Won Chang (장원, 張源) Associate Professor
Department of Statistics
Seoul National Universityemail : wonchang at snu.ac.kr
Curriculum Vitae
Education and Training
Postdoctoral Scholar, Department of Statistics, University of Chicago, August 2014 - July 2016 (mentored by Dr. Michael L. Stein and co-mentored by Dr. Elisabeth J. Moyer)
Ph.D. Statistics, Pennsylvania State University, 2014.
Thesis title: Climate model calibration using high-dimensional and non-Gaussian spatial data (Thesis advisor: Dr. Murali Haran, Thesis co-advisor: Dr. Klaus Keller).
M.S. Statistics, Korea University, 2009.
Thesis title: Estimating volatility and distribution of European option prices using Bayesian UHF GARCH-M model (Thesis advisor: Dr. Yousung Park).
B.S. Statistics, Korea University, 2007.
Employment
Associate Professor, Department of Statistics, Seoul National University Sep 2024 -
Associate Professor, Department of Mathematical Sciences, University of Cincinnati, July 2022 - Aug 2024
Assistant Professor, Department of Mathematical Sciences, University of Cincinnati, August 2016 - June 2022
Research
- Interests: The key topics of my recent work include
- Uncertainty quantification for computer model experiments using deep learning and Gaussian processes.
- Spatial data analysis for for various fields of application including atmospheric science, hydrology, and genetics.
- Publications:
Hwang, Y., Kim, H. J., Chang, W., Hong, C., MacEachern, S. N. (2024+) Bayesian Model Calibration and Sensitivity Analysis for Oscillating Biological Experiments, submitted (arXiv:2110.10604 [stat.AP])
Xie, J., Jeon, H., Chang, W., Jeon, Y., Li, Z., Ma, Q., Chung, D. (2024+) spaDesign: A Statistical Framework to Improve the Design of Sequencing-based Spatial Transcriptomics Experiments, submitted
Chang, W, Haran, M. (2024+), Computer Model Emulation and Calibration for Discrete Data. In Bingham, D., Haran, M., Oakley, J., Sanso, B. (Ed.), Handbook of Statistical Methods for Computer Modeling, accepted for publication
Lee, K., Chang, W. Cao, X. (2024+) The joint local dependence Cholesky prior for bandwidth selection across multiple groups, accepted for publication in Bayesian Analysis
Cho, D., Chang, W., Park, J. (2024+) Fast Computer Model Calibration using Annealed and Transformed Variational Inference, accepted for publication in the Journal of Computational and Graphical Statistics
Bonas, M., Datta, A., Wikle, C. K., Boone, E. L., Alamri, F., Hari, B. V., Kavila, I., Simmons, S. J., Jarvis, S. M., Burr, W. S., Pagendam, D., Chang, W., Castruccio, S. (2024+) Assessing Predictability of Environmental Time Series with Statistical and Machine Learning Models, accepted for publication in Environmetrics
Park, Y., Chang, W. (2024+) A Personalized Dose-Finding Algorithm Based on Adaptive Gaussian Process Regression, accepted for publication in Pharmaceutical Statistics
Jeon, Y., Chang, W., Jeong, S., Park, J. (2024) A Bayesian Convolutional Neural Network-based Generalized Linear Model, Biometrics, 80 (2), ujae057
Kim, H., Chang, W., Chae, S. J., Park, J-E, Seo, M., Kim, J. K. (2024), scLENS: Data-driven signal detection for unbiased scRNA-seq data analysis, Nature Communications, 15, 3575
Jo, H., Hong, H., Hwang, H. J., Chang, W., Kim, J. K. (2024) Density Physics-Informed Neural Network reveals sources of cell heterogeneity in signal transduction, Patterns, 5 (2), 100899
Wang, S., Kim, S., Cho, H., Chang, W. (2024) Nonparametric Predictive Model for Sparse and Irregular Longitudinal Data, Biometrics, 80 (1), ujad023
Park, J., Yi, S., Chang, W., Mateu, J. (2023) A Spatio-Temporal Dirichlet Process Mixture Model for Coronavirus Disease-19, Statistics in Medicine, 42 (30), 5555-5576
Lee, M. P., Hoang, K., Park, S., Song, Y. M., Joo, E. Y., Chang, W., Kim, J. H. , Kim, J. K. (2023) Imputing Missing Sleep Data from Wearables with Neural Networks in Real-World Settings, SLEEP, 47 (1), zsad266
Allen, C., Chang, Y., Neelon, B., Chang, W., Kim, H. J., Li, Z., Ma, Q., Chung, D. (2023) A Bayesian Multivariate Mixture Model for Spatial Transcriptomics Data, Biometrics, 73 (3), 1775-1787
