Yize Zhao PhD
Associate Professor · Yale University
Biostatistics · Biomedical Informatics & Data Science
I am a tenured Associate Professor of Biostatistics at Yale School of Public Health, with a joint appointment in Biomedical Informatics & Data Science at Yale School of Medicine. My contributions to statistics, artificial intelligence (AI), machine learning, and data science encompass Bayesian learning, multimodal data integration, and inference for complex networks. This research spans molecular and imaging data, electronic health records, and, more recently, clinical audio and video, connecting methodological advances with questions in biomedical science, clinical medicine, and public health. My publication record extends across leading journals in statistics, science, and medicine, as well as major machine learning conferences. I have been named a National Academy of Medicine Scholar in Diagnostic Excellence. My other honors include the Committee of Presidents of Statistical Societies (COPSS) Emerging Leader Award, the IMS Thelma and Marvin Zelen Emerging Women Leaders in Data Science Award, and Anthropic’s AI for Science Program Award. I serve as principal investigator on multiple projects supported by the National Institutes of Health and provide analytical leadership for several major center grants and a large national clinical research initiative. My affiliations at Yale include the Alzheimer’s Disease Research Center, Wu Tsai Institute, Institute for Foundations of Data Science, Biomedical Imaging Institute, Center for Brain & Mind Health, Computational Biology & Biomedical Informatics, and Center for Analytical Sciences. I serve as an Associate Editor for the Journal of the American Statistical Association (JASA) and Biometrics and as a standing member of the NIH Biodata Management and Analysis study section.
Office: Suite 523, 300 George Street · New Haven, CT 06511
Selected work
- LatentSNA · Nature Methods, 2026
A Bayesian framework connecting brain networks to behavior, with uncertainty quantification and open-source tools.
- Structure–function gradient coupling · Nature Communications, 2026
A measure linking brain anatomy and function with development, behavior, and genetic influences.
- Bayesian hypergraph inference · ICML 2026 Oral
Learning overlapping disease risk pathways from electronic health records.
- Heterogeneity in brain connectivity and neurocognition · JASA, 2024
Bayesian learning of individual differences in brain–behavior relationships across multiple cognitive states.