Preprints
color indicates supervised student
Diffusion non-additive model for multi-fidelity simulations with tunable precision
3. Heo, J., Boutelet, R., and Sung, C.-L. (2025)Active learning with adaptive non-stationary kernel for continuous-fidelity surrogate models
2. Boutelet, R. and Sung, C.-L. (2025)Uncertainty-aware out-of-distribution detection with Gaussian processes
1. Chen, Y., Sung, C.-L., Kusari, A., Song, X., and Sun, W. (2024)Publications
color indicates supervised student
Advancing inverse scattering with surrogate modeling and Bayesian inference for functional inputs
22. Sung, C.-L., Song, Y., and Hung, Y. (2025)SIAM/ASA Journal on Uncertainty Quantification, 13(2), 339-517.
Region-optimal Gaussian process surrogate model via Dirichlet process for cold-flow and combustion emulations
21. Zhou, M., Zuo, R., Sung, C.-L., Tong, Y., and Wang, X. (2025)Computer Methods in Applied Mechanics and Engineering, 439, 117894.
Active learning for a recursive non-additive emulator for multi-fidelity computer experiments
19. Heo, J. and Sung, C.-L. (2025)Technometrics, 67(1), 58-72.
★ Winner of INFORMS 2023 QSR Best Student Paper 🔗 • Winner of 2024 ASA SPES + Q&P Best Student Paper
Modeling with uncertainty quantification identifies essential features of a non-canonical algal carbon-concentrating mechanism
20. Steensma, A. K., Kaste, J. A. M., Heo, J., Orr , D., Sung, C.-L., Shachar-Hill, Y., and Walker, B. J. (2025)Plant Physiology, 197(2), kiae629.
★ Media coverage on interdisciplinary plant science collaboration 🔗
Category tree Gaussian process for computer experiments with many-category qualitative factors and application to cooling system design
18. Lin, W.-A., Sung, C.-L., and Chen, R.-B. (2024)Journal of Quality Technology, 56(5), 391-408.
★ C. Z. Wei Memorial Award (CIPS) 🔗
Functional-input Gaussian processes with applications to inverse scattering problems
17. Sung, C.-L., Wang, W., Cakoni, F., Harris, I., and Hung, Y. (2024)Statistica Sinica, 34(4), 1883-1902.
Mesh-clustered Gaussian process emulator for partial differential equation boundary value problems
16. Sung, C.-L., Wang, W., Ding, L., and Wang, X. (2024)Technometrics, 66(3), 406-421.
Stacking designs: designing multifidelity computer experiments with target predictive accuracy
15. Sung, C.-L., Ji, Y., Mak, S., Wang, W., and Tang, T. (2024)SIAM/ASA Journal on Uncertainty Quantification, 12(1), 157-181.
A review on computer model calibration
14. Sung, C.-L. and Tuo, R. (2024)WIREs Computational Statistics, 16(1), e1645.
★ Top 10 most-cited papers of 2024 in the journal — certificate
Efficient calibration for imperfect epidemic models with applications to the analysis of COVID-19
13. Sung, C.-L. and Hung, Y. (2024)Journal of the Royal Statistical Society: Series C, 73(1), 47-64.
Modeling of thermophysical properties and vapor-liquid equilibrium using Gaussian process regression
12. Zhou, M., Ni, C., Sung, C.-L., Ding, S., and Wang, X. (2024)International Journal of Heat and Mass Transfer, 219, 124888.
Data-driven modeling of general fluid density under subcritical and supercritical conditions
11. Zhou, M., Chen, W. , Su, X., Sung, C.-L., Wang, X., and Ren, Z. (2023)AIAA Journal, 61(4), 1519-1531.
A clustered Gaussian process model for computer experiments
10. Sung, C.-L., Haaland, B., Hwang, Y., and Lu, S. (2023)Statistica Sinica, 33(2), 893-918.
Calibration of inexact computer models with heteroscedastic errors
9. Sung, C.-L., Barber, B. D., and Walker, B. J. (2022)SIAM/ASA Journal on Uncertainty Quantification, 10(4), 1733-1752.
★ Media coverage on interdisciplinary plant science collaboration 🔗
Estimating functional parameters for understanding the impact of weather and government interventions on COVID-19 outbreak
8. Sung, C.-L. (2022)Annals of Applied Statistics, 16(4), 2505-2522.
Calibration for computer experiments with binary responses and application to cell adhesion study
7. Sung, C.-L., Hung, Y., Rittase, W., Zhu, C., and Wu, C. F. J. (2020)Journal of the American Statistical Association, 115(532), 1664-1674.
A generalized Gaussian process model for computer experiments with binary time series
6. Sung, C.-L., Hung, Y., Rittase, W., Zhu, C., and Wu, C. F. J. (2020)Journal of the American Statistical Association, 115(530), 945-956.
Multi-resolution functional ANOVA for large-scale, many-input computer experiments
5. Sung, C.-L., Wang, W., Plumlee, M., and Haaland, B. (2020)Journal of the American Statistical Association, 115(530), 908-919.
Kernel-smoothed proper orthogonal decomposition (KSPOD)-based emulation for prediction of spatiotemporally evolving flow dynamics
4. Chang, Y.-H., Zhang, L., Wang, X., Yeh, S.-T., Mak, S., Sung, C.-L., Wu, C. F. J., and Yang, V. (2019)AIAA Journal, 57(12), 5269-5280.
An efficient surrogate model for emulation and physics extraction of large eddy simulations
3. Mak, S., Sung, C.-L., , Yeh, S.-T., Wang, X., Chang, Y.-C., Joseph, V. R., Yang, V., and Wu, C. F. J. (2018)Journal of the American Statistical Association, 113(524):1443-1456.
★ SPES Award (ASA) 🔗
Data-driven analysis and mean flow prediction using a physics-based surrogate model for design exploration
2. Yeh, S.-T., Wang, X., Sung, C.-L., Mak, S., Chang, Y.-H., Wu, C. F. J., and Yang, V. (2018)AIAA Journal, 56(6):2429-2442.
Exploiting variance reduction potential in local Gaussian process search
1. Sung, C.-L., Gramacy, R. B., and Haaland, B. (2018)Statistica Sinica, 28(2):577-600.