RTG: Program in Computation- and Data-Enabled Science

波特兰州立大学的计算和数据驱动科学（CADES）研究培训小组（RTG）旨在培训学生和博士后计算数学和统计学，并使他们能够对数据驱动科学中的当前问题有广泛的了解。有针对性的社会影响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。研究，在数学，统计学和计算的交叉点，其特点是技术的智力多样性。这些学科之间的整合预计将导致提高研究生产力和独特的合格的受训人员。对于这个RTG，八名教师专家将研究和培训与城市和当地社区的服务相结合。该研究小组的工作整合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本征解算器模拟微结构光学器件中的光传播，通过时空方法改进时间演化模拟，有无因果关系，从噪声数据中学习动力系统，随机控制试验的内核方法，复杂系统预测的先进数据同化，以及多设施位置和机器学习的优化方法。加快学员进入这些研究课题的机制已纳入该方案。该项目将建立一个咨询实验室，使用真实世界的数据进行基于客户的研究和培训，其副产品是为区域客户创建新的咨询服务。培训创新包括一个新的研讨会，有利于对话独白，从该领域的领导者作为外部考官，夏季靴子营地，以克服预期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专题实习，以及城市为基础和社区服务的本科生的研究经验。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招聘活动的目标是增加代表性不足群体的参与。来自需要美国公民员工的外部合作伙伴的参与增加了受训人员的就业前景，并有助于解决技术劳动力短缺的问题。该奖项反映了NSF的法定使命，并通过使用基金会的智力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.