REU Site: Graph Learning and Network Analysis: from Foundations to Applications (GraLNA)

REU 网站：图学习和网络分析：从基础到应用 (GraLNA)

• 批准号: 2349369
• 负责人: Chunjiang Zhu
• 金额: $ 37.2万
• 依托单位: University of North Carolina Greensboro
• 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=2349369&HistoricalAwards=false
• 关键词: REU; Site; Graph; Learning; Network; REU; Site; Graph; Learning; Network

This project establishes a new Research Experiences for Undergraduates (REU) Site hosted by the Department of Computer Science at UNC Greensboro. Ten students will receive research training each summer in the foundations of graph machine learning and network analysis and their concrete applications in real-life networks. Graphs and networks have become ubiquitous in various scientific disciplines ranging from the Internet of Things, online social networks, brain networks, and molecules to protein-protein interaction networks. Analysis of large-scale networks can bring significant advances to our understanding of complex systems. Existing methods are purely empirical or lack in-depth foundational exploration, thus limited in processing complex graph and network data. This project aims to provide students the opportunity to undertake cutting-edge research in graphs and networks at a major research and minority serving institute. The research training on Graph Learning and Network Analysis will contribute to developing a competitive next-generation network and AI workforce. Through various activities such as orientation workshops, invited lectures, hands-on projects, presentations, demos, and other professional development opportunities, undergraduate students will also enhance their professional skills.The first objective of this GraLNA project is to provide an experience of doing solid research for a diverse group of students, including underrepresented minorities in STEM, especially those from Primarily Undergraduate Institutions. Students will gain an increased proficiency in research skills as well as oral and written communication skills. The second objective is to advance the theoretical understanding of graph learning and optimization, and to also develop new approaches to handling diverse types of complexities in graph and network data. Notable types of complexities include the distributed nature of many realworld graph data, privacy concerns arising from sensitive relationships and interactions encoded in graphs and networks, and specialized network data that involves rich domain knowledge and regulatory constraints. Student participants will engage with research projects centered around distributed graph analysis, federated learning, optimization, private graph analysis, network security, and structural and functional brain network analysis. The third objective of this project is to provide for both student participants and faculty mentors professional training and growth through a series of professional development activities, and also provide junior faculty and Ph.D. students mentoring and co-advising experience respectively.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.

该项目为UNC Greensboro计算机科学系主持的本科生（REU）网站建立了新的研究经验。每年夏天，有十名学生将在Graph Machine学习和网络分析的基础上接受研究培训及其在现实生活网络中的具体应用。图形和网络在各种科学学科中变得无处不在，从物联网，在线社交网络，大脑网络和分子到蛋白质 - 蛋白质相互作用网络。大规模网络的分析可以为我们对复杂系统的理解带来重大进步。现有方法纯粹是经验或缺乏深入的基础探索，因此在处理复杂图和网络数据方面有限。该项目旨在为学生提供机会在主要研究和少数民族服务学院进行图形和网络的尖端研究。图形学习和网络分析的研究培训将有助于发展竞争性的下一代网络和AI劳动力。通过各种活动，例如导向研讨会，受邀的讲座，动手项目，演示，演示和其他专业发展机会，本科生也将增强他们的专业技能。这个Gralna项目的第一个目标是为包括在STEM中尤其是来自原始研究生的STEM不足的少数群体（包括较少的少数人）提供扎实研究的经验。学生将培养研究技能以及口头和书面沟通技巧的熟练程度。第二个目标是提高对图形学习和优化的理论理解，并开发出处理图和网络数据中各种复杂性的新方法。值得注意的复杂性类型包括许多Realworld图数据的分布式性质，隐私问题是由敏感的关系和图形和网络中编码的交互所引起的，以及涉及丰富的领域知识和调节约束的专业网络数据。学生参与者将参与围绕分布式图分析，联合学习，优化，私人图分析，网络安全以及结构和功能性脑网络分析的研究项目。该项目的第三个目标是通过一系列专业发展活动为学生参与者和教职员工指导专业培训和成长，并提供初级教师和博士学位。该奖项分别反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评论标准来评估的，这一奖项分别反映了NSF的法定任务。