Themed Joint Session 10 Big Data, Big Computing, Bigger Science: Earth Data for AI

Tuesday, 8 January 2019: 1:30 PM-2:30 PM
North 124B (Phoenix Convention Center - West and North Buildings)
Hosts: (Joint between the 18th Conference on Artificial and Computational Intelligence and its Applications to the Environmental Sciences; the Ninth Symposium on Advances in Modeling and Analysis Using Python; the Fifth Symposium on High Performance Computing for Weather, Water, and Climate; and the 32nd Conference on Climate Variability and Change )
Timothy S. Sliwinski, Texas Tech University, Department of Geosciences, Atmospheric Science Group, Lubbock, TX; Surya Karthik Mukkavilli, University of New South Wales/CSIRO Australia/World Energy Meteorology Council UK/GroundObs Ltd., UNSW, SPREE/CSIRO, O&A/WEM Council, Technical Advisory Group/GroundObs, Founder & Director, Auckland; Auroop Ganguly, Northeastern University, Civil and Environmental Engineering, Boston, MA; Scott Collis, Argonne National Laboratory, Environmental Science Division, Argonne, IL; Gerald J. Creager, CIMMS/University of Oklahoma and NOAA/NSSL, FRDD, Norman, OK and Philippe Tissot, Texas A&M University‚ąíCorpus Christi, Conrad Blucher Institute, Corpus Christi, TX

Many challenges exist with respect to the collection, storage, transfer, and analysis of "Big Data" across the Earth sciences. New Earth observing sensors and models generate richer and bigger datasets, and many methods are being used or developed to overcome the difficulty of working with them. This session discusses ongoing efforts to identify, assemble, and make large multivariate Earth datasets widely accessible for comparative AI research. Speakers will address real-world Big Data challenges they have encountered in their workflows and/or the solutions they have used to overcome them. Solutions could include data mining or machine learning algorithms, high-performance parallel computing systems or platforms, advanced data storage and transfer capabilities, and more.

1:30 PM
AI for Earth (Core Science Keynote)
Lucas Joppa, Microsoft, Redmond, WA
2:00 PM
The Need for HPC for Deep Learning with Real-Time Satellite Observations
Jebb Q. Stewart, NOAA, Boulder, CO; and C. Bonfanti, I. Jankov, L. Trailovic, and M. W. Govett
2:15 PM
Machine Learning Applications of the Earth Data Analytic Services (EDAS) Framework
Thomas P. Maxwell, NASA, Greenbelt, MD; and D. Duffy, G. L. Potter, and L. Carriere
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