Scientific Computing Research Environments for the Mathematical Sciences (SCREMS)

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This project will provide computing support forresearch on computationally intensive appliedstatistical methods: wavelet methods for findingfiliments in two and three dimensional data,microarray data analysis of gene expression data,Monte Carlo method of finding interpretableapproximations to black box functions,spatial smoothing of functional MRI images,and automatic clustering of flow cytometry data.The research frontiers of statistics are now afar cry from the t-tests and chi-squared tests ofintroductory courses. Modern scientific equipmentgathers millions of data measurements in complexsettings. For example, biologists measure theexpression levels of thousands of genes on a singlechip, and brain scientists watch maps of brainactivity as a subject performs a task. Handlingdata from these and related problems requires newstatistical methods to sort out patterns fromrandomness. These methods in turn requiresubstantial computating power, in both speed andmemory. This project will pay for computatingresources to support five such research areas.