REU Site: Multidisciplinary Graph Data Analytics

佐治亚州立大学的本科生研究经验（REU）网站：多学科图形数据分析是一个为期八周的暑期研究项目，为本科生提供研究密集型培训，并提供积极参与多学科数据分析项目的宝贵机会。该奖项每年夏天将从研究能力有限、少数族裔（如佐治亚州和邻近州的非洲裔美国人和西班牙裔）高度集中的大学中招收10名本科生。该项目的目标包括：(1)为本科生提供高质量的研究经验；(2)增加女性和代表性不足的少数民族在数据分析（特别是图形数据分析）方面的参与，这将有助于扩大计算领域的多样性；(3)为学生在研究型职位上攻读研究生课程和职业生涯做好准备。参与者将在导师的指导和指导下从事图形数据分析的研究项目，并在社交网络、生物信息学和商业分析方面进行实际应用。此外，学生将通过实地考察和嘉宾演讲获得对行业研究实践的见解。完成课程后，参与者将获得在科学和技术领域取得成功必不可少的强大技能，特别是在不断发展的数据科学领域，这一领域预计将在未来的专业领域中保持关键地位。这个REU网站旨在吸引本科生学习经验，以提高他们进行基础研究的兴趣和能力，特别是在图数据分析方面。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），同时在社交网络，生物信息学和商业分析中的实际项目中工作。研究项目将分为以下几类：1)基于图压缩的图神经网络；2)基于商业网络的影响最大化；3)基于知识图的社会网络分析；4)基于异构图的生物医学数据分析。通过每周一次的研讨会，学生将进一步了解数据分析中固有的道德挑战，从隐私问题到应用于有偏见数据集的机器学习出现的问题。通过定期的会议，各种各样的问题和经验被分享和知识的交流，学生们不仅会深入研究他们的项目，也会接触到其他正在进行的项目。该奖项反映了美国国家科学基金会的法定使命，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: 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.