REU Site: Research for Undergraduates Summer Institute of Statistics at Oregon State University -- RUSIS@OSU

REU 网站：俄勒冈州立大学本科生暑期统计研究所研究 -- RUSIS@OSU

• 批准号: 1852213
• 负责人: Javier Rojo
• 金额: $ 38万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-15 至 2022-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1852213&HistoricalAwards=false
• 关键词: REU; Site; Research; Undergraduates; Summer

The Research for Undergraduates Summer Institute of Statistics at Oregon State University (RUSIS@OSU) is the continuation of a successful (AMS award-winning) summer REU site. The underlying motivation for the Institute is to attract more students into graduate work in the Mathematical and Statistical Sciences, and facilitate their transition into research work at a time when demand for human resources with data analytic skills easily exceeds the supply. RUSIS @OSU will recruit selected underrepresented minority students and students with no easy access to a research experience at their institutions, including students from Community Colleges, Tribal Colleges, Historically Black Colleges (HBCUs) and Hispanic Serving Institutions (HSIs), with the specific goal of mentoring and training them for careers in research in the mathematical sciences and more specifically in Statistics. RUSIS@OSU will prepare students to compete globally in a vibrant world where data-driven decisions are becoming more prominent in all human endeavors. While the program has been successful in motivating and enticing students to pursue graduate careers in the statistical sciences, it also provides the necessary tools for students to join the workforce in a wide range of professional positions. From academic jobs, to jobs in the pharmaceutical companies, private industry, government agencies and national laboratories, professional sports as data analysts, and climate scientists, the job outlook for statisticians and data scientists is excellent. The goals of the summer at RUSIS@OSU are accomplished through the following mechanisms: (1) an intensive short course in probability and statistics and an intensive seminar in computation. In addition, topics in stochastic processes, and statistical inference, with special emphasis on areas of current interest will be discussed; (2) engaging the students in research projects from areas of current interest. Statistical thinking is ubiquitous in every aspect of modern life. Data Mining, Big Data, Data Analytics, Knowledge Discovery are terms that have been coined in the past to denote essentially the same thing: activities related to the extraction of information from data to advance science, technology, and society. Through mentoring and supervision by faculty, the students develop skills that will help them during graduate school and their graduate research stages; (3) the students, motivated by current interest in the development of new methodologies to respond to needs in high dimensional data analyses, will engage in research projects that will be selected from areas of current applied and theoretical interest (e.g. multiple comparisons, dimension 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 dimension reduction methodologies provide interesting problems; (4) students will present their results at national meetings and they will be mentored in the preparation and presentation of their talks. Short courses in LaTeX, and software used for research purposes (Mathematica, MatLab, R) will be taught; (5) visits to scientific facilities (e.g., Linus Pauling Institute @ OSU) will be organized; (6) the progress of students for seven years (expected time for them to finish graduate school) after their participation will be monitored and annual evaluation of the program by the participating students and an external advisory committee will take place.More details about the REU Site are avaialble at https://stat.oregonstate.edu/rusis@OSUThis 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.

俄勒冈州立大学（RUSIS@OSU）的本科生夏季统计研究所的研究是成功（AMS获奖）夏季REU网站的延续。该研究所的基本动机是吸引更多的学生从事数学和统计科学领域的研究生工作，并促进他们在对具有数据分析技能的人力资源的需求轻松超过供应时过渡到研究工作的。 Rusis @OSU将招募选定的人为代表性不足的少数群体和学生，在其机构中不容易获得研究经验，包括社区学院，部落学院，历史上的黑人学院（HBCUS）和西班牙裔服务机构（HSIS）（HSIS）的学生，具有指导和培训他们在数学方面的专门研究和更多专门研究的研究，并培训了他们的特定目标。 Rusis@OSU将使学生在充满活力的世界中，在所有人类努力中都变得越来越突出。尽管该计划已成功激励和吸引学生从事统计科学领域的研究生职业，但它还为学生提供了必要的工具，让学生以各种专业职位加入劳动力。从学术工作到制药公司，私营企业，政府机构和国家实验室的工作，作为数据分析师的专业运动以及气候科学家，统计学家和数据科学家的工作前景非常好。 Rusis@OSU的夏季目标是通过以下机制来实现的：（1）概率和统计的强化简短课程以及计算中的强化研讨会。此外，将讨论随机过程和统计推断的主题，并特别强调当前感兴趣的领域； （2）从当前感兴趣的领域吸引学生参与研究项目。统计思维在现代生活的各个方面都无处不在。数据挖掘，大数据，数据分析，知识发现是过去所创造的术语，以表示基本上相同的事情：与从数据中提取以促进科学，技术和社会有关的活动。通过教师的指导和监督，学生会发展技能，可以在研究生院和研究生研究阶段帮助他们； （3）由当前对高维数据分析中需求的新方法发展的兴趣所激发的学生，将参与研究项目，这些项目将从当前应用和理论兴趣的领域中选择（例如，多重比较，降低尺寸降低，随机矩阵，随机矩阵，极值，极值理论，多变量存活分析，统计遗传学，统计遗传学，大量的数据和其他模型，以及其他模型，构成了启动型和模型。 使用统计技术，例如模式识别，统计学习和降低维度的方法，可以提供有趣的问题； （4）学生将在全国会议上提出结果，并将在准备和演讲中进行指导。将教授乳胶的简短课程以及用于研究目的的软件（Mathematica，Matlab，R）； （5）将组织对科学设施的访问（例如Linus Pauling Institute @ OSU）； (6) the progress of students for seven years (expected time for them to finish graduate school) after their participation will be monitored and annual evaluation of the program by the participating students and an external advisory committee will take place.More details about the REU Site are avaialble at https://stat.oregonstate.edu/rusis@OSUThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's智力优点和更广泛的影响审查标准。