Rahat, S., H., Steissberg, T., Chang, W., Chen, X., Mandavya, G., Tracy, J., Wasti, A., Atreya, G., Saki, S., Bhuiyan, M. D. A., Ray, P. (2023) Remote Sensing-Enabled Machine Learning for River Water Quality Modeling Under Multidimensional Uncertainty, Science of the Total Environment, 898, 165504
Deng, Q., Nam, J. H., Yilmaz, A. S., Chang, W., Pietrzak, M., Li., L., Kim, H. J., Chung, D. (2023) graph-GPA 2.0: improving multi-disease genetic analysis with integration of functional annotation data, Frontiers in Genetics, 14
Jeong, J, and Chang, W. (2023) Analysis of East Asia Wind Vectors Using Space–Time Cross-Covariance Models, Remote Sensing, 15 (11), 2860
Jeon H, Xie J, Jeon Y, Jung KJ, Gupta A, Chang W, Chung D. (2023) Statistical Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments: Review, Tutorial, and Perspectives, Biomolecules, 13 (2), 221
Wikle, C. K., Datta, A., Hari, B. V., Boone, E. L., Sahoo, I., Kavila, I., Castruccio, S., Simmons, S. J., Burr, W. S., Chang, W. (2022). An illustration of model agnostic explainability methods applied to environmental data, Environmetrics, 34 (1), e2772.
Chang, W., Konomi, B. A., Karagiannis, G., Guan, Y., Haran, M. (2022) Ice model calibration using semi-continuous spatial data, the Annals of Applied Statistics, 16 (3), 1937-1961
Bhatnagar, S., Chang, W., Kim., S., Wang, J. (2022) Computer Model Calibration with Time Series Data using Deep Learning and Quantile Regression, SIAM/ASA Journal on Uncertainty Quantification, 10 (1), 1-26 (Winner of the 2021 American Statistical Association Section on Statistics and the Environment Student Paper Competition)
Park, J., Chang, W., Choi, B. (2022) An interaction Neyman-Scott point process model for Coronavirus Disease-19, Spatial Statistics, 47, 100561
Tracy, J., Chang, W., Freeman, S., Brown, C., Palma, A., Ray, P. (2021) Enabling Dynamic Emulation of High-Dimensional Model Outputs: Demonstration for Mexico City Groundwater Management, Environmental Modelling & Software, 147, 105238
Wang, J., Liu, Z., Foster, I., Chang, W., Kettimuthu, R., Kotamarthi, R. (2021) Fast and accurate learned multiresolution dynamical downscaling for precipitation, Geoscientific Model Development, 14 (10), 6355-672
Kim, S., DeSarbo, W., and Chang, W. (2021) Note: A new approach to the modeling of spatially dependent and heterogeneous geographical regions, International Journal of Research in Marketing, 38 (3), 792-803
Plumlee, M., Asher, T. G., Chang, W., Bilskie, M. (2021) High-fidelity hurricane surge forecasting using emulation and sequential experiments, the Annals of Applied Statistics, 15 (1), 460-480
Chang, W., Wang, J., Marohnic, J., Kotamarthi, V.R., and Moyer, E. J. (2020) Diagnosing added value of convection-permitting regional models using precipitation event identification and tracking, Climate Dynamics, 55, 175-192
Chang, W., Kim, S., Chae, H. (2020) A regularized spatial market segmentation method with Dirichlet process Gaussian mixture prior, Spatial Statistics, 35, 100402
Guan, Y., Sampson, C., Tucker, D., Chang, W., Mondal, A., Haran, M., Sulsky, D. (2019) Computer model calibration based on image warping metrics: an application for sea ice deformation, the Journal of Agricultural, Biological and Environmental Statistics, 24(3), 444-463.
Olson, R., An, S.-I., Fan, Y., Chang, W., Evans, J. P., & Lee, J.-Y. (2019). A novel method to test non-exclusive hypotheses applied to Arctic ice projections from dependent models, Nature Communications, 10 (1), 3016
Hwang, Y., Kim, H.J., Chang, W., Yeo, K., Kim., Y. (2019) Bayesian pollution source identification via an inverse physics model, Computational Statistics & Data Analysis, 134, 76-92
Olson, R., Ruckert, K. L., Chang, W., Keller, K., Haran, M., and An, S.-I. (2018) Stilt: easy emulation of AR(1) computer model output in multidimensional parameter space, the R Journal , 10 (2), 209--225
Chang, W., and Chen, X. (2018) Monthly Rainfall-Runoff Modeling at Watershed Scale: A Comparative Study of Data-Driven and Theory-Driven Approaches, Water, 10 (9), 1116
Haran, M., Chang, W., Keller, K., Nicholas, R., and Pollard, D. (2017) Statistics and the Future of the Antarctic Ice Sheet, Chance, 30 (4), 37-44.
