning approaches to model patterns of orphan gene expression in big heterogeneous RNA-Seq data.