Department of Data Science, Dana Farber Cancer Institute
450 Brookline Avenue
Boston, Massachusetts 02115
- 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.
- Supervised and unsupervised learning using multiple studies; cross-study replicability and validation of genomics results.
- Statistical methods for complex medical decisions: decision trees and dynamic programming.
- Bayesian modeling and computation: decision theoretic approaches to inference; sequential experimental design, Markov chain Monte Carlo methods.
Ph.D. in Statistics, Department of Statistics, Carnegie Mellon University, 1990.
M.S. in Statistics, Department of Statistics, Carnegie Mellon University, 1987.
B.S. (cum laude), in Economics and Social Sciences, Università L. Bocconi, Milano, 1984.