IRES: Track II: The Coastal Processes & Machine Learning Advanced Studies Institute

海岸线拥有人口中心、基础设施、多样化的生态系统，并提供宝贵的旅游和娱乐机会。沿海地区也容易发生风暴、海平面上升和慢性侵蚀等灾害。研究海岸过程和未来海岸线动态的科学家可以获得越来越多的数据。一系列可用于从这些数据中提取知识和洞察力的新工具和方法，特别是机器学习技术。然而，这些新方法通常不是沿海科学家研究生课程的一部分。该项目的重点是在新西兰奥克兰建立一个高级研究所(ASI)，向20名研究海岸过程和海岸地貌学的美国研究生教授机器学习方法。ASI的参与者是下一代美国沿海科学家，他们将在工业、机构和学术环境中找到工作。ASI由几名专注于将机器学习方法应用于沿海问题的科学家教授。该研究所直接影响了20名美国研究生参与者，并为他们提供了在沿海背景下发展机器学习技能的重点体验。ASI旨在利用新西兰独有的资源，包括多个高保真数据集。ASI的所有课程材料将使用开放源码软件构建并存储在开放知识库中，以便于在ASI以外的其他沿海教学和学习环境中使用。项目评估将调查ASI的学习目标(事后和纵向)。ASI的成果和学习材料将在印刷、在线和科学会议上传播。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Coastlines host population centers, infrastructure, diverse ecosystems, and provide valuable tourism and recreational opportunities. Coastal regions are also prone to hazards such as storms, sea level rise, and chronic erosion. There is a growing volume of data available to scientists who study coastal processes and the future dynamics of coastlines. A range of new tools and methods that can be used to extract knowledge and insight from these data, especially machine learning techniques. However these new methods are not typically part of the graduate curriculum for coastal scientists. This project is focused on developing an advanced studies institute (ASI) in Auckland, New Zealand to teach machine learning methods to 20 US graduate students studying coastal processes and coastal geomorphology. The ASI participants are the next generation of US coastal scientists, who will get jobs in industry, agency, and academic settings.The ASI is taught by several scientists who focus on applying machine learning methods to coastal problems. The institute directly impacts 20 US graduate student participants, and provides a focused experience for them to develop machine learning skills in a coastal context. The ASI is designed to leverage resources that are unique to New Zealand, including multiple high-fidelity datasets. All course materials for the ASI will be built with open source software and stored in open repositories to facilitate use in other coastal teaching and learning settings beyond this ASI. A project evaluation will investigate the ASI learning goals (post-event and longitudinally). ASI outcomes and learning materials will be disseminated in print, online, and at scientific conferences.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.