I direct the Machine Learning for Learning Health Systems lab, whose work focuses on developing, validating, and evaluating the effectiveness of machine learning models within health systems. This includes projects such as a machine learning-supported patient educational platform (https://ask.musicurology.com) to support decision-making for patients with urological conditions. In additional to my predictive modeling research, I study patient-facing mobile apps and have published on this topic in Health Affairs, the Journal of General Internal Medicine, and the Clinical Journal of the American Society of Nephrology, among others. I have additional leadership roles that recognize my expertise in machine learning at a local and regional level. I chair the Michigan Medicine Clinical Intelligence Committee, which oversees implementation of predictive models across our health system, and I serve on the Michigan Economic Development Corporation’s Artificial Intelligence Advisory Board, where I contribute to the state of Michigan’s vision on artificial intelligence. I also teach a health data science and machine learning course to over 60 graduate students per year.
My research interests are in data-driven sequential decision making and optimization under uncertainty with applications to medicine. I have a cross-appointment in the School of Medicine and I am a member of the Cancer Center and the Institute for Healthcare Policy and Innovation (IHPI) at University of Michigan. My current research projects are investigating new ways to use longitudinal data to improve decisions related to the using of biomarkers, imaging, and medication for early detection of cancer and prevention of cardiovascular disease.