PhD in Operations Research
Georgia Institute of Technology, 2018
Assistant Professor
Thrust of Data Science and Analytics
Department of Mathematics
Scopus: 57208692777
ResearcherID: FYX-1477-2022
Google Scholar: EKS1sO0AAAAJ
ORCID: 0000-0001-9219-0494
Computer Experiments
Uncertainty Quantification
Machine Learning
Nonparametric Statistics
Computer Experiments
Wang, W., & Jing, B.-Y. (2022). Convergence of Gaussian process regression: Optimality, robustness, and relationship with kernel ridge regression. Journal of Machine Learning Research, 23(193):1−67, 2022.
Wang, W., Yue, X., Haaland, B., & Wu, C. F. J. (2022). Gaussian Process with Input Location Error and Applications to the Composite Parts Assembly Process. SIAM/ASA Journal on Uncertainty Quantification 10.2 (2022): 619-650.
Wang, W. (2021). On the Inference of Applying Gaussian Process Modeling to a Deterministic Function. Electronic Journal of Statistics, 15 (2) 5014 – 5066.
Wang, W., & Zhou, Y.-H. (2021). Eigenvector-Based Sparse Canonical Correlation Analysis: Fast Computation for Estimation of Multiple Canonical Vectors. Journal of Multivariate Analysis, 104781.
Lee, C., Wu, J., Wang, W., & Yue, X. (2021). Neural Network Gaussian Process Considering Input Uncertainty for Composite Structures Assembly. IEEE/ASME Transactions on Mechatronics, (accepted).
Tuo, R.*, & Wang, W.* (2020). Kriging Prediction with Isotropic Matérn Correlations: Robustness and Experimental Designs. Journal of Machine Learning Research, 21(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,
Sung, C.-L.*, Wang, W.*, Plumlee, M., & Haaland, B. (2020). Multi-Resolution Functional ANOVA for Large-Scale, Many-Input Computer Experiments. Journal of the American Statistical Association, 115:530, 908-919.
Wang, W., & Haaland, B. (2019). Controlling Sources of Inaccuracy in Stochastic Kriging. Technometrics, 61(3): 309-321.
Hu, T.*, Wang, J.*, Wang, W.*, & Li, Z. (2022). Understanding Square Loss in Training Overparametrized Neural Network Classifiers. Neural Information Processing Systems (NIPS), 2022. (Spotlight).
Tuo, R.*, & Wang, W.*. (2022). Uncertainty Quantification for Bayesian Optimization. In the 25th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 151:2862-2884, 2022.
Hu, T.*, Wang, W.*, Lin, C., & Cheng, G. (2021). Regularization Matters: A Nonparametric Perspective on Overparametrized Neural Network. In the 24th International Conference on Artificial Intelligence and Statistics (AISTATS). PMLR 130:829-837, 2021.
Doctor of Philosophy: