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Statistics and Probability

Graduate study in statistics and probability has taken on a new look and increased importance in the last two decades due to dramatically increased computational power and the aggressive and highly successful application of statistical methods by our competitors in the world marketplace. In particular, the Japanese have extensively employed design of experiments, data analysis, and statistical process control to improve the quality of their processes and the quality of their manufactured products. Recently a number of major U.S. corporations began emulating the Japanese approach by getting management to support the introduction of “statistical thinking” throughout the company, and requiring that the people running their processes have sufficient formal training in statistics to properly implement and monitor statistical process control programs.


  • A. Brown: Bayesian analysis, neuroimaging data analysis, large-scale inference
  • C. Gallagher: limit theorems, time series, modeling heavy-tailed data
  • R. Lund: time series, applied probability, statistics in climatology
  • C. McMahan: Categorical data analysis, group testing, survival analysis, Bayesian estimation, statistical computing
  • X. Sun: statistical decision theory, Bayesian Statistics, multivariate analysis, and bioinformatics
  • C. L. Williams: biostatistics, computational statistics, categorical data
  • Q. Zhang: Experimental design and statistical modeling for computer experiments, Uncertainty quantification

Curriculum and Course Descriptions

Additional Statistics Links