HR job title
- Assistant Professor
- Contact Info
- Websites
Tuo, Rui Assistant Professor
Positions
- Assistant Professor, Industrial and Systems Engineering, College of Engineering
- Overview
- Publications
- Research
- Teaching
- Background
- Contact
- Other
- View All
Overview
Publications
selected publications
-
academic article
-
Chen, G., & Tuo, R.
(2022).
Projection pursuit Gaussian process regression. IISE TRANSACTIONS.
1-11.
-
Jin, S., Tuo, R., Tiwari, A., Bukkapatnam, S., Aracne-Ruddle, C., Lighty, A., Hamza, H., & Ding, Y. u.
(2022).
Hypothesis tests with functional data for surface quality change detection in surface finishing processes. IISE TRANSACTIONS.
1-17.
-
Prakash, A., Tuo, R., & Ding, Y. u.
(2022).
The Temporal Overfitting Problem with Applications in Wind Power Curve Modeling. Technometrics.
1-13.
-
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. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION.
1-15.
-
Zhang, W., Krehbiel, S., Tuo, R., Mei, Y., & Cummings, R.
(2021).
Single and Multiple Change-Point Detection with Differential Privacy. JOURNAL OF MACHINE LEARNING RESEARCH.
22.
-
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. Annals of Applied Statistics.
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: a journal of theoretical and applied statistics.
54(6), 1255-1275.
-
Chen, Z., Tuo, R., & Zhang, W.
(2020).
A BALANCED OVERSAMPLING FINITE ELEMENT METHOD FOR ELLIPTIC PROBLEMS WITH OBSERVATIONAL BOUNDARY DATA. 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
-
Chen, G., & Tuo, R.
(2020).
Projection Pursuit Gaussian Process Regression
-
Wang, W., Tang, S., Li, C., Chen, J., Li, H., Su, Y., & Ning, B.
(2020).
Specific Brain Morphometric Changes in Spinal Cord Injury: A Voxel-Based Meta-Analysis of White and Gray Matter Volume. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION.
36(15), 2348-2357.
-
Tuo, R., & Wang, W.
(2020).
Uncertainty Quantification for Bayesian Optimization
-
Tuo, R., Wang, Y., & Wu, C.
(2020).
On the Improved Rates of Convergence for Matern-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.
31, 10825-10834.
-
Tuo, R., & Wu, C.
(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 alpha-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., & Wu, C.
(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-+.
-
Chen, G., & Tuo, R.
(2022).
Projection pursuit Gaussian process regression. IISE TRANSACTIONS.
1-11.
-
conference paper
-
Tuo, R., & Wang, W.
(2022).
Uncertainty Quantification for Bayesian Optimization
-
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.
(2020).
Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features
-
Ding, L., Tuo, R., & Shahrampour, S.
(2019).
Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features
-
Tuo, R., & Wang, W.
(2022).
Uncertainty Quantification for Bayesian Optimization
-
institutional repository document
-
Jin, S., Tuo, R., Tiwari, A., Bukkapatnam, S., Aracne-Ruddle, C., Lighty, A., Hamza, H., & Ding, Y. u.
(2022).
Hypothesis Tests with Functional Data for Surface Quality Change Detection in Surface Finishing Processes
-
Chen, H., Ding, L., & Tuo, R.
(2022).
Kernel Packet: An Exact and Scalable Algorithm for Gaussian Process Regression with Matrn Correlations
-
Ding, L., Tuo, R., & Shahrampour, S.
(2021).
A Sparse Expansion For Deep Gaussian Processes
-
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
-
Ding, L., Tuo, R., & Zhang, X.
(2021).
High-Dimensional Simulation Optimization via Brownian Fields and Sparse Grids
-
Prakash, A., Tuo, R., & Ding, Y. u.
(2020).
The temporal overfitting problem with applications in wind power curve modeling
-
Ding, L., Zou, L. u., Wang, W., Shahrampour, S., & Tuo, R.
(2020).
High-Dimensional Non-Parametric Density Estimation in Mixed Smooth Sobolev Spaces
-
Chen, G., & Tuo, R.
(2020).
Projection Pursuit Gaussian Process Regression
-
Prakash, A., Tuo, R., & Ding, Y. u.
(2020).
Gaussian process aided function comparison using noisy scattered data
-
Ding, L., Tuo, R., & Shahrampour, S.
(2020).
Generalization Guarantees for Sparse Kernel Approximation with Entropic Optimal Features
-
Tuo, R., Wang, Y., & Wu, C.
(2020).
On the Improved Rates of Convergence for Matrn-type Kernel Ridge Regression, with Application to Calibration of Computer Models
-
Wang, Y., Yue, X., Tuo, R., Hunt, J. H., & Shi, J.
(2019).
Effective Model Calibration via Sensible Variable Identification and Adjustment, with Application to Composite Fuselage Simulation
-
Tuo, R., & Wang, W.
(2019).
Kriging prediction with isotropic Matrn correlations: Robustness and experimental design
-
Cummings, R., Krehbiel, S., Mei, Y., Tuo, R., & Zhang, W.
(2018).
Differentially Private Change-Point Detection
-
Joseph, V. R., Wang, D., Gu, L. i., Lv, S., & Tuo, R.
(2017).
Deterministic Sampling of Expensive Posteriors Using Minimum Energy Designs
-
Wang, W., Tuo, R., & Wu, C.
(2017).
On Prediction Properties of Kriging: Uniform Error Bounds and Robustness
-
Tuo, R.
(2017).
Adjustments to Computer Models via Projected Kernel Calibration
-
Tuo, R., & Wu, C.
(2017).
Prediction based on the Kennedy-O'Hagan calibration model: asymptotic consistency and other properties
-
Chen, Z., Tuo, R., & Zhang, W.
(2017).
A finite element method for elliptic problems with observational boundary data
-
Chen, Z., Tuo, R., & Zhang, W.
(2017).
Stochastic Convergence of A Nonconforming Finite Element Method for the Thin Plate Spline Smoother for Observational Data
-
Tuo, R., & Wu, C.
(2015).
A theoretical framework for calibration in computer models: parametrization, estimation and convergence properties
-
Tuo, R., & Wu, C.
(2015).
Efficient Calibration for Imperfect Computer Models
-
Jin, S., Tuo, R., Tiwari, A., Bukkapatnam, S., Aracne-Ruddle, C., Lighty, A., Hamza, H., & Ding, Y. u.
(2022).
Hypothesis Tests with Functional Data for Surface Quality Change Detection in Surface Finishing Processes
Research
principal investigator on
Teaching
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
Background
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
Contact
first name
- Rui
has last name
- Tuo
Other
- ruituo@tamu.edu