Environmental Statistics & Data Science: Where do we go from here?

Music City Center 

Room: CC-207B 

Environmental statistics and data science (ESDS) is the scientific field associated with developing the statistical, mathematical and computational tools necessary to analyze data arising from complex environmental and ecological questions. Over the years, ESDS has guided inter-disciplinary research to further understand environmental and ecological phenomena and has played a critical role in framing evidence-based environmental policy that mitigates environmental and ecological degradation. Despite these historical successes, the onset of data science is leading to a new identity for the ESDS community that requires it to look to the future to optimize its usefulness and impact. This session brings together ESDS professionals in academics, industry and government to discuss current and future needs of ESDS professionals and associated collaborators on science and policy. The panel will discuss their view on important questions such as "What current scientific demands does ESDS need to meet?", "How can ESDS statisticians utilize new developments in AI and machine learning?", "Where does ESDS need to be in 10 years?" "How is ESDS currently engaging with the community?" and "How can ESDS be used going forward to protect the environment?".

This session is particularly timely as machine and deep learning along with artificial intelligence are transforming various fields across the scientific spectrum. However, the application of such methods in ESDS is still in its infancy. As such, this session will provide important guidance on how to develop these tools to meet the needs of environmental and ecological scientists. With this focus, this session appeals to the entire ESDS community as well as anyone working with environmental data.

The session includes 5 panelists who will, initially, be given 10 minutes to share their vision of the future of ESDS. Following these brief presentations, the session chair will moderate a discussion between the panelists and attendees covering the questions mentioned above. The outcome of this session will, hopefully, be a joint paper to be published in a peer-reviewed journal for the entire community to comment on and read.

Yes

Section on Statistics and the Environment

Section on Statistical Learning and Data Science

The International Environmetrics Society