Professor, Department of Statistics, University of Cincinnati
Won Chang is currently a professor at the University of Cincinnati. Before this new appointment, Won was a postdoctoral scholar in Department of Statistics at the University of Chicago. His 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.
- Big data issues in climate research:
- Modeling non-Gaussian and high-dimensional space-time data
- Climate model emulation and calibration
- Conditional simulation of climate processes
Current Research Project at RDCEP:
- Chang, W., Haran, M., Applegate, P.J., Pollard, D. (2015) Calibrating an ice sheet model using high-dimensional non-Gaussian spatial data, accepted for publication in Journal of the American Statistical Association.
- 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., 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