I have broad research interests in data science, management, and trustworthy computing. I believe that the analysis of large quantities of health and molecular information has the potential to refine phenotype definitions into significantly more nuanced models, as well as novel clinical concepts, which associate with differential response to interventions. My research has demonstrated that we can make rapid progress in this direction by integrating novel computing infrastructures with statistically-driven methods to learn patterns and test predictive models. However, to maximize the potential for data science in clinical investigations, we must make data available on a broad scale without violating the rights of the people to whom it corresponds. As such, a great deal of my research focuses on the development of multidisciplinary approaches to privacy preservation that draw upon methods from computer science, biomedical knowledge modeling, policy analysis, and economics.