Bio
I’m a postdoc and resident at Memorial Sloan Kettering, where I split my time between treating patients as a Radiation Oncology resident in the Holman Pathway and building computational tools to make clinical cancer data more informative. As a postdoctoral researcher in Computational Oncology, I work with Nikolaus Schultz, Francisco Sanchez-Vega, and Sohrab Shah and lead the Pathology Data Mining team within the Cancer Data Science Initiative.
I got my PhD and MD through the Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program, doing my dissertation in Computational Oncology with Sohrab Shah via MSK-MIND. Before that, I studied Biomedical Engineering at Yale.
The through-line in my work is using machine learning to pull clinically actionable insights out of data that’s already being generated during routine cancer care — pathology slides, sequencing panels, clinical records. I’m especially drawn to digital pathology and multimodal ML, where there’s a massive opportunity to connect what we see under the microscope with what’s happening at the molecular level, and how they impact response to specific therapies. Some of my prior work applied this idea to breast cancer: we built ML to identify patients who stand to benefit from chemotherapy after surgery.
I’m a PI on grants from the Fund for Innovation in Cancer Informatics and aiTDIF, a member of the MSK Entrepreneurship Initiative, and have given talks at AACR: Special Conference on AI and ML and ESMO AI. I was recently awarded the AACR-Margaret Foti Foundation Scholar-in-Training Award for our most recent work in progress.