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

“ENVR 2022研讨会：环境和生态统计研究与社会影响应用”将于2022年10月6日至8日在犹他州的普罗沃举行。环境统计和数据科学（ESDS）正在迅速成为环境科学跨学科研究的一个组成部分。 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，以了解和减轻环境退化，促进人类和生态福祉。为了管理和促进ESDS的发展，并培养新的跨学科研究团队，本项目将通过为ESDS初级研究人员提供差旅支持来支持上述研讨会。 该研讨会的目标是（一）促进跨学科研究，（二）目前国家的最先进的方法，（三）发展ESDS的新人才。 研讨会将包括各种活动，包括关于ESDS的当前和未来状态的小组讨论，关于ESDS跨学科主题的特邀科学报告，短期课程，海报会议，团队建设和头脑风暴练习，以建立新的研究方向和团体。环境科学中的现代复杂问题越来越多地转向来自各种来源的更复杂的数据集来寻求答案。 这种复杂数据集的例子包括但不限于从气候模型和遥感生成的大量相关数据集、在非欧几里德空间（如河流网络）上收集的数据、关于气象极端事件的罕见事件数据以及多变量时空数据（如污染成分）。 从如此复杂的数据集得出的科学答案需要经过培训的统计学家和数据科学家的专业技能，以便与环境科学家进行匡威和合作。 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。 来自统计和应用环境科学的发言者将介绍他们处理环境科学数据的最新科学方法。 讨论的方法将包括环境数据的深度学习，海量空间相关数据的计算，利用河流网络收集的数据中的网络信息，以及处理点模式（位置）数据的现代技术。 这些介绍，沿着小组讨论，将概述未来在环境统计和数据科学方面的跨学科研究。关于研讨会的更多细节可以在以下网站找到：https://community.amstat.org/envr/events/workshops/envr2022workshop.This奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

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.