Lu Tang, PhD

Assistant Professor, Biostatistics

Contact

A740 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 prediction and decision making based on large scale data sets, for example, the detection of heterogeneous subpopulations with differential treatment effects. I also develop methods and tools for high-dimensional data. Most of my work is inspired by and closely related to applications in bioinformatics, clinical trials, electronic health records, environmental health sciences, and health policies. More infomation can be found on my personal website
 
Research interest: data integration, federated learning, generalized linear models, causal inference, subgroup analysis, decision rule learning.
 
Collaboration: Investigators may feel free to reach me with regards to statistical supports for publications and grant applications.
 
Advising: Pitt students may feel free to reach me with regards to statistical application and methodological research projects.


Education

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


Teaching

Upcoming: BIOST2079 | Introductory Statistical Learning for Health Sciences | Fall 2022

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

BIOST2079 | Introductory Statistical Learning for Health Sciences | Fall 2020, Fall 2021

BIOST2080 | Advanced Statistical Learning | Spring 2020, Spring 2021


News


Selected Publications

Google Scholar 

 

Tang, L., and Song, P.X. (2021). Poststratification fusion learning in longitudinal data analysis. Biometrics. https://doi.org/10.1111/biom.13333 [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. https://doi.org/10.1111/insr.12402.

 

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. https://doi.org/10.1016/j.jmva.2019.104567 [Packages]

 

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