REU Site: Research Projects in Data-Enabled Industrial Mathematics

这个位于恩布里-里德尔航空大学的REU网站每年为10名本科生提供机会，在2021-2023年夏季期间参与数据启用的工业数学的真实研究项目。这一独特的研究体验的目标是让学生为数据驱动的数学挑战做好准备，并为他们提供在当今数据丰富的世界中有能力导航的技能。学生将学习将企业、行业或政府提出的技术、组织和数据支持的问题转换为可解的数学问题。该计划将从不同的群体中招收有才华的学生，包括女性、代表性不足的少数民族、经济困难的学生和第一代大学生。参与将使对工业数学研究感兴趣的学生对数据分析、数学建模和解决问题的能力以及有效的沟通技能有深刻的理解。每个研究项目的进展和结果的视频纪录片将被用来促进更广泛的受众的本科生研究和数据素养。这个网站的最终想法是模拟真实世界的工业研究经验，关注学生的成长，并鼓励学生在超越课堂环境的水平上与教育材料互动。学生们将就企业、政府和行业直接提供的问题进行协作。教师导师和行业合作伙伴的两个视角是这种独特体验的关键：教师导师明确关注项目核心的数学和数据科学方面，而行业合作伙伴提供相关行业需求和背景的激励。学生将通过实例了解数据科学技能如何对实际应用至关重要，这种经验将促进明智的职业决策和促进专业发展。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 REU site at Embry-Riddle Aeronautical University provides ten undergraduate students each year with opportunities to participate in authentic research projects in data-enabled industrial mathematics during the summers 2021-2023. The objectives of this unique research experience are to prepare students for challenges in data-driven mathematics and to provide them with skills to navigate capably in today’s data-rich world. Students will learn to transform technical, organizational, and data-enabled problems posed by business, industry, or government into solvable mathematical questions. The program will recruit talented students from diverse groups, including females, underrepresented minorities, financially disadvantaged students, and first-generation college students. Participation will equip students interested in industrial mathematics research with a strong understanding of data analytics, mathematical modeling and problem-solving abilities, and effective communication skills. Video documentaries of the progress and results of each research project will be used to promote undergraduate research and data literacy for a broader audience. The ultimate idea of this site is to model real-world industrial research experiences, focusing on student growth and encouraging students to interact with the educational material at a level that goes beyond a classroom setting. The students will work collaboratively on problems provided directly by business, government, and industry. The two perspectives of the faculty mentor and of the industrial partners are keys to this unique experience: the faculty mentor maintains a clear focus on the mathematics and data science aspects at the core of the project, while industrial partners provide an incentive of relevant industrial needs and contexts. Students will learn through examples how data science skills can be critical for practical applications, and the experience will foster informed career decisions and promote professional development.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.