I am a 4th year Biomedical Informatics Ph.D. Candidate in the Department of Biomedical Data Science (BDS). Co-advised by Profs. Parag Mallick (Radiology) and Sylvia Plevritis (Radiology, BDS), my research broadly aims to better our understanding of spatial systems and their key actors through the lenses of computer vision, spatial statistics, and graph representation learning. Specifically, my thesis work develops novel machine learning and interpretability methods for multi-scale salient object detection (SOD) in multiplexed and megapixel images. Primary applications seek to improve cancer prognostics by identifying histopathological biomarkers of cancer progression in the tumor microenvironment through the use of multiplexed immunofluorescence (mIF) tissue microscopy images. In collaboration with Prof. Pascal Geldsetzer (Medicine), secondary applications focus on global health monitoring by predicting maternal and child health outcomes of Earth’s remote villages through the use of satellite imagery, government surveys, and other geotagging and remote sensing data. While my research is driven by biomedical applications, I am also interested in understanding the theoretical guarantees of our computational methods.
I completed my undergraduate training at University of California, Berkeley in Applied Mathematics and Bioengineering. Outside of research, I am passionate about teaching and mentoring and furthering diversity, equity, and inclusion (DEI) in academia. In my free time, you can find me working out, cooking up a storm, listening to live music, exploring East Bay’s cafés and bookstores, and spending weekends hiking and camping in California’s State and National Parks.