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.
Gramacy has taught at both the undergraduate and graduate levels. Prior to joining 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 of the Statistics and Applied Probability department at UC Santa Barbara.
An important aspect of Gramacy’s research is implementation. Trained as an engineer, Gramacy believes that releasing high quality open source software for new statistical methodologies is just as important as putting them in print. His software packages for R include the widely used tgp package for nonparametric regression. This emphasis on software development also defines Gramacy’s teaching style. His lectures regularly include live demonstrations, and he asks students to demonstrate understanding by producing their own code.
Gramacy earned four degrees from the University of California, Santa Cruz. In 2001 he was awarded a B.A. (Honors) in Mathematics and a B.Sc. (Highest Honors) in Computer Science. In 2003 he earned a M.Sc. in Computer Science, and in 2005 a Ph.D. in Applied Mathematics & Statistics. Gramacy was honored with the Savage Award in 2006 for his Ph.D. thesis “Bayesian treed Gaussian Process models.”
His hobbies include cycling, traveling, and ice hockey.
2013 - 2014 Course Schedule
||Applied Regression Analysis
Cycling, travel, music, and hockey.
Bayesian modeling methodology, statistical computing, Monte Carlo inference, nonparametric regression, sequential design, and optimization under uncertainty. Application areas include spatial data, sequential computer experiments, ecology, epidemiology, finance and public policy.
With M.A. Taddy and N.G. Polson, “Dynamic trees for learning and design,” Journal
of the American Statistical Association (2011).
With E. Pantaleo, “Shrinkage regression for multivariate inference with missing data, and an application to portfolio balancing,” Bayesian Analysis (2010).
With R.J. Samworth and Ruth King, “Importance tempering,” Statistics and Computing (2010).
With D. Merl, L.R. Johnson, and M.S. Mangel, “A statistical framework for the adaptive management of epidemiological interventions,” PLoS ONE (2009).
With H.K.H. Lee, “Bayesian treed Gaussian process Models with an application to computer modeling,” Journal of the American Statistical Association (2008).