Duchwan Ryu, Ph.D.
Associate Professor, Director of Graduate and Undergraduate Studies
Duchwan Ryu has extensive experience in Bayesian models for various types of data, including, but not limited to, nonparametric regressions, longitudinal measurements, statistical classifications, LASSO regressions, measurements with random effects, state-space models, particle filters, survival data analysis, and other complicated and high-throughput data analysis, as well as functional data analysis, which enables us to develop appropriate statistical models and effective computational algorithms for the Bayesian functional data analysis in the detection of differentially methylated genomic regions and survival analysis.
Duchwan has successfully developed Bayesian models and has published papers in top journals for statistics and epidemiology. For very complicated longitudinal measurements, where the responses depend on the previous design points, he has proposed a unified, fully Bayesian framework to fit a flexible nonparametric regression model. Under the presence of measurement errors in predictors, he proposed a Bayesian nonparametric regression model for non-Gaussian responses. For high-dimensional data requiring extensive computation, he has applied Bayesian nonparametric regression models as an application of particle filters, which analyze spatio-temporal data.
Duchwan earned his Ph.D. from Texas A&M University and joined NIU in 2014.
Ryu, D., Bilgili, D. Ergonul, O., and Ebrahimi, N. (2018). Bayesian analysis of multiple-inflation Poisson models and its application to infection data. Brazilian Journal of Probability and Statistics, 32(2), 239-261.
Bilgili, D., Ryu, D., Ergonul, O., and Ebrahimi, N. (2016). Bayesian framework for para-metric bivariate accelerated lifetime modeling and its application to hospital acquired infections. Biometrics, 72(1), 56-63.
Ryu, D., Liang, F., and Mallick, B. (2013). Sea surface temperature modeling using radial basis function networks with a dynamically weighted particle filter. Journal of the American Statistical Association, 108(501), 111-123.
Ryu, D., Li, E., and Mallick, B. (2011). Bayesian nonparametric regression analysis of data with random effects covariates from longitudinal measurements. Biometrics, 67, 454-466.
Ryu, D., Sinha, D., Mallick, D., Lipsitz, S., and Lipshultz, S. (2007). Longitudinal studies with outcome-dependent follow-up: Models and Bayesian regression. Journal of the American Statistical Association, 102, 952-961.