Data Science

The Yale School of Public Health has a long history of contributing to the development of quantitative methodologies and tools for rigorous scientific research to address the most challenging problems in biology, medicine and public health.

Our researchers are working to:

• Improve public health and epidemiological study design
• Data collection
• Computation
• Statistical analysis
• Interpretation of findings.

The school is a leader in:

• The development and application of statistical methods in genetics/genomics and bioinformatics
• Epidemiology
• Causal inference
• Clinical trials
• Spatial/spatiotemporal data
• High-dimensional analysis
• Machine learning
• Biomedical imaging
• Network analysis and statistical computation
• Implementation science and health informatics

• Genome Biology
  SUPERGNOVA: Local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits
• The Annals of Applied Statistics
  A Hierarchical Baysian Model for Single-cell CLustering Using RNA-Sequencing Data
• PNAS
  A Polynomial Algorithm for Best-subset Selection Problem
• Journal of the National Cancer Institute
  Effect Sizes of Somatic Mutations in Cancer
• Epidemiology
  Transmission Modeling with Regression Adjustment for Analyzing Household-based Studies of Infectious Disease: Application to Tuberculosis