About Zihang
PhD, Biostatistics, Dalla Lana School of Public Health, University of Toronto, 2020
MSc, Biostatistics, Dalla Lana School of Public Health, University of Toronto, 2013
Dr. Zihang Lu's research develops and applies innovative statistical and machine learning methods to address complex health research questions, with expertise in Bayesian and latent-variable modeling, longitudinal and functional data analysis, causal inference, and multimodal data integration.
His interdisciplinary research spans public health, medicine, and kinesiology, with a particular focus on wearable sensor data, physical activity, and digital health. Through methodological innovation and interdisciplinary collaborations, his research aims to advance our understanding of human health, movement behaviours, and chronic disease prevention.
Visit his website for more information about research and opportunities in the lab.
Applications
- Digital health
- Wearable sensor data and physical activity
- Population health, chronic disease, and environmental epidemiology
Statistical and Data Science Methodology
- Bayesian and latent-variable modeling
- Longitudinal and functional data modeling
- Causal inference
- Multimodal data integration
Kinesiology and Health Research
Lu, Z., Petersen, C., Dai, R., Vargas, M., Ahmadiankalati, M., Sifuentes, E., Miliku, K., Moraes, T., Mandhane, P., Becker, A., Azad, M., Simons, E., Lou, W., Ambalavanan, A., Duan, Q., Turvey, S., and Subbarao, P. (2025). Early preschool wheeze trajectories are predominantly non-allergic with distinct biologic and microbiome traits. Journal of Allergy and Clinical Immunology, 156 (6): 1556-1572.
Taccardi, D., Zacharias, A.; Gowdy, H.; Knezic, M., Parisien, M., Bisson, E., Fang, Z., Stickley, S., Brown, E., Camire, D., Wilson, R., Singer, L., Daly-Cyr, J., Choiniere, M., Lu, Z., Diatchenko, L., Duan, Q., Ghasemlou, N. (2025). Circadian rhythmicity and neutrophil activation as biomarkers of pain intensity and opioid use. The Journal of Clinical Investigation, 135 (19).
Rossi, A., Chen, Z., Ahmadiankalati, M., Campisi, S., Myrtha, R., Dempsey, K., Jenkins, D., O’Connor, D., El-Sohemy, A., Mandhane, P., Simons, E., Turvey, S., Moraes, T., Lu, Z., Subbarao, P., Miliku, K. (2025). Determining the Interplay of Prenatal Parental BMI in Shaping Child BMI Trajectories: The CHILD Cohort Study. International Journal of Obesity, 49(6): 1-8.
Soussi, S., Tarvasmaki, T., Kimmoun, A., Ahmadiankalati, M., Azibani, F., Santos, C., Duarte, K., Gayat, E., Jentzer, J., Harjola, V., Hibbert, B., Jung, C., Johan, L., Levy B., Lu, Z., Lawler, P., Marshall, J., Poss, J., Sadoune, M., Nguyen, A., Raynor, A., Peoc’h, K., Thiele, H., Mathew, R., Mebazaa, A. (2025). Identifying biomarker-driven subphenotypes of cardiogenic shock: Analysis of prospective cohorts and randomized controlled trials. The Lancet Discovery Science eClinicalMedicine, 79.
Wai, G., Lu, Z., Gill, S., Henderson, I., Auais, M. (2024). Impact of the End PJ Paralysis interventions on patient health outcomes at the participating hospital units in Alberta. Disability and Rehabilitation, 46(26), 6391–6401.
Statistical and Data Science Research
Alnajjar, A., Bian, H., Lu, Z. (2025). A Fast Integrative Clustering and Feature Selection for Multiview and High-Dimensional Data. Statistical Methods in Medical Research. Available online.
Lu, Z., Chandra, N. (2024). A Sparse Factor Model for Clustering High-Dimensional Longitudinal Data. Statistics in Medicine, 43(19): 3633-3648. https://doi.org/10.1002/sim.10151.
Ahmadiankalati, M., Boury, H., Subbarao, P., Lou, W., Lu, Z. (2024). Bayesian additive regression trees for predicting childhood asthma in the CHILD cohort study. BMC Medical Research Methodology, 24(1), p.262. .
Lu, Z., Subbarao, P., Lou, W. (2023). A Bayesian latent class model for integrating multi-source longitudinal data: application to the CHILD cohort study. Journal of the Royal Statistical Society Series C, 73(2): 398-419.
Lu, Z., Lou, W. (2022). Bayesian consensus clustering for multivariate longitudinal data. Statistics in Medicine, 41(1): 108-127.
Reviews and Tutorials
Lu, Z. (2025). Clustering Longitudinal Data: A Review of Methods and Software Packages. International Statistical Review, 93(3): 425-458. https://doi.org/10.1111/insr.12588.
Lu, Z., Ahmadiankalati, M., Tan, Z. (2023). Joint Clustering Multiple Longitudinal Features: a Comparison of Methods and Software Packages with Practical Guidance. Statistics in Medicine, 42(29): 5513-5540. https://doi.org/10.1002/sim.9917.
Lu, Z., Lou, W. (2021). Bayesian approaches to variable selection: a comparative study from practical perspectives. International Journal of Biostatistics, 18(1): 83-108. https://doi.org/10.1515/ijb-2020-0130.
Software Packages
Alnajjar, A., Lu, Z. (2024). iClusterVB: an R package for fast integrative clustering and feature selection for high-dimensional data. https://cran.r-project.org/web/packages/iClusterVB/index.html
Tan, Z., Lu, Z., Shen, C. (2022). BCClong: an R package for performing the Bayesian Consensus Clustering model for clustering continuous, discrete and categorical longitudinal data. https://cran.r-project.org/web/packages/BCClong/index.html
KIN8325 Advanced Biostatistics