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SUMMARY:IMOS x AI Center Seminar - AI for Science series - Prof. Johannes 
 Brandstetter
DTSTART:20250212T100000
DTEND:20250212T110000
DTSTAMP:20260429T200016Z
UID:7225670d057519cb7757b9ed663776d01b89286873cfb0e54f5dfc3e
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Johannes Brandstetter\nThe talk is jointly organized by 
 the EPFL IMOS Lab and the EPFL AI Center.\n\nHosting professor: Prof. O
 lga Fink\n\nTitle\nClosing the Gap Between Scientific Foundation Models an
 d Real-World Applications\n\nAbstract\nIn the era of LLM models\, one gets
  notoriously confronted with the question of where we stand with applicabi
 lity of large-scale deep learning models within scientific or engineering 
 domains. The discussion starts by reiterating on recent triumphs in weathe
 r and climate modeling. Further\, we discuss recent breakthroughs in fluid
  dynamics and related engineering fields\, and subsequently extrapolate in
 sights applicable to relatively untouched scientific and engineering field
 s. Finally\, we outline challenges and potential solutions when it comes t
 o scalability beyond traditional numerical schemes and discuss the respect
 ive impact on industry and scientific environments.\n\nBio: Prof. Johann
 es Brandstetter is leading a group “AI for data-driven simulations” at
  the Institute for Machine Learning at the Johannes Kepler University (J
 KU) Linz. Additionally\, he's a Chief Researcher at NXAI - the new Europ
 ean AI hub in Linz (Austria). He has obtained his PhD after working severa
 l years at the CMS experiment at CERN. During this time\, he had the priv
 ilege of learning from brilliant minds from all around the world\, and got
  the chance to co-author seminal papers in the realm of Higgs boson physic
 s. In 2018\, after completing his PhD\, his career trajectory shifted towa
 rds machine learning\, and he was fortunate to join the research group of 
 Mr LSTM Sepp Hochreiter in Linz. Under Sepp’s mentorship\, he delved int
 o the intricacies of machine learning and modern deep learning over a span
  of 2.5 years.
LOCATION:ELE 117 https://plan.epfl.ch/?room==ELE%20117
STATUS:CONFIRMED
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