Our research uses computational and statistical modeling to understand the genetic and epigenetic bases of gene regulation in the context of several important systemic, infectious, and immune-related diseases. We work on bringing systematic and unbiased approaches to help develop and test specific hypotheses in human genetics, molecular biology, and immunology. We are particularly interested in the analysis and modeling of the 3D genome organization from high-throughput chromatin conformation capture data to understand how changes in this 3D structure affect outcomes such as development, differentiation, and disease progression. Our lab develops broadly used computational methods based in statistics, graph theory, data mining, and machine learning for the analysis of high-throughput data sets. We also have ongoing interests in systems-level analysis and reconstruction of regulatory networks, inference of enhancer-promoter contacts, predictive models of gene expression, analysis of single-cell data, as well as integrative, comparative, and high-resolution analysis of chromosomes conformation data such as Hi-C and HiChIP.