Physician scientist (MD/PhD) working on building safe and reliable AI systems for medicine and biotech. During my PhD (Computer Science and Engineering @ UW), I developed methods for AI interpretability and robustness, with applications in computer vision (radiology, dermatology), natural language processing, and biology (bulk/single-cell transcriptomics). Outside of research, I also do consulting work on projects at the intersection of AI/medicine/biology. I'm currently a radiology resident at Stanford.
A selection of projects representative of my research interests.
An evaluation dataset for AI systems intended to benchmark capabilities foundational to scientific research in biology.
A system leveraging large language models and retrieval-augmented generation to provide guideline-grounded clinical management recommendations in oncology.
We used interpretability techniques based on generative image models to audit COVID-19 deep learning classifiers, and proposed changes to dataset construction to improve generalization.