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Won Chang

Title: Assistant Professor in Statistics
Office: 5516 French Hall
Tel: 513-556-4069
Email: won.chang@uc.edu
Web: http://www.wonchang.net/

My research focuses on statistical methods for simulating important aspects of future climate such as storm patterns and sea level changes. The statistical challenges of this work involve performing conditional simulation and climate model calibration using high-dimensional and non-Gaussian space-time data. 

  • My research encompasses these key areas:
     
    • 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 analysis

Education

  • Ph.D. , Pennsylvania State University, University Park, 2014 (Statistics).
  • M.S., Korea University, Seoul, 2009 (Statistics).
  • B.S., Korea University, Seoul, 2007 (Statistics).

Research Information

Research Interests

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

Publications

Peer-reviewed Publications

  • Chang, W., Stein, M. L., Jiali, W., 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., Applegate, P.J., Pollard, D. (2016). Calibrating an ice sheet model using high-dimensional binary spatial data. Journal of the American Statistical Association, 111 (513), 57.
  • 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., Applegate, P.J., Pollard, D. (2016). Improving ice sheet model calibration using paleoclimate and modern data. the Annals of Applied Statistics, 10 (4), 2274.
  • Pollard, D., Chang, W., Haran, M., Applegate, P., and DeConto, R. (2015). Large ensemble modeling of last deglacial retreat of the West Antarctic Ice Sheet: Comparison of simple and advanced statistical techniques. Geoscientific Model Developement, 9, 1697.
  • 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.
  • 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., 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.
  • Jeon, S., Chang, W., Park, Y. (2016). An Option Pricing Model using High Frequency Data. Procedia Computer Science, 91, 175.
  • 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.

Presentations & Lectures

Invited Presentations

  • Won Chang (11-2016). Calibrating an ice sheet model using high-dimensional binary spatial data University of Akron, Department of Statistics, Akron, OH.
  • Won Chang (05-2017). Calibrating an ice sheet model using high-dimensional binary spatial data Korean Statistical Society Spring Meeting 2017, Seoul, Korea.
  • Won Chang (01-2017). Improving ice sheet model calibration using paleoclimate and modern data Korean National Institute for Mathematical Sciences, Daejeon, Korea.
  • 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.
  • 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.
  • 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.
  • Won Chang (12-2017). Changes in Spatio-temporal Precipitation Patterns in Changing Climate Conditions IISA International Conference on Statistics 2017, Hyderabad, India.
  • Won Chang (02-2017). Improving ice sheet model calibration using paleoclimate and modern data Department of Geography, University of Cincinnati, Cincinnati, Cincinnati, OH.

Paper Presentations

  • Won Chang Improving ice sheet model calibration using paleoclimate and modern data. ECOSTA 2017, Hong Kong.
  • Won Chang (07-2017). Improving ice sheet model calibration using paleoclimate and modern data. Spatial Statistics 2017, Lancaster, UK.

Poster Presentations

  • 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.

Experience & Service

Post Graduate training and Education

  • 08-2014 to 07-2016, Postdoctoral Scholar, University of Chicago, Chicago.
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