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Big data and machine learning in geosciences (co-organized)
Convener: Mikhail Kanevski  | Co-Conveners: Reik Donner , Philip Brown , Karsten Steinhaeuser , Nicolas Brodu , Rosa Lasaponara , Nicolas Younan , Kwo-Sen Kuo , Peter Baumann , Morris Riedel , Sandro Fiore 
 / Tue, 19 Apr, 10:30–12:00  / 13:30–15:00
 / Attendance Tue, 19 Apr, 17:30–19:00

This session aims to bring together researchers working with big data sets generated from extensive observational campaigns (e.g., via ground and remote sensing) and extremely detailed modeling efforts (such as in meteorology, climatology, risk modeling) across various fields of geosciences.

Topics of this session will include the identification and handling of specific problems arising from the need to analyze such large-scale data sets, together with methodological approaches towards automatically inferring relevant patterns in time and space aided by computer science-inspired techniques. Among others, this session shall address approaches from the following fields:

* Graph analysis and analytics
* Complex networks
* Dimensionality and complexity of big data sets
* Informatics and data science
* Data mining
* Machine learning
* Climate informatics
* Visualization and visual analytics of big data