I develop methods, applications, and tools to understand people and optimize their interactions with technology through data-driven user modeling, personalization, and machine learning in large-scale digital environments. My research includes statistical and computational methods for modeling complex user behavior from massive and complex data; developing algorithms for adaptive digital environments, personalized interventions, and user pathways; and building tools to support other researchers in implementing these methods across domains.
This research addresses questions relevant to many domains, including high-dimensional model selection, social network-augmented classification, next-action recommendation, and reproducible research with massive private datasets. To date, my research has evaluated diver settings, from Massive Open Online Courses (MOOCs) and traditional higher education institutions to digital games and large government datasets. The unifying theme of my research is developing tools and methods for robust and reproducible machine learning and applying those tools to drive real-world impact.
I am currently pursuing a PhD in computer science at the University of Washington's Paul G. Allen School of Computer Science & Engineering. Previously, I received a Master of Science in Applied Statistics and a Master of Science in Information from the University of Michigan.