Assistant professor at the University of Virginia with joint appointments in the Quantitative Psychology program and the School of Data Science.
Broadly builds and implements quantitative tools to estimate, model, and predict network structure, connectivity, and change over time.
Quantitative expertise includes graph theory, control methods, Bayesian estimation, and exponential random graph models.
Substantive expertise spans functional neuroimaging, neurodevelopmental disorders (e.g., autism, ADHD), and natural language processing (e.g., political blog topic analysis, semantic networks).
Currently focused on developing network methods for functional and structural neuroimaging, while also exploring commonalities and unique challenges of network data across disciplines—from sociology and business to psychiatry and medicine.