REU Site: Research for Undergraduates Summer Institute of Statistics at Indiana University

点击翻译按钮获取中文摘要

The Research for Undergraduates Summer Institute of Statistics at Indiana University (RUSIS@IU) will bring 10 students to Indiana University for 10 weeks for each of 3 summers to study problems in statistics and its applications. The Institute strives to attract students into graduate work in the mathematical and statistical sciences and to facilitate their transition into research in areas of high demand and national interest, including sectors of the workforce that require data analytic skills where demands for human resources exceeds the supply of qualified professionals. RUSIS @IU will recruit students from underrepresented groups in STEM as well as students with limited access to research opportunities at their home institutions, including students from Community Colleges, Tribal Colleges, Historically Black Colleges and Universities, and Hispanic Serving Institutions. While the program aims to motivate students to pursue graduate studies in the statistical sciences, it will also prepare students for a wide range of professional positions that require high level training in statistics and data science. The Institute’s objectives are accomplished through the following mechanisms. Students will take an intensive short course in probability and statistics and an intensive seminar in computation. In addition, topics in stochastic processes, and statistical inference, with a special emphasis on topics of current interest will be discussed. Students will then participate in research projects from areas of current interest in high dimensional data analyses, where statistical thinking can enable the extraction of information from data to advance science, technology, and society. Topics include multiple comparisons, dimensionality reduction, random matrices, extreme value theory, multivariate survival analysis, statistical genetics, massive data methodologies, and modeling of the power grid and other complex urban infrastructures. Autonomous systems (e.g. autonomous cars) that make use of statistical techniques such as pattern recognition, statistical learning, and dimensionality reduction methodologies will provide interesting test cases. Students will also participate in professional development activities including the facilitation of student presentations at national meetings, short courses in LaTeX and software used for research such as Mathematica, MatLab, and R, and visits to scientific facilities for career development.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.