Analytic Tools

  • Handling genetic data: Python
  • Statistics/graphs: R, SAS, Stata,
  • Also have used/familiarity with: SQL, Octave/Mathlab

Analytic Experience:

  • causal inference
  • survival analysis and cumulative risk
  • prospective (longitudinal) and retrospective analyses
  • handling missing data and imputation
  • GLM and model selection
  • Within genetics: PCA, K-means clustering


  • Mitochondrial DNA and phylogeny
  • Head and neck cancer
  • Modifiable exposures and cancer
  • Modifiable exposures and chronic neurological disease
  • Cancer trends
  • Disaster aftermath and recovery (e.g. World Trade Center)