Our lab leverages machine learning and experimental genomics to map the gene regulatory networks driving cardiovascular disease to develop network-correcting therapies. We develop novel machine learning models that can be pretrained on large-scale biological data to gain a fundamental understanding of network dynamics that can then improve predictions in a multitude of downstream applications through transfer learning. We apply these approaches to advance our understanding of the regulatory circuitry governing human development and tissue maintenance and to determine how network rewiring drives the progression of cardiovascular disease. We then design innovative network-based screens to identify molecules that correct these disrupted networks back to the normal state, thereby accelerating the development of much-needed treatments for patients.