My research interests are in the development and application of statistical methods to genomic data from population or family samples, or experimental cell lines. The most notable features of such data include ultra-high dimensionality, complex dependency, sparsity, heterogeneity, and incompleteness. My methodological contributions aim at providing more suitable statistical models, analytical methods, and efficient computational approaches that accommodate specific features of data. My research is typically motivated by collaborative projects with colleagues in the medical sciences; we consider a host of genomic data types, including genomic variations, gene expression, methylation, chromatin interactions, and metagenomic samples. Over the past twenty years, I have collaborated with researchers to understand the genetic basis to a number of complex diseases, including cancer, multiple sclerosis, tuberculosis, and diabetes.