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.

俄勒冈州州立大学的本科生夏季统计研究所（RUSIS@OSU）是一个成功的（AMS获奖）夏季REU网站的延续。该研究所的根本动机是吸引更多的学生进入数学和统计科学的研究生工作，并促进他们过渡到研究工作的时候，人力资源与数据分析技能的需求很容易超过供应。RUSIS @OSU将招募选定的代表性不足的少数民族学生和学生在他们的机构，包括来自社区学院，部落学院，历史上的黑人学院（HBCU）和西班牙裔服务机构（HSI）的学生没有容易获得的研究经验，指导和培训他们的职业生涯的具体目标在数学科学和更具体的统计研究。RUSIS@OSU将帮助学生在一个充满活力的世界中进行全球竞争，在这个世界中，数据驱动的决策在所有人类的努力中变得越来越突出。虽然该计划已经成功地激励和吸引学生追求统计科学的研究生职业，它也为学生提供了必要的工具，以加入劳动力在广泛的专业职位。从学术工作，到制药公司，私营企业，政府机构和国家实验室，专业体育数据分析师和气候科学家的工作，统计学家和数据科学家的工作前景非常好。夏季在RUSIS@OSU的目标是通过以下机制完成：（1）在概率和统计密集的短期课程和密集的研讨会计算。此外，还将讨论随机过程和统计推断的主题，特别强调当前感兴趣的领域;（2）让学生参与当前感兴趣领域的研究项目。统计思维在现代生活的方方面面无处不在。数据挖掘，大数据，数据分析，知识发现是过去创造的术语，基本上表示相同的事情：与从数据中提取信息以推进科学，技术和社会有关的活动。通过教师的指导和监督，学生发展技能，这将有助于他们在研究生院和研究生研究阶段;（3）学生们将从当前应用和理论感兴趣的领域中选择研究项目，（例如多重比较、降维、随机矩阵、极值理论、多变量生存分析、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）。 利用模式识别、统计学习和降维方法等统计技术的自主系统（例如自主汽车）提供了有趣的问题;（4）学生将在全国会议上展示他们的结果，他们将在准备和演讲时得到指导。将教授LaTeX和用于研究目的的软件（Mathematica，MatLab，R）的短期课程;（5）参观科学设施（例如，Linus Pauling Institute @ OSU）将被组织;（6）学生在参与后七年（他们完成研究生院的预期时间）的进展将被监测，参与学生和外部咨询委员会将对该计划进行年度评估。有关REU网站的更多详细信息可在www.example.com上获得https://stat.oregonstate.edu/rusis@OSUThis奖项反映了NSF的法定使命，并被认为值得支持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。