I develop methods, applications, and tools to investigate and support adaptivity, personalization, and learning in large-scale digital environments. More broadly, my work touches on statistical and computational methods for generating dynamic, data-driven digital environments at Internet scale. This work supports machine learning and data mining in environments with fine-grained, large-scale data and has potential applications for adaptive digital environments, personalized interventions and user pathways, reproducible computational research, and applied machine learning.
My work has addressed diverse questions relevant to many domains, including high-dimensional model selection, network-augmented classification, 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.