My research interests span target trial emulation using real-world data, multi-source data integration (including transfer learning and federated learning), and statistical modeling and inference for high-dimensional and complex structured data (such as neuroimaging and network data). My methodological pursuits have been driven by leveraging real-world data to address pressing scientific and clinical inquiries, with a particular focus on vaccine efficacy, Long COVID, health disparities, pharmacovigilance, and psychiatry.
2015 | Zhejiang University, Hangzhou, China | Bachelor in Mathematics and Applied Mathematics
2017 | George Washington University, Washington, DC | Master in Statistics
2021 | University of Maryland, College Park, MD | PhD in Statistics
Wu, Q., Zhang, B., Tong, J., Bailey, L. C., Bunnell, H. T., Chen, J., ... & Chen, Y. (2025). Real-world effectiveness and causal mediation study of BNT162b2 on long COVID risks in children and adolescents. eClinicalMedicine, 79. https://doi.org/10.1016/j.eclinm.2024.102962.
Wu, Q., Pajor, N. M., Lu, Y., Wolock, C. J., Tong, J., Lorman, V., ... & Chen, Y. (2024). A latent transfer learning method for estimating hospital-specific post-acute healthcare demands following SARS-CoV-2 infection. Patterns, 5(11).
Wu, Q., Wang, C., Chen, Y., Heterogeneous latent transfer learning in Gaussian graphical models, Biometrics, Volume 80, Issue 3, September 2024, ujae096.
Wu, Q., Zhang, Y., Huang, X., Ma, T., Kochunov, P., Hong, L. E., & Chen, S., A multivariate to multivariate approach for voxel-wise genome-wide association analysis. Statistics in Medicine. 2024; 43(20): 3862-3880. doi: 10.1002/sim.10101.
Wu, Q.*, Tong, J.*, Zhang, B., Zhang, D., Xu, J., Shen, Y., Li, L., Bailey, C. L., Bian, J., Christakis, A. D., et. al. Real-world Effectiveness of BNT162b2 Against Infection and Severe Diseases in Children and Adolescents. Annals of Internal Medicine. 2024 Feb;177(2):165-176. doi: 10.7326/M23-1754. (* co-first author)
Wu, Q., Schuemie, M. J., Suchard, M. A., Ryan, P., Hripcsak, G. M., Rohde, C. A., & Chen, Y. (2023). Padé approximant meets federated learning: A nearly lossless, one-shot algorithm for evidence synthesis in distributed research networks with rare outcomes. Journal of Biomedical Informatics, 145, 104476. https://doi.org/10.1016/j.jbi.2023.104476.
Chen, S., Zhang, Y., Wu, Q., Bi, C., Kochunov, P., & Hong, L. E. (2023). Identifying covariate-related subnetworks for whole-brain connectome analysis. Biostatistics, Volume 25, Issue 2, April 2024, Pages 541–558, https://doi.org/10.1093/biostatistics/kxad007.
Wu, Q., Huang, X., Culbreth, A., Waltz, J., & Chen, S. (2021) Extracting Brain Disease Related Connectome Subgraphs by Adaptive Dense Graph Discovery. Biometrics. 2021; 1– 13.
Wu, Q., Ma, T., Liu, Q., Milton, D., Zhang, Y., & Chen, S. (2021). ICN: Extracting interconnected communities in gene Co-expression networks, Bioinformatics, Volume 37, Issue 14, July 2021, Pages 1997–2003, https://doi.org/10.1093/bioinformatics/btab047.
Wu, Q., Zhang, Z., Waltz, J., Ma, T., Milton, D., & Chen, S. (2021). Link predictions for incomplete network data with outcome misclassification. Statistics in Medicine. 2021; 40: 1519–1534. https://doi.org/10.1002/sim.8856.
Culbreth, A. J., Wu, Q., Chen, S., Adhikari, B. M., Hong, L. E., Gold, J. M., & Waltz, J. A. (2020). Temporal-thalamic and cingulo-opercular connectivity in people with schizophrenia. NeuroImage: Clinical, 29, 102531.
