I develop methods, applications, and tools to optimize and accelerate human learning through data-driven user modeling, personalization, and machine learning in large-scale digital environments. My work includes statistical and computational methods for generating dynamic digital environments at Internet scale; applying machine learning and data mining to model complex user behavior in environments with fine-grained, large-scale 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.
My work has addressed diverse questions relevant to many domains, including high-dimensional model selection, social network-augmented classification, next-action recommendation, and reproducible research with massive private datasets. This work has involved domains ranging from MOOCs and traditional higher education to digital games and large government datasets. A key focus area of my work is developing tools and methods for robust and reproducible data science research in both computer science and applied statistics, and in 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.