Department of Biostatistics
Biostatistics: In Conversation with the Yale School of Public Health Department Chair
Yale School of Public Health biostatisticians are making significant real-world impacts. They have contributed to COVID modeling, directly impacting how governments and organizations have addressed the pandemic. They have worked on how to better design, direct, and analyze clinical studies worldwide. And they have helped us better understand cancer, cardiovascular disease, and mental health, as well as how we treat these complex diseases.
"I think right now is the best timing to start studying biostatistics," says Interim Department Chair Shuangge Steven Ma, PhD. "Public health and medical research, nowadays, rely more and more on data. Biostatistics is the only field that can combine the department of mathematics, statistics, computer science, and health informatics."
Research and Study
The Yale School of Public Health Biostatistics department helps research and study the most challenging problems in biology, medicine & public health. The Department of Biostatistics develops quantitative methodologies and tools for rigorous scientific research to solve the world’s most challenging problems in biology, medicine and public health. Our view of biostatistics encompasses all aspects of the scientific method, from study design and data collection to computation, statistical analysis and interpretation of findings.
Biostats faculty are leaders in the research, development and application of statistical methods in the areas below.
Division of Health Informatics
The Division of Health Informatics is invested in training and research around issues surrounding big data acquisition and its management in health care as well as biomedical and public health research. The science of health informatics drives innovation-defining future approaches to information and knowledge management in biomedical research, clinical care and public health.
Areas of Interest
Statistical Genetics/Genomics, Spatial Statistics and Modeling
Causal Inference, Observational Studies and Implementation Science Methodology
Health Informatics, Data Science and Reproducibility
Clinical Trials and Outcomes
Machine Learning and High Dimensional Data
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