Yiwen Liu, Ph.D, is an Assistant Professor of Practice in the Department of Epidemiology and Biostatistics at the Mel and Enid Zuckerman College of Public Health. Her research primarily focuses on developing statistical methods and theory to harness the variety of issues in analyzing the high dimensional data or the complex data set. More specifically, her research interests include developing model-free dimension reduction methods for high dimensional data regression and integration methods for multiple sources data. Another continuing theme is to develop statistical tools to solve scientific problems arising from biological research. She has been actively engaged in collaborative and interdisciplinary work. 

Research interests

Dimension reduction and variable selection, big data analytics, data integration

Selected Publications

1. Valenti MA, Farland LV, Huang K, Liu Y, Beitel SC, Jahnke SA, Hollerbach B, St. Clair CC, Gulotta JJ, Kolar JJ, Urwin DJ. Evaluating the effect of depression, anxiety, and post-traumatic stress disorder on anti-Müllerian hormone levels among women firefighters. Journal of Women's Health. 2025 Mar 1;34(3):354-61.
2. Zhang M, Parker J, An L, Liu Y, Sun X. Flexible analysis of spatial transcriptomics data (FAST): a deconvolution approach. BMC Bioinformatics. 2025 Jan 31;26(1):35.
3. Zhong W, Liu Y, Zeng P. A model-free variable screening method based on leverage score. Journal of the American Statistical Association. 2023 Jan 2;118(541):135-46.
4. Liu Y, Sun X, Zhong W, Li B. B-scaling: A novel nonparametric data fusion method. The Annals of Applied Statistics. 2022 Sep;16(3):1292-312.
5. Zhang M, Liu Y, Zhou H, Watkins J, Zhou J. A novel nonlinear dimension reduction approach to infer population structure for low-coverage sequencing data. BMC Bioinformatics. 2021 Jun 26;22(1):348.
6. Sun X, Liu Y, An L. Ensemble dimensionality reduction and feature gene extraction for single-cell RNA-seq data. Nature Communications. 2020 Nov 17;11(1):5853.