CEDAR email: Opportunity to optimize visibility across communities at AGU
Mcgranaghan, Ryan (335G-Affiliate)
ryan.mcgranaghan at jpl.nasa.gov
Wed Jul 25 13:57:49 MDT 2018
Since we have not received enough AGU session announcements… I thought I would share one (a particularly exciting and new session).
It’s important that the CEDAR community becomes involved in the AGU Earth and Space Science Informatics (ESSI) section, so we are convening a radically new session offered through the ESSI section and cross-listed and organized with the AGU Space Physics and Aeronomy (SPA) and Earth Science (ES) Sections. We welcome you to participate in this importance conversation around convergence.
A full announcement is below. Please consider contributing to this unique opportunity to optimize the visibility of your excellent work across communities.
We looking forward to your participation!
Ryan McGranaghan (ryan.mcgranaghan at jpl.nasa.gov<mailto:ryan.mcgranaghan at jpl.nasa.gov>)
Kerstin Lehnert (lehnert at ldeo.columbia.edu<mailto:lehnert at ldeo.columbia.edu>)
Derek Posselt (Derek.Posselt at jpl.nasa.gov<mailto:Derek.Posselt at jpl.nasa.gov>)
We eagerly invite your participation in an exciting session at Fall AGU 2018 devoted to new directions, innovation, and convergence between scientific disciplines through data science and informatics: Convergence in Space Physics and Earth Science: Discovery through machine learning<https://agu.confex.com/agu/fm18/gateway.cgi>. We encourage potential speakers and thought-leaders to contact us to be involved.
This session is targeted to bring together a multi-disciplinary group from across the disciplines of space physics, earth science, statistical analysis, and computer and data sciences to:
1. Discuss the application of cutting-edge data science approaches (e.g., machine learning) across space physics and earth science;
2. Provide a forum to navigate the intersection between innovative data science tools and established methods and models; and
3. Address the convergence<https://www.nsf.gov/od/oia/convergence/index.jsp> between scientific disciplines through data science and methodology transfer.
The session abstract and details are:
Session ID: 46030
Session Title: IN018. Convergence in Space Physics and Earth Science: Discovery through machine learning
Section/Focus Group: Earth and Space Science Informatics
View Session Details: https://agu.confex.com/agu/fm18/gateway.cgi
Machine learning is increasingly used in a wide variety of scientific disciplines to advance knowledge in the age of big data and novel data analytics. However, application of machine learning algorithms to Earth and Space Science datasets is still in its infancy, and the question of how to incorporate uncertainties and physics knowledge remains unanswered. The common paradigm of applying machine learning to create new knowledge in both Space Physics and Earth Science provides a foundation to discuss their convergence<https://www.nsf.gov/od/oia/convergence/index.jsp>. Besides being the pinnacle of evolutionary integration across disciplines, convergent research is now a central investment focus at both NASA and NSF. We invite contributions that exemplify the application of machine learning in Space Physics and Earth Sciences. The session will spark discussion of how machine learning methodology transfer may serve to bridge disciplines, leading to the opening of new research vistas and collaboration among researchers seeking future funding opportunities.
The abstract submission deadline is Aug. 1, 23:59 EDT (Submit here<https://fallmeeting.agu.org/2018/abstract-submissions/>).
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