I am an assistant professor at the Department of Biostatistics, University of Pittsburgh. I received my Ph.D. in Biostatistics from the University of Michigan, supervised by Dr. Peter X.K. Song. Before that, I received my B.A. in Mathematics and M.S. in Statistics from the University of Virginia.

I am especially interested in developing and applying statistical methods for biomedical data that inform health decisions made by individuals and health care providers. My current methodological research is driven by heterogeneous subpopulations detection, and mainly focuses on developing scalable and robust algorithms for statistical inference and data integration. I am currently working on collaborative projects in personalized medicine, health policy, environmental health, and metabolomics, developing useful tools for analyzing high-dimensional and complicated data (genomic, accelerometer, EHR, etc.). I am open to all kinds of collaboration opportunities.

  • Data integration and meta-analysis
  • Unsupervised learning and subgroup analysis
  • High-dimensional data analysis
  • Longitudinal data analysis
  • Statistical inference


  • University of Michigan - Ph.D. in Biostatistics (2018)
  • University of Virginia - M.S. in Statistics (2013)
  • University of Virginia - B.A. in Mathematics (2012)
  • Sun Yat-sen University - Information and Computational Science (2008-2010)


* pdf copies available upon request
  • Distributed simultaneous inference in generalized linear models via confidence distribution
    [Link] -- Tang, L., Zhou, L., and Song, P.X.
    2019+ -- Journal of Multivariate Analysis
  • Method of contraction-expansion (MOCE) for simultaneous inference in linear models
    [Link] -- Wang, F., Zhou, L., Tang, L., and Song, P.X.
    2019 -- arXiv
  • Urate and nonanoate mark the relationship between sugar-sweetened beverage intake and blood pressure in adolescent girls: A metabolomics analysis in the ELEMENT cohort.
    [Link] -- Perng, W., Tang, L., Song, P.X., Goran, M., Tellez-Rojo, M.M., Cantoral, A., and Peterson, K.E.
    2019 -- Metabolites
  • Fusion learning algorithm to combine partially heterogeneous Cox models
    [Link] -- Tang, L., Zhou, L., and Song, P.X.
    2019 -- Computational Statistics
  • Metabolomic profiles and development of metabolic risk during the pubertal transition: a prospective study in the ELEMENT project
    [Link] -- Perng, W., Tang, L., Song, P.X., Tellez-Rojo, M.M., Cantoral, A., Peterson, K.E.
    2019 -- Pediatric Research
  • Learning large scale ordinal ranking model via divide-and-conquer technique
    [Link] -- Tang, L., Chaudhuri, S., Bagherjeiran, A., Zhou, L.
    2018 -- Companion Proceedings of the Web Conference 2018
  • A LASSO method to identify protein signature predicting post-transplant renal graft survival
    [Link] -- Zhou, L., Tang, L., Song, A.T., Cibrik, D., and Song, P.X.
    2017 -- Statistics in Biosciences
  • Method of divide-and-combine in regularized generalized linear models for big data
    [Link] -- Tang, L., Zhou, L., and Song, P.X.
    2016 -- arXiv
  • Fused lasso approach in regression coefficients clustering -- learning parameter heterogeneity in data integration
    [Link] -- Tang, L., and Song, P.X.
    2016 -- Journal of Machine Learning Research
  • Lipid Metabolism is a key mediator of developmental epigenetic programming
    [Link] -- 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 -- Scientific Reports
  • Automatic quality control of transportation reports using statistical language processing
    [Link] -- Gerber, M.S., and Tang, L.
    2013 -- IEEE Transactions on Intelligent Transportation Systems


  • pgee: R implementation of penalized GEE with LASSO, SCAD and MCP [GitHub]
  • modac: method of divide-and-combine for penalized GLM
    Map-reduce functions in Python for fitting GLM when a dataset is large and stored on distributed Hadoop clusters. The method provides stable inference. [GitHub]
  • metafuse: fused lasso approach for data integration
    The package allows detection of heterogeneous effects across multiple independent datasets when analyzed jointly. It provides visualization of covariate-specific effect subgrouping via dendrograms, and enables variable selection. [CRAN] metafuse


  • Outside of work, I like to swim, run, and spend time with my family.
  • This site is largely built upon the website of Jake Abernethy.

This page was last modified on: 8/5/2019