Robert Gramacy studies Bayesian modeling methodology, statistical computing, Monte Carlo inference, nonparametric regression, sequential design, and optimization under uncertainty. His application areas of interest include spatial data, sequential computer experiments, ecology, epidemiology, finance, and public policy. Having trained as an engineer, he believes in the importance of releasing high quality open source software for new statistical methodologies. His software packages for R include the widely used tgp package for nonparametric regression.
Gramacy has taught at both the undergraduate and graduate levels. Prior to joining Chicago Booth in 2010, he was a lecturer in the Statistical Laboratory at the University of Cambridge and a fellow of Jesus College. He also was a visitor in the Statistics and Applied Probability Department at the University of California, Santa Barbara.
Gramacy earned four degrees from the University of California, Santa Cruz. In 2001, he was awarded a BA (honors) in mathematics and a BSc (highest honors) in computer science. In 2003, he earned a MSc in computer science and in 2005 a PhD in applied mathematics and statistics. He was honored with the Savage Award in 2006 for his PhD thesis, “Bayesian Treed Gaussian Process Models.”
Gramacy joined the University of Chicago faculty in 2010.