I am a 4th year PhD candidate in the Stanford Machine Learning Group co-advised by Andrew Ng and Percy Liang. I am interested in engineering artificial intelligence algorithms for medical applications, and exploring how these algorithms can assist expert medical decision making. I have developed several high-performance ML algorithms for clinical medicine (CheXNet, CheXNeXt, MRNet), curated large datasets that can facilitate advancements of ML methods (SQuAD, MURA, CheXpert), and led several interdisciplinary research teams as part of the AI for Healthcare Bootcamp at Stanford.
My research has been published in medical journals including Nature Medicine and PLOS Medicine, and CS conferences including ACL (best paper award), EMNLP (best paper award), AAAI and CHI. My research has also been covered in the popular press, including NPR, Washington Post, The Economist, WIRED, and MIT Technology Review. Outside research, I enjoy building apps and co-founded Edusalsa, a course planning platform that has been used by over 3000 Stanford students. I’m a Stanford Accel Innovation Scholar ‘18, and a Tau Beta Pi graduate ‘15. Prior to my PhD, I received my B.S. from Stanford in Computer Science with distinction and honors in 2015 and my M.S. also from Stanford in 2018.
I lead the AI For Healthcare bootcamp at Stanford, a selective research program to train Stanford students in both Computer Science and Medicine to do cutting-edge research at the intersection of machine learning for healthcare. Students work in interdisciplinary teams of 3-6 with me and with a faculty in the medical school. I’ve worked with 10 medical school faculty as part of this bootcamp, and trained 49 undergraduate, masters and medical students over the past 15 months.