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, health policies, and nutritional sciences. More infomation can be found on my personal website
Potential collaborators: feel free to reach me with regards to statistical supports for publications and grant applications.
Potential students at Pitt Biostat: feel free to reach me with regards to methodological research projects.


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


Current: BIOST2080 | Advanced Statistical Learning | Spring 2021 (syllabus)

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

BIOST2079 | Introductory Statistical Learning for Health Sciences | Fall 2020

BIOST2080 | Advanced Statistical Learning | Spring 2020


  • February 5, 2021 --- Stay tuned for the 2021 Biostatistics Research Day, which is scheduled for March 4, 2021Dr. Kelley Kidwell '12 will be our alumnus speaker for that day! Make sure to submit your abstract by Feb 22!
  • January 31, 2021 --- Huge congrats to Xiaoqing (Ellen) Tan for receiving Honorable Mention in the ASA Student Paper Award competition (SLDS section) for her paper "A Tree-based Federated Learning Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources"! She will be presenting this work at JSM 2021.

Selected Publications

Google Scholar 


Tang, L., and Song, P.X. (2020+). Poststratification fusion learning in longitudinal data analysis. Biometrics. [R code]


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.


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, 18(3), 409-432. [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, 176. [Packages]


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]

Lu  Tang