• Assistant Professor, Industrial & Systems Engineering
  • 2022 Texas A&M Institute of Data Science Career Initiation Fellow
  • Affiliated Faculty, Department of Statistics
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Rui Tuo

Educational Background

  • Ph.D., Statistics, University of Chinese Academy of Sciences, 2013

Research Interests

    • Design and analysis of computer experiments
    • Uncertainty quantification
    • Statistical inference in random fields
    • Nonparametric and semiparametric statistics
    • Bayesian inference and computation

Awards & Honors

  • Career Initiation Fellow, Texas A&M Institute of Data Science, 2022

Selected Publications

  • Tuo, R. and Wang, W., (2022) "Uncertainty Quantification for Bayesian Optimization," The 25th International Conference on Artificial Intelligence and Statistics, accepted.
  • Prakash, A., Tuo, R., and Ding, Y., (2021+) “Gaussian Process Aided Function Comparison Using Noisy Scattered Data,” Technometrics, to appear.
  • Tuo, R., He, S., Pourhabib, A., Ding, Y., and Huang, J., (2021+) “A Reproducing Kernel Hilbert Space Approach to Functional Calibration of Computer Models,” Journal of the American Statistical Association, to appear.
  • Zhang, W., Krehbiel, S., Tuo, R., Mei, Y., and Cumming, R., (2021) “Single and Multiple Change-Point Detection with Differential Privacy,” Journal of Machine Learning Research, 22(29):1−36.
  • Wang, Y., Yue, X., Tuo, R., Hunt, J. H., and Shi, J., (2020) “Effective Model Calibration via Sensible Variable Identification and Adjustment, with Application to Composite Fuselage Simulation,” Annals of Applied Statistics, 14(4), 1759-1776.
  • Tuo, R., and Wang, W., (2020) “Kriging prediction with isotropic Matérn correlations: robustness and experimental design,” Journal of Machine Learning Research, (187), 1−38.
  • Wang, W., Tuo, R., Wu, C. F. J., (2020) “On Prediction Properties of Kriging: Uniform Error Bounds and Robustness,” Journal of the American Statistical Association, 115(530), 920-930.
  • Ding, L., Tuo, R., Shahrampour, S., (2020) “Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features,” In International Conference on Machine Learning (ICML), (pp. 2875-2884).
  • Tuo, R., (2019) “Adjustments to Computer Models via Projected Kernel Calibration,” SIAM/ASA Journal on Uncertainty Quantification, 7(2), 553-578.
  • Tuo, R., and Wu, C. F. J., (2018) “Prediction Based on the Kennedy-O’Hagan Calibration Model: Asymptotic Consistency and Other Properties”, Statistica Sinica, 28(2), 743-759
  • Tuo, R., and Wu, C. F. J., (2016) “A Theoretical Framework for Calibration in Computer Models: Parametrization, Estimation and Convergence Properties,” SIAM/ASA Journal on Uncertainty Quantification, 4(1), 739-766
  • Tuo, R., and Wu, C. F. J., (2015) “Efficient Calibration for Imperfect Computer Models,” The Annals of Statistics, 43(6), 2331-2352
  • Plumlee, M., and Tuo, R. (2014) “Building Accurate Emulators for Stochastic Simulations via Quantile Kriging,” Technometrics, 56(4), 466-473
  • Tuo, R., Wu, C. F. J., and Yu, D. (2014) “Surrogate Modeling of Computer Experiments with Different Mesh Densities,” Technometrics, 56(3), 372-380
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