Human diseases often have complex causes and effects on the body. Data needed to analyze human diseases is similarly complex and multi-faceted. These data are also often difficult to acquire, leading to relatively limited dataset sizes. These and other challenges necessitate going beyond application of existing computational methods. Thus, we actively engage in computational research, to develop novel computational models, inference algorithms, integrated pipelines, and hardware. To accomplish this, we leverage a variety of advanced computational disciplines, including Bayesian nonparametric statistics, deep learning and control theory.