Assistant Professor
NCI-Designated Cancer Center
My goal is to develop large-scale datasets and artificial intelligence as primary scientific instruments, not simply auxiliary tools.
Instead of focusing on individual genes or pathways in isolation, we want to create programs that integrate massive, multi-modal amounts of information, including single-cell and spatially resolved molecular profiles generated across many conditions and disease states, to reconstruct how cells encode identity, adapt to stress and coordinate behavior within tissues.
This data-centric framework helps move science from only descriptive biology to predictive interrogation. By learning from thousands to millions of cellular measurements simultaneously, emerging models capture both common principles and rare but critical cell states that drive therapy resistance and disease progression.
These representations make it possible to simulate how targeted perturbations are likely to reshape cellular programs and tissue organization, guiding experimental design and drug discovery toward the most informative and actionable directions.
