Xuerong Meggie Wen is a Professor of Statistics in the Department of Mathematics & Statistics at Missouri University of Science and Technology, where she conducts research in advanced statistical methodologies. She is affiliated with the College of Arts, Sciences, and Education and maintains an active research program in dimension reduction, variable selection, survival analysis, and longitudinal data modeling. Ph.D. in Statistics, University of Minnesota M.S. in Operations Research, Chinese Academy of Sciences B.S. in Probability and Statistics, Peking University, China Her research focuses on sufficient dimension reduction , variable and model selection , and survival and recurrent event data analysis . She develops model-free and nonparametric methods for high-dimensional and complex data structures, with applications in science and engineering. Her work emphasizes conditional independence, sparsity, and efficient estimation without restrictive parametric assumptions. The recent publications show a strong trend in sufficient dimension reduction across multi-population and multi-index settings, with increasing emphasis on sparsity , link-free methods , and conditional screening . Her research bridges theoretical statistics with practical applications, particularly in handling censored, recurrent, and high-dimensional data. Dr. Wen has not been explicitly mentioned to have received scientific awards in the provided text. She has advised and collaborated with several researchers, including Lu Li, Zhou Yu, Lei Huo, and Xuejing Liu, indicating an active role in mentoring graduate students and junior researchers. While no grants are listed, her sustained publication record suggests ongoing research support. She teaches courses in statistics, accessible through the university’s Canvas platform. Dr. Wen leads a research group focused on nonparametric and semiparametric statistical methods, particularly in dimension reduction and variable selection for complex data. Her team works on both theoretical development and computational implementation of novel statistical techniques.
