Robert B. Gramacy Professor of Statistics

Monty the Null Hippopotamus Book on Surrogate Modeling

Biography

I am a Professor of Statistics, and Head of the Department of Statistics in the College of Science at Virginia Polytechnic and State University (Virginia Tech/VT). At VT, I also serve as affiliate faculty in the Computational Modeling and Data Analytics (CMDA) program, and core faculty in the Center for Ecosystem Forecasting. My research interests include Bayesian modeling methodology, statistical computing, Monte Carlo inference, nonparametric regression, surrogate modeling, uncertainty quantification, sequential design, and optimization under uncertainty.

Latest News

Soft open of Monty the Null Hippopotamus.
In Aug 2025 I took over as Department Head of Statistics at VT.
In 2023 I began work on an NSF project forecasting phytoplankton blooms for drinking water quality.
From 2023-2025 I served as Editor-in-Chief of Technometrics.
In 2022 I received an Outstanding Service Award from the ASA Section on Physical and Engineering Sciences.

Research

The papers listed on the right are chosen to represent my current research interests. I’ve lately been very excited about large scale computer model emulation for calibration and optimization.

For a more complete picture, see my research page, my full publication list, or my Google Scholar page.

Select Pubs & Tech Reports

Vecchia approximated Bayesian heteroskedastic Gaussian processes (2025) with Parul Patil, Cayelan Carey, Quinn Thomas; preprint on arXiv:2507.07815
Monotonic warpings for additive and deep Gaussian processes (2025) with Steven Barnett, Lauren Beesley, Annie Booth and Dave Osthus. Statistics and Computing, 35(65); preprint on arXiv:2408.01540
Contour location for reliability in airfoil simulation experiments using deep Gaussian processes (2025) with Annie Booth and Ashwin Renganathan. Annals of Applied Statistics, 19(1), pp. 191-211; preprint on arXiv:2308.04420
Active learning of deep Gaussian process surrogates (2023) with Annie Sauer and Dave Higdon. Technometrics, 65(1), pp. 4-18; preprint on arXiv:2012.08015
Vecchia-approximated deep Gaussian processes for computer experiments (2023) with Annie Sauer and Andrew Cooper. Journal of Computational and Graphical Statistics, 32(3); preprint on arXiv:2204.02904
Triangulation candidates for Bayesian optimization (2022) with Annie Sauer and Nate Wycoff. 36th Conference on Neural Information Processing Systems (NeurIPS); preprint on arXiv:2112.07457