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: 1560460
• 负责人: Javier Rojo
• 金额: $ 39.81万
• 依托单位: Board of Regents, NSHE, obo University of Nevada, Reno
• 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: 2016-05-01 至 2017-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1560460&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.

内华达大学里诺大学（Rusis@unr）的本科生夏季统计研究所的研究是成功（AMS屡获殊荣）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@UNR计划将使学生在充满活力的世界中竞争全球竞争，在这个充满活力的世界中，数据驱动的决定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@UNR目标将通过以下机制来实现：（1）指导和研究监督，对15个被选定的代表性较低的少数族裔本科生和学生进行，而在其机构中不容易获得职业经验，这是通过概率和统计数据的强化核心课程。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。 统计信息提供了所需的工具（从概念和方法到算法到操作软件），从大量来源的大数据集中提取有效和可靠的信息，并提供了验证模型并量化固有不确定性的方法。 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统计技术固有的，（c）使用统计技术（例如模式识别，统计学习和降低维度减少方法）的自治系统（例如自动驾驶汽车）。 在过去的几年中，已经有几项国家计划需要新的统计工具和理论，并增加了现代训练的统计学家的生产。 这些倡议中很重要的是：大脑倡议，智能电网计划，大数据和数据分析，气候变化和精密医学； （3）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）； （4）将组织对科学设施（例如沙漠研究所，劳伦斯·伯克利国家实验室）的访问。 将在参与他们的参与之后的七年学生（预计将完成研究生院的时间），并将由参与的学生和外部咨询委员会对计划的年度评估。