Scientific Computing Research Environments for the Mathematical Sciences (SCREMS)

数学科学的科学计算研究环境 (SCREMS)

• 批准号: 0215486
• 负责人: David Donoho
• 金额: $ 6万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-08-15 至 2003-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0215486&HistoricalAwards=false
• 关键词: Scientific; Computing; Research; Environments; Mathematical

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.

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寻找细丝的小波方法，基因表达数据的微阵列数据分析，寻找黑箱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，以及流式细胞术数据的自动聚类。统计学的研究前沿现在与入门课程的t检验和卡方检验相去甚远。现代科学设备在复杂的环境中收集数以百万计的数据测量。例如，生物学家测量单个芯片上数千个基因的表达水平，大脑科学家观察受试者执行任务时的大脑活动图。处理这些和相关问题的数据需要新的统计方法来从随机中找出规律。这些方法反过来需要大量的计算能力，无论是速度还是内存。该项目将支付计算资源以支持五个这样的研究领域。