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The official Jupyter Conference
Aug 21-22, 2018: Training
Aug 22-24, 2018: Tutorials & Conference
New York, NY
Ryan Abernathey

Ryan Abernathey
Assistant Professor, Columbia University

Website | @rabernat

Ryan Abernathey is an assistant professor of Earth and environmental science at Columbia University and Lamont Doherty Earth Observatory. Ryan is a physical oceanographer who studies the large-scale ocean circulation and its relationship with Earth’s climate. High-resolution numerical modeling and satellite remote sensing are key tools in this research, which has led to an interest in high-performance computing and big data. Previously, he held a postdoc at Scripps Institution of Oceanography. In 2016, Ryan was awarded an Alfred P. Sloan Research Fellowship in ocean sciences and an NSF CAREER award for a project entitled “Evolution of Mesoscale Turbulence in a Changing Climate” and received a NASA New Investigator Award in 2013. He is an active participant in and advocate for open source software, open data, and reproducible science. He holds a PhD from MIT and a BA from Middlebury College.


1:50pm–2:30pm Thursday, August 23, 2018
Ryan Abernathey (Columbia University), Yuvi Panda (Data Science Education Program (UC Berkeley))
Average rating: *****
(5.00, 1 rating)
Climate science is being flooded with petabytes of data, overwhelming traditional modes of data analysis. The Pangeo project is building a platform to take big data climate science into the cloud using SciPy and large-scale interactive computing tools. Join Ryan Abernathey and Yuvi Panda to find out what the Pangeo team is building and why and learn how to use it. Read more.
9:10am–9:25am Friday, August 24, 2018
Location: Grand Ballroom
Ryan Abernathey (Columbia University)
Average rating: ****.
(4.33, 3 ratings)
Drawing on his experience with the Pangeo project, Ryan Abernathey makes the case for the large-scale migration of scientific data and research to the cloud. The cloud offers a way to make the largest datasets instantly accessible to the most sophisticated computational techniques. A global scientific data commons could usher in a golden age of data-driven discovery. Read more.