REU Site: Multidisciplinary Graph Data Analytics

REU 网站：多学科图数据分析

• 批准号: 2349486
• 负责人: Esra Akbas
• 金额: $ 37.24万
• 依托单位: Georgia State University Research Foundation, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2349486&HistoricalAwards=false
• 关键词: REU; Site; Multidisciplinary; Graph; Data

The Research Experience for Undergraduates (REU) site: Multidisciplinary Graph Data Analytics at Georgia State University is an eight-week summer research program to provide undergraduates a research-intensive training and offer valuable opportunities to actively engage in multidisciplinary data analytics projects. This award will recruit ten undergraduate students each summer from colleges with limited research capabilities and high concentrations of underrepresented minority populations such as African Americans and Hispanics in Georgia and neighboring states. The goals of the project include (1) providing a quality research experience for undergraduates, (2) increasing participation of female and under-represented minorities in data analytics (particularly graph data analytics), which will contribute to the broadening of diversity in computing fields, and (3) preparing students to pursue graduate studies and professional careers in research-oriented positions. The participants will engage in research projects in graph data analytics with practical applications in social networks, bioinformatics, and business analytics under faculty mentors' mentorship and guidance. Additionally, students will gain insights into industry research practices through field trips and guest speaker sessions. Upon completion of the program, participants are expected to acquire a robust skill set essential for successful careers in science and technology, particularly in the ever-growing field of data science—an area projected to remain pivotal in the future professional landscape.This REU site aims to engage undergraduates in learning experiences that increase their interest and ability to conduct basic research, especially on graph data analytics. Students will learn how to develop and use different graph machine learning (e.g., graph neural networks), graph data mining (e.g., graph clustering), and statistical methods (e.g., regression) while working on real-world projects with applications in social networks, bioinformatics, and business analytics. The research projects will fall into the following categories: 1) Graph Neural Networks with Graph Compressing, 2) Influence Maximization on Business Networks, 3) Social Network Analysis using Knowledge Graphs, and 4) Biomedical Data Analysis using Heterogeneous Graphs. Students will further learn about the ethical challenges inherent in data analytics, from privacy issues to problems emerging from machine learning applied to biased datasets via weekly seminars. Through regular meetings, where diverse problems and experiences are shared and knowledge is exchanged, students will not only delve into their projects but also gain exposure to other ongoing projects.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.

佐治亚州立大学的本科生研究经验（REU）网站：多学科图数据分析是一项为期八周的夏季研究计划，旨在为大学生提供研究密集型培训，并为积极参与多学科数据分析项目提供宝贵的机会。该奖项将每年夏天从学院招募十个本科生，研究能力有限，高度占代表性不足的少数民族人口，例如非裔美国人和佐治亚州和邻国的西班牙裔。该项目的目标包括（1）为本科生提供优质的研究经验，（2）女性和代表性不足的少数群体参与数据分析（部分图形数据分析），这将有助于扩大计算领域的多样性，以及（3）为学生购买研究生研究和职业护理领域的准备工作。参与者将在教师指导和指导下的社交网络，生物信息学和业务分析中的实际应用中从事图形数据分析。此外，学生将通过实地考察和演讲嘉宾会议获得对行业研究实践的见解。该计划完成后，预计参与者将获得对科学和技术成功职业至关重要的强大技能，尤其是在不断增长的数据科学领域，该领域预计将在未来的专业景观中保持关键性。本REU网站旨在使本科生参与学习经验，从而增加他们的兴趣和能力，以提高他们从事基础研究的能力，尤其是在图形数据分析方面。学生将学习如何开发和使用不同的图形机器学习（例如图形神经元网络），图形数据挖掘（例如图形聚类）和统计方法（例如，回归），同时使用社交网络，生物信息信息和业务分析的应用程序进行实际项目。研究项目将属于以下类别：1）图形压缩的图形神经网络，2）影响业务网络的最大化，3）使用知识图的社交网络分析，以及4）使用异构图的生物医学数据分析。学生将进一步了解数据分析中固有的道德挑战，从隐私问题到通过每周的SEMIAR应用于有偏见数据集的机器学习出现的问题。通过常规会议，分享潜水员的问题和经验并交换知识，学生不仅会深入研究自己的项目，而且还会接触其他正在进行的项目。该奖项反映了NSF的法定任务，并认为值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来获得支持。