REU Site: Research for Undergraduates Summer Institute of Statistics at The University of Nevada Reno (RUSIS@UNR)

REU 网站：内华达大学里诺分校夏季统计学院本科生研究 (RUSIS@UNR)

• 批准号: 1731082
• 负责人: Javier Rojo
• 金额: $ 34.56万
• 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: 2016
• 资助国家: 美国
• 起止时间: 2016-12-01 至 2019-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1731082&HistoricalAwards=false
• 关键词: REU; Site; Research; Undergraduates; Summer

Research for Undergraduates Summer Institute of Statistics at The University of Nevada Reno (RUSIS@UNR) 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. The RUSIS@UNR program 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 RUSIS@UNR objectives will be accomplished through the following mechanisms: (1) Mentoring and research supervision, of 15 selected underrepresented minority undergraduate students and students with no easy access to a career experience at their institution, through an intensive core course in probability and statistics. 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. Statistics provides the tools (from concepts and methods to algorithms to operational software) needed to extract valid and reliable information from large data sets of diverse origin, and provides the methodologies to validate the models and quantify the inherent uncertainty. The students will engage in projects selected from (a) areas of genomics, metabolomics, proteomics, gene annotation, brain imaging, that rely to a very high degree on large and high-dimensional data sets, (b) environmental sciences, geophysics (including climate studies) astrophysics, cosmology, that regularly collect terabytes of high-dimensional data containing essential information about our World, and rely on knowledge extraction using tools inherent in statistical techniques, and (c) autonomous systems (e.g. autonomous cars) that make use of statistical techniques such as pattern recognition, statistical learning, and dimension reduction methodologies. In the last few years, there have been several national initiatives that require new statistical tools and theories, as well as an increased production of modernly trained statisticians. Prominent among these initiatives are: The brain initiative, the smart power grid initiative, big data and data analytics, climate change, and precision medicine; (3) 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; (4) Visits to scientific facilities (e.g., Desert Research Institute, Lawrence Berkeley National Laboratory) will be organized. 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.

内华达大学里诺分校的本科生暑期统计研究所（RUSIS@UNR）是一个成功的（AMS获奖的）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@UNR计划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@UNR的目标将通过以下机制实现：(1)通过概率和统计的强化核心课程，指导和研究监督15名被选中的代表性不足的少数民族本科生和在其所在机构不易获得职业经验的学生。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。统计学提供了从不同来源的大型数据集中提取有效和可靠信息所需的工具（从概念和方法到算法到操作软件），并提供了验证模型和量化固有不确定性的方法。学生将从以下领域选择项目：(a)基因组学、代谢组学、蛋白质组学、基因注释、脑成像，这些领域在很大程度上依赖于大型和高维数据集；(b)环境科学、地球物理学（包括气候研究）、天体物理学、宇宙学，这些领域定期收集tb级的高维数据，其中包含有关我们世界的基本信息，并依赖于使用统计技术固有工具提取知识。(c)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（例如自动驾驶汽车）。在过去几年中，有几项国家倡议需要新的统计工具和理论，并需要更多受过现代训练的统计学家。这些倡议中最突出的是：大脑倡议、智能电网倡议、大数据和数据分析、气候变化和精准医疗；(3)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；(4)组织参观科学设施（如沙漠研究所、劳伦斯伯克利国家实验室）。学生参加后的七年（预计完成研究生学业的时间）的进度将被监测，并由参与的学生和外部咨询委员会对项目进行年度评估。