Scientific Computing Research Environments for the Mathematical Sciences (SCREMS)

该项目将为计算密集型应用统计方法的研究提供计算支持：用于在二维和三维数据中发现细丝的小波方法，基因表达数据的微阵列数据分析，发现黑盒函数的可解释近似的蒙特卡罗方法，功能性MRI图像的空间平滑，目前统计学的研究前沿已远远落后于教学课程中的t检验和卡方检验。 现代科学设备在复杂的环境中收集了数百万的数据测量。 例如，生物学家在一个计算机上测量数千个基因的表达水平，脑科学家在受试者执行任务时观察大脑活动的地图。 处理这些数据和相关问题需要新的统计方法来从随机性中挑选出模式。 这些方法反过来又需要大量的计算能力，包括速度和内存。该项目将支付计算资源，以支持五个这样的研究领域。

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.