Faculty Profile

Yi Li

Yi Li, PhD

  • M. Anthony Schork Collegiate Professor of Biostatistics
  • Professor, Global Public Health

Li has contributed to a wide range of statistical areas, including survival analysis, data science, high-dimensional inference, machine learning, deep learning, spatial data analysis, random-effects models, clinical trial design, and infectious disease modeling.

He is interested in cancer genetics/genomics, radiomics, racial disparity analysis, chronic disease research and opioid overuse research. He has published more than 230 papers in major statistical journals, such as JASA, Biometrika, JRSSB, and Biometrics, as well as premier subject matter journals, such as PNAS, JAMA and JCO. His methodologic research is funded by various NIH statistical grants starting from year 2003. Li is actively involved in collaborative research in cutting-edge clinical and observational studies with researchers from the University of Michigan and Harvard University.

  • Postdoctor, Biostatistics, Harvard, 1999-2000
  • PhD, Biostatistics, University of Michigan, 1999
  • MS, Biostatistics, University of Michigan, 1996

Research Interests:
Survival analysis, data science, high-dimensional inference, machine learning, deep learning, spatial data analysis, random-effects models, clinical trial design, and infectious disease/COVID modeling, with applications in cancer genetics/genomics, radiomics, racial disparity analysis, chronic disease research and opioid overuse research.

Research Projects:
  • New Statistical Methods for Modelling Cancer Outcomes
  • Detecting Racial Disparities in Cancer Survival by Integrating Multiple High-dimensional Observational Studies

Sun, Y., Kang, J., Brummett, C. and Li, Y. (2022) Individualized risk assessment of pre-operative opioid use by interpretable neural network regression. Annals of Applied Statistics, in press. https://www.e-publications.org/ims/submissioOAS/user/submissionFile/50515?confirm=19ac3225

Salerno, S. and Li, Y. (2022) High-dimensional survival analysis: methods and applications. Annual Review of Statistics and Its Application, in press. https://www.annualreviews.org/doi/abs/10.1146/annurev-statistics-032921-022127

Zhang, E. and Li, Y. (2022) High dimensional Gaussian graphical regression models with covariates. Journal of the American Statistical Association, in press. https://www.tandfonline.com/doi/abs/10.1080/01621459.2022.2034632

Salerno, S., Messana, J., Gremel, G., Dahlerus, C., Hirth, R., Han, P., Segal, J., Xu, T., Shaffer, D., Jiao, A., Simon, J., Tong, L., Wisniewski, K., Nahra, T., Padilla, R., Sleeman, K., Shearon, T., Callard, S., Yaldo, A., Borowicz, L., Agbenyikey, W., Horton, G., Roach, J. and Li, Y. (2021) COVID-19 risk factors and mortality outcomes among Medicare patients receiving long-term dialysis. JAMA Network Open, 2021;4(11):e2135379. https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2786199

Salerno, S., Sun, Y., Morris, E., He, X., Li, Y., Pan, Z., Han, P., Kang, J., Sjoding, M. and Li, Y. (2021) Comprehensive evaluation of COVID-19 patient short- and long-term outcomes: racial disparities in healthcare utilization and outcomes. PLOS ONE, 16(10): e0258278. https://doi.org/10.1371/ journal.pone.0258278.


Fei, Z., Zheng, Q., Hong, H. and Li, Y. (2021) Inference for high dimensional censored quantile regression. Journal of the American Statistical Association, in press. doi:10.1080/01621459.2021. 1957900 https://www.tandfonline.com/doi/abs/10.1080/01621459.2021.1957900?journalCode=uasa20

Email: yili@umich.edu

Address:
M2102 SPH II
1415 Washington Heights
Ann Arbor, MI 48109

For media inquiries: sph.media@umich.edu

Areas of Expertise: Biostatistics,  Cancer,  Chronic Disease,  COVID-19,  Firearm Injury Prevention,  Genetics,  Global Public Health,  Health Care,  Health Equity,  Health Policy,  Infectious Disease,  Substance Use