Lu Tang, PhD

Assistant Professor, Biostatistics


7124 Public Health, 130 DeSoto Street, Pittsburgh, PA 15261
R-znvy: yhgnat@cvgg.rqh
Primary Phone: 967-838-5151
Web site:

Personal Statement

I am interested in developing statistical methods for integrative data analysis that combines data sets from multiple sources or knowledge of different types to achieve higher power, also known as data integration. My current research focuses on fusion learning and distributed computing that support the detection of heterogeneous subpopulations and differential (treatment) effects in large scale data analyses. I also develop methods and tools for analyzing high-dimensional metabolomic data, accelerometer data and epigenetic data, with the goals of statistical inference, prediction, and cluster detection. Most of my work is inspired by and closely related to applications in bioinformatics, clinical trials, electronic health records, environmental health sciences, and nutritional sciences.


2018 | University of Michigan, Ann Arbor, MI | PhD in Biostatistics

2013 | University of Virginia, Charlottesville, VA | MS in Statistics

2012 | University of Virginia, Charlottesville, VA | BA in Mathematics


BIOST2079 | Introductory Statistical Learning for Health Sciences | Fall 2020

BIOST2080 | Advanced Statistical Learning | Spring 2020

BIOST2025 | Biostatistics Seminar | Fall 2018, Spring 2019, Fall 2019

Selected Publications

Google Scholar 


Tang, L., Zhou, Y., Wang, L., Purkayastha, S., Zhang, L., He, J., Wang, F., and Song, P.X. (2020). A review of multi-compartment infectious disease models. International Statistical Review. Accepted.


Wang, L., Zhou, Y., He, J., Zhu, B., Wang, F., Tang, L., Kleinsasser, M., Barker, D., Eisenberg, M., and Song, P.X. (2020). An epidemiological forecast model and software assessing interventions on COVID-19 epidemic in China. Journal of Data Science. In press. [MedRxiv preprint] [R package] [R shiny app]


Tang, L., Zhou, L., and Song, P.X. (2019+) Distributed simultaneous inference in generalized linear models via confidence distribution. Journal of Multivariate Analysis, DOI: 10.1016/j.jmva.2019.104567.[Packages]


Perng, W., Tang, L., Song, P.X., Tellez-Rojo, M.M., Cantoral, A., and Peterson, K.E. (2019) Metabolomic profiles and development of metabolic risk during the pubertal transition: A prospective study in the ELEMENT project. Pediatric Research, 85(3), 262-268.


Tang, L., Chaudhuri, S., Bagherjeiran, A., and Zhou, L. (2018) Learning large scale ordinal ranking model via divide-and-conquer technique. Companion Proceedings of the Web Conference 2018, 1901-1909.


Tang, L., and Song, P.X. (2016) Fused LASSO approach in regression coefficients clustering – Learning parameter heterogeneity in data integration. Journal of Machine Learning Research, 17(113), 1-23. [R package]


Marchlewicz, E.H., Dolinoy, D.C., Tang, L., Milewski, S., Jones, T.R., Goodrich, J.M., Soni, T., Domino, S.E., Song, P.X., Burant, C., and Padmanabhan, V. (2016) Lipid metabolism is a key mediator of developmental epigenetic programming. Scientific Reports, 6, 34857.

Lu  Tang