I am a 5th year Ph.D. student advised by Professor John Guttag in the Data-Driven Inference Group at MIT CSAIL. I did my undergraduate in Applied Mathematics at Harvard University. I completed an internship at Google in Summer 2014. I am interested in using machine learning to improve healthcare, and the role of public policy in shaping research and education in science and technology.
My research focuses on developing models to predict rare adverse events. A primary challenge in clinical risk model development is the scarcity of data relevant to the patient population and task of interest: there are often few data and a large class imbalance. My work leverages knowledge from auxiliary sources (e.g., data from similar patient populations, expert-encoded ontologies) to improve the performance of these risk models.
Recently, I have worked to predict adverse events in patients after cardiac surgery in collaboration with clinicians at the Massachusetts General Hospital and the Brigham and Women's Hospital. In addition, I have worked on predicting risk of adverse events such as mortality and extended length of stay in intensive care subjects using the publicly available MIMIC dataset. I also collaborate with domain experts at the Massachusetts General Hospital Institute of Health Professions to automatically detect and diagnose speech and language impairments in children.
See my C.V. for a complete list of presentations and publications.