REU Site: Diverse Undergraduate Research Experiences in Statistics

美国统计协会（ASA）将通过为本科生网站实施分布式研究经验网络，为统计领域的本科生研究界注入活力。在每个站点，学生团队将处理来自不同科学和工程学科的复杂数据集，其中统计学在数据分析中起着关键作用。在三年的时间里，该项目将有9个站点，每个站点有4名学生，允许36名学生在资助期间获得统计学研究经验。招聘对象将是妇女、少数民族和残疾人。支持的学生必须是美国公民、国民或永久居民。成功的关键指标将是学生是否继续研究生学习，他们做出什么样的职业选择，以及他们的统计培训如何影响他们的职业选择和他们在这些职业中的成功。这一举措将在整个统计学科中激发和维持本科生研究的新兴奋，并有可能影响依赖统计的领域，如工程、大气科学、医疗保健和各种公共政策。资助提案的一个统一主题是，学生将参与数据分析周期的所有阶段，包括：数据验证，数据清理和数据可视化，一直到统计建模，预测和数据挖掘/机器学习/计算统计，作为他们统计研究的一个组成部分。这些reu的一个结果是将有更多的学生沉浸在科学和工程的每个分支中出现的数据科学分析中。每个项目至少需要2名教师导师。优先考虑以下两种教师：(a)过去作为本科生研究人员的导师有良好的记录，和/或(b)在独立研究方面有良好的记录，并且渴望开始与本科生合作。ASA认为，通过协调教师培训课程，这项新举措也是一个教育教师的机会。

The American Statistical Association (ASA) will invigorate the undergraduate research community in statistics by implementing a distributed network of research experiences for undergraduates sites. At each site, student teams will work with complex data sets from varied scientific and engineering disciplines in which statistics plays a key role for data analysis. Over a three year period, the project will have nine sites with four students per site, allowing 36 students to have a research experience in statistics during the lifetime of the grant. Recruitment will target women, minorities, and persons with disabilities. Supported students must be U.S. citizens, nationals, or permanent residents. Key metrics for success will be whether the students subsequently pursue graduate studies, what kind of career choices they make, and how their statistics training impacts their choice of careers and their success in such careers. This initiative should spark and sustain a new excitement about undergraduate research throughout the statistics discipline and has the potential to impact fields that rely on statistics such as engineering, atmospheric science, healthcare, and all kinds of public policy. A unifying theme of the funded proposals is that students will engage in all stages of the data analysis cycle, including: data verification, data cleaning, and data visualization, all the way to statistical modeling, prediction, and data mining/machine learning/computational statistics, as an integral part of their statistics research. One result of these REUs will be a larger number of students who are immersed in the kind of data science analysis that is emerging in every branch of science and engineering. At least 2 faculty mentors are necessary for each project. Priority will be given to faculty who either (a) have a strong track record as a mentor with undergraduate student researchers in the past, and/or (b) have a strong track record of independent research and who are eager to begin working with undergraduate students. The ASA views this new initiative as an opportunity to teach the teachers, too, by coordinating faculty training sessions.