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Robert B. Gramacy

Robert B. Gramacy

D-Index & Metrics

Engineering and Technology

D-Index
42
Citations
6824
World Ranking
6584
National Ranking
1804

Research.com Recognitions

  • 2020 - Fellow of the American Statistical Association (ASA)

Overview

Robert B. Gramacy is affiliated with Virginia Tech in the United States. Their research primarily focuses on computer science, with significant contributions in areas such as artificial intelligence, computational theory and mathematics, management science and operations research, statistics, probability and uncertainty, and environmental engineering.

The scientist's main topics of work include advanced multi-objective optimization algorithms, Gaussian processes and Bayesian inference, optimal experimental design methods, probabilistic and robust engineering design, soil geostatistics and mapping, machine learning and data classification, and fault detection and control systems.

Robert B. Gramacy has published extensively, with notable papers including:

  • "Analyzing Stochastic Computer Models: A Review with Opportunities," 2022, Statistical Science
  • "hetGP: Heteroskedastic Gaussian Process Modeling and Sequential Design in R," 2021, Journal of Statistical Software
  • "Vecchia-Approximated Deep Gaussian Processes for Computer Experiments," 2022, Journal of Computational and Graphical Statistics
  • "Traditional kriging versus modern Gaussian processes for large-scale mining data," 2023, Statistical Analysis and Data Mining The ASA Data Science Journal
  • "Entropy-based adaptive design for contour finding and estimating reliability," 2022, Journal of Quality Technology

The scientist has collaborated frequently with several co-authors, including Annie S. Booth, Leah R. Johnson, Ryan B. Christianson, Mickaël Binois, and Annie Sauer.

Robert B. Gramacy has contributed to a range of publication venues, with frequent appearances in:

  • arXiv (Cornell University)
  • Technometrics
  • The Annals of Applied Statistics
  • Statistical Analysis and Data Mining The ASA Data Science Journal
  • Statistics and Computing

In 2020, they were named a Fellow of the American Statistical Association (ASA).

Best Publications

  • Bayesian Treed Gaussian Process Models With an Application to Computer Modeling

    Robert B Gramacy;Herbert K. H Lee

  • Local Gaussian Process Approximation for Large Computer Experiments

    Robert B. Gramacy;Daniel W. Apley

  • Cases for the nugget in modeling computer experiments

    Robert B. Gramacy;Herbert K. Lee

  • Adaptive Design and Analysis of supercomputer Experiments.

    Robert B. Gramacy;Herbert K. H. Lee

  • tgp: An R Package for Bayesian Nonstationary, Semiparametric Nonlinear Regression and Design by Treed Gaussian Process Models

    Robert B. Gramacy

  • Practical Heteroscedastic Gaussian Process Modeling for Large Simulation Experiments

    Mickael Binois;Robert B. Gramacy;Michael Ludkovski

  • Importance tempering

    Robert Gramacy;Richard Samworth;Ruth King

  • Optimization Under Unknown Constraints

    R B Gramacy;Lee Hkh.;C Holmes;M Osborne

  • Categorical Inputs, Sensitivity Analysis, Optimization and Importance Tempering with tgp Version 2, an R Package for Treed Gaussian Process Models

    Robert B. Gramacy;Matthew Alan Taddy

  • laGP: Large-Scale Spatial Modeling via Local Approximate Gaussian Processes in R

    Robert B. Gramacy

  • Modeling an Augmented Lagrangian for Blackbox Constrained Optimization

    Robert B. Gramacy;Genetha A. Gray;Sébastien Le Digabel;Herbert K. H. Lee

  • amei: An R Package for the Adaptive Management of Epidemiological Interventions

    Daniel Merl;Leah R. Johnson;Robert B. Gramacy;Marc Mangel

  • Dynamic Trees for Learning and Design

    Matthew A. Taddy;Robert B. Gramacy;Nicholas G. Polson

  • Particle Learning of Gaussian Process Models for Sequential Design and Optimization

    Robert B. Gramacy;Nicholas G. Polson

  • ACME: Adaptive Caching Using Multiple Experts

    Ismail Ari;Ahmed Amer;Robert B. Gramacy;Ethan L. Miller

  • Replication or Exploration? Sequential Design for Stochastic Simulation Experiments

    Mickaël Binois;Jiangeng Huang;Robert B. Gramacy;Mike Ludkovski

  • Bayesian treed gaussian process models

    Robert B. Gramacy;Herbert K. Lee

  • Surrogates: Gaussian Process Modeling, Design, and Optimization for the Applied Sciences

    Robert B. Gramacy

  • Gaussian Process Single-Index Models as Emulators for Computer Experiments.

    Robert B. Gramacy;Heng Lian

  • Parameter space exploration with Gaussian process trees

    Robert B. Gramacy;Herbert K. H. Lee;William G. Macready

  • Adaptive Caching by Refetching

    Robert B. Gramacy;Manfred K Warmuth;Scott A. Brandt;Ismail Ari

Frequent Co-Authors

Christian Franzke
Christian Franzke Universität Hamburg
Stefan M. Wild
Stefan M. Wild Lawrence Berkeley National Laboratory
Nicholas G. Polson
Nicholas G. Polson University of Chicago
Sadie J. Ryan
Sadie J. Ryan University of Florida
Garth N. Wells
Garth N. Wells University of Cambridge
Douglas Nychka
Douglas Nychka Colorado School of Mines
Jason R. Rohr
Jason R. Rohr University of Notre Dame
Len Thomas
Len Thomas University of St Andrews
Stephen T. Buckland
Stephen T. Buckland University of St Andrews
Rita R. Colwell
Rita R. Colwell University of Maryland, College Park

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