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

印第安纳州大学（RUSIS@IU）的本科生暑期统计研究所将在3个夏季中的每个夏季为印第安纳州大学带来10名学生，为期10周，学习统计及其应用问题。该研究所致力于吸引学生进入数学和统计科学的研究生工作，并促进他们过渡到高需求和国家利益领域的研究，包括需要数据分析技能的劳动力部门，这些部门对人力资源的需求超过了合格专业人员的供应。RUSIS @IU将从STEM代表性不足的群体中招收学生，以及在家乡机构获得研究机会有限的学生，包括来自社区学院，部落学院，历史上的黑人学院和大学以及西班牙裔服务机构的学生。虽然该计划旨在激励学生攻读统计科学的研究生课程，但它也将为学生准备各种需要统计和数据科学高水平培训的专业职位。研究所的目标是通过以下机制实现的。学生将参加概率和统计的强化短期课程以及计算的强化研讨会。此外，随机过程和统计推断的主题，特别强调当前感兴趣的主题将进行讨论。然后，学生将参与当前感兴趣的高维数据分析领域的研究项目，其中统计思维可以从数据中提取信息，以推进科学，技术和社会。主题包括多重比较，降维，随机矩阵，极值理论，多元生存分析，统计遗传学，海量数据方法，以及电网和其他复杂城市基础设施的建模。自动系统（例如自动汽车）利用统计技术，如模式识别，统计学习和降维方法将提供有趣的测试案例。学生还将参加专业发展活动，包括在全国会议上促进学生演讲，LaTeX短期课程和用于研究的软件，如Mathematica，MatLab和R，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.