RTG: Program in Computation- and Data-Enabled Science

波特兰州立大学计算和数据驱动科学（CADES）研究训练组（RTG）旨在培养学生和博士后在计算数学和统计学方面的能力，并使他们能够对数据驱动科学的当前问题有广泛的理解。社会影响的目标研究方向包括模拟驱动当今互联网的光纤，预测天气，空气质量和干旱，了解癌症和痴呆症等疾病的进展，以及优化仓库位置和无线服务。该研究是数学、统计学和计算机的交叉领域，其特点是技术上的智力多样性。这些学科之间的整合有望提高研究生产力，培养出独特的合格学员。对于这个RTG，八位教师专家将研究和培训与城市和当地社区的服务结合起来。研究小组的努力集成了偏微分算子的数值技术，数据密集型统计学习和数据科学的优化方法。具体项目包括使用先进的特征解算器模拟光在微结构光学器件中的传播，通过有或没有因果关系的时空方法改进时间演化模拟，从噪声数据中学习动态系统，随机对照试验的核方法，用于预测复杂系统的先进数据同化，以及用于多设施定位和机器学习的优化方法。加速学员进入这些研究课题的机制被纳入该计划。该项目将建立一个咨询实验室，利用真实世界的数据进行基于客户的研究和培训经验，其副产品是为区域客户创造新的咨询服务。培训创新包括一个新的研讨会，倾向于对话而不是独白，从该领域的领导者那里获得外部考官的支持，夏季新兵训练营，以克服预期的缺乏跨学科交叉的实习生先决条件，确定选定的外部合作机构进行专题实习，以及为本科生提供基于城市和社区服务的研究经验。所有项目都需要高性能计算和开源科学软件产品。这些主题的培训是有价值的副产品。RTG征聘活动的目标是增加代表性不足群体的参与。需要美国公民雇员的外部合作伙伴的参与增加了受训者的就业前景，并有助于解决技术劳动力短缺的问题。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 Research Training Group (RTG) in Computation- and Data-Enabled Science (CADES) at Portland State University is designed to train students and postdocs in computational mathematics and statistics, as well as enable them to develop a broad understanding of current issues in data-driven science. Targeted research directions of societal impact include simulation of optical fibers that drive today's internet, forecasting of weather, air quality, and drought, understanding progression of diseases such as cancer and dementia, and optimizing warehouse locations and wireless services. The research, at the intersection of mathematics, statistics, and computing, is characterized by intellectual diversity of techniques. Integration across these disciplines is expected to result in enhanced research productivity and uniquely qualified trainees. For this RTG, eight faculty experts integrate research and training with service for the city and the local community. The research group effort integrates numerical techniques for partial differential operators, data-intensive statistical learning, and optimization methods for data science. Specific projects include simulation of light propagation in microstructured optical devices using advanced eigensolvers, improvements to time-evolving simulations by spacetime approaches with and without causality, learning dynamical systems from noisy data, kernel methods for randomized control trials, advanced data assimilation for prediction of complex systems, and optimization methods for multifacility location and machine learning. Mechanisms to accelerate the entry of trainees into these research topics are integrated into the program. The project will establish a Consulting Lab for client-based research and training experiences using real-world data, a byproduct of which is the creation of new consulting services for regional clients. Training innovations include a new seminar favoring dialogue over monologue, buy-in from leaders in the field as external examiners, summer boot camps to overcome anticipated lack of trainee prerequisites for transdisciplinary crossovers, identification of selected external partnering institutions for topical internships, and city-based and community-serving research experiences for undergraduates. All projects require high performance computing and open-source scientific software products. Training in these topics are valued byproducts. The RTG recruitment activities are targeted to increase participation of underrepresented groups. Engagement from external partners in need of US citizen employees augments job prospects for the trainees and helps address shortages in the technical workforce.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.