REU Site: Georgia Institute of Technology Mathematics Research Experiences for Undergraduates

REU数学项目的主要目标是：(I)为不同类型的本科生提供高质量的研究经验和专业发展活动；(Ii)激励有才华的本科生继续攻读研究生并取得成功；(Iii)招收不同类型的本科生；(Iv)吸引研究活动有限的机构的学生；以及(V)在暑期项目结束后，参与者返回母校后继续参与。参与者将单独或与个别教职员工团队合作，指导他们完成研究经验。每周开展团队建设和专业发展活动，包括广泛的研究生院指南，并最终为所有参与者举办海报会议。参与者准备好在全国范围内的会议上展示他们的工作，并牢牢掌握研究生院的好处以及申请研究生院的时间表。本科生将参与一系列不同的数学项目，从绘制班级小组的探索到现代数据科学的主题。所有主题都反映了当前数学研究的重要趋势。从建筑到流体混合，再到现代信号分析，许多应用都强烈地推动了这些应用的发展。例如，贴图类组是从曲面的所有变形的集合中构建的。对象本身有额外的结构，继承自表面，但用一种完全不同的语言表达。这些特性反过来又提供了一些见解，例如，如何最有效地混合两种非常坚硬的流体。其他例子来自数据科学，这是一个非常广泛的学科，其应用范围不断扩大，包括从手写数字到社区结构的模式自动检测。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。

The primary aims of this REU program in mathematics are to: (i) provide high-quality research experiences, together with professional development activities, for diverse cohorts of undergraduates; (ii) motivate talented undergraduates to pursue, and succeed in, graduate study; (iii) recruit a diverse cohort of undergraduates; (iv) attract students from institutions that have limited research activities; and (v) continue engagement of the participants beyond the summer program when they return to their home institutions. Participants will work individually or in teams with individual faculty members who guide them through a research experience. Weekly team-building and professional development activities take place, including an extensive guide to graduate school, and culminating in a poster session for all participants. The participants leave the program prepared to present their work at conferences nationwide and with a firm grasp of the benefits of graduate school as well as a timeline for applications to graduate school.Undergraduates will participate in a diverse array of projects in mathematics, ranging from explorations in mapping class groups to topics in modern data science. All topics reflect important current trends in mathematics research. Many are strongly motivated by applications, ranging from architecture, to mixing of fluids, to modern signal analysis. For example, mapping class groups are built from the collection of all deformations of a surface. The object itself has additional structure, inherited from the surface, but expressed in an entirely different language. Those properties in turn yield insights into, for instance, how to most efficiently mix two types of very stiff fluids. Other examples come from data science, a very broad subject with an ever-increasing set of applications, including automatic detection of patterns, from hand written digits to community structures.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.