REU Site: Interdisciplinary Integration in Statistical Learning and Data Mining

REU 网站：统计学习和数据挖掘的跨学科整合

• 批准号: 1659288
• 负责人: Cuixian Chen
• 金额: $ 25.37万
• 依托单位: University of North Carolina at Wilmington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1659288&HistoricalAwards=false
• 关键词: REU; Site; Interdisciplinary; Integration; Statistical

This NSF supported REU Site will provide undergraduate students interdisciplinary research experience in statistical learning and data mining with applications in computer vision and pattern recognition at the University of North Carolina Wilmington (UNCW), for ten weeks during each summer of 2017-2019. UNCW has an institutional commitment to undergraduate education and research through applied learning, and is ideally suited to provide a complete REU experience for the participants. This project is motivated by the shortage of data scientists with analytical skills, recent surge of interests from students, and bringing in awareness of data science career options in academics, industry, and government. The program is designed to involve students in undergraduate research experiences through applied learning and to provide opportunities to develop quantitative and critical thinking skills, and opportunities to improve effective communication skills with professionals from other disciplines. The intellectual focus of the program is to introduce contemporary statistical learning theory and data mining techniques, with applications in analyzing human facial features. Students will be given lectures on the significant impact of computer vision and pattern recognition, challenges in human image analysis, review of fundamentals in mathematics and statistics, image preprocessing, and contemporary statistical learning theory and data mining techniques. The following topics will be discussed in detail: data cleaning and visualization, dimension reduction, regression and classification, software engineering, high performance computing, etc. Research projects are applications of these techniques and emphasize on real world application with interdisciplinary integration. The values of the problem, background, modeling assumptions, statistical theory, numerical solutions, and visualization with computational technology and its interpretation will be well articulated among the participants. Critical and reflective thinking are encouraged, under proper intervenes by mentors, through team-based collaboration and cooperation, group meetings and feedback from weekly presentation and reports.

这个NSF支持的REU网站将为本科生提供统计学习和数据挖掘方面的跨学科研究经验，并在北卡罗来纳州威尔明顿大学（UNCW）的计算机视觉和模式识别中应用，在2017-2019年的每个夏天为期十周。UNCW通过应用学习对本科教育和研究做出了机构承诺，非常适合为参与者提供完整的REU体验。该项目的动机是缺乏具有分析技能的数据科学家，最近学生的兴趣激增，以及在学术界，工业界和政府中引入数据科学职业选择的意识。该项目旨在通过应用学习让学生参与本科研究体验，并提供培养定量和批判性思维技能的机会，以及提高与其他学科专业人士有效沟通技能的机会。 该计划的智力重点是介绍当代统计学习理论和数据挖掘技术，并应用于分析人类面部特征。学生将获得讲座计算机视觉和模式识别的重大影响，在人类图像分析的挑战，在数学和统计学的基础知识，图像预处理，以及当代统计学习理论和数据挖掘技术的审查。以下主题将被详细讨论：数据清洗和可视化，降维，回归和分类，软件工程，高性能计算等研究项目是这些技术的应用，并强调真实的世界的应用与跨学科的整合。问题的价值，背景，建模假设，统计理论，数值解，以及计算技术的可视化及其解释将在参与者之间得到很好的阐述。在导师的适当干预下，通过团队协作与合作、小组会议以及每周演示和报告的反馈，鼓励批判性和反思性思维。

项目成果

Preliminary Studies on a Large Face Database

• DOI: 10.1109/bigdata.2018.8622432
• 发表时间: 2018-11
• 期刊: 2018 IEEE International Conference on Big Data (Big Data)
• 作者: B. Yip;G. Bingham;K. Kempfert;J. Fabish;T. Kling;Cuixian Chen;Yishi Wang

A comparison study on nonlinear dimension reduction methods with kernel variations: Visualization, optimization and classification

• DOI: 10.3233/ida-194486
• 发表时间: 2020-01-01
• 期刊: INTELLIGENT DATA ANALYSIS
• 影响因子: 1.7
• 作者: Kempfert, Katherine C.;Wang, Yishi;Wong, Samuel W. K.
• 通讯作者: Wong, Samuel W. K.

Gender Effect on Face Recognition for a Large Longitudinal Database

• DOI: 10.1109/wifs.2018.8630762
• 发表时间: 2018-11
• 期刊: 2018 IEEE International Workshop on Information Forensics and Security (WIFS)
• 作者: Caroline Werther;M. Ferguson;K. Park;T. Kling;Cuixian Chen;Yishi Wang