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

通过研究生和博士后教育，统计系力求增加能够在统计与自然科学和社会科学特定领域之间开展工作的研究人员数量。应用的目标领域是遗传学和谱系分析、医学图像分析、大气物理和地球物理科学、天体物理学和金融。 在过去的几年里，有关广泛人类谱系的遗传数据的可用性以爆炸性的速度增加，而且几乎可以肯定，这一速度在未来十年还会增加。 与使用多个标记对复杂遗传疾病进行连锁分析相关的推论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。 芝加哥活跃的团体包括糖尿病研究组格雷姆·贝尔 (Graeme Bell) 和哮喘研究组卡罗尔·奥伯 (Carole Ober)，他们两人都与统计部门的奥古斯丁·孔 (Augustine Kong) 和玛丽·萨拉·麦克皮克 (Mary Sara McPeek) 保持着长期合作。 Yali Amit 与放射科的 Chin-Tu Chen 合作处理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编程。 形状和图像识别是未来工作的挑战。 该计划要求该项目支持的博士后和研究生接受统计遗传学、图像分析、大气或天文现象的时空建模或与应用领域相关的其他主题的培训。就地球物理科学而言，平流层臭氧消耗等问题是通过采用从时间序列和空间过程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部的John Frederick、商学院的George Tiao、阿贡国家实验室的Barry Lesht以及统计学部的Michael Stein和孟小丽。 该系的目标是增加接受过统计学培训的研究人员数量，他们能够在自然科学和社会科学的许多领域进行合作研究。 该计划是招募统计学或相关学科的博士，并向他们提供必要的培训，以缩小统计学和科学之间的差距。 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。这些领域满足对成功至关重要的三个标准：迫切需要更广泛的合作、活跃在这些领域的统计系教师以及愿意担任导师的大学或阿贡国家实验室的科学家。过去几年，随着研究人员意识到识别疾病易感基因的潜在好处，有关广泛人类谱系的遗传数据的可用性呈爆炸式增长。 随着人类基因组计划的进展，未来十年步伐将会加快，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，平流层臭氧消耗等问题是通过采用从时间序列和空间过程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部的John Frederick、商学院的George Tiao、阿贡国家实验室的Barry Lesht以及统计学部的Michael Stein和孟小丽。 本次活动的经费将由数学科学处和公安部多学科活动办公室提供。

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.