This project will provide computing support for research on computationally intensive applied statistical methods: wavelet methods for finding filiments in two and three dimensional data, microarray data analysis of gene expression data, Monte Carlo method of finding interpretable approximations to black box functions, spatial smoothing of functional MRI images, and automatic clustering of flow cytometry data.
The research frontiers of statistics are now a far cry from the t-tests and chi-squared tests of introductory courses. Modern scientific equipment gathers millions of data measurements in complex settings. For example, biologists measure the expression levels of thousands of genes on a single chip, and brain scientists watch maps of brain activity as a subject performs a task. Handling data from these and related problems requires new statistical methods to sort out patterns from randomness. These methods in turn require substantial computating power, in both speed and memory. This project will pay for computating resources to support five such research areas.
