PARTNER: An AI/ML Collaborative for Southeast Florida Coastal Environmental Data and Modeling Center

这个项目是佛罗里达国际大学(FIU)、北卡罗来纳州立大学(NCSU)、德克萨斯A&A&M语料库克里斯蒂(TAMU-CC)和人工智能天气、气候和沿海海洋学值得信赖的人工智能研究所(AI2ES)之间的扩展人工智能伙伴关系。在这个项目中，一个为少数群体服务的机构领导着与另外两个MSI和一个人工智能研究所的新合作，专注于扩大其机构已经建立的研究和教育项目，并围绕发展人工智能寻求共同、互补的目标，同时考虑到对社会的使用，以及培养下一代人工智能教育和劳动力人才。合作研究的重点是开发人工智能，以管理沿海人口稠密地区面临的洪水破坏和其他环境压力的威胁，重点是佛罗里达州东南部的环境影响。该项目还将围绕这个新的人工智能卓越中心建立社区，在那里，此类活动以前并未得到很好的发展。该项目在佛罗里达国际大学(FIU)建立了南佛罗里达海岸环境数据和建模中心，促进综合研究和教育努力，以开发人工智能和机器学习技术，以了解和预测影响海洋、城市、农业和自然系统的关键过程，并研究对南佛罗里达州重要的沿海环境问题。该区域包括生态敏感和经济重要的地区，如比斯坎湾、城市化走廊、与比斯坎湾接壤的支流流域，以及区域流域的地表水和地下水系统。由于房地产价值高、人口密集、地势低洼，该地区极易受到洪水破坏和其他环境压力的影响，如海平面上升、城市洪水、水质监测和有害藻华检测。该项目的重点是四个方面。首先，该中心管理并使经过管理的数据集为人工智能做好准备并公开可用，以测试AI/ML技术和开发解决方案。其次，研究人员合作开发可信赖的人工智能，以了解和预测沿海环境现象，包括开发物理信息的人工智能模型、可靠的人工智能系统和可解释的人工智能方法。第三，研究人员将AI/ML应用于南佛罗里达海岸环境问题的研究，如海平面变化、复合海岸洪水和有害藻类水华。最后，FIU和AI2ES为FIU的AI/ML和环境科学专业的不同本科生和研究生提供教育、培训和劳动力发展机会，包括开发本科跨学科课程，加强两年制大学学生的途径课程，共享教育材料，通过实地考察、实习机会以及跨机构研讨会和研讨会促进研究交流和建立机构之间的联盟。该项目的部分资金来自NSF在STEM卓越公平分部内的Louis Stokes少数群体参与联盟(LSAMP)计划。该奖项反映了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。

This project is an ExpandAI Partnership between the Florida International University (FIU), North Carolina State University (NCSU), Texas A&M Corpus Christi (TAMU-CC), and the AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES). In this project, a minority-serving institution leads a new collaboration with two other MSIs and an AI Institute focused on scaling up already-established research and education programs at their institutions and to pursue shared, complementary goals around developing AI with use for society in mind and for developing the next generation of AI education and workforce talent. The collaborative research focuses on the development of AI to manage threats of flood damage and other environmental stresses faced by areas with high coastal population, with an emphasis on environmental impacts in Southeast Florida. The project will also build community around this new center of excellence in AI where such activities were not previously well developed. This project establishes the South Florida Coastal Environmental Data and Modeling Center at Florida International University (FIU), promoting integrated research and education efforts to develop artificial intelligence and machine learning techniques for understanding and predicting the key processes affecting ocean, urban, agricultural, and natural systems, and for studying coastal environmental issues important to South Florida. This region encompasses ecologically sensitive and economically important areas, such as Biscayne Bay, urbanized corridors, tributary watersheds bordering the Bay, and the surface and groundwater systems in regional watersheds. Due to high property values, dense population, and vulnerable low-lying areas, the region is highly susceptible to flood damage and other environmental stresses such as sea-level rise, urban flooding, water quality monitoring, and harmful algae bloom detection. The project focuses on four areas. Firstly, the Center manages and makes curated datasets AI-ready and publicly available for testing AI/ML techniques and developing solutions. Secondly, the researchers collaborate on trustworthy AI for understanding and predicting coastal environmental phenomena, including developing physics-informed AI models, reliable AI systems, and explainable AI methods. Thirdly, the researchers apply AI/ML to study South Florida coastal environmental problems, such as sea-level variability, compound coastal flooding, and harmful algae blooms. Lastly, FIU and AI2ES provide education, training, and workforce development opportunities for diverse undergraduate and graduate students at FIU in AI/ML and Environmental Science, including developing an undergraduate interdisciplinary program, strengthening pathway programs for students from 2-year colleges, sharing education materials, and facilitating research exchange and building alliances among institutions via site visits, internship opportunities, and cross-institution workshops and seminars. The project is partially funded by NSF’s Louis Stokes Alliances for Minority Participation (LSAMP) program within the Division of Equity for Excellence in STEM.”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.