To showcase the breadth and impact of our research, our Biostatistics Program organizes our expertise into eight Biostatistics Research Hubs. Each hub highlights affiliated faculty, core methods, data resources, and collaborative projects across Fred Hutch and beyond.
Explore the hubs to connect with top biostatisticians and learn more about the scientific questions driving our work.
Research Hubs
Statistical Learning and Data Science
Faculty develop statistical and machine learning methods for complex biomedical data, supporting individualized prediction, adaptive trial design, and data integration in population health studies.
Statistics in Genomics and Genetics
Faculty design statistical tools for high-throughput molecular data, including transcriptomics, epigenomics, and gene–environment interactions. Their work supports discovery in cancer genomics, integrative multi-omics, and population-scale risk modeling.
Statistics in Imaging
Faculty develop methods for extracting quantitative information from biomedical images, including MRI, pathology, and radiomics. Their work supports spatial modeling, early detection, and image-based monitoring of cancer progression and treatment response.
Clinical Trials and Data Coordination
Faculty lead the design and coordination of clinical trials and data centers, developing methods that support precision medicine, prevention, and real-world impact.
Statistics in Epidemiology
Faculty advance statistical methods for epidemiologic research, including observational studies, screening programs, and longitudinal population data. Their work supports causal inference, biomarker validation, and cancer prevention strategies that inform clinical guidelines and public health policy.
Health Policy Statistics
Faculty design models to evaluate healthcare interventions, screening strategies, and policy alternatives. Their work supports decision-making through simulation, comparative effectiveness, and equity assessment, translating real-world data into evidence for national health guidelines and system planning.
Mobile and Wearable Data Science
The Mobile and Wearable Data Science Hub develops statistical and machine-learning methods for analyzing smartphone- and wearable-derived health data. Work spans high-frequency longitudinal sensor streams, linkage with clinical data, and tools for monitoring, surveillance, and personalized intervention.
Teaching Statistics in the Health Sciences
Faculty lead reproducible and accessible education initiatives in statistical science for health researchers and professionals. Their work fosters innovation in teaching, mentorship, and curriculum development using real-world biomedical data and open-source tools.