Chang, W., Stein, M. L., Wang, J., Kotamarthi, V. R., and Moyer, E. J. (2016) Changes in spatio-temporal precipitation patterns in changing climate conditions, Journal of Climate, 29 (23), 8355-8376
Chang, W., Haran, M., Applegate, P.J., and Pollard, D. (2016) Improving ice sheet model calibration using paleoclimate and modern data, the Annals of Applied Statistics, 10 (4), 2274-2302
Jeon, S., Chang, W., and Park, Y. (2016) An option pricing model using high frequency data, Procedia Computer Science, 91, 175-179
Pollard, D., Chang, W., Haran, M., Applegate, P., and DeConto, R. (2016) Large ensemble modeling of last deglacial retreat of the West Antarctic Ice Sheet: Comparison of simple and advanced statistical techniques, Geoscientific Model Development, 9, 1697-1723.
Chang, W., Haran, M., Applegate, P.J., and Pollard, D. (2016) Calibrating an ice sheet model using high-dimensional binary spatial data, Journal of the American Statistical Association, 111 (513), 57-72. (a minor correction for figures here)
Chang, W., Haran, M., Olson, R., and Keller, K. (2015) A composite likelihood approach to computer model calibration with high-dimensional spatial data, Statistica Sinica, 25 (1), 243-260
Chang, W., Haran, M., Olson, R., and Keller, K. (2014) Fast dimension-reduced climate model calibration and the effect of data aggregation, the Annals of Applied Statistics, 8 (2), 649-673 (Winner of the 2014 American Statistical Association Section on Statistics and the Environment Student Paper Competition)
Chang, W., Applegate, P.J., Haran, M. and Keller, K. (2014) Probabilistic calibration of a Greenland Ice Sheet model using spatially-resolved synthetic observations: toward projections of ice mass loss with uncertainties, Geoscientific Model Development, 7, 1933-1943
Olson, R., Sriver, R., Chang, W., Haran, M., Urban, N.M., and Keller, K. (2013) What is the effect of unresolved internal climate variability on climate sensitivity estimates?, Journal of Geophysical Research - Atmospheres, 118 (10), 4348-4358
Professional Services
Associate Editor, Journal of Korean Statistical Society, 2023 - present
Associate Editor, Journal of Agricultural, Biological and Environmental Statistics, 2023 - present
Associate Editor, Stat, 2023 - present
Honors and Awards
Elected Member, International Statistical Institute (ISI), 2021
Career Development Award, Korean International Statistical Society, 2019
Winner, American Statistical Association Section on Statistics and the Environment Student Paper Competition, 2014
Graduate Fellow, Eberly College of Science, The Pennsylvania State University, Fall 2009 - Spring 2010
Teaching
Instructor for Statistical Seminar (Uncertainty Quantification for Deep Learning), Fall 2024
Instructor for Machine Learning and Statistics, Spring 2020, Spring 2021, Spring 2022, Spring 2023, and Spring 2024
Instructor for Bayesian Data Science, Fall 2023
Instructor for Introduction to Data Science, University of Cincinnati, Spring 2020, Spring 2021, Spring 2022, Spring 2023, Fall 2023 and Spring 2024
Instructor for Applied Statistics I, University of Cincinnati, Fall 2017, Fall 2018, Fall 2019, Fall 2020 and Fall 2021
Instructor for Statistical Computing with R and SAS, Fall 2021 Instructor for Spatial Statistics, University of Cincinnati, Spring 2017, Spring 2019
Instructor for Probability and Statistics I, University of Cincinnati, Fall 2016 - Fall 2019
Instructor for Introduction to Statistics, Pennsylvania State University, Summer 2012.
Instructor for Introduction to Biometry, Pennsylvania State University, Spring 2012.
Media Coverage
"Understanding Uncertainty in Glacier Models — Before the Ice Melts", Siam News
UC Professor: Two-Degree Increase Enough To End Arctic Sea Ice, WVXU News
Arctic Ocean could have no September sea ice if global average temperatures increase by 2 degrees, UC News
