NRT-DESE: Team Science for Integrative Graduate Training in Data Science and Physical Science

大规模并行计算机以以前无法想象的规模模拟分子现象的数据，卫星扫描地球捕获关于生态系统健康的大量测量数据，粒子加速器产生揭示物质最小组成部分的基本属性的海量数据；所有这些都在药物发现、能源保护和材料科学等领域具有潜在的广泛社会效益。要充分实现这些好处，将需要一支拥有从海量科学数据集中提取有用信息的技术技能的劳动力，这要求采取新的方法来培养研究生，强调数据驱动的科学专业知识。授予加州大学欧文分校(UCI)的国家科学基金会研究培训(NRT)奖将通过创建一个培训生态系统来应对这一挑战，该培训生态系统由UCI、国家实验室和私营部门的领先研究人员组成，涵盖粒子物理、地球科学、化学、统计学和机器学习；所有这些都通过新兴科学团队科学的专业知识联系在一起。该项目预计将培训60多名MS和博士生，其中包括20名资助的受训人员，他们来自计算统计、机器学习、地球科学、粒子物理、合成化学和团队科学等不同背景。毕业后，该项目的学生将拥有技术和团队科学技能，成为新兴数据驱动科学领域的领导者，并参与和领导国家实验室、学术界和工业实验室的跨学科研究团队。该项目的研究议程寻求在传统的科学路线和数据驱动的建模方法之间建立桥梁，传统的科学路线基于物理原理建立可解释的模型，但数据驱动的建模方法可以提供高保真预测，但在基础科学方面可能缺乏明确的解释能力。该计划将涉及信息和物理科学中多个学科的一些相互关联的研究主题，包括机器学习(例如，时间和空间数据建模、多尺度模型、深度学习和可扩展学习算法)、粒子和天体粒子物理(例如，基于加速器的实验)、地球系统科学(例如，减少生态系统响应预测的不确定性)和化学(例如，预测小分子的物理性质)。该计划的一个重要方面是强调团队科学作为核心主题。学生们将在由拥有不同学科技能的学生和教师组成的小型跨学科研究团队中进行合作，并将参加团队科学研讨会，以期在项目的第三到第五年由学生主导开发团队科学证书。学生在国家和行业研究实验室的暑期实习将通过参与大型跨学科数据科学研究项目来加强学生的学术培训。NSF研究培训(NRT)计划旨在鼓励为STEM研究生教育培训开发和实施大胆的、具有潜在变革意义的新模式。培训路径致力于通过创新的、基于证据的、与不断变化的劳动力和研究需求保持一致的综合培训模式，在高度优先的跨学科研究领域对STEM研究生进行有效培训。

Massively parallel computers simulate data about molecular phenomena at previously unimaginable scales, satellites scan the planet capturing vast sets of measurements about ecosystem health, and particle accelerators generate tremendous amounts of data revealing fundamental properties of the smallest building blocks of matter; all with potentially broad societal benefits in areas such as drug discovery, energy conservation, and materials science. To fully realize these benefits will require a workforce with the technical skills to extract useful information from massive scientific data sets, calling for new approaches to graduate student training that emphasize expertise in data-driven science. This National Science Foundation Research Traineeship (NRT) award to the University of California Irvine (UCI) will tackle this challenge by creating a training ecosystem comprised of leading UCI, national-laboratory, and private-sector researchers across particle physics, earth science, chemistry, statistics and machine learning; all bound together by expertise in the emerging Science of Team Science. The project anticipates training over sixty (60) MS and PhD students, including twenty (20) funded trainees, from diverse backgrounds in computational statistics, machine learning, earth science, particle physics, synthetic chemistry, and team science. After graduation, students from this program will have both the technical and team-science skills to be leaders in the emerging field of data-driven science, and to participate in and lead interdisciplinary research teams at national laboratories, in academia, and in industry labs.The research agenda of the program seeks to create the foundation from which bridges can be built between the traditional scientific route of building interpretable models based on physical principles and data-driven modeling approaches that can provide high fidelity predictions but may lack clear interpretability in terms of the underlying science. The program will involve a number of interrelated research themes across multiple disciplines in the information and physical sciences, including machine learning (e.g. temporal and spatial data modeling, multi-scale models, deep learning, and scalable learning algorithms), particle and astroparticle physics (e.g. accelerator based experiments), earth systems science (e.g. reducing ecosystem response prediction uncertainties), and chemistry (e.g. prediction of physical properties of small molecules). A significant aspect of the program is an emphasis on team science as a core theme. Students will collaborate in small interdisciplinary research teams consisting of students and faculty with different disciplinary skills, and will take part in team-science workshops leading to student-led development of a team-science certificate in years 3 to 5 of the program. Summer internships for student participants, at both national and industry research laboratories, will serve to reinforce the students' academic training via participation in large-scale interdisciplinary data science research projects.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 Traineeship Track is dedicated to effective training of STEM graduate students in high priority interdisciplinary research areas, through the comprehensive traineeship model that is innovative, evidence-based, and aligned with changing workforce and research needs.