Robert B. Gramacy Professor of Statistics

Robert Gramacy in Action

Biography

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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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

  • 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