Ph.D., 2007, University of Wisconsin - Madison, Statistics
M.S., 2002, Tianjin University, China, Applied Mathematics
B.S., 2000, Tianjin University, China, Applied Mathematics
Dr. Ruiyan Luo received her Ph.D. in Statistics from University of Wisconsin-Madison in 2007. Subsequently she was a postdoctoral at Yale University, Department of Epidemiology and Public Health. In 2010, she joined the Department of Mathematics and Statistics in GSU as an Assistant Professor.
Dr. Luo joined the School of Public Health as an Assistant Professor in Biostatistics in August 2012. Her research focuses on functional data analysis, Bayesian statistics, and high dimensional data analysis. She is interested in developing novel methods for linear and nonlinear functional regression models, with functional responses and/or multiple or even thousands of functional predictors. She also is interested in developing Bayesian statistical models to address problems in public health and biology, and developing novel statistical methods for inferring biological networks and analyzing high dimensional data emerging from genetics.
Click here to view free software packages created for biostats researchers by Dr. Luo.
Ruiyan Luo and Xin Qi. (Accepted) Interaction model and model selection for function-on-function regression. Journal of Computational and Graphical Statistics.
Xin Qi and Ruiyan Luo. (Accepted) Nonlinear functional regression with functional response and multiple functional predictors. Statistica Sinica. DOI: 10.5705/ss.202017.0249
Xin Qi, and Ruiyan Luo. (2018) Function on function regression with thousands of predictive curves. Journal of Multivariate Analysis. 163(C): 51-66.
Ruiyan Luo, and Xin Qi (2017) Signal extraction approach for sparse multivariate response regression. Journal of Multivariate Analysis. 153: 83-97.
Ruiyan Luo, and Xin Qi (2017) Function-on-function linear regression by signal compression. Journal of American Statistical Association.112(518):690-705.
Ruiyan Luo, and Xin Qi (2016) Functional wavelet regression for function-on-function linear models. Electronic Journal of Statistics. 10(2): 3179-3216