• This program trains students for academic research careers. The foundation is a sequence of courses in probability, mathematical statistics, linear models and statistical computing. The program also encourages study in a cognate area of application. 
  • Major areas of departmental research include: analysis of observational studies; Bayesian inference, bioinformatics; computational biology; decision theory; game theory; genomics; high dimensional inference; information theory; machine learning; model selection; nonparametric function estimation; and time series analysis.
  • Students typically have a strong undergraduate background in mathematics. Knowledge of linear algebra and advanced calculus is required, and experience with real analysis is helpful. Although some exposure to undergraduate probability and statistics is expected, skills in mathematics and computer science are more important.