EMSW21-RTG Statistics for Physical and Engineering Sciences: A Plan for the Establishments of a Research Training Group

点击翻译按钮获取中文摘要

NATIONAL SCIENCE FOUNDATIONArlington, Virginia 22230ABSTRACTResearch Training Groups ProgramFY 2005Prop ID: DMS-0502347 PI: Carriquiri, Alicia L.NSF Program: WORKFORCE IN THE MATHEMATICAL SCIENCES Institution: Iowa State University Title: EMSW21-RTG statistics for Physical and engineering sciences: A plan for the establishment of a research training groupPanel and ad-hoc Ratings: E, E, G, V, V, V.Statisticians are playing an increasingly important role in cross-disciplinary research addressing important, challenging scientific problems. The overall objective of this RTG program is to train young statisticians in Statistics for the Physical and Engineering Sciences in a cross-disciplinary, experiential (first-hand exposure) research environment, that brings together scientists from industry, government, and academia. This program aims to solve problems of national importance through collaboration with external partner scientists, and by educating and training undergraduates and graduate students, in cross-disciplinary research. Activities planned to achieve the objective include student internships providing an experiential research component and shorter-term visits between partner scientists and ISU faculty members to initiate and extend research collaboration. The activities will also include joint Ph.D. program expansion in the Iowa State Statistics Department, new course offerings, weekly RTG seminars, and two workshops on topics of research in the RTG. The five initial focus areas for the RTG are: reliability of critical systems, nano-metrology and high throughput technologies, calibration of computer models of complex physical systems, accelerated discovery of useful new chemical compounds and behavior of complicated systems such as large wireless networks. The creation and development of this experiential, cross-disciplinary research environment for educating and training the nation's next-generation statisticians will provide a national model for advancing scientific discovery while promoting education and learning. Expected results have the potential to be implemented widely and to provide solutions in important areas of application, such as complex physical and information networks, nano-technology, and high-performance material discovery. Statisticians are playing an increasingly important role in cross-disciplinary research addressing important, challenging scientific problems. The overall objective of this RTG program is to train young statisticians in Statistics for the Physical and Engineering Sciences in a cross-disciplinary, experiential (first-hand exposure) research environment, that brings together scientists from industry, government, and academia. This program aims to solve problems of national importance through collaboration with external partner scientists, and by educating and training undergraduates and graduate students, in cross-disciplinary research. Activities planned to achieve the objective include student internships providing an experiential research component and shorter-term visits between partner scientists and ISU faculty members to initiate and extend research collaboration. The activities will also include joint Ph.D. program expansion in the Iowa State Statistics Department, new course offerings, weekly RTG seminars, and two workshops on topics of research in the RTG. The five initial focus areas for the RTG are: reliability of critical systems, nano (very small-scale)-metrology and high throughput technologies, calibration of computer models of complex physical systems, accelerated discovery of useful new chemical compounds and behavior of complicated systems such as large wireless networks. The creation and development of this experiential, cross-disciplinary research environment for educating and training the nation's next-generation statisticians will provide a national model for advancing scientific discovery while promoting education and learning. Expected results have the potential to be implemented widely and to provide solutions in important areas of application, such as complex physical and information networks, nano (very small-scale)-technology, and high-performance material discovery