My research in applied statistics focuses on methods for causal inference using large, administrative datasets, primarily with applications in learning sciences and social sciences. I have developed and worked on methods combining machine learning with design-based analysis of randomized trials and matched observational studies, principal stratification and mediation analysis using log data from intelligent tutoring systems, and regression discontinuity designs. In my teaching and mentoring, I try to present good statistical practice as central to rigorous science, and I am looking forward to teaching, mentoring, and working alongside WPI students.