Human disease emerges through changes in gene regulation, cellular states, and interactions within tissues. Our research asks how these molecular and cellular processes can be reconstructed from genomic measurements, and how genetic variation and perturbations reshape them. Single-cell and spatial omics make it possible to resolve these processes in their biological context, but the data are sparse, heterogeneous, and incomplete. We develop computational models that connect genomic sequence, chromatin accessibility, gene expression, and tissue organization while making those limitations explicit.
Our approach combines AI method development with large-scale human genomics. We build models of regulatory networks and cellular dynamics, integrate measurements across assays and samples, and use computational perturbations to generate testable hypotheses. Work in ENCODE, PsychENCODE, and SCORCH connects these methods to shared genomic resources and disease-focused collaborations. Across the four programs below, we aim to identify which molecular programs are shared across conditions, which are specific to a cell type or tissue context, and which regulatory changes warrant experimental follow-up for therapeutic discovery.