Scotland C. Leman

His main research interests include Bayesian statistics on both a theoretical and inferential level; MCMC mixing theory; data augmentation for efficient simulation; and large-scale stochastic modeling. Additionally, he has a strong interest in visualization techniques, which involve Human-Computer-Interaction. More specifically, given visual displays, he is interested in how users can inject feedback so that resulting displays are a merger between the data, visualization model, and the user’s cognitive insights. Such methods prove to be exceedingly useful in exploring relevant information in very high-dimensional spaces.

He earned a master of science degree in computational from Stanford University and his Ph.D. from the Department of Statistical Science at Duke University.