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.
