David Zhang is an Assistant Professor in the Departments of Biostatistics and Genetics. His research focuses on the development of novel machine learning frameworks for analyzing high-dimensional, multi-modal, multi-scale data, especially those originating from spatial omics, computational pathology, and medical imaging. The overarching goal of his research is to harness the power of AI to answer the most pressing scientific, engineering, and clinical needs in biomedicine and health care.
Research Activities
Methodology: computer vision, language models, generative AI Applications: spatial omics, computational pathology, medical imaging
Service Activities
Referee for: American Journal of Human Genetics Nature Communications Nature Communications Biology Genome Biology Cell Reports Methods Biometrics Statistics in Medicine Annals of Applied Statistics Biostatistics
Key Publications
Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology. D. Zhang, A. Schroeder, H. Yan, H. Yang, J. Hu, M. Y. Y. Lee, K. S. Cho, K. Susztak, G. X. Xu, M. D. Feldman, E. B. Lee, E. E. Furth, L. Wang, and M. Li. (2024). Nature Biotechnology.
Image response regression via deep neural networks. D. Zhang, L. Li, C. Sripada, and J. Kang. Journal of the Royal Statistical Society Series B: Statistical Methodology.
Density regression and uncertainty quantification with bayesian deep noise neural networks. D. Zhang, T. Liu, and J. Kang. (2023). Stat, 12(1).
Fast and robust ancestry prediction using principal component analysis. . D. Zhang, R. Dey, and S. Lee. (2020). Bioinformatics, 36(11), 3439–3446.
Education
PhD, Biostatistics and Scientific Computing, University of Michigan, 2021
