Sushmita Roy's research focuses on developing and applying computational methods based on statistical machine learning for the inference, analysis and interpretation of molecular regulatory networks that control gene expression patterns in living cells. I am interested in identifying networks under different environmental, developmental, disease and evolutionary contexts, comparing these networks across contexts, and understanding how changes in the regulatory network affect overall organism state. Our approaches harness the increasingly available repertoires of high-throughput molecular measurements and are applicable to diverse yeast, plant and mammalian systems. Our methods can be used to study a variety of biological processes ranging from infectious disease, cell fate specification, and host microbe interactions, that all have a shared goal to understand the underlying regulatory network. Four major directions of research in my group are to develop methods for: (a) Genome-scale transcriptional regulatory network inference, (b) Inference of cell-type specific regulatory networks on developmental lineages, (c) Evolutionary analysis of gene regulatory networks and regulatory genomic datasets, (d) Understanding 3D Genome organization.