Computational Oncology studies that integrate clinical, genomic, and imaging data to explore the dynamics of tumor heterogeneity and evolution in response to treatment.
Tumors are continually evolving collections of cells, characterized by a dynamic interplay among heterogeneous sub-clonal populations that expand and contract under innate and imposed selective pressures. My research couples deep learning imaging techniques with high-resolution molecular assays and matched clinical information to analyze tumors through a framework of evolution. We study the impact of treatment on the dynamics of the tumor ecosystem to elucidate resistance mechanisms and identify potential targets for intervention.
M.S., Computer and Systems Sciences
M.S., Computational Biology & Bioinformatics
Ph.D., Computational Biology & Bioinformatics
Yale-New Haven Hospital
General Surgery Resident
Memorial Sloan Kettering Cancer Center
Complex Surgical Oncology Fellow
University of Connecticut
Assistant Professor of Surgery