Lu Tang

I am an assistant professor at the Department of Biostatistics, University of Pittsburgh. I received my PhD degree in Biostatistics from the University of Michigan. Before that, I got my BA in Mathematics and MS in Statistics from the University of Virginia. I was once an undergraduate student at the Sun Yat-sen University. My research lies at the intersection of biostatistics and machine learning, with a broad goal of promoting and propelling health data science. I am particularly interested in developing statistical methods for integrative data analysis that combines data sets from multiple sources or knowledge of different types to achieve higher precision and power. With this in mind, my current research program focuses on developing methods that support regression, prediction and decision making based on large scale distributed data sets. I also develop data processing tools for analyzing 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, fairness and disparity, and health policies. Please see below a list of my work and relevant publications. For more information, please visit https://sites.pitt.edu/~lutang/.

Contributions to Public Health

• Learning of individualized treatment effect and individualized treatment rules for sepsis.
  • Tan, X., Chang, C.H., Zhou, L., and Tang, L.* (2022). A tree-based model averaging approach for personalized treatment effect estimation from heterogeneous data sources. Proceedings of the 39^th International Conference on Machine Learning (ICML) 2022.
  • Tan, X., Qi, Z., Seymour, C.W., and Tang, L.* (2022). RISE: Robust individualized decision learning with sensitive variables. Advances in Neural Information Processing Systems (NeurIPS) 2022.
• Distributed data analysis for studying the use of medication for treating opioid use disorder.
  • Donohue, J.M., Jarlenski, M., Kim, J.Y., Tang, L., et al. and Medicaid Outcomes Distributed Research Network (MODRN) Investigators. (2021). Use of medications for treatment of opioid use disorder among US Medicaid enrollees in 11 states, 2014-2018. Journal of the American Medical Association, 326(2), 154-164.
  • Burns, M., Tang, L., Chang, C.H., Kim, J.Y., Ahrens, K., Lindsay, A., Cunningham, P., Gordon, A., Jarlenski, M.P., Lanier, P., Mauk, R., McDuffie, M.J., Mohamoud, S., Talbert, J., Zivin, K., and Donohue, J. (2022). Duration of medication treatment for opioid-use disorder and risk of overdose among Medicaid enrollees in eleven states: A retrospective cohort study.  Addiction. DOI: 10.1111/add.15959.
• High-dimensional data analysis for the selection of biomarkers.
  • Zhou, L., Tang, L., Song, A.T., Cibrik, D., and Song, P.X. (2017). A LASSO method to identify protein signature predicting post-transplant renal graft survival. Statistics in Biosciences, 9(2), 431-452.
  • Demirci, H., Tang, L.+, Demirci, F.Y., Ozgonul, C., Weber, S., and Sundstrom, J. (2023). Investigating vitreous cytokines in choroidal melanoma. Cancers, 15(14), 3701.
• Epidemiological forecasting model for infectious diseases.
  • 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.
  • 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, 88(2), 462–513.
• Fusion learning and transfer learning for the analysis of heterogeneous data.
  • Tang, L.*, and Song, P.X. (2020). Post-stratification fusion learning in longitudinal data analysis. Biometrics, 77(3), 914-928.
  • Xiang, P., Zhou, L., and Tang, L.* (2024). Transfer learning via random forests: a one-shot federated approach. Computational Statistics and Data Analysis, 197, 107975.