Division Director
Biostatistics and Bioinformatics
Biostatistics and bioinformatics are the information sciences of epidemiology, public health, and medicine because they transform complex data into reliable evidence that guides action. Biostatistics underpins study design, risk assessment, and the analysis of clinical trials and health data, ensuring that medical and public health decisions are statistically sound and ethically grounded. Bioinformatics enables the organization and interpretation of large-scale genomic and molecular datasets, revealing mechanisms of disease and informing surveillance, outbreak investigation, and precision public health strategies. In medicine, biostatistics supports evidence-based practice by evaluating treatment safety and efficacy, advancing clinical research, and informing personalized care tailored to individual patients. Together, these disciplines form a foundation for improving population health and patient outcomes in an increasingly data-rich world.
Advancing biostatistics and bioinformatics requires creatively solving methodological problems that arise from rapidly growing, complex, and often messy real-world data. Biostatisticians and bioinformaticians must also develop new ways to integrate causal inference with machine learning and AI so that predictions can be translated into credible explanations and decision-making. Modern research in epidemiology, public health, and medicine also needs innovative methods that handle high-dimensional, heterogeneous, and biased data streams, as well as resource-efficient study designs that make the best use of limited time, data, and computing.
Mission Statement
The Division of Biostatistics and Bioinformatics advances the development of state-of-the-art analytic approaches and their application to epidemiology, public health, and medicine.
Our division is involved in research and education:
- Advance methodological research in biostatistics and bioinformatics to address emerging challenges in epidemiology, public health, and clinical medicine, including complex, high-dimensional, and real-world data.
- Provide rigorous quantitative collaboration and support for epidemiologic, public health, and biomedical research across the school of medicine, from study design through data analysis and interpretation.
- Develop and promote innovative approaches that integrate causal inference with machine learning and AI to generate robust, actionable evidence for prevention, diagnosis, and treatment.
- Train the next generation of investigators by offering high-quality education and mentoring in biostatistics, bioinformatics, and data science for medical, public health, and graduate students, as well as fellows and faculty.
- Build and sustain strategic partnerships with clinical departments, health systems, and external collaborators to translate quantitative methods into improved patient care, population health, and health equity.