REU Site: University of North Carolina at Greensboro - Complex Data Analysis using Statistical and Machine Learning Tools

REU 站点：北卡罗来纳大学格林斯伯勒分校 - 使用统计和机器学习工具进行复杂数据分析

• 批准号: 2244160
• 负责人: Sat Gupta
• 金额: $ 40.5万
• 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: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2244160&HistoricalAwards=false
• 关键词: REU; Site; University; North; Carolina

Technological innovations now allow the collection of massive amounts of data at low cost. Appropriate handling and analysis of such data is vital training for the future generation of graduates. This award will support the University of North Carolina at Greensboro Complex Data Analysis using Statistical and Machine Learning Tools Research Experiences for Undergraduates site, which will provide 10 weeks of training and research opportunities in the analysis of complex data to ten undergraduates each summer, 2023-2025. The program will aim to recruit students from diverse backgrounds and educational institutions. This research experience is intended to motivate student participants to go on to graduate study in mathematical sciences and to become skilled data analytics professionals. The program will introduce students to a range of analysis techniques for complex data. The research projects will introduce them to a range of techniques appropriate to different types of data complexity. Potential projects include working with geospatial sources (such as Census, election, and epidemiological data) and human genome sequencing data. Students in the program will also receive professional skills training and give presentations on their work, submit papers for publication, and participate in conferences.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.

技术创新现在允许以低成本收集大量数据。对此类数据的适当处理和分析是对未来毕业生的重要培训。该奖项将利用本科生网站的统计和机器学习工具研究经验为北卡罗来纳大学的格林斯伯勒分析分析，该研究经验将在每年夏天2023 - 2025年为十个本科生提供10周的培训和研究机会。该计划将旨在招募来自不同背景和教育机构的学生。这项研究经验旨在激励学生参与者继续学习数学科学研究生学习，并成为熟练的数据分析专业人员。该计划将向学生介绍复杂数据的一系列分析技术。研究项目将向他们介绍一系列适合不同类型数据复杂性的技术。潜在项目包括使用地理空间来源（例如人口普查，选举和流行病学数据）和人类基因组测序数据。该计划中的学生还将接受专业技能培训，并提供有关其工作的演讲，提交论文出版并参加会议。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估标准来通过评估来获得支持的。