James G. Scott joined The University of Texas at Austin in 2009. His research focuses on statistical methodology for high-dimensional data sets, with applications in a diverse set of areas spanning the social, physical, and biomedical sciences. Three areas of methodological focus include (1) large-scale multiple testing, anomaly-detection and screening problems, where the rate of false discoveries must be controlled in order to yield viable inferences; (2) inference in sparse models; and (3) the application of data-augmentation theory and algorithms to improve the efficiency of Bayesian inference in large-scale models for discrete data sets. His recent applied work has included collaborations in health care, demography, linguistics, biology, and neuroscience. He is the associate editor for The Annals of Applied Statistics and Journal of Computational and Graphical Statistics. He received the UT System Regents’ Outstanding Teaching Award in 2014.
Fields of Interest
- Bayesian Statistics
- Monte Carlo and MCMC Methods
- Ph.D. in Statistics, Duke University, 2009