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SUMMARY:Gaussian processes and active learning: three recent chemistry-rel
 ated developments
DTSTART:20250318T151500
DTSTAMP:20260415T183743Z
UID:775e1f0a266578cf31a8d9e66e0fd708a135db5c782b4a465b302767
CATEGORIES:Conferences - Seminars
DESCRIPTION:David Ginsbourger is heading the Uncertainty Quantification an
 d Spatial Statistics Group and serving as Director of Studies in Statistic
 s at the University of Bern\, where I he is co-directing the Institute of 
 Mathematical Statistics and Actuarial Science. At the University of Bern\,
  he is also a member of the Oeschger Center for Climate Change Research\, 
 the Center for Artificial Intelligence in Medecine\, and the Multidiscipli
 nary Center for Infectious Diseases. On the editorial side\, he is serving
  as Associate Editor of SIAM/ASA Journal on Uncertainty Quantification\, T
 echnometrics\, and regularly as Area Chair / Meta-Reviewer for major Machi
 ne Learning conferences.\nIn this talk\, I will give an overview of three 
 different research collaborations pertaining to Gaussian Process modelling
  and active learning algorithms applied to chemistry-related challenges. I
 n particular\, I will present recent work on targeted sequential design fo
 r uncovering high-activity molecules under precise and imprecise measureme
 nts schemes\, and also present fast approaches to encode stochastic equiva
 riances within kernel methods applied to dipole moment prediction.   
LOCATION:https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYI
 NRdz09
STATUS:CONFIRMED
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