Team

Isabel is a physician-scientist in the Reilly Lab whose research integrates 3D genome organization, epigenomic regulation, and machine learning–based interpretation of noncoding variants to uncover the mechanisms underlying human disease. As a clinician, she brings a translational perspective to the lab’s work, connecting computational and mechanistic discoveries to their potential impact on patient care. She is a dedicated, compassionate, and forward-thinking physician who believes that the most meaningful advances in medicine arise from bridging bedside observations with bench-side discovery. Isabel completed her MD/PhD through the UCLA–Caltech Medical Scientist Training Program, earning her PhD in Biochemistry and Molecular Biophysics in the laboratory of Mitchell Guttman at Caltech. There, she investigated the interplay between transcription and 3D genome architecture and contributed to the development of ChIP-DIP and SPIDR, multiplexed technologies that enable the mapping of hundreds of protein interactions with DNA and RNA. She is now continuing her physician-scientist training at Yale, where she is completing a residency in Internal Medicine followed by a fellowship in Medical Oncology–Hematology. In the Reilly Lab, Isabel applies machine learning approaches to dissect the epistatic effects of noncoding variants (co-mentored by Stephanie Halene, MD, PhD) and to identify pathogenic noncoding variants in patients with cholestatic liver disease (co-mentored by Silvia Vilarinho, MD, PhD). Through these efforts, she aims to advance the understanding of how noncoding genetic variation contributes to human disease and to improve the diagnosis and treatment of patients. Outside of her research and clinical training, Isabel co-leads Yale’s AI and Innovation in Medicine Distinction Pathway, helping to foster the next generation of physicians at the intersection of medicine, data science, and technology.