Models and software for predicting who is at risk of carrying genetic variants that confer susceptibility to cancer. Application to breast, ovarian, colorectal, pancreatic and skin cancer.
Statistical methods for the analysis of high throughput genomic data: analysis of cancer genome sequencing projects; integration of genomic information across technologies; cross-study validation of genomics results.
Statistical methods for complex medical decisions: comprehensive models for lifetime history of chronic disease outcomes; decision trees and dynamic programming.
Bayesian modeling and computation: multilevel models; decision theoretic approaches to inference; sequential experimental design, Markov chain Monte Carlo methods.