Department of Mathematical Sciences
University of Cincinnati
email : changwn at ucmail.uc.edu   or   won.chang at uc.edu
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.
Assistant Professor, Department of Mathematical Sciences, University of Cincinnati, August 2016 -
- Interests: My current research focuses on resolving "big data'' issues in uncertainty quantification and spatial modeling for environmental research and business analytics. My methodological work pertains developing new computationally efficient approaches to analyzing large data sets with complex dependence structures and distributional properties for which traditional methods are not scalable. The key topics of my recent work include
- Analysis of high-dimensional non-Gaussian spatial data
- Computer model emulation and calibration using dimension reduction, Gaussian processes, and deep learning
- Spatial data analysis for for various fields of application including atmospheric science, hydrology, and business analytics
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, submitted
Park, J., Chang, W., Choi, B. (2021) An interaction Neyman-Scott point process model for Coronavirus Disease-19, submitted (arXiv:2102.02999 [stat.AP])
Wang, J., Liu, Z., Foster, I., Chang, W., Kettimuthu, R., Kotamarthi, R. (2021) Fast and accurate learned multiresolution dynamical downscaling for precipitation, submitted (arXiv:2101.06813 [cs.LG])
Bhatnagar, S., Chang, W., Kim., S., Wang, J. (2021) Computer Model Calibration with Time Series Data using Deep Learning and Quantile Regression, submitted (arXiv:2008.13066 [stat.ML]) (Winner of the 2021 American Statistical Association Section on Statistics and the Environment Student Paper Competition)
Chang, W., Konomi, B. A., Karagiannis, G., Guan, Y., Haran, M. (2021) Ice model calibration using semi-continuous spatial data, submitted (arXiv:1907.13554 [stat.ME])
Plumlee, M., Asher, T. G., Chang, W., Bilskie, M. (2020) High-fidelity hurricane surge forecasting using emulation and sequential experiments, the Annals of Applied Statistics, 15 (1), 460-480
Kim, S., DeSarbo, W., and Chang, W. (2020) Note: A new approach to the modeling of spatially dependent and heterogeneous geographical regions, International Journal of Research in Marketing, in press
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, 30, 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. (2018) 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 Xi, C. (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
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
Instructor for Machine Learning and Statistics, Spring 2020 and Spring 2021
Instructor for Introduction to Data Science, University of Cincinnati, Spring 2020, Spring 2021
Instructor for Applied Statistics I, University of Cincinnati, Fall 2017, Fall 2018, Fall 2019, Fall 2020
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.