• Associate 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
    • Probabilistic Machine Learning Methods
    • Nonparametric and semiparametric statistics

Awards & Honors

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

Selected Publications

  • Zhao, W., Chen, H., Liu, T., Tuo, R., and Tian, C., (2025) "From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior Approximation", The 28th International Conference on Artificial Intelligence and Statistics, pp. 4231-4239. PMLR.
  • Ding, L., Tuo, Rui., Shahrampour, S., (2024). "A Sparse Expansion for Deep Gaussian Processes," IISE Transactions, 56(5), 599-572.
  • Chen, H., Ding, L., and Tuo, R., (2022) "Kernel Packet: An Exact and Scalable Algorithm for Gaussian Process Regression with Matérn Correlations," Journal of Machine Learning Research, 23(127), 1−32.
  • Wang, W., Tuo, R., and 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.
  • Tuo, R., and Wu, C. F. J., (2015) “Efficient Calibration for Imperfect Computer Models,” The Annals of Statistics, 43(6), 2331-2352.
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