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

俄勒冈州立大学本科生暑期统计研究所（RUSIS@OSU）是一个成功的（AMS获奖的）暑期REU网站的延续。该研究所的根本动机是吸引更多的学生从事数学和统计科学的研究生工作，并在对具有数据分析技能的人力资源的需求很容易超过供应的时候，促进他们过渡到研究工作。RUSIS @OSU将招收代表性不足的少数民族学生和在其机构中不易获得研究经验的学生，包括来自社区学院，部落学院，历史上的黑人学院（HBCUs）和西班牙裔服务机构（HSIs）的学生，其具体目标是指导和培训他们从事数学科学研究，更具体地说，是在统计学方面。RUSIS@OSU将帮助学生在一个充满活力的世界中参与全球竞争，在这个世界中，数据驱动的决策在所有人类活动中变得越来越重要。虽然该计划在激励和吸引学生追求统计科学的研究生职业生涯方面取得了成功，但它也为学生加入广泛的专业职位的劳动力提供了必要的工具。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，专业体育数据分析师和气候科学家，统计学家和数据科学家的工作前景非常好。RUSIS@OSU暑期课程的目标是通过以下机制完成的：(1)概率和统计方面的短期强化课程和计算方面的强化研讨会。此外，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出来的术语，本质上是指同一件事：从数据中提取信息以推进科学、技术和社会的活动。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)受当前对开发新方法以响应高维数据分析需求的兴趣的激励，学生将从当前应用和理论兴趣领域（如多重比较，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，电网和其他复杂城市基础设施建模）中选择研究项目。自主系统（如自动驾驶汽车）利用统计技术，如模式识别、统计学习和降维方法，提供了有趣的问题；(4)学生将在全国会议上展示他们的成果，他们将在准备和演讲方面得到指导。将教授LaTeX和用于研究目的的软件（Mathematica, MatLab， R）的短期课程；(5)组织参观科学设施（如俄勒冈州立大学莱纳斯·鲍林研究所）；(6)对学员参加后七年（预计完成研究生学业的时间）的学习进度进行监测，并由学员和外部咨询委员会对项目进行年度评估。有关REU站点的更多细节可在https://stat.oregonstate.edu/rusis@OSUThis上获得，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 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.