Knowledge Resource Center for Ecological Environment in Arid Area
| 项目编号 | 1731082 |
| REU Site: Research for Undergraduates Summer Institute of Statistics at The University of Nevada Reno (RUSIS@UNR) | |
| Javier Rojo | |
| 主持机构 | Oregon State University |
| 开始日期 | 2016-12-01 |
| 结束日期 | 2019-04-30 |
| 资助经费 | 345551(USD) |
| 项目类别 | Standard Grant |
| 资助机构 | US-NSF（美国国家科学基金会） |
| 项目所属计划 | RSCH EXPER FOR UNDERGRAD SITES, WORKFORCE IN THE MATHEMAT SCI |
| 语种 | 英语 |
| 国家 | 美国 |
| 英文简介 | 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. |
| 来源学科分类 | Mathematical and Physical Sciences |
| URL | https://www.nsf.gov/awardsearch/showAward?AWD_ID=1731082 |
| 资源类型 | 项目 |
| 条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/342954 |
| 推荐引用方式 GB/T 7714 | Javier Rojo.REU Site: Research for Undergraduates Summer Institute of Statistics at The University of Nevada Reno (RUSIS@UNR).2016. |
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