REU Site: Interdisciplinary Integration in Statistical Learning and Data Mining

University Of North Carolina At Wilmington, Wilmington NC

Investigators

Abstract

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.