MIT Computational Biology Lab (Kellis Lab)
Computer Science and Artificial Intelligence Laboratory (CSAIL)
Massachusetts Institute of Technology (MIT)
Broad Institute of MIT and Harvard
Hello! I'm a postdoc at MIT CSAIL and the Broad Institute, advised by Manolis Kellis. I also work closely with Rich Caruana at MSR. I completed my PhD in Computer Science and MS in Machine Learning at Carnegie Mellon University, advised by Eric Xing. I received my BS in Computer Science and BS in Mathematics from Penn State in 2015.
I develop machine learning methods to understand complex diseases and advance precision medicine. See here for more details.
I am on the 2023-2024 academic job market.
Office: D-528 Stata Center, MIT
New talk: Excited to present "Contextualized learning for adaptive yet persistent AI in biomedicine" at ETH Zurich, Duke University, University of Wisconsin, Penn State, and University of Colorado!
New talk: Honored to present "Beyond Zero-to-One" at Mt Sinai's AI and Human Health Seminar Series!
New preprint: Our study of Contextualized Networks and implications in cancer is now available on Biorxiv!
New preprint: Contextualized Machine Learning is now available on Arxiv!
New preprint: Contextualized Policy Recovery is now available on Arxiv!
Award: Honored to be selected as a "Rising Star in Data Science" by UChicago & UCSD!
Context-Adaptive Systems (Meta- and Contextualized Learning): How do we build AI agents that adapt to context?
Prior Knowledge as Context: Connecting Statistical Inference to Foundation Models
Interpretable Representations of Complex and Nonlinear Systems: How can we build models that summarize complicated patterns in interpretable ways?
Clinical Tools for Personalized Medicine: How can we analyze real-world evidence to improve care for every patient?