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和生态统计研究及应用”将于2022年10月6-8日在犹他州普罗沃举行。环境统计和数据科学(ESDS)正迅速成为环境科学跨学科研究的一个不可或缺的领域。环境统计学家负责处理、总结和分析来自气候模型、现场观测和遥感测量的大量环境数据，以了解和缓解环境退化，促进人类和生态福祉。为了管理和促进ESD的发展，并培养新的跨学科研究团队，这项计划将通过为ESD初级研究人员提供参加工作坊的差旅支持来支持上述工作坊。工作坊的目标是(I)促进跨学科研究，(Ii)介绍最先进的方法，以及(Iii)培养ESD方面的新人才。工作坊将包括多项活动，包括就环境影响及排放系统的现状及未来状况进行小组讨论、邀请有关环境影响及排放系统跨学科课题的科学报告、短期课程、海报讲座，以及团队建设和集思广益练习，以建立新的研究方向和小组。环境科学中的现代复杂问题正越来越多地从不同来源的更复杂的数据集中寻找答案。这类复杂数据集的例子包括但不限于气候模型和遥感产生的大量相关数据集、在河流网络等非欧几里得空间收集的数据、关于气象极端情况的罕见事件数据以及污染成分等多变量时空数据。从如此复杂的数据集得出的科学答案需要统计学家和数据科学家的专业技能，他们接受过与环境科学家交谈和合作的培训。该项目将提供资金，支持初级环境数据科学研究人员的参与。统计学和应用环境科学的演讲者将介绍他们处理环境科学中产生的数据的最先进的科学方法。讨论的方法包括对环境数据的深度学习、对大量空间相关数据的计算、在河流网络上收集的数据中利用网络信息，以及处理点模式(位置)数据的现代技术。这些介绍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/events/workshops/envr2022workshop.This奖项反映了国家科学基金会的法定使命，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。

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.