ENVR 2022 Workshop: Environmental and Ecological Statistical Research and Applications with Societal Impacts

ENVR 2022 研讨会：具有社会影响的环境与生态统计研究与应用

• 批准号: 2224121
• 负责人: Matthew Heaton
• 金额: $ 2.4万
• 依托单位: Brigham Young University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-15 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2224121&HistoricalAwards=false
• 关键词: ENVR; 2022; Workshop; Environmental; Ecological

The "ENVR 2022 Workshop: Environmental and Ecological Statistical Research and Applications with Societal Impacts" will be held in Provo, Utah, on October 6-8, 2022. Environmental Statistics and Data Science (ESDS) is quickly emerging as an integral field for conducting interdisciplinary research in the environmental sciences. Environmental statisticians are charged with processing, summarizing, and analyzing a massive inflow of environmental data from climate models, in situ observations, and remote sensing measurements, to name a few, to understand and mitigate environmental degradation and foster human and ecological well-being. To manage and facilitate growth in ESDS and foster new interdisciplinary research teams, this project will support the above workshop by providing travel support for junior ESDS researchers to attend the workshop. The goals of the workshop are to (i) facilitate interdisciplinary research, (ii) present state-of-the-art methods, and (iii) develop new talent in ESDS. The workshop will include various activities, including panel discussions on the current and future state of ESDS, invited scientific presentations on interdisciplinary topics in ESDS, short courses, a poster session, and team building and brainstorming exercises to establish new research directions and groups.Modern complex questions in the environmental sciences are increasingly turning to more complex datasets from a wide variety of sources for answers. Examples of such complex datasets include, but are not limited to, massive correlated datasets generated from climate models and remote sensing, data collected on non-Euclidean spaces such as river networks, rare-event data on meteorological extremes, and multivariate spatio-temporal data such as pollution compositions. Scientific answers derived from such complex datasets require specialized skills from statisticians and data scientists trained to converse and work with environmental scientists. This project will provide funding to support the participation of junior environmental data science researchers. Speakers from statistics and applied environmental sciences will present their state-of-the-art scientific methods for dealing with data arising in the environmental sciences. Methods discussed will include deep learning for environmental data, computation for massive spatially correlated data, exploiting network information in data collected on river networks, and modern techniques for dealing with point pattern (location) data. These presentations, along with panel discussions, will outline future interdisciplinary research in environmental statistics and data science. More details about the workshop can be found at: https://community.amstat.org/envr/events/workshops/envr2022workshop.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.

“ Envr 2022：环境和生态统计研究以及具有社会影响的环境和生态统计研究和应用”将于2022年10月6日至8日在犹他州普罗沃举行。环境统计学和数据科学（ESDS）迅速成为环境科学中进行跨学科研究的整体领域。 环境统计学家负责处理，总结和分析气候模型，原位观察和遥感测量值的大量环境数据的大量流入，以了解和减轻环境退化并促进人类和生态健康。为了管理和促进ESD的增长和培养新的跨学科研究团队，该项目将通过为初级ESDS研究人员提供旅行支持以参加研讨会来支持上述研讨会。 研讨会的目标是（i）促进跨学科研究，（ii）当前的最新方法，以及（iii）在ESD中发展新的人才。 研讨会将包括各种活动，包括有关当前和未来状况的小组讨论，邀请了有关ESD中的跨学科主题的科学演示，短期课程，海报会议以及团队建设和集思广益练习，以建立新的研究指示和集团。在环境科学中，越来越多地转向更复杂的数据集中的多种多样的数据。 这种复杂数据集的示例包括但不限于气候模型和遥感产生的大规模相关数据集，在非欧几里得空间上收集的数据，例如河流网络，关于气象极端的罕见事业数据，以及多变的时空 - 静态数据，例如污染构成。 从这样的复杂数据集中得出的科学答案需要统计学家和受过培训的数据科学家的专业技能，可以与环境科学家进行交谈和合作。 该项目将提供资金，以支持初级环境数据科学研究人员的参与。 统计和应用环境科学的发言人将提出其最先进的科学方法，以处理环境科学中产生的数据。 讨论的方法将包括有关环境数据的深度学习，用于大规模的空间相关数据的计算，利用河网络收集的数据中的网络信息以及用于处理点模式（位置）数据的现代技术。 这些演示文稿以及小组讨论将概述未来在环境统计和数据科学方面的跨学科研究。有关研讨会的更多详细信息，请访问：https：//community.amstat.org/envr/envr/events/workshops/envr2022workshop.tworkshop.tworkshop.tworkshop.this Award反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛影响的评估来评估的支持。