Assistant Professor, Electrical Engineering and Computer Science
My primary research interests lie at the intersection of machine learning, data mining, and healthcare. Within machine learning, I am particularly interested in time-series analysis, transfer/multitask learning, causal inference, and learning intelligible models. 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 patient 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.