Networks are Everywhere
Intrinsic connectivity in the brain. Social networks of peer affiliation. Disease transmission. Gut protein interactions. Topic networks across social media. Algorithms that predict stock exchange patterns. What do these all have in common? Networks. Broadly, I build and implement quantitative tools that estimate, model, and predict network structure, connectivity, and change over time. My research focuses on developing and building innovative methods, tools, and R packages for network science and dynamical systems modeling for use in neuroscience, developmental science, clinical science, data science, and beyond.
I am an assistant professor at the University of Virginia with joint faculty appointments in the Quantitative Psychology program and the School of Data Science. My quantitative expertise is in graph theory, control methods, Bayesian estimation, and exponential random graph models. My substantive expertise is in functional neuroimaging, neurodevelopmental disorders (e.g., autism, ADHD), and natural language processing (e.g., political blog topic analysis, semantic networks). Although I am currently focused on building network methods for functional and structural neuroimaging, I am also fascinated by the underlying similarities vs. unique challenges that network data pose across all different types of disciplines--from sociology and business to psychiatry and medicine.
I will not be taking graduate students this coming cycle (2021-2022), although I do plan on taking another graduate student in the following cycle (2022-2023).
Functional Connectivity
Examining the brain on a functional connectivity level.
Network Psychometrics & Social Networks
Modeling complex dependencies using cutting edge statistical methods.
Language Networks
Networks of texts and texts of networks.