Dr. Quefeng Li is an associate professor in the Department of Biostatistics. He earned his doctoral degree in statistics in 2013 from University of Wisconsin, Madison.
Dr. Li’s research is focused on developing new statistical tools for analyzing complex data that arose in biomedical science studies. He has developed new methodology for the classification, variable selection and robust estimation of high dimensional data. He has also worked on the development of new data integration tools, which target to extract key information from multiple datasets and improve the reproducibility of science studies. Such tools are applicable for synthetic analysis of datasets from popular data consortia, such as GEO and TCGA.
High dimensional data analysis with application in biomedical research
Integrative analysis of omics data
Estimation of High-Dimensional Mean Regression in Absence of Symmetry and Light-tail Assumptions. Fan, J., Li, Q., and Wang, Y. (2016). Journal of the Royal Statistical Society, Series B.
Sparse Quadratic Discriminant Analysis for High Dimensional Data. Li, Q., and Shao, J. (2015). Statistica Sinica, 25.
Regularized Outcome Weighted Subgroup Identification for Differential Treatment Effects. Xu, Y., Yu, M., Zhao, Y. Q., Li, Q., Wang, S., and Shao, J. (2015). Biometrics, 71.
Meta-analysis Based Variable Selection for Gene Expression Data. Li, Q., Wang, S., Huang, C., Yu, M., and Shao, J. (2014). Biometrics, 70.