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outreach
portfolio
Design and analysis of multi-fidelity computer experiments
I’m interested in developing new statistcal emulators and experimental designs for multi-fidelity computer experiments. The hope is to
- maximize predictive power of the multi-fidelity emulator via a careful design of experiments, and
- ensure this model achieves a desired error tolerance with confidence.
publications
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.
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.
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 from ASA]
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.
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.
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.
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.
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 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. [story about the collaboration with plant scientists]
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.
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.
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.
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.
A review on computer model calibration
14. Sung, C.-L. and Tuo, R. (2024)WIREs Computational Statistics, 16(1), e1645.
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.
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.
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.
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 from CIPS]
Active learning for a recursive non-additive emulator for multi-fidelity computer experiments
19. Heo, J. and Sung, C.-L. (2024+)Technometrics, accepted. [Winner of INFORMS 2023 QSR 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. (2024+)Plant Physiology, accepted.
software
students
submitted
Advancing inverse scattering with surrogate modeling and Bayesian inference for functional inputs
1. Sung, C.-L., Song, Y., and Hung, Y. (2023+)talks
Spring Research Conference (SRC) 2016
2016 ICSA Symposium
NAE Regional Meeting
Statistical Perspectives of Uncertainty Quantification (SPUQ) 2017
ISBIS 2017 Meeting
Joint Statistical Meetings (JSM) 2017 Conference
Georgia Statistics Day 2017
INFORMS 2017 Conference
SIAM Conference on Uncertainty Quantification (UQ18)
Workshop on Computer Experiments, Academia Sinica, Taiwan
INFORMS 2018 Conference
Research Colloquium, Purdue University
Seminar, Institute of Statistics, National Tsing Hua University
Seminar, Academia Sinica
The 28th South Taiwan Statistics Conference
The 3th International Conference on Econometrics and Statistics (EcoSta 2019)
The Fifth International Conference on the Interface between Statistics and Engineering (ICISE) 2019
International Conference on Statistical Distributions and Applications (ICOSDA) 2019
INFORMS 2019 Conference
Colloquium, Michigan State University
Seminar, University of California, Los Angeles
Joint Statistical Meetings (JSM) 2020 Conference
UQ Seminar, Academy of Mathematics and Systems Science, Chinese Academy of Sciences
Joint Statistical Meetings (JSM) 2021 Conference
INFORMS 2021 Conference
Seminar, Institute of Statistics, National Tsing Hua University
The 5th International Conference on Econometrics and Statistics (EcoSta 2022)
Seminar, Institute of Statistical Science, Academia Sinica
Joint Statistical Meetings (JSM) 2022 Conference
Seminar, Department of Statistics, Virginia Tech
Seminar, School of Mathematics and Statistics, University of St Andrews
Spring Research Conference (SRC) 2023
2023 ICSA Applied Statistics Symposium
ISI World Statistics Congress 2023
Industry 4.0 Technology Implementation Workshop
Seminar, TAMIDS, Texas A&M University
Seminar, Department of Statistics, National Chengchi University
Seminar, Institute of Statistical Science, Academia Sinica
Annual Meeting and Conference of Chinese Statistical Association
ICSDS2024: 2024 International Conference for Statistics and Data Science
teaching
STT 801: Design of Experiments
Graduate course, Michigan State University
Semester: 2023 Spring, 2022 Spring, 2021 Spring
Textbook: Experiments: Planning, Analysis, and Optimization, 2nd Edition
Design of experiments (DOE) is the laying out of a detailed experimental plan in advance of doing the experiment. In this course, students will learn various experimental designs and their applications. Students will also learn to analyze data and determine the relationship between potential factors and the output of that process from planned experiments, using the statistical software (R). This course covers the following topics:
- experimental designs: fractional factorial experiments, Latin squares, orthogonal arrays, robust parameter designs, and other designs
- data analysis for experimental data: linear regression model, analysis of variance and covariance (ANOVA), analysis of covariance (ANCOVA), random effect model, response surface methodology, and others
- statistical software: R
- model interpretation
- presentation and data visualization
STT 481: Capstone in Statistics
Undergraduate course, Michigan State University
Semester: 2024 Fall, 2023 Spring, 2022 Fall, 2022 Spring, 2021 Fall, 2021 Spring, 2020 Fall, 2020 Spring, 2019 Fall, 2019 Spring, 2018 Fall
Textbook: An Introduction to Statistical Learning with Applications in R
Statistical capstone experiences are essential for statisticians to perform an in-depth analysis of real-world data. Capstone experiences can develop statistical thinking by engaging in a consulting-like experience that requires skills outside the scope of traditional courses: defining a complex problem, analyzing data, building a strong team, and communicating effectively. In this course, selected projects will be given to illustrate special problems encountered by professional statisticians in their roles as consultants, educators, and analysts. This course covers the following topics:
- problem formulation
- advanced statistical modeling, preliminary data analysis, and machine learning
- Statistical software (R)
- thorough and elaborate statistical analyses of data
- final presentation and data visualization
STT 997: Advanced Topics in Statistics
PhD-level course, Michigan State University
Semester: 2024 Spring
Textbook: Surrogates: Gaussian Process Modeling, Design, and Optimization for the Applied Sciences
Explore the fundamental concepts of Uncertainty Quantification (UQ) in this special-topic course. Uncertainty is inherent in various fields, from engineering to finance, and understanding its impact is crucial for informed decision-making. Through a combination of theoretical insights and practical applications, this course will cover key UQ principles, modern statistical methods, and computational tools. This course covers the following topics:
- Gaussian process (GP) regression
- Space-filling designs and model-based designs
- Sensitivity analysis
- Computer model calibration
- Bayesian optimization
- Multi-fidelity simulations
STT 442: Probability and Statistics II: Statistics
Undergraduate course, Michigan State University
Semester: 2024 Fall
Textbook: Mathematical Statistics and Data Analysis (3rd edition)
This course provides a comprehensive introduction to key concepts in probability and statistics, with a balanced emphasis on both theoretical principles and practical applications. The course material will be drawn from Chapters 5-9 and Chapters 11-14 of the textbook, and lecture notes on time series. Specifically, the curriculum covers a wide range of topics, including:
- Limit theorems
- Hypothesis testing
- Survey sampling
- Parameter estimation
- Analysis of variance (ANOVA)
- Categorical data analysis
- Time series (ARMA model, data analysis, forecasting)