selected publications academic article Prakash, A., Tuo, R., & Ding, Y. u. (2022). Gaussian Process-Aided Function Comparison Using Noisy Scattered Data. Technometrics. 64(1), 92-102. Tuo, R., He, S., Pourhabib, A., Ding, Y. u., & Huang, J. Z. (2021). A Reproducing Kernel Hilbert Space Approach to Functional Calibration of Computer Models. J Am Stat Assoc. 1-15. Wang, Y., Yue, X., Tuo, R., Hunt, J. H., & Shi, J. (2020). Effective model calibration via sensible variable identification and adjustment with application to composite fuselage simulation. Ann Appl Stat. 14(4), 1759-1776. Prakash, A., Tuo, R., & Ding, Y. u. (2020). The temporal overfitting problem with applications in wind power curve modeling Tuo, R., & Wang, W. (2020). Kriging Prediction with Isotropic Matern Correlations: Robustness and Experimental Designs. JOURNAL OF MACHINE LEARNING RESEARCH. 21. Wang, Y., & Tuo, R. (2020). Semi-parametric adjustment to computer models. Statistics. 54(6), 1255-1275. sci, Z. C. (2020). A Balanced Oversampling Finite Element Method for Elliptic Problems with Observational Boundary Data. JOURNAL OF COMPUTATIONAL MATHEMATICS. 38(2), 355-374. Ding, L., Zou, L. u., Wang, W., Shahrampour, S., & Tuo, R. (2020). High-Dimensional Non-Parametric Density Estimation in Mixed Smooth Sobolev Spaces Wang, W., Tuo, R., & Jeff Wu, C. F. (2020). On Prediction Properties of Kriging: Uniform Error Bounds and Robustness. J Am Stat Assoc. 115(530), 920-930. Chen, G., & Tuo, R. (2020). Projection Pursuit Gaussian Process Regression Tuo, R., & Wang, W. (2020). Uncertainty Quantification for Bayesian Optimization Tuo, R., Wang, Y., & Jeff Wu, C. F. (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), 1522-1547. Joseph, V. R., Wang, D., Gu, L. i., Lyu, S., & Tuo, R. (2019). Deterministic Sampling of Expensive Posteriors Using Minimum Energy Designs. Technometrics. 61(3), 297-308. Tuo, R. (2019). Adjustments to Computer Models via Projected Kernel Calibration. SIAM/ASA Journal on Uncertainty Quantification. 7(2), 553-578. Su, H., Tuo, R., & Wu, C. (2019). PIG PROCESS: JOINT MODELING OF POINT AND INTEGRAL RESPONSES IN COMPUTER EXPERIMENTS. International Journal for Uncertainty Quantification. 9(4), 331-349. Cummings, R., Krehbiel, S., Mei, Y., Tuo, R., & Zhang, W. (2018). Differentially Private Change-Point Detection. ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS (NEURIPS 2020). 31, 10825-10834. Tuo, R., & Wu, J. (2018). Prediction based on the Kennedy-O’Hagan calibration model: asymptotic consistency and other properties. STATISTICA SINICA. 28(2), 743-759. Chen, Z., Tuo, R., & 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. Tuo, R. (2018). Uncertainty Quantification with α-Stable-Process Models. STATISTICA SINICA. 28(2), 553-576. Chen, Z., Tuo, R., & Zhang, W. (2017). A finite element method for elliptic problems with observational boundary data He, X. u., Tuo, R., & Wu, C. (2017). Optimization of Multi-Fidelity Computer Experiments via the EQIE Criterion. Technometrics. 59(1), 58-68. Tuo, R., & Jeff Wu, C. F. (2016). A Theoretical Framework for Calibration in Computer Models: Parametrization, Estimation and Convergence Properties. SIAM/ASA Journal on Uncertainty Quantification. 4(1), 767-795. Tuo, R., & Wu, C. (2015). Efficient calibration for imperfect computer models. ANNALS OF STATISTICS. 43(6), 2331-2352. Joseph, V. R., Dasgupta, T., Tuo, R., & Wu, C. (2015). Sequential Exploration of Complex Surfaces Using Minimum Energy Designs. Technometrics. 57(1), 64-74. Plumlee, M., & Tuo, R. (2014). Building Accurate Emulators for Stochastic Simulations via Quantile Kriging. Technometrics. 56(4), 466-473. Tuo, R., Wu, C., & Yu, D. (2014). Surrogate Modeling of Computer Experiments With Different Mesh Densities. Technometrics. 56(3), 372-380. Tuo, R., Qian, P., & Wu, C. (2013). Comment: A Brownian Motion Model for Stochastic Simulation With Tunable Precision. Technometrics. 55(1), 29-31. Zhang, W., Krehbiel, S., Tuo, R., Mei, Y., & Cummings, R. Single and Multiple Change-Point Detection with Differential Privacy. JOURNAL OF MACHINE LEARNING RESEARCH. 22. conference paper Ding, L., Tuo, R., & Shahrampour, S. (2020). Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features. 2875-2884. Ding, L., Tuo, R., & Shahrampour, S. Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features. 22ND INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 89. institutional repository document Ding, L., Tuo, R., & Zhang, X. (2021). High-Dimensional Simulation Optimization via Brownian Fields and Sparse Grids
principal investigator on Collaborative Research: Uncertainty Quantification, Optimal Designs and Calibration in Computer Experiments awarded by Directorate for Mathematical & Physical Sciences - (Arlington, Virginia, United States) 2019 - 2022
teaching activities ISEN414 Total Quality Engr Instructor ISEN616 Des & Analy Ind Exper Instructor ISEN619 Analysis & Prediction Instructor ISEN689 Sptp: Computer Experiments Instructor ISEN691 Research Instructor
education and training Ph.D. in Statistics, Chinese Academy of Sciences - (Beijing, Beijing, China) 2013 M.S. in Statistics, Chinese Academy of Sciences - (Beijing, Beijing, China) 2010 B.S. in Statistics, University of Science and Technology of China - (Hefei, China) 2008