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BEGIN:VEVENT
DTSTAMP:20260317T121102Z
UID:https://www.ipp.mpg.de/events/43889/14160
DTSTART:20260109T093000Z
DTEND:20260109T110000Z
CLASS:PUBLIC
CREATED:20251119T134237Z
DESCRIPTION: The term "big data" governs not only social media and online s
 tores but also most modern research fields. It obviously also applies to m
 aterials science\, revolutionizing many of its aspects. But what does "big
 " mean in the context of typical materials-science machine-learning proble
 ms? This question involves not only data volume\, but also data quality an
 d veracity as much as infrastructure issues. We ask\, how models generaliz
 e to similar datasets or how high-quality datasets can be gathered from he
 terogeneous sources. Likewise\, we explore how the feature set and complex
 ity of a model can affect expressivity. And what requirements does this al
 l impose on data infrastructures for creating and hosting large datasets a
 nd training models? Through selected examples\, I will demonstrate that bi
 g data presents unique challenges in many aspects that may often be overlo
 oked but would deserve more attention. I will also discuss how a scalable 
 data infrastructure can make our research data AI ready\, and thus contrib
 ute to solving the problem.\nSpeaker: Prof. Claudia Draxl
LAST-MODIFIED:20251212T105452Z
LOCATION:IPP\, Room: Günter-Grieger Lecture Hall (Greifswald) and Zoom
ORGANIZER;CN=Dmitry Moseev:mailto:dmitry.moseev@ipp.mpg.de
SUMMARY:Institutskolloquium: Machine learning and Big Data in materials sci
 ence: How big is Big? 
URL;VALUE=URI:https://www.ipp.mpg.de/events/43889/14160
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