Duchwan Ryu, Ph.D.

Associate Professor

Duchwan Ryu earned his Ph.D. from Texas A&M University and joined NIU in 2014.

He develops Bayesian statistical methods for complex data and has published in top journals in statistics and epidemiology.

Education

Ph.D., Texas A&M University — Bayesian Analysis, Statistical Modeling, and Data Science Applications

Research

Ryu develops Bayesian statistical models for complex, high-dimensional data in genomics, neuroscience, epidemiology and engineering, drawing on Bayesian nonparametric regression, functional data analysis and sequential Monte Carlo methods. A major focus is identifying differentially methylated regions in DNA — with direct applications to cancer research and epigenomics — and his work extends to spatio-temporal modeling, Bayesian analysis of infectious disease and statistical modeling of neural activity. He has been supported by the Department of Energy and King Abdullah University of Science and Technology and has published in the Journal of the American Statistical Association, Biometrics and the Journal of Multivariate Analysis.

Selected Publications

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.