People

The Division of Statistics and Data Science currently has 9 core research faculty and 2 affiliate faculty. In addition, some, mostly undergraduate, statistics courses are also taught by faculty in the Department in the Department of Mathematical Sciences, whose information can be viewed following the People link in the Department’s page. The division has about 30 graduate students pursuing MS in Statistics and PhD in Mathematics with concentration in Statistics. 

Faculty

Headshot of Xuan Cao

Xuan Cao

Assistant Professor in Statistics , A&S Mathematical Sciences

5307 French Hall

513-556-4853

Education

BS in Mathematics, Nanjing University 2013

PhD in Statistics, University of Florida 2018

Publications
Peer Reviewed Publications

Cao, X., Khare, K., Ghosh, M. (2019. )High-dimensional graph selection and estimation consistency for Bayesian DAG models .Annals of Statistics, ,47 (1 ),319-348

Q. Huang, R. Zhang, X. Hu, S. Ding, X. Cao, L. Tao, Z. Qian, and H. Liu. (2014. )Disturbed small-world networks and neurocognitive function in frontal lobe low-grade glioma patients .PLOS ONE, ,9 (4 ),

Cao, X., Khare, K., Ghosh, M. (2018. )High-dimensional posterior consistency for hierarchical nonlocal priors in regression .

Huang, Q., Cao, X., Chai, X., Wang, X., Xiao, C., Yang, K (2019. )3 dimensional pseudocontinuous arterial spin labeling and susceptibility-weighted magnetic resonance imaging associated with clinical progression in mild cognitive impairment and Alzheimer's disease .

Huang, Q., Cao, X., Chai, X., Xiao, C., Yang, K., Wang, J. (2019. )Values of 3-dimensional pseudocontinuous arterial spin labeling and protein cofilin-1 in follow-up of the postoperative gliomas compared with tumor grades .

Cao, X. (2019. )A permutation-based Bayesian approach for inverse covariance estimation .

Huang, Q., Cao, X., Chai, X., Wang, X., Xiao, C. (2019. )The radiological imaging features of easily misdiagnosed epithelioid glioblastoma in seven patients.World Neurosurgery, ,

Research and Practice Interests

High-dimensional inference on graphical models; High-dimensional model selection in regression; Statistical applications in neuroscience

Preferred Information

Assistant Professor in Statistics, https://xuan-cao.github.io

Headshot of Won Chang

Won Chang

Assistant Professor in Statistics , A&S Mathematical Sciences

5516 French Hall

513-556-4069

  • My current research focuses on resolving "big data'' issues in uncertainty quantification and spatial modeling for environmental research and business analytics. The key topics of my recent methodological work include
    • Analysis of high-dimensiolnal non-Gaussian spatial data
    • Computer model calibration using dimension reduction, Gaussian processes, and deep learning
    • Spatial modeling for marketing analysis using variable selection and Dirichlet processes
    • Image component analysis for spatio-temporal pattern of precipitation in high resolution climate models and observational data
Education

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

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

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

Publications
Peer Reviewed Publications

Chang, W., Wang, J., Marohnic, J., Kotamarthi, V.R., and Moyer, E. J. (2018. )Diagnosing added value of convection-permitting regional models using precipitation event identification and tracking .Climate Dynamics , ,doi:10.1007/s00382-018-4294-0 ,

Chang, W., and Xi, C. (2018. )Rainfall-runoff modeling at watershed scale: a machine learning approach with exploratory modeling capability .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, ,doi:10.32614/RJ-2018-049 ,

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, ,

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

Jeon, S., Chang, W., Park, Y. (2016. )An Option Pricing Model using High Frequency Data .Procedia Computer Science, ,91 ,175

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., 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. (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

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

Presentations
Invited Presentations

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 St