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.

针对本科和教师网站的Emory研究经验着重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基本和战略性的重要性，几乎影响了科学的每个领域。但是，学术培训机会和熟练工人的数量并没有跟上私人和公共实体需求的迅速增长。 该网站强调开发研究和专业技能，以提高参与者理解，进行和有效地传达数据科学和计算数学研究的能力。为期三年的计划将培训十二名本科生和四名教师六个星期。埃默里（Emory）数学和计算机科学系的教职员工将监督和指导参与者。该网站的活动将使本科生在该领域开展职业所需的数学和计算技能，并激励他们攻读研究生学位。学生招募将在全国范围内进行，重点是代表性不足的群体和该领域研究机会有限的大学的学生。 通过参与研究经验的Serpervice K-12教师，该网站将扩大其对高中生的影响，并帮助创新课程设计并改善职业咨询。教师将在全国各地的亚特兰大都会区和其他地区招募。REU/RET网站将向本科生和教师介绍用于应用程序中的数学理论和计算工具，从数据吸收到机器学习到机器学习，并使他们通过解决团队中的研究问题来推进这些领域。该网站的活动将集中在每年不同的共同主题上。该网站的年度研究主题将分别从图像中学习，将模型与数据和社会正义的数据科学结合在一起。在每个主题中，教师导师将至少提出四个研究问题，并建议学生老师团队为创新解决方案提供工作。将在学生/教师创作的出版物，开源软件和在线博客中创建和传播与更广泛的科学界相关的新见解。 老师还将创建并免费提供教室使用的材料。研究项目将使参与者超出标准课程。该网站的教育组成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，部分微分方程和统计数据。教师导师还将为受训者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。每周一次的研讨会将以小组活动和教师为主导的数据和道德，算法偏见，公共奖学金。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛的影响来通过评估来支持的。

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