Dr. Gill is currently Distinguished Professor of Government, Professor of Statistics, and a Member of the Center for Behavioral Neuroscience, all at American University.
He has done extensive work in the development of Bayesian hierarchical models, nonparametric Bayesian models, elicited prior development from expert interviews, as well in fundamental issues in statistical inference. Dr. Gill has extensive expertise in statistical computing, Markov chain Monte Carlo (MCMC) tools in particular.
Most sophisticated Bayesian models for the social or medical sciences require complex, compute-intensive tools such as MCMC to efficiently estimate parameters of interest. Gill is an expert on these statistical and computational techniques and uses them to contribute to empirical knowledge in the biomedical and social sciences. His current theoretical work builds logically on his prior applied work and adds opportunities to develop new hybrid algorithms for statistical estimation with multilevel specifications and complex time-series and spatial relationships. Current applied work includes: mode effects in polling and surveys, applied Bayesian meta-analysis, Bayesian lassos, energetics and cancer, long-term mental health outcomes from children's exposure to war, pediatric head trauma, and human circulation.