NSF Convergence Accelerator: Symposium on Predicting Extremes by Data-Driven Analytics

NSF 融合加速器：通过数据驱动分析预测极端情况研讨会

• 批准号: 2035365
• 负责人: A Surjalal Sharma
• 金额: $ 10万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2022-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2035365&HistoricalAwards=false
• 关键词: NSF; Convergence; Accelerator; Symposium; Predicting

The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future. This symposium on Predicting Extremes by Data-Driven Analytics will help identify topic areas for new tracks in the NSF Convergence Accelerator. Extreme events and associated hazards in natural, commercial, and security systems underlie the most devastating catastrophes in society. The societal risks arising from extreme events are very high both in terms of likelihood and impact on society. The increasing impact of disasters and the consequent risks have led leading organizations and institutions around the world to develop new approaches to mitigate the impacts and develop strategies for resilience. Improved predictive capability for extreme events is a critical scientific need in order to achieve effective disaster risk assessment, which is a product of three factors: the probability of the underlying events, vulnerability of the system and consequences therein. The understanding and modeling of extreme events contributes towards developing the probabilities of occurrence. This symposium will bring together participants from academia, government and industry to develop data-driven analytics as a pathway for predicting extreme events in natural and anthropogenic systems, including applications in terrestrial and space weather, finance and economics, and cybersecurity. The symposium is planned as a 3-day event to be run as a virtual meeting. It will emphasize participation by researchers and students from underrepresented communities that may be especially vulnerable to the effects of extreme events.An essential step toward achieving better predictability is uncertainty quantification, which is an inherently interdisciplinary endeavor, requiring convergence across multiple disciplines and participation by multiple stakeholders across academia, industry and government. A Convergence Accelerator track on this theme would benefit multiple sectors. Developing a framework for predicting extremes requires the harnessing of massive data from a variety of sources. The symposium will discuss the idea of developing a common platform for stakeholders in government, industry and nonprofits as part of this potential new track in the Convergence Accelerator. The themes of the symposium are of general interest due to their potential for high impact on society. A symposium proceedings will be produced, which will provide broad exposure to the potential outcomes of convergence research in this topic area.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.

NSF融合加速器支持以团队为基础的多学科努力，这些努力应对国家重要性的挑战，并将在不久的将来为社会带来价值的可交付成果。关于通过数据驱动的分析预测极端的研讨会将有助于确定NSF收敛加速器中新轨道的主题领域。自然，商业和安全系统中的极端事件以及相关的危害是社会上最具破坏性的灾难。在可能性和对社会的影响方面，由极端事件引起的社会风险都很高。灾难的日益影响以及随之而来的风险导致世界各地的领先组织和机构开发新的方法来减轻影响并制定弹性战略。为了实现有效的灾害风险评估，提高极端事件的预测能力是一项至关重要的科学需求，这是三个因素的产物：基础事件的概率，系统的脆弱性以及其中的后果。极端事件的理解和建模有助于发展发生的概率。该研讨会将使来自学术界，政府和行业的参与者聚集在一起，以开发数据驱动的分析，作为预测自然和人为系统中极端事件的途径，包括在陆地和太空天气，金融和经济学，网络安全中的应用。该研讨会计划是为期3天的活动，作为虚拟会议。它将强调来自代表性不足的社区的研究人员和学生的参与，这些社区可能特别容易受到极端事件的影响。实现更好可预测性的基本步骤是不确定性量化，这是不确定性的量化，这是一项固有的跨学科努力，需要在多个学科和各个股份范围内跨越学院的各种学科和政府参与。该主题的收敛加速器轨道将使多个部门受益。开发一个预测极端的框架需要利用各种来源的大量数据。该研讨会将讨论为政府，行业和非营利组织中利益相关者开发一个共同平台的想法，这是融合加速器这一潜在新轨道的一部分。专题讨论会的主题由于对社会的高影响而具有普遍关注。将制定一个研讨会会议，这将为该主题领域的融合研究的潜在结果提供广泛的曝光。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估审查标准来通过评估来获得支持的。