REU SITE: DATA SCIENCE RESEARCH FOR HEALTHY COMMUNITIES IN THE DIGITAL AGE

REU 网站：数字时代健康社区的数据科学研究

基本信息

批准号：
1852498
负责人：
Elke Rundensteiner
金额：
$ 36.67万
依托单位：
Worcester Polytechnic Institute
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-03-01 至 2024-02-29
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1852498&HistoricalAwards=false
关键词：
REU SITE DATA SCIENCE RESEARCH

项目摘要

Critical applications are emerging in the interdisciplinary field of Data Science in the area of community health analytics. Yet despite the high potential impacts of data science on the community, not enough opportunities for undergraduate education and research immersion in data science are available. Supporting communities in the digital age rests on comprehensive data science solutions from machine learning, artificial intelligence to big data technologies. With an interdisciplinary team, participants in this project improve the state-of-art in data science to solve real-world problems from personalized health and well-being, public health by mental health screening tools, early alerts for infection spread, to environmental and transportation improvements. This Research Experiences for Undergraduates (REU) site helps to sustain our community's innovation-based economy by training students to acquire data science skills. This site provides access to data science research opportunities otherwise extremely limited. A goal is to boost retention of female students and underrepresented groups such as African Americans in Science, Technology, Engineering, and Mathematics (STEM) professions. Undergraduate research programs are known to improve students' interpersonal communication, professional networking, and research skills that they would not encounter in the classroom, and to encourage students to find their interest and seriously consider graduate studies. This REU site is a continuation of an REU site in Data Science successfully operated by the Worcester Polytechnic Institute Data Science program since 2016. This new site offers 30 additional students from a range of geographically diverse locations around the country access to vibrant scientific research projects and undergraduate mentorship. Comprehensive support for the REU participants, from close research mentoring to professional development, are provided. Students have the opportunity to network with professionals from the data science advisory board, alumni and industrial partners from companies and organizations as diverse as Google, MathWorks, and MITRE Corporation, thereby encouraging them to pursue Data Science in their careers.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.

社区健康分析领域的数据科学跨学科领域正在出现关键应用。然而，尽管数据科学对社区具有巨大的潜在影响，但本科教育和沉浸在数据科学中的研究机会却不够。对数字时代社区的支持依赖于从机器学习、人工智能到大数据技术的全面数据科学解决方案。通过跨学科团队，该项目的参与者提高了数据科学的最新水平，以解决现实世界的问题，从个性化健康和福祉、心理健康筛查工具的公共卫生、感染传播的早期警报到环境和健康问题。交通改善。这个本科生研究经验 (REU) 网站通过培训学生掌握数据科学技能，帮助维持我们社区的创新型经济。该网站提供的数据科学研究机会极其有限。目标是提高科学、技术、工程和数学 (STEM) 专业中女学生和非裔美国人等代表性不足群体的保留率。众所周知，本科研究项目可以提高学生在课堂上不会遇到的人际沟通、专业网络和研究技能，并鼓励学生找到自己的兴趣并认真考虑研究生学习。该 REU 站点是伍斯特理工学院数据科学项目自 2016 年以来成功运营的 REU 数据科学站点的延续。这个新站点为来自全国各地不同地理位置的另外 30 名学生提供了参与充满活力的科学研究项目和本科生导师。为 REU 参与者提供从密切研究指导到专业发展的全面支持。学生有机会与来自数据科学咨询委员会的专业人士、来自 Google、MathWorks 和 MITRE Corporation 等不同公司和组织的校友和工业合作伙伴建立联系，从而鼓励他们在职业生涯中追求数据科学。该奖项反映了 NSF 的法定奖项使命，并通过使用基金会的智力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

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Automated Construction of Lexicons to Improve Depression Screening With Text Messages

DOI：
10.1109/jbhi.2022.3203345
发表时间：
2022-08
期刊：
IEEE Journal of Biomedical and Health Informatics
影响因子：
7.7
作者：
M. L. Tlachac;Avantika Shrestha;M. Shah;Benjamin R. Litterer;Elke A. Rundensteiner
通讯作者：
M. L. Tlachac;Avantika Shrestha;M. Shah;Benjamin R. Litterer;Elke A. Rundensteiner

HAR-CTGAN: A Mobile Sensor Data Generation Tool for Human Activity Recognition

DOI：
10.1109/bigdata55660.2022.10020848
发表时间：
2022-01-01
期刊：
2022 IEEE International Conference on Big Data (Big Data)
影响因子：
0
作者：
Deoliveira, Joshua;Gerych, Walter;Agu, Emmanuel
通讯作者：
Agu, Emmanuel

Verifying Adversarial Robustness of 3D Object Detectors for Autonomous Vehicles

DOI：
10.1109/urtc56832.2022.10002253
发表时间：
2022-09
期刊：
2022 IEEE MIT Undergraduate Research Technology Conference (URTC)
影响因子：
0
作者：
Rebecca Dollahite;Kevin Wang;Kaidong Li;Yiqing Zhang;Ziming Zhang
通讯作者：
Rebecca Dollahite;Kevin Wang;Kaidong Li;Yiqing Zhang;Ziming Zhang

Are Fair Learning To Rank Models Really Fair? An Analysis Using Inferred Gender

公平学习对模型进行排名真的公平吗？

DOI：
10.1109/urtc56832.2022.10002233
发表时间：
2022
期刊：
2022 IEEE MIT Undergraduate Research Technology Conference (URTC
影响因子：
0
作者：
Pietrick, Alexander;Romportl, Alyssa;Smith, Shailen;Olulana, Oluseun;Cachel, Kathleen;Rundensteiner, Elke
通讯作者：
Rundensteiner, Elke

AutoOD: Automatic Outlier Detection