McMicken College of Arts & SciencesMcMicken College of Arts & SciencesUniversity of Cincinnati

McMicken College of Arts & Sciences

Department of Mathematical Sciences

Department of Mathematical Sciences

People in the Department

Open Positions
View Opportunities
Update Personal Profile
eProfessional
< Back to list

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). (Under Review). 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.

Experience & Service

Post Graduate training and Education

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