Dr. Kundaje's primary research interests are computational biology and applied machine learning with a focus on gene regulation. In the lab, they develop machine learning methods to learn predictive gene regulatory networks from heterogeneous functional genomic data in order to understand the natural dynamics, variation and divergence of gene regulatory mechanisms across cell-types, individuals and species, explore causality and correlation in regulatory responses by analyzing temporal (e.g. differentiation) and perturbation (e.g. drug response and knockdown), detect disruption and disregulation of regulatory pathways in disease by analyzing the molecular effects of natural and disease associated genetic variants, and develop large-scale, statistical data processing pipelines for the analysis of functional genomic data.