REU Site: Big Data Analytics at Oklahoma State University

俄克拉荷马州州立大学（OSU）的大数据分析本科生研究经验（REU）网站是一个为期十周的暑期项目，旨在从研究能力有限的大学和俄克拉荷马州及邻近州的非裔美国人和美洲原住民等少数民族人口高度集中的大学招募十名本科生。参与者将在教师导师的指导和指导下从事大数据分析的研究项目，并允许学生参与跨学科的跨领域研究。该项目的目标包括：（1）为本科生提供高质量的研究体验;（2）增加女性和代表性不足的少数民族在计算领域（特别是大数据分析）的参与，这将有助于扩大计算机科学的多样性;（3）为学生在计算和数据科学领域攻读研究生课程做好准备，并建立一个大数据分析研究人员社区。参与者还将通过实地考察和外部演讲者接触行业的研究活动。这种接触将告知学生在学术界以及工业环境中的潜在职业道路的未来选择。通过该计划的结束，学生应该获得的技能，将导致奖励的科学和技术的职业生涯，特别是在数据科学，预计将继续成为未来最重要的领域之一。这个REU网站旨在吸引本科生的学习经验，提高学生的兴趣和能力进行计算机科学的初步研究，特别是大数据分析研究。学生将学习如何开发和使用不同的机器学习（例如，神经网络），数据挖掘（例如，聚类），和统计方法（例如，回归），并应用于图论、文本挖掘、图像处理和生物信息学。他们将被介绍到大数据分析的不同方面，同时使用不同类型的数据（包括网络，健康和图像数据）进行实际项目。他们将开发高效的新算法来分析大量的现实社会和信息网络，分析从电子健康记录中收集的健康数据，并提取未标记数据的有意义的视觉表示，以更好地进行视觉理解。研究主题包括使用大数据来描述社交媒体中的仇恨言论，以了解COVID-19的传播。学生将为前沿研究做出贡献，并经常在顶级场所发表和展示。REU的经验将扩大学生对研究的理解，将学生放在包括其他本科生和研究生在内的团队中，由PI和其他教师导师指导。他们还将了解大数据分析所固有的道德挑战，从隐私问题到应用于有偏见数据集的机器学习所出现的问题。通过每周的会议和研讨会，他们还将了解其他项目，与其他学生分享他们在项目中的经验，形成一个群体。主要重点是招募女性和代表性不足的少数民族，使UG学生准备好从事研究型职位的职业生涯，并为扩大计算机科学的多样性做出贡献。该项目由计算机和信息科学与工程的信息和智能系统部门和刺激竞争研究的既定计划（EPSCoR）共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The Research Experience for Undergraduates (REU) site on big data analytics at Oklahoma State University (OSU) is a ten-week summer program that seeks to recruit ten undergraduate students from colleges with limited research capabilities and high concentrations of underrepresented minority populations such as African Americans and Native Americans in Oklahoma and neighboring states. The participants will engage in research projects in big data analytics under faculty mentors' mentorship and guidance and allow students to participate in interdisciplinary research that crosses a variety of fields. The goals of the project include (1) providing a quality research experience for undergraduates, (2) increasing participation of female and under-represented minorities in computing fields (especially big data analytics), which will contribute to the broadening of diversity in computer science, (3) preparing students to pursue graduate studies in computing and data science fields, and building a community of big data analytic researchers. The participants will also be exposed to research activities in the industry through field trips and external speakers. This exposure will inform students' future choices about potential career paths within academia as well as within industrial settings. By the end of the program, the students should acquire skills that will lead to rewarding professional careers in science and technology, specifically in data science, expected to continue to be one of the most important fields of the future.This REU site aims to engage undergraduates in learning experiences that increase students` interest and ability to conduct primary research in computer science, especially big data analytics research. Students will learn how to develop and use different machine learning (e.g., neural networks), data mining (e.g., clustering), and statistical methods (e.g., regression), with applications to graph theory, text mining, image processing, and bioinformatics. They will be introduced to different aspects of big data analytics while working on real-world projects with different types of data, including network, health, and image data. They will develop efficient novel algorithms to analyze massive real-world social and information networks, to analyze the health data collected from electronic health records and to extract meaningful visual representations of unlabeled data for better visual understanding. Research topics range from using big data to characterize hate speech in social media to understand the COVID-19 spread. Students will contribute to cutting-edge research and often publish and present in top venues. The REU experience will expand the students' understanding of research by placing students in teams that include other undergraduate students and graduate students under the mentorship of the PIs and other faculty mentors. They will also learn about the ethical challenges inherent in big data analytics, from issues of privacy to problems emerging from machine learning applied to biased datasets. With weekly meetings and seminars, they will also learn about other projects, share their experience in their project with other students to form a cohort. The primary focus is to recruit female and underrepresented minorities, make UG students ready to pursue professional careers in research-oriented positions and contribute to the broadening of diversity in computer science.This project is jointly funded by Computer and Information Science and Engineering’s Information and Intelligent Systems division and the Established Program to Stimulate Competitive Research (EPSCoR).This 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.