NRT-HDR: Graduate Traineeship in Data Science Technologies and Applications

数据科学和分析是一个新兴的跨学科领域，包括计算、统计和各种应用领域，包括医学、护理、工业和商业应用等。目前美国研究生课程的一个重大缺陷是，科学家和工程师在他们自己的专业领域受过良好的训练，但缺乏数据科学和分析所带来的新科学发现和行业应用所需的综合知识。美国国家科学基金会授予佛罗里达大西洋大学（FAU）的研究实习生奖将通过提出一种通过体验式学习的融合教育新模式来解决这些缺点。跨学科教育以和谐的方式整合不同的学科，构建新的知识，提升学生的认知能力和持续知识和技能的更高领域。该培训计划预计将为大约160名研究生（160人）提供一个独特而全面的培训机会，其中包括35名受资助的受训人员。来自5个学院和10个系的30名教员将参加该项目。该计划有可能对未来数据科学专业人员的培训实践产生重大影响。课程的主要训练要素将包括规范化课程的开发、为各种应用领域创建不同的测试平台、训练营、深入选修课程和专业研讨会。标准化课程将用于解决进入该计划的学生的不同背景。融合研究主题将集中在三个数据科学和分析领域：(i)医疗和保健应用，（ii）行业应用，以及（iii）数据科学和人工智能技术。为了解决这些问题，我们的目标是为数据科学和分析的研究生创建一个课程，其中每门课程将由来自两个不同学科的至少两名教师开发。为了整合研究和培训，将在新创建的数据科学和人工智能实验室中开发不同应用领域的多个测试平台。每个试验台都与一个研究项目相关，将包括一个计算机平台、软件工具和一组学习模块。研究项目将与行业合作伙伴共同制定，这些合作伙伴是FAU的NSF产学研合作研究CAKE（先进知识实现中心）的成员。该计划将培养具有技术深度和对数据科学技术和应用的理解的毕业生。美国国家科学基金会研究实习生（NRT）计划旨在鼓励开发和实施大胆的、具有潜在变革性的STEM研究生教育培训新模式。该项目致力于通过创新、循证、适应不断变化的劳动力和研究需求的综合培训模式，在高优先级跨学科或融合研究领域对STEM研究生进行有效培训。该奖项反映了美国国家科学基金会的法定使命，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。

Data science and analytics is an emerging transdisciplinary area comprising computing, statistics, and various application domains including medicine, nursing, industry and business applications among others. A significant shortcoming of the current graduate curricula in the U.S. is that scientists and engineers are well trained in their own areas of specialty but lack the integrative knowledge needed for new scientific discoveries and industry applications made possible by data science and analytics. The National Science Foundation Research Traineeship award to Florida Atlantic University (FAU) will address these shortcomings by proposing a new model of convergent education through experiential learning. Transdisciplinary education brings integration of different disciplines in a harmonious manner to construct new knowledge and uplift the student to higher domains of cognitive abilities and sustained knowledge and skills. The traineeship anticipates providing a unique and comprehensive training opportunity for approximately one hundred sixty graduate students (160), including thirty five (35) funded trainees. Thirty faculty members from five colleges and ten departments will participate in the program. The program has the potential to have a significant impact on training practices for future data science professionals. Primary training elements of the curriculum will include the development of normalization courses, the creation of different testbeds for the various application domains, boot-camps, in-depth elective courses, and professional workshops. Normalization courses will be used to address various background of students entering the program. The convergent research themes will focus on three data science and analytics areas: (i) medical and healthcare applications, (ii) industry applications, and (iii) data science and AI technologies. To address these, the goal is to create a curriculum for graduate students in data science and analytics, where each course will be developed by at least two faculty members from two different disciplines. In order to integrate research and training, multiple testbeds for different application domains will be developed in a newly created Data Science and Artificial Intelligence Laboratory. Each testbed, which relates to a research project, will include a computer platform, software tools, and a set of learning modules. Research projects will be formulated jointly with industry partners who are members of the NSF Industry/University Cooperative Research CAKE (Center for Advanced Knowledge Enablement) at FAU. The program will produce graduates with technical depth and understanding of data science technologies and applications.The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.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.