Chih-Li Sung

Chih-Li Sung

Assistant Professor, Department of Statistics and Probability

Posts by Collection

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.

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.

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.

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.

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.

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.

software

students

submitted

Uncertainty-aware out-of-distribution detection with Gaussian processes

1. Chen, Y., Sung, C.-L., Kusari, A., Song, X., and Sun, W. (2024)

Active learning for finite element simulations with adaptive non-stationary kernel function

2. Boutelet, R. and Sung, C.-L. (2025)

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

Seminar, Department of Industrial and Systems Engineering, University of Washington

Workshop, UQ and Trustworthy AI Algorithms for Complex Systems and Social Good

teaching

STT 801: Design of Experiments

Graduate course, Michigan State University
Semester: 2025 Spring, 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)