Giovanni Parmigiani


Scientific Interests:

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.


GIANT (Genomic Integration Across N Technologies) Group A group of faculty and students working on methods for integrating information from multiple studies, techniques, and aspects of the genome and transcriptome.

BayesMendel Group A group of faculty and students working on Mendelian models for cancer genes. It grew out of the work that Don Berry and I did on the BRCAPRO model when we were at Duke University. The group is supported by a GI spore project and an R01, and is tied to the activities of the CGN. It maintains and improves BayesMendel, a free and open-source environment for Mendelian risk prediction modeling.

Hopkins Expressionists A weekly discussion group about genomics and computational genetics. It is attended by people with a wide range of backgrounds and interests. Discussions cover work in progress, reviews of papers, software tutorials, or just whatever is going on.

Predoctoral Training Program in Biostatistics for Genetics/Genomics This is a program designed to integrate rigorous training in biostatistics and bioinformatics design and analysis methods with training and direct participation in translational and cross-disciplinary research in molecular and population genetics . The program is housed in the Department of Biostatistics and supported by faculty in the Departments of Biostatistics, Applied Mathematics & Statistics, Epidemiology, Molecular Microbiology & Immunology, and Oncology.

SKCCC Bioinformatics Core This is a shared reource for all members of the Kimmel Cancer Center. It provides initial consultation and assistance with all design and data analysis challenges arising from high-throughput technologies in molecular biology.