My primary research interests lie at the intersection of machine learning, data mining, and medicine. Within machine learning, I am particularly interested in time-series analysis, transfer/multitask learning, intelligible models, and causal inference. The overarching goal of my research is to develop the computational methods needed to help organize, process, and transform patient data into actionable knowledge.

My work has applications in modeling disease progression and predicting adverse outcomes. For several years now, I have been focused on developing accurate patient risk stratification approaches that leverage spatiotemporal data, with the ultimate goal of reducing the rate of healthcare-associated infections among patients admitted to hospitals in the US.

In addition to my research in the healthcare domain, I also spend a portion of my time developing new data mining techniques for analyzing player tracking data from the NBA. In general, I enjoy tackling the challenges that develop when working with large complex datasets.



Women in Tech Shown | Scientific American | ACP Hospitalist Article | MIT Tech Review Article | SSAC16 | Invited Talk at Wellesley College: Big Data's Impact in Medicine, Finance, and Sports | SSAC13: To Crash or not to Crash | ESPN TrueHoop TV: Interview with Henry Abbott | ESPN TrueHoop: Commentary | Grantland Interview | NIPS 2012 Spotlight | NIPS Workshops 2011 Spotlight