AGU 2018

IS-GEO is organizing THREE sessions at the AGU Fall meeting, spanning research and education.  Come present your latest research and education ideas on machine learning and other computational methods, visualization and data coordination approaches in the geosciences. The IS-GEO Sessions at AGU Fall Meeting are listed below.

1) Research session

IN045: IS-GEO: Augmenting intelligent systems and advancing geoscientific discoveries with AI and knowledge centered research

To get involved: Please submit your abstract!

Advanced computing and artificial intelligence are paving new pathways for geoscientific discoveries.  Leveraging knowledge from across computational and geoscience fields is achieving new insight at a rapid pace.  This session brings together researchers that have already successfully applied AI research in the geosciences, as well as those who are interested in getting involved in this important area.  The EarthCube sponsored Research Coordination Network for Intelligent Systems in Geosciences, or IS-GEO, is an emerging community of researchers with active working groups that support methodology and technique sharing, encourage cross-disciplinary research collaborations, and development of data benchmarks to accelerate progress.  AI and knowledge-based reasoning promise to accelerate discovery in the next century as approaches in adaptive sensing, machine learning, knowledge graphing and other approaches are increasingly applied across the geosciences.

2) Education session

IN030 : Educating the next generation of scientists: Interdisciplinary education bridging geoscience and data science

To get involved: Please submit your abstract!  Since this is co-organized as an Education session, an abstract submitted here does not count as your one allowed abstract.  See also this AGU chart on how submissions count.

This session invites presentations that focus on the development of education materials for learning at the intersection of earth and data science.  Given the emergence of huge amounts of observed and model output data becoming available in the geosciences, it is crucial to educate the next generation of geoscientists to have significant skills not only in geosciences, but also in data analysis and related areas like machine learning, artificial intelligence, statistics and other types of applied mathematics.  Presentations will discuss coursework, curricula, interdisciplinary education, and other aspects along with the challenges and success that educators have had during development and implementation.

3) Panel session

IN008: Augmenting Advances in the Next Century: Why AI and knowledge-centered research in Geosciences is important now and how it will change the next century

To get involved:  Attend the panel and ask questions!!

This panel session seeks to bridge the gap between Intelligent Systems and AI researchers and researchers from the geosciences, including Earth, atmospheric, ocean, and space sciences.  Sponsored by the EarthCube IS-GEO RCN, the panel will address specific topics and key questions:

  1. What are AI and knowledge-centered approaches that are emerging and promising for geosciences
  2. How can the latest AI advancements contribute to Earth sciences?
  3. What is special about the Earth sciences that makes it challenging to apply standard AI algorithms?
  4. What are examples of successful AI research in the field of Earth sciences?
  5. Where are the greatest opportunities and needs for AI in Earth science?
  6. How can we prepare the next generation of students to work at the intersection of these fields?

AGU is in Washington DC December 10-14, 2018.  Abstracts are due 1 August 2018, 11:59 P.M. EDT. Be eligible to win a $100.00 USD gift card — submit your abstract by 25 July 2018, 11:59 P.M. EDT

For any questions or suggestions, please contact the organizing committee for IS-GEO sessions at AGU Fall meeting:

  • Suzanne A Pierce (spierce at tacc.  utexas . edu), Texas Advanced Computing Center
  • Deana D Pennington (ddpennington at utep.  edu), University of Texas at El Paso
  • Mary C Hill (mchill at ku.  edu), University of Kansas
  • Imme Ebert-Uphoff (iebert at colostate . edu), Colorado State University
  • Daniel Fuka (drfuka at vt . edu), Virginia Tech
  • Jo Martin (jmartin at oberlin . edu), University of Vermont
  • Natalie Freed (nfreed at tacc . utexas . edu), Texas Advanced Computing Center
  • Daniel Garijo (dgarijo at isi . edu),  University of Southern California
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