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。该研究所的基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，并在具有数据分析技能的人力资源容易供不应求的情况下为他们过渡到研究工作提供便利。Rusis@OSU将招收选定的少数族裔学生和在其机构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(HBCU)和西班牙裔服务机构(HSI)的学生，具体目标是指导和培训他们从事数学科学研究的职业，特别是统计学方面的研究。Rusis@OSU将为学生在充满活力的世界中进行全球竞争做好准备，在这个世界中，数据驱动的决策在所有人类努力中变得更加突出。虽然该计划在激励和诱使学生在统计科学领域追求研究生职业生涯方面取得了成功，但它也为学生在广泛的专业职位上加入劳动力大军提供了必要的工具。从学术工作，到制药公司、私营行业、政府机构和国家实验室的工作，数据分析师的职业运动，以及气候科学家，统计学家和数据科学家的就业前景都很好。Rusis@OSU的暑期目标是通过以下机制实现的：(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，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。统计思维普遍存在于现代生活的方方面面。数据挖掘、大数据、数据分析、知识发现是过去创造的术语，表示本质上相同的东西：与从数据中提取信息以促进科学、技术和社会发展相关的活动。通过教师的指导和指导，学生将发展在研究生院和研究生研究阶段帮助他们的技能；(3)学生将从事从当前应用和理论感兴趣的领域(例如，多元比较、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)中挑选出来的研究项目，以满足当前对高维数据分析需求的开发兴趣。利用模式识别、统计学习和降维方法等统计技术的自动驾驶系统(例如自动驾驶汽车)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。将教授乳胶和用于研究目的的软件的短期课程(MATHEMICA、MATLAB、R)；(5)组织参观科学设施(如莱纳斯·鲍林研究所@俄亥俄州立大学)；(6)学生在参与后的七年内(预计他们完成研究生学业的时间)的进展将受到监督，参与学生和外部咨询委员会将对该项目进行年度评估。有关该项目的更多细节可在https://stat.oregonstate.edu/rusis@OSUThis奖上获得，这反映了美国国家科学基金会的法定使命，并通过使用基金会的智力优势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.