My research is on scalable methods and flexible models for spatiotemporal statistics and Bayesian machine learning, applied to public policy and social science. I’ve worked on application areas that include public health, orphanhood, deep learning from satellite and street-level imagery, crime, voting patterns, filter bubbles / echo chambers in media, the big data paradox, and the regulation of machine learning algorithms.