My research interests include end-to-end research on medical embedded systems and the application of data mining and machine learning algorithms necessary to make personalized, preventative medical treatments possible through advanced health analytics . My background is in embedded systems design, where I studied sensor fusion, reconfigurable architectures and systems, hardware accelerators, and gpu computing. During my Ph.D. I applied data mining and machine learning techniques to these systems to develop a personalized, exercise-level activity-recognition video game with wearable sensors. I am now primarily concerned with the ability to use supervised and unsupervised techniques to learn more about medical prediction and risk-stratification in order to better develop personalized medical systems, prediction models, comparative effectiveness techniques, and combine wearable sensors and other necessary data to make a clinical impact at the system level, provider level, and patient level.