Our methodological research consideres statisical and machine learning challenges that we think are critical for better developing and evaluating prediction models. Examples of ongoing projects include: 

1. Accounting for misreporting of family history in Mendelian risk prediction models. 
2. Using a frailty model to account for unobserved genetic and environmental effects in Mendelian risk prediction models.
3. Accounting for missing data in Mendelian risk prediction models.  
4. Combining predictions from multiple risk models. 
5. Developing statistical methods to conduct meta-analysis of penetrance estimates. 
6. Developing statistical methods to conduct meta-analysis of prevalence estimaets. 
7. Developing Mendelian risk models that consider multiple genetic syndromes together. 
8. Developing computational approaches to extend Mendelian models to large number of genes and cancers. 
9. Updating and extending existing Mendelian models including; BRCApro, MMRPRO, PancPro, MelaPro, and LFSpro.
10. Developing machine learning tools that can learn mendelian inheritance patterns.