Robert B. Gramacy Professor of Statistics
Biography
I am a Professor of Statistics in the College of Science at Virginia Polytechnic and State University (Virginia Tech/VT) and affiliate faculty in VT's Computational Modeling and Data Analytics program. Previously I was an Associate Professor of Econometrics and Statistics at the Booth School of Business, and a fellow of the Computation Institute at The University of Chicago. My research interests include Bayesian modeling methodology, statistical computing, Monte Carlo inference, nonparametric regression, sequential design, and optimization under uncertainty.
Latest News
- I am recruiting a postdoc, applications due March 15 2024: details.
- In 2023 I began work on an NSF project forecasting phytoplankton blooms for drinking water quality.
- In 2022 I received an Outstanding Service Award from the ASA Section on Physical and Engineering Sciences.
- In 2022 I began work on an NSF project studying local Gaussian processes for jumps in engineering systems.
- As of Jan 1 2023, I am Editor-in-Chief of Technometrics.
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
- Contour location for reliability in airfoil simulation experiments using deep Gaussian processes (2023) with Annie Booth and Ashwin Renganathan; 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
- Entropy-based adaptive design for contour finding and estimating reliability (2023) with Austin Cole, James E. Warner, Geoffrey F. Bomarito, Patrick E. Leser, William P. Leser. Journal of Quality Technology, 55(1), pp. 70-87; preprint on arXiv:2105.11357
- 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
- Sensitivity prewarping for local surrogate modeling (2022) with Nate Wycoff and Mickael Binois. Technometrics, 64(4), pp. 535-547; preprint on arXiv:2101.06296