Mathematical Sciences/GIG: Graduate & Postdoctoral Education in Cross-Disciplinary Research

数学科学/GIG：研究生

• 批准号: 9709696
• 负责人: Peter McCullagh
• 金额: $ 58.6万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-09-01 至 2001-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9709696&HistoricalAwards=false
• 关键词: Mathematical; Sciences; GIG; Graduate; &amp

Through graduate and postdoctoral education, the Department of Statistics seeks to increase the number of research workers who are capable of working at the interface between statistics and specific areas in the natural and social sciences. The target areas of application are genetics and pedigree analysis, medical image analysis, atmospheric physics and geophysical sciences, astrophysics, and finance. The availability of genetic data on extensive human pedigrees has increased at an explosive rate over the past few years, and the pace is almost certain to increase in the next decade. Inferential and computational problems associated with linkage analysis for complex genetic diseases using multiple markers are non-standard, so the demand for statisticians knowledgeable in quantitative statistical genetics is very high. Groups active at Chicago include Graeme Bell on diabetes, and Carole Ober on asthma, both of whom have long-standing collaborations with Augustine Kong and Mary Sara McPeek from Statistics. Yali Amit's work with Chin-Tu Chen from Radiology deals with model registration and image matching for various types of medical images. The techniques used include deformable templates, and dynamic programming on decomposable graphs. Shape and image recognition is a challenge for future work. The plan calls for postdocs and graduate students supported by this project to be trained in statistical genetics, image analysis, spatio-temporal modelling of atmospheric or astronomical phenomena, or other topics relevant to the area of application. In the case of geophysical sciences, issues such as stratospheric ozone depletion are tackled by adapting statistical methods derived from time-series and spatial processes. The personnel involved in this project include John Frederick from Geophysical Sciences, George Tiao from the Graduate School of Business, Barry Lesht from Argonne, and Michael Stein and Xiao-Li Meng from Statistics. The Department aims to increase the number of research wo rkers trained in statistics who are capable of collaborative research in a number of areas of the natural and social sciences. The plan is to recruit doctorates in statistics or in the relevant discipline and to provide them with the necessary training to bridge the gap between statistics and science. The targeted areas of application include quantitative genetics, medical image analysis, atmospheric physics and geophysical sciences, astrophysics and finance. These are areas that meet three criteria crucial to success: a critical need for more extensive collaboration, faculty in the Department of Statistics active in these areas, and scientists at the University or at Argonne willing to serve as mentors. The availability of genetic data on extensive human pedigrees has increased at an explosive rate over the past few years, as research workers have realized the potential benefits from identifying disease-susceptibility genes. With the progress of the human genome project, the pace will increase in the next decade, so the demand for statisticians knowledgeable in quantitative statistical genetics is certain to increase. In the case of geophysical sciences, issues such as stratospheric ozone depletion are tackled by adapting statistical methods derived from time-series and spatial processes. The personnel involved in this project include John Frederick from Geophysical Sciences, George Tiao from the Graduate School of Business, Barry Lesht from Argonne, and Michael Stein and Xiao-Li Meng from Statistics. Funding for this activity will be provided by the Division of Mathematical Sciences and the MPS Office of Multidisciplinary Activities.

通过研究生和博士后教育，统计学系旨在增加能够在统计与自然科学和社会科学特定领域之间工作的研究工作者的数量。应用的目标领域是遗传学和系谱分析、医学图像分析、大气物理学和地球物理科学、天体物理学和金融。在过去几年中，广泛的人类谱系遗传数据的可用性以爆炸性的速度增长，而且在未来十年中，这一速度几乎肯定会增加。使用多个标记进行复杂遗传疾病连锁分析的推理和计算问题是非标准的，因此对具有定量统计遗传学知识的统计学家的需求非常高。活跃在芝加哥的研究小组包括研究糖尿病的格雷姆·贝尔和研究哮喘的卡罗尔·奥伯，他们都与统计学的奥古斯丁·孔和玛丽·萨拉·麦克皮克有长期的合作。Yali Amit与来自放射科的Chin-Tu Chen的工作涉及各种类型医学图像的模型配准和图像匹配。使用的技术包括可变形模板和可分解图的动态规划。形状和图像识别是未来工作的一个挑战。本项目支持的博士后和研究生在统计遗传学、图像分析、大气或天文现象的时空建模或其他与应用领域相关的主题方面进行培训。就地球物理科学而言，平流层臭氧耗损等问题是通过调整从时间序列和空间过程中得出的统计方法来解决的。参与该项目的人员包括地球物理科学系的John Frederick、商学院的George Tiao、Argonne的Barry Lesht、统计学的Michael Stein和xiaoli孟。该司的目标是增加受过统计训练的研究人员的人数，使他们能够在自然科学和社会科学的一些领域进行合作研究。计划是征聘统计学或有关学科的博士，并向他们提供必要的培训，以弥合统计学与科学之间的差距。目标应用领域包括定量遗传学、医学图像分析、大气物理学和地球物理科学、天体物理学和金融学。这些领域都符合成功的三个关键标准：迫切需要更广泛的合作，统计学系的教师积极参与这些领域，以及哈佛大学或阿贡国家实验室的科学家愿意担任导师。在过去几年中，由于研究人员已经认识到识别疾病易感基因的潜在好处，广泛的人类谱系遗传数据的可用性以爆炸性的速度增长。随着人类基因组计划的进展，在未来十年，步伐将加快，因此对具有定量统计遗传学知识的统计学家的需求肯定会增加。就地球物理科学而言，平流层臭氧耗损等问题是通过调整从时间序列和空间过程中得出的统计方法来解决的。参与该项目的人员包括地球物理科学系的John Frederick、商学院的George Tiao、Argonne的Barry Lesht、统计学的Michael Stein和xiaoli孟。这项活动的经费将由数学科学司和MPS多学科活动办公室提供。