Robert B. Gramacy Professor of Statistics

Robert Gramacy in Action


I am a Professor of Statistics in the College of Science at Virginia Polytechnic and State University (Virginia Tech). 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.

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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

  • Replication or exploration? Sequential design for stochastic simulation experiments (2017) with Mickael Binois, Jiangeng Huang and Mike Ludkovski; preprint on arXiv:1710.03206
  • Phenomenological forecasting of disease incidence using heteroskedastic Gaussian processes: a dengue case study (2017) with Leah Johnson, Jeremy Cohen, Erin Mordecai, Courtney Murdock, Jason Rohr, Sadie Ryan, Anna Stewart-Ibarra and Daniel Weikel. To appear in Annals of Applied Statistics; preprint on arXiv:1702.00261
  • Practical heteroskedastic Gaussian process modeling for large simulation experiments (2016) with Mickael Binois and Mike Ludkovski; preprint on arXiv:1611.05902
  • Modeling an augmented Lagrangian for blackbox constrained optimization (2016) with Genetha Gray, Sebastien Le Digabel, Herbie Lee, Pritam Ranjan, Garth Wells and Stefan Wild; Technometrics (with discussion), 58(1), pp. 1-11; preprint on arXiv:1403.4890
  • Local Gaussian process approximation for large computer experiments (2015) with Dan Apley; Journal of Computational and Graphical Statistics, 24(2), pp. 561-578; preprint on arXiv:1303.0383