REU/RET Site: Computational Mathematics for Data Science

REU/RET 网站：数据科学的计算数学

• 批准号: 2051019
• 负责人: Lars Ruthotto
• 金额: $ 39.72万
• 依托单位: Emory University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2051019&HistoricalAwards=false
• 关键词: REU; RET; Site; Computational; Mathematics

The Emory Research Experience for Undergraduates and Teachers site focuses on computational mathematics and its applications in data science. Data science is of fundamental and strategic importance to the US and impacts nearly every field of science. However, the number of academic training opportunities and skilled workers has not kept pace with the rapid growth in demand from private and public entities. The site emphasizes developing research and professional skills that will increase the participants' ability to understand, conduct, and effectively communicate research in data science and computational mathematics. The three-year program will train twelve undergraduates and four teachers annually for six weeks. Faculty members from Emory's Departments of Mathematics and Computer Science will supervise and mentor the participants. The site's activities will equip undergraduate students with the mathematical and computational skills required to launch careers in this area and will motivate them to pursue a graduate degree. Student recruitment will be nationwide, with a strong focus on underrepresented groups and students enrolled in colleges with limited research opportunities in this area. By involving in-service K-12 teachers in the research experience, the site will extend its impact to high-school students and help innovate curricula design and improve career counseling. The teachers will be recruited from the diverse Atlanta metro area and other districts nationwide.The REU/RET site will introduce undergraduate students and teachers to the mathematical theory and computational tools used in applications ranging from data assimilation to machine learning and enable them to advance these fields by solving research problems in teams. The site's activities will be centered around a common theme that differs each year. The site's annual research themes will be Learning from Images, Combining Models with Data, and Data Science for Social Justice, respectively. Within each theme, faculty mentors will pose at least four research problems and advise student-teacher teams to work on innovative solutions. New insights of relevance to the broader scientific community will be created and disseminated in student/teacher-authored publications, open-source software, and online blogs. The teachers will also create and make freely available materials for classroom-use. The research projects will take participants beyond standard coursework. The site's educational component will introduce the participants to a range of mathematical techniques, including machine learning, deep neural networks, numerical linear algebra, optimization, partial differential equations, and statistics. The faculty mentors will also provide their mentees with professional and computational skills, including scientific writing, oral and poster presentations, and cloud computing. The weekly seminar will feature group activities and faculty-led presentations on data and ethics, algorithmic bias, public scholarship.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.

Emory Research Experience for Undergraduates and Teachers网站专注于计算数学及其在数据科学中的应用。数据科学对美国具有重要的基础和战略意义，几乎影响到所有科学领域。然而，学术培训机会和熟练工人的数量跟不上私营和公共实体需求的快速增长。 该网站强调发展研究和专业技能，以提高参与者理解，进行和有效沟通数据科学和计算数学研究的能力。这个为期三年的项目每年将培训12名本科生和4名教师，为期六周。来自埃默里大学数学系和计算机科学系的教师将监督和指导参与者。该网站的活动将使本科生具备在这一领域开展职业所需的数学和计算技能，并将激励他们攻读研究生学位。招生将在全国范围内进行，重点关注代表性不足的群体和在该领域研究机会有限的大学就读的学生。 通过让在职K-12教师参与研究，该网站将扩大其对高中学生的影响，并帮助创新课程设计和改善职业咨询。REU/RET网站将向本科生和教师介绍从数据同化到机器学习等应用中使用的数学理论和计算工具，并使他们能够通过团队解决研究问题来推进这些领域。该网站的活动将围绕一个共同的主题，每年不同。该网站的年度研究主题将分别是从图像中学习，将模型与数据相结合，以及数据科学促进社会正义。在每个主题中，教师导师将提出至少四个研究问题，并建议学生教师团队研究创新的解决方案。与更广泛的科学界相关的新见解将在学生/教师撰写的出版物，开源软件和在线博客中创建和传播。 教师还将创建并免费提供课堂使用的材料。研究项目将使参与者超越标准课程。该网站的教育部分将向参与者介绍一系列数学技术，包括机器学习，深度神经网络，数值线性代数，优化，偏微分方程和统计学。教师导师还将为学员提供专业和计算技能，包括科学写作，口头和海报演示以及云计算。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Comparison of atlas-based and neural-network-based semantic segmentation for DENSE MRI images

基于图集和基于神经网络的 DENSE MRI 图像语义分割的比较

• DOI: 10.48550/arxiv.2109.14116
• 发表时间: 2022
• 期刊: SIAM undergraduate research online
• 作者: Buser, Elle;Hart, Emma;Huenemann, Ben
• 通讯作者: Huenemann, Ben

Comparing Shallow and Deep Graph Models for Brain Network Analysis

比较脑网络分析的浅层图模型和深层图模型

• DOI: 10.1109/bigdata55660.2022.10020640
• 发表时间: 2022
• 期刊: 2022 IEEE International Conference on Big Data (Big Data
• 作者: Choi, Erica;Smith, Sally;Young, Ethan
• 通讯作者: Young, Ethan

A Tensor SVD-based Classification Algorithm Applied to fMRI Data

• DOI: 10.1137/21s1456522
• 发表时间: 2021-10
• 期刊: ArXiv
• 作者: K. Keegan;T. Vishwanath;Yihua Xu

Alternating Minimization for Computed Tomography with Unknown Geometry Parameters

• DOI: 10.1137/21s1441638
• 发表时间: 2021-08
• 期刊: ArXiv
• 作者: Mai Phuong Pham Huynh;M. Santana;Ana Castillo