Dr. Wei Tu, MSc, PhD

Canadian Cancer Trials Group, Senior Biostatistician

Dr. Tu is a Senior Biostatistician/Data Scientist in the Canadian Cancer Trials Group at the Queen’s Cancer Research Institute and an Assistant Professor in the Department of Public Health Sciences. After receiving his undergraduate training in Mathematics from Harbin Institute of Technology in China, he moved to Edmonton and completed a MSc and PhD in Statistics from the University of Alberta.  

Dr. Tu’s research lies in the intersection of health care and the emerging data science. With the advancement of digital technologies, different sources of data (genetic, imaging, electronic health records, etc.) are available in health care. Trained as a Statistician, Dr. Tu is interested in integrating these different sources of high-dimensional data and translate into informed clinical decision-making.

Areas of expertise:

  • Biostatistics
  • Machine learning
  • Clinical trials
  • High dimensional statistics
  • Data privacy
  • Functional data analysis

Research interests:

  • Personalized medicine
  • Robust dimensionality reduction 
  • Data Privacy and its application in clinical trials 
  • Interpretable machine learning 

Selected Publications:

  • Tu, W., Chen, P. A., Koenig, N., Gomez, D., Fujiwara, E., Gill, M. J., ... & Power, C. (2020). Machine learning models reveal neurocognitive impairment type and prevalence are associated with distinct variables in HIV/AIDS. Journal of neurovirology, 26(1), 41-51.
  • Karunamuni, R. J., Kong, L., & Tu, W. (2019). Efficient robust doubly adaptive regularized regression with applications. Statistical methods in medical research, 28(7), 2210-2226.
  • Tu, W., Liu, P., Zhao, J., Liu, Y., Kong, L., Li, G., ... & Yao, H. (2019, November). M-estimation in Low-rank Matrix Factorization: a General Framework. In 2019 IEEE International Conference on Data Mining (ICDM) (pp. 568-577). IEEE.
  • Tu, W., Yang, D., Kong, L., Che, M., Shi, Q., Li, G., & Tian, G. (2019). Ensemble-based Ultrahigh-dimensional Variable Screening. In IJCAI (pp. 3613-3619).
  • Tu, W., Kong, L., Karunamuni, R., Butcher, K., Zheng, L., & McCourt, R. (2019). Nonlocal spatial clustering in automated brain hematoma and edema segmentation. Applied Stochastic Models in Business and Industry, 35(2), 321-329.

Dr. Tu’s publications: Google Scholar