Tenglong Li


Dr. Tenglong Li is an applied statistician who has extensive experience in epidemiological, biomedical and social science research. His main research interest includes Tuberculosis, COVID-19, batch effects in genomic data and infectious disease modeling. His methodological work focuses on Bayesian inference, causal inference and statistical computation. He teaches courses such as Advanced Methods in Biostatistics and Statistical Computing with SAS. He also has collaborated with educational, sociological and psychological researchers as a statistical consultant during his career.

Dr. Tenglong Li obtained his Ph.D. degree in Measurement & Quantitative Methods and a Master degree in Statistics from Michigan State University. He completed his postdoctoral training at Boston University (in the department of biostatistics and department of computational biomedicine). He was also a lecturer and faculty affiliate at Northeastern University, teaching courses for the graduate programs in Analytics and Commerce and Economics Development.
  • Qualifications

    • B.A., Huazhong University of Science & Technology, June 2010
    • M.S., Michigan State University, May 2012
    • Ph.D., Michigan State University, Jan 2018
  • Experience

    • Assistant Professor, Xi'an Jiaotong-Liverpool University, 2021-Current
    • Lecturer & Faculty Affiliate, Northeastern University, U.S., 2018-2021
    • Postdoctoral Associate, Boston University, U.S., 2018-2021
  • Research interests

    • Methodologies: Bayesian Inference, Causal Inference, Statistical Computation, Bayesian Networks
    • Applications: Tuberculosis, Batch effects, COVID-19, Infectious Disease Modeling, Clinical Trials, Genomic Data Science
  • Articles

    • Li, T., Zhang, Y., Patil, P., & Johnson, W. E. Overcoming the impacts of two-step batch effect correction on gene expression estimation and inference. Biostatistics. (Accepted; preprint in bioRxiv: https://doi.org/10.1101/2021.01.24.428009).
    • Li, T., & Frank, K. A. The probability of a robust inference for internal validity: A counterfactual approach for regression models. arXiv: https://arxiv.org/abs/2005.12784
    • Li, T., & Lawson, J. A generalized bootstrap approach for estimating the standard errors and confidence intervals in regression models with propensity score weighting. (Under Review; preprint in arXiv: https://arxiv.org/abs/2109.00171).
    • Li, T., Martinez, L., Jones-Lopez, E., & White, L. F. The integrated approach of learning tuberculosis transmission within and outside households via random directed graph models. (Under Review; preprint in medRxiv: https://doi.org/10.1101/2020.07.30.20165506).
    • Rodriguez, C. A., Li, T., Self, J. L., Horsburgh, C. R., Jenkins, H. E., & White, L. F. (2021). MIRU-VNTR genotyping indicates marked heterogeneity of tuberculosis transmission in the United States, 2009–2018. Epidemiology and Infection. (Accepted).
    • Li, T., & White, L. F. (2021). Bayesian back-calculation and nowcasting for line list data during the COVID-19 pandemic. PLOS Computational Biology, 17(7): e1009210. https://doi.org/10.1371/journal.pcbi.1009210.
    • Li, T., & Frank, K. A. (2020). The probability of a robust inference for internal validity. Sociological Methods & Research. https://doi.org/10.1177/0049124120914922.
    • Raykov, T., Marcoulides, G. A., & Li, T. (2018). On the unlikely case of an error-free principal component from a set of fallible measures. Educational and Psychological Measurement, 78(4), 708-712.
    • Raykov, T., Marcoulides, G. A., & Li, T. (2017). On the fallibility of principal components in research. Educational and Psychological Measurement, 77(1), 165-178.
    • Raykov, T., Marcoulides, G. A., & Li, T. (2016). Evaluation of measurement instrument criterion validity in finite mixture settings. Educational and Psychological Measurement, 76(6), 1026-1044.
  • Conference presentations

    • Tenglong Li. “Multilevel Modeling of Mathematics Achievement: A Report of Evidence from Education Longitudinal Study”. Poster presented at the American Educational Research Association 2015 Annual Meeting. Chicago IL. April 2015.
    • Tenglong Li. “The Bayesian Paradigm of the Robustness Indices of Causal Inferences”. Poster presented at the American Educational Research Association 2017 Annual Meeting. San Antonio TX. May 2017.
    • Tenglong Li. “Identifying the community and household transmission of tuberculosis via random directed graphs: Findings based on a Brazilian household contact study”. Poster presented at EPIDEMIC 7 – International conference on infectious disease dynamics. Charleston SC. December 2019.
    • Tenglong Li. “Addressing the exaggerated significance problem in batch effect adjustments”. Oral presentation presented at 2020 Evans Day – Annual research day for Dept. of Medicine at Boston University. Boston MA. November 2020.
  • Grants

    • Tenglong Li. 05/2015 - 08/2015. Principal Investigator. "Applying Hansen-Hurwitz Estimator to Propensity Score Analysis". Summer Research Fellowship from College of Education, Michigan State University.
  • Professional service activities

    • Reviewer for Scientific Reports, Journal of Educational and Behavioral Statistics, American Journal of Epidemiology, PLOS ONE, BMC Genomics, Journal of Inverse and Ill-posed Problems, Journal of Community Medicine and Public Health, Archives of Epidemiology, International Journal of Quantitative Research in Education, Journal of Health Economics and Outcomes Research, American Journal of Health Behavior.
  • Teaching activities

    • APH 417: Statistical Computing using SAS
    • APH 413: Advanced Methods in Biostatistics
  • Courses taught

    • Courses taught in Northeastern University: ALY 6010: Probability Theory and Introductory Statistics (2018 Fall I), ALY 6015: Intermediate Analytics (2018 Fall II, 2019 Winter I, 2019 Winter II, 2019 Spring I, 2019 Spring II, 2019 Fall II, 2020 Winter I, 2020 Winter II, 2020 Spring II, 2020 Summer, 2020 Fall II), CED 6030: Mathematical Methods for Economists I (2018 Fall I, 2019 Fall I, 2020 Spring I).
    • Lectures taught in Michigan State University: CEP 932: Quantitative Methods in Educational Research I (2015 Fall, 2016 Fall), CEP 933: Quantitative Methods in Educational Research II (2015 Spring, 2016 Spring, 2016 Summer), CEP 934: Multivariate Data Analysis I (2013 Fall and 2014 Fall), CEP 935: Advanced Multivariate Data Analysis II (2014 Fall) and CEP 938: Latent Variable and Structural Equation Modeling (2016 Spring).
  • Professional memberships

    • American Educational Research Association
    • American Statistical Association
  • 电话

    +86 (0)512 85186479
  • 电子邮件

  • 地址

    Public Building
    PB 246
    Suzhou Dushu Lake Science and Education Innovation District
    Suzhou Industrial Park