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., 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.
