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难以想象的规模模拟分子现象的数据，卫星扫描地球，获取关于生态系统健康的大量测量数据，粒子加速器产生大量数据，揭示物质最小构建块的基本特性；在药物发现、能源节约和材料科学等领域都具有潜在的广泛社会效益。为了充分实现这些好处，需要具备从大量科学数据集中提取有用信息的技术技能的劳动力，这就要求研究生培训采用新的方法，强调数据驱动科学的专业知识。这项授予加州大学欧文分校（UCI）的国家科学基金会研究培训（NRT）奖将通过创建一个由领先的UCI、国家实验室和私营部门研究人员组成的培训生态系统来应对这一挑战，这些研究人员横跨粒子物理、地球科学、化学、统计学和机器学习；所有这些都是由新兴的团队科学的专业知识联系在一起的。该项目预计将培养60多名硕士和博士研究生，其中包括20名受资助的学员，他们来自计算统计学、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，本课程的学生将具备在新兴的数据驱动科学领域成为领导者的技术和团队科学技能，并参与和领导国家实验室、学术界和行业实验室的跨学科研究团队。该项目的研究议程旨在为基于物理原理构建可解释模型的传统科学路线和数据驱动的建模方法之间建立桥梁奠定基础，这些方法可以提供高保真的预测，但在基础科学方面可能缺乏明确的可解释性。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（例如时空数据建模、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学（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）、还有化学（比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。学生将在由具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，从而在该计划的第3至5年以学生为主导开发团队科学证书。学生参与者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训练。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。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，培训项目致力于在高优先级跨学科研究领域对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.