Publications

Publications and Accepted Manuscripts

        Research Articles

1.       Tuo, R., Wu, C. F. J., and Yu, D. (2014) “Surrogate Modeling of Computer Experiments with Different Mesh Densities,” Technometrics, 56(3), 372-380.

2.       Plumlee, M., and Tuo, R. (2014) “Building Accurate Emulators for Stochastic Simulations via Quantile Kriging,” Technometrics, 56(4), 466-473.

3.       Joseph, V. R., Dasgupta, T., Tuo, R., and Wu, C. F. J. (2015) “Sequential Exploration of Complex Surfaces Using Minimum Energy Designs,” Technometrics, 57(1), 64-74.

4.       Tuo, R., and Wu, C. F. J., (2015) “Efficient Calibration for Imperfect Computer Models,” The Annals of Statistics, 43(6), 2331-2352.

5.       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.

6.       He, X., Tuo, R., and Wu, C. F. J., (2017) “Optimization of Multi-fidelity Computer Experiments via the EQIE Criterion,” Technometrics, 59(1), 58-68.

7.       Tuo, R. (2018) “Uncertainty Quantification with α-Stable-Process Models,” Statistica Sinica, 28(2), 553-576.

8.       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.

9.     Chen, Z., Tuo, R., and Zhang, W., (2018) “Stochastic Convergence of a Nonconforming Finite Element Method for the Thin Plate Spline Smoother for Observational Data,” SIAM Journal on Numerical Analysis, 56(2), 635-659.

10.   Tuo, R., (2019) “Adjustments to Computer Models via Projected Kernel Calibration,” SIAM/ASA Journal on Uncertainty Quantification, 7(2), 553-578.

11.   Su, H., Tuo, R., and Wu, C. F. J., (2019) “PIG Process: Joint Modeling of Point and Integral Responses in Computer Experiments,” International Journal for Uncertainty Quantification, 9(4), 331–349.

12.   Joseph, V. R., Wang, D., Gu, L., Lyu, S., and Tuo R., (2019) “Deterministic Sampling of Expensive Posteriors Using Minimum Energy Designs,” Technometrics, 61(3), 297-308.

13.   Du, H., Hu, X., Duan, H., Yu, L., Qu, F., Huang, Q., ..., Tuo, R., … & Wang, C. (2019). "Principles of inter-amino-acid recognition revealed by binding energies between homogeneous oligopeptide", ACS Central Science, 5(1), 97-108. I am the 10th author, contributing significantly to the data analysis and interpretation.

14.   Chen, Z., Tuo, R. and Zhang, W., (2020) “A Balanced Oversampling Finite Element Method for Elliptic Problems with Observational Boundary Data,” Journal of Computational Mathematics, 30(2), 355-374.

15.   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.

16. Tuo, R., and Wang, W., (2020) “Kriging Prediction with Isotropic Matérn Correlations: Robustness and Experimental Design,” Journal of Machine Learning Research, (187), 1−38, 2020.

17. 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,” The Annals of Applied Statistics, 14(4), 1759-1776.

18. Tuo, R., Wang, Y, and Wu, C. F. J., (2020) “On the Improved Rates of Convergence for Matérn-type Kernel Ridge Regression, with Application to Calibration of Computer Models.” SIAM/ASA Journal on Uncertainty Quantification, 8(4), 1522-47.

19. Wang, Y, and Tuo, R., (2020) “Semi-parametric Statistical Adjustment Method for Computer Models,” Statistics, 54(6), 1255-1275.

20. 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.

21. Prakash, A., Tuo, R., and Ding, Y., (2022) “Gaussian Process Aided Function Comparison Using Noisy Scattered Data,” Technometrics, 64(1), 92-102.

22. 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.

23. Prakash, A., Tuo, R., and Ding, Y., (2023). “The Temporal Overfitting Problem with Applications in Wind Power Curve Modeling,” Technometrics, 65(1), 70-82.

24. Tuo, R., He, S., Pourhabib, A., Ding, Y., and Huang, J., (2023) “A Reproducing Kernel Hilbert Space Approach to Functional Calibration of Computer Models,” Journal of the American Statistical Association, 118(542), 883-897.

25. Chen, G., and Tuo, R., (2023). "Projection Pursuit Gaussian Process Regression," IISE Transactions, 55(9), 901-911.

26.  Jin, S., Tuo, R., Tiwari, A., Bukkapatnam, S., Aracne-Ruddle, C., Lighty, A., Hamza, H., and Ding, Y., (2023). “Hypothesis Tests with Functional Data for Surface Quality Change Detection in Surface Finishing Processes,” IISE Transactions, 55(9), 940-956.

27. Ding, L., Tuo, Rui., Shahrampour, S., (2024). "A Sparse Expansion for Deep Gaussian Processes," IISE Transactions, 56(5), 599-572.

28. Chen, G., Zhou, Y., Zhang, X., Tuo, R., (2024+) "Renewing Iterative Self-labeling Domain Adaptation with Application to the Spine Motion Prediction," IEEE Transactions on Automation Science and Engineering, to appear.

Review Articles and Comments

1. Tuo, R., Qian, P. Z. G, and Wu, C. F. J. (2013) “A Brownian Motion Model for Stochastic Simulation with Tunable Precision,” comments on “Quantile-Based Optimization of Noisy Computer Experiments with Tunable Precision,” by Picheny et al., Technometrics, 55(1), 29-31.

2.  Sung, C-L., and Tuo, R. (2024) “A Review on Computer Model Calibration,” WIREs Computational Statistics, 16(1), e1645.

In Refereed Conference or Symposium Proceedings

1. Cummings, R., Krehbiel, S., Mei, Y., Tuo, R., and Zhang, W., (2018) “Differentially Private Change-Point Detection,” In Advances in Neural Information Processing Systems (NeurIPS), pp. 10825-10834.

2. Ding, L., Tuo, R., and Shahrampour, S., (2020) “Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features,” In International Conference on Machine Learning (ICML), pp. 2875-2884.

3. Tuo, R. and Wang, W., (2022) "Uncertainty Quantification for Bayesian Optimization", Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, PMLR 151:2862-2884.