Won Chang

personal webpage: http://wonchang.net

Ph.D. : Pennsylvania State University University Park, 2014 (Statistics)

M.S.: Korea University Seoul, 2009 (Statistics)

B.S.: Korea University Seoul, 2007 (Statistics)

High-dimensional spatial data analysis, Non-Gaussian spatial data analysis, Data-driven simulation of climate processes, Computer model emulation and calibration, Composite likelihood, Statistical methods in environmental science, Bayesian inference, Time series

Grant: #1000006747/GR#127836(R21HG012482) Investigators:Chang, Won 06-01-2022 -05-31-2024 National Human Genome Research Institute Statistical Power Calculation Framework for Spatially Resolved Transcriptomics Experiments Role:PI 12563.00 Hold Level:Federal

Peer Reviewed Publications

Park, J., Chang, W., Choi, B. (2022. ) An interaction Neyman-Scott point process model for Coronavirus Disease-19 .Spatial Statistics, , 47 ,100561

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

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

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

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

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

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

Chang, W., Kim, S., Chae, H. (2020. ) A regularized spatial market segmentation method with Dirichlet process Gaussian mixture prior .Spatial Statistics, , 30 ,100402

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

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

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

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

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

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 ) ,

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

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

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

Jeon, S., Chang, W., and Park, Y. (2016. ) An option pricing model using high frequency data .Procedia Computer Science, , 91 ,175

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

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

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

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

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

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

Invited Presentations

Won Chang (10-2021. ) Bayesian Model Calibration and Sensitivity Analysis for Oscillating Biological Experiments .Department of Biostatistics, University of Louisville, Louisville, KY. Level:Department

Won Chang (06-2021. ) New Statistical Framework for Large Scale Computer Model Calibration Using Deep Learning .Ecosta 2021, Hong Kong. Level:International

Won Chang (05-2021. ) Statistical Inference with Neural Network Imputation for Item Nonresponse .Department of Statistics, Korea University, Seoul. Level:Department

Won Chang (03-2021. ) Computer Model Emulation and Calibration Using Complex Spatial and Temporal Data .Department of Statistics, Yonsei University, Seoul. Level:Department

Won Chang (12-2020. ) Computer Model Emulation and Calibration Using Complex Spatial and Temporal Data .LANL Stats Seminar, Los Alamos National Laboratory, NM. Level:Prof. Org.

Won Chang (11-2020. ) Computer Model Emulation and Calibration Using Complex Spatial and Temporal Data .Departmental Colloquium, Department of Statistics, University of Illinois, Urbana-Champaign, IL. Level:Department

Ice Model Calibration Using Semi-continuous Spatial Data (10-2020. ) Statistics Colloquium, Department of Mathematics and Statistics, University of Maryland, Baltimore County, MD. Level:Department

Won Chang (09-2020. ) Ice Model Calibration Using Semi-continuous Spatial Data .UC Day at JPL, NASA Jet Propulsion Laboratory, CA. Level:Prof. Org.

Won Chang (10-2019. ) Ice Model Calibration using Semi-continuous Spatial Data .ICOSDA 2019, Grand Rapids, MI. Level:National

Won Chang (10-2019. ) Ice Model Calibration using Semi-continuous Spatial Data . Department of Statistics, Ohio State University, Columbus, OH. Level:Department

Won Chang (08-2019. ) New Statistical Framework for Large Scale Computer Model Calibration Using Deep Learning .SAMSI Deep Learning Workshop, Research Triangle Park, NC. Level:National

Won Chang (05-2019. ) Ice Model Calibration using Zero-Inflated Continuous Spatial Data .SAMSI MUMS Closing Workshop, Research Triangle Park, NC. Level:National

Won Chang (11-2018. ) ‘Bit Data’ Challenges in Uncertainty Quantification and Environmental Statistics .Department of Physics, University of Dayton, Dayton, OH. Level:Department

Won Chang (08-2018. ) Computer Model Emulation and Calibration using High-dimensional and Non-Gaussian Spatial Data .SAMSI MUMS Opening Workshop, Research Triangle Park, NC. Level:National

Won Chang (07-2018. ) A Bayesian Spatial Market Segmentation Method Using Dirichlet Process Gaussian Mixture Model and LASSO regularization .ISBA-EAC, Seoul, Korea. Level:International

Won Chang (07-2018. ) Computer Model Emulation and Calibration using High-dimensional and Non-Gaussian Spatial Data .Young Statistician's Meeting, Yangpyeong, Korea. Level:National

Won Chang (06-2018. ) A Bayesian spatial market segmentation method using Dirichlet process-Gaussian mixture models .Ecosta 2018, Hong Kong. Level:International

Won Chang (06-2018. ) Calibrating an ice sheet model using high-dimensional binary spatial data .IMS-APRM, Singapore. Level:International

Won Chang (05-2018. ) Ice Model Calibration using Zero-Inflated Continuous Spatial Data .SAMSI CLIM Transition Workshop, Research Triangle Park, NC. Level:National

Won Chang (12-2017. ) Diagnosing added value of convection-permitting regional models using precipitation event identification and tracking .Department of Atmospheric Sciences, University of Illinois, Champaign, IL. Level:Department

Won Chang (12-2017. ) Changes in Spatio-temporal Precipitation Patterns in Changing Climate Conditions .IISA International Conference on Statistics 2017, Hyderabad, India. Level:International

Won Chang (11-2017. ) Calibrating an ice sheet model using high-dimensional binary spatial data .Department of Mathematics and Statistics, University of North Carolina, Charlotte, NC. Level:Department

Won Chang (08-2017. ) Diagnosing added value of convection-permitting regional models using precipitation event identification and tracking .SAMSI Mathematical and Statistical Methods for Climate and Earth Systems Program Opening Workshop, Durham, NC. Level:National

Won Chang (05-2017. ) Calibrating an ice sheet model using high-dimensional binary spatial data .Korean Statistical Society Spring Meeting 2017, Seoul, Korea. Level:International

Won Chang (02-2017. ) Improving ice sheet model calibration using paleoclimate and modern data .Department of Geography, University of Cincinnati, Cincinnati, Cincinnati, OH. Level:Department

Won Chang (01-2017. ) Improving ice sheet model calibration using paleoclimate and modern data .Korean National Institute for Mathematical Sciences, Daejeon, Korea. Level:International

Won Chang (11-2016. ) Calibrating an ice sheet model using high-dimensional binary spatial data .University of Akron, Department of Statistics, Akron, OH. Level:Department

Poster Presentations

Won Chang (12-2018. ) New Statistical Framework for Large Scale Computer Model Calibration Using Deep Learning .AGU 2018 Fall Meeting, Washington DC. . Level:National

Won Chang (12-2017. ) Diagnosing added value of convection-permitting regional models using precipitation event identification and tracking .AGU 2017 Fall Meeting, New Orleans, LA. . Level:National

Paper Presentations

Won Chang (08-2018. ) Changes in Spatiotemporal Precipitation Patterns in Changing Climate Conditions .Vancouver, BC. Conference. Level:International

Won Chang (07-2017. ) Improving ice sheet model calibration using paleoclimate and modern data .Lancaster, UK. Conference. Level:International

Won Chang Improving ice sheet model calibration using paleoclimate and modern data .Hong Kong. Conference. Level:International

04-2021 Elected Member, International Statistical Institute (ISI)

08-2014-07-2016 Postdoctoral Scholar, (with Dr. Michael Stein and Dr. Elisabeth Moyer) , University of Chicago, , Chicago

Academic - Office
5516 French Hall
Cincinnati  Ohio, 45221
Phone: 513-556-4069
changwn@ucmail.uc.edu