NRT-AI: Data in Engineering and Society: Converging Applications, Research, and Training Enhancements for Students

这是一个技术突飞猛进的时代，尤其是在数据科学和人工智能领域。然而，包括2019冠状病毒病大流行在内的经济和其他挑战推迟了这一进展对大部分人口的好处。在夏威夷，数据科学的进步有可能解决电力、交通、医疗保健和通信方面的关键需求。但为了造福广大人民，技术进步必须考虑到经济、商业和社会因素。美国国家科学基金会研究培训（NRT）奖授予夏威夷大学马诺阿分校，将开发一个创新的研究生项目，将工程、计算机科学、社会科学、商业和医学结合起来，利用数据科学的变革力量。这个跨学科项目预计将培养61名新一代研究生，其中包括41名受资助的学员。该项目采用了量身定制的课程模块等新机制，以更好地为学员提供带来包容性数据革命所需的独特技能，并使他们对传统上在STEM研究生课程中被区别对待的领域之间的相互作用有更广阔的视野。在这些领域有效地利用数据革命需要处理来自不同来源的动态和/或不完整的数据，用新数据改进决策，并用科学和社会领域知识补充算法。NRT学员将在基础和应用数据科学研究的队列团队中工作，应用于：(i)夏威夷电力和交通部门的脱碳，使用我们的当地利益相关者网络、公用事业和大学校园已成为的“生活实验室”的数据；（ii）医疗保健，利用可能是目前最大的儿科心电图数据集，带来新的诊断方法，并通过研究流行病模型；（iii）具有增强通信、新型传感、弹性和效率的下一代无线系统。培训模式将在课程中开发和部署紧凑的模块，以个性化指导，并为每位学员提供适合其先前经验和未来职业目标的技能。针对夏威夷原住民、女性和军人的外展活动将利用和扩大机构项目，以扩大STEM劳动力的参与。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。该项目由NRT计划和促进竞争研究的既定计划（EPSCoR）共同资助。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。

This is a time of extraordinary technological progress, particularly in data science and artificial intelligence. However, economic and other challenges, including the COVID-19 pandemic, have delayed the benefits of this progress for significant sections of the population. In Hawaii, progress in data science has the potential to address critical needs in power, transportation, healthcare and communications. But to benefit the population at large, technological progress must take economic, business and social factors into consideration. The National Science Foundation Research Traineeship (NRT) award to the University of Hawaii at Manoa will develop an innovative graduate program that brings together engineering, computer science, social science, business and medicine to harness the transformative power of data science. This cross-disciplinary program anticipates training a new generation of 61 graduate students, including 41 funded trainees. The program incorporates novel mechanisms such as tailored course modules to better prepare the trainees with the unique skills needed for bringing about an inclusive data revolution and equips them with a broader perspective on the interplay between areas traditionally treated disparately in the a STEM graduate curriculum.Harnessing the data revolution effectively in these areas requires tackling dynamic and/or incomplete data from disparate sources, refining decisions with new data, and supplementing algorithms with scientific and social domain knowledge. NRT trainees will work in cohort teams on fundamental and applied data science research, applying to: (i) decarbonization of the electricity and transportation sectors in Hawaii, using data from our network of local stakeholders, utilities and the "living lab" that the University campus has become; (ii) healthcare, leveraging possibly the largest current pediatric electrocardiogram dataset to bring about novel diagnosis approaches, as well as through research that models epidemics; (iii) next-generation wireless systems with enhanced communication, novel sensing, resiliency and efficiency. The traineeship model will develop and deploy compact modules across courses to personalize instruction and prepare each trainee with skills tailored to their prior experience and future career goals. Outreach activities to Native Hawaiians, women, and members of the military will leverage and amplify institutional programs to broaden participation in the STEM workforce. 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 project is jointly funded by the NRT program and the Established Program to Stimulate Competitive Research (EPSCoR).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.