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HDR DSC: Engaging Undergraduates in Data and Decisions Research at the Engineering/Biology Interface

HDR DSC：让本科生参与工程/生物学界面的数据和决策研究

基本信息

批准号：
1922516
负责人：
David Schmale
金额：
$ 118.61万
依托单位：
Virginia Polytechnic Institute and State University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1922516&HistoricalAwards=false
关键词：
HDR DSC Engaging Undergraduates Data

项目摘要

Recent studies have documented the overall failure of undergraduate programs to prepare students for the complex, professional lives that lie ahead for them. This project addresses a number of these shortfalls, training students to navigate the more complex and uncertain professional terrain associated with interdisciplinary scholarship. The project will launch a unique data sciences program at Virginia Tech (coordinating organization), Morehouse College (HBCU for men, Georgia, implementing organization), Bennett College (HBCU for women, North Carolina, implementing organization), and Hampden-Sydney College (all-male college, Virginia, implementing organization). Our ultimate goal is to provide interdisciplinary education and research opportunities in data and decision science for undergraduate students who are experts in a core discipline of engineering or biology, but who are also proficient in the alternate discipline. Undergraduates from biology and engineering will take classes and conduct research in data science at the engineering/biology interface. A new collaborative, multi-university capstone course "Data and Decisions at the Engineering/Biology Interface" will be launched simultaneously at all four universities. This new course will be driven by the needs of stakeholders from agriculture, conservation, search and rescue, water quality, transportation in inclement weather, and global health and emergency relief.The program will provide unique research opportunities in data and decision science for at least 75 students over 3 years, with about half coming from the two HBCUs. Multi-university student teams (comprised of biologists and engineers) will work together to identify broad social, global, economic, cultural and technical needs/constraints, and determine ways in which their complementary technical skills contribute to addressing complex data science grand challenges at the engineering/biology interface. The teams will submit their data science challenge ideas using sensor-based assets and computational-based assets, competing for slots to participate in a coordinated field campaign in which they will collect data, and learn to make decisions from these data. Team projects will be developed in response to stakeholder needs, using sensor assets available from the participating universities and stakeholders. Students will become well-grounded in the language and tools of computational modeling and data analytics, including machine learning, data-driven discovery of equations and causality, clustering, and neural networks. Students will learn to communicate effectively with fellow students, policymakers, and the public. Following their data sciences experiences, the students are expected to: (1) be conversant with data science research in a second discipline, open to its methods, culture, and perspectives; (2) be able to integrate the second discipline into sustainable new data science research; and (3) conduct interdisciplinary data science research with team members from other fields. The program will provide insights into the attitudes of students towards interdisciplinary data science research, and explore how conceptions of collaboration and career path are affected by their participation in the program.NSF's Harnessing the Data Revolution Data Science Corps program focuses on building capacity for harnessing the data revolution at the local, state, national, and international levels to help unleash the power of data in the service of science and society. Projects in this program are being jointly funded by the NSF's Harnessing the Data Revolution Big Idea; the Directorate for Computer and Information Science and Engineering, Division of Information and Intelligent Systems; the Directorate for Education and Human Resources, Division of Undergraduate Education; the Directorate for Mathematical and Physical Sciences, Division of Mathematical Sciences; and the Directorate for Social, Behavioral and Economic Sciences, Office of Multidisciplinary Activities and Division of Behavioral and Cognitive Sciences.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.

最近的研究表明，本科课程在为学生未来复杂的职业生活做准备方面总体上是失败的。该项目解决了这些不足之处，培养学生导航与跨学科奖学金相关的更复杂和不确定的专业领域。该项目将在弗吉尼亚理工大学（协调组织）、莫尔豪斯学院（格鲁吉亚男子HBCU，实施组织）、班尼特学院（北卡罗来纳州女子HBCU，实施组织）和汉普顿-悉尼学院（弗吉尼亚州全男子学院，实施组织）推出一个独特的数据科学项目。我们的最终目标是为本科生提供数据和决策科学的跨学科教育和研究机会，这些本科生是工程或生物学核心学科的专家，但也精通替代学科。来自生物学和工程学的本科生将在工程/生物学界面上上课并进行数据科学研究。一个新的合作，多大学的顶点课程“数据和决策在工程/生物学接口”将同时在所有四所大学推出。这门新课程将满足农业、自然保护、搜索和救援、水质、恶劣天气下的交通以及全球健康和紧急救援等利益相关者的需求。该计划将为至少75名学生提供数据和决策科学方面的独特研究机会，为期3年，其中约一半来自两个HBCU。多所大学的学生团队（由生物学家和工程师组成）将共同努力，以确定广泛的社会，全球，经济，文化和技术需求/限制，并确定他们的互补技术技能有助于解决工程/生物学接口复杂的数据科学重大挑战的方式。这些团队将使用基于传感器的资产和基于计算的资产提交他们的数据科学挑战想法，竞争参与协调的现场活动的名额，他们将在其中收集数据，并学习根据这些数据做出决策。团队项目将根据利益相关者的需求开发，使用参与大学和利益相关者提供的传感器资产。学生将在计算建模和数据分析的语言和工具方面打下良好的基础，包括机器学习，数据驱动的方程和因果关系发现，聚类和神经网络。学生将学会与同学，政策制定者和公众进行有效沟通。根据他们的数据科学经验，学生将：（1）熟悉第二学科的数据科学研究，对其方法，文化和观点持开放态度;（2）能够将第二学科融入可持续的新数据科学研究;（3）与其他领域的团队成员进行跨学科的数据科学研究。该计划将深入了解学生对跨学科数据科学研究的态度，并探索他们参与该计划如何影响合作和职业道路的概念。NSF的利用数据革命数据科学团计划侧重于在地方，州，国家，和国际水平，以帮助释放数据的力量，为科学和社会服务。该计划中的项目由美国国家科学基金会利用数据革命大创意联合资助;信息和智能系统部计算机和信息科学与工程理事会;本科教育部教育和人力资源理事会;数学科学部数学和物理科学理事会;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

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Is the Finite-Time Lyapunov Exponent Field a Koopman Eigenfunction?

有限时间李亚普诺夫指数场是库普曼本征函数吗？

DOI：
10.3390/math9212731
发表时间：
2021
期刊：
Mathematics
影响因子：
2.4
作者：
Bollt, Erik M.;Ross, Shane D.
通讯作者：
Ross, Shane D.

In the wind: Invasive species travel along predictable atmospheric pathways

在风中：入侵物种沿着可预测的大气路径传播

DOI：
10.1002/eap.2806
发表时间：
2023
期刊：
Ecological Applications
影响因子：
5
作者：
Pretorius, Ilze;Schou, Wayne C.;Richardson, Brian;Ross, Shane D.;Withers, Toni M.;Schmale, David G.;Strand, Tara M.
通讯作者：
Strand, Tara M.

Transition criteria and phase space structures in a three degree of freedom system with dissipation

耗散三自由度系统中的转变准则和相空间结构