REU Site: Data Analytics and Information Retrieval

这个为期三年的数据分析和信息检索本科生研究体验网站由北得克萨斯大学(UNT)主办。这个项目的目的是为学生提供一个支持性和参与性的研究和学习环境，以建立数据分析和信息检索方面的研究经验，发展广泛适用的研究技能，增强研究协作和沟通的技能，并探索数据科学的研究兴趣。它每年夏天为10名本科生提供为期10周的研究项目。在为期十周的时间里，每个学生在一名教师导师的建议下，在数据分析和信息检索的一个主要研究课题上进行密切指导的研究。师生互动，以及学生之间的互动，有许多不同的形式。该项目支持在暑期课程结束后对学生进行持续指导。该网站利用教师导师在数据分析、计算语言学和信息检索方面的专业知识，开发数据分析和信息检索方面最先进的综合研究项目，以便更有效地为学生提供研究建议。这些研究项目涵盖了数据分析和信息检索的算法、模型、工具和应用方面的基本问题。样本研究项目包括：1.生物医学图像的数据分析，2.基于内容的图像检索，3.大规模文本集合的信息检索，以及4.用于灾难研究的社交媒体信息检索。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This three-year Research Experiences for Undergraduates site in Data Analytics and Information Retrieval is hosted at the University of North Texas (UNT). The objective of this project is to provide a supportive and engaging research and learning environment for students to build research experiences in data analytics and information retrieval, develop broadly applicable research skills, enhance skills in research collaboration and communication, and explore research interests in data science. It offers a ten-week research program for ten undergraduate students each summer. During the ten-week period, each student engages in closely mentored research, under advising of a faculty mentor, in one major research topic in data analytics and information retrieval. The faculty-student interaction, as well as interaction among students, take many different forms. The project supports continual mentoring of students after the summer program.The site leverages the expertise of the faculty mentors in data analytics, computational linguistics, and information retrieval to develop state-of-the-art integrated research projects in data analytics and information retrieval for more effectively advising students in research. The research projects cover the fundamental issues in algorithms, models, tools, and applications of both data analytics and information retrieval. The sample research projects include: 1. Data analytics of biomedical images, 2. Content based image retrieval, 3. Information retrieval of large-scale text collections, and 4. Social media information retrieval for disaster research.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.