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BEGIN:VEVENT
DTSTAMP:20260429T133517Z
DTSTART;VALUE=DATE-TIME:20260302T130000
DTEND;VALUE=DATE-TIME:20260302T140000
SUMMARY:WCPM: Brennon Shanks\, Institute of Organic Chemistry and Biochem
 istry of the Czech Academy of Sciences
TZID:Europe/London
UID:20260302-8ac672c49b9d1da0019b9daeb499064d@warwick.ac.uk
CREATED:20260121T133208Z
DESCRIPTION:Networking Lunch: The Recharge Room\, next to Lecture Theatre
  004\, from 12:30pm - 1pm. Title: Embracing Uncertainty in Computational
  Chemistry the Bayesian Way Abstract: Uncertainty is a fundamental aspec
 t of modeling complex physical systems\, arising from limited data\, app
 roximate models\, and simplifying assumptions in both experiments and si
 mulations. Bayesian methods provide a principled framework for quantifyi
 ng this uncertainty and propagating it through predictive models. This s
 eminar introduces the Bayesian perspective from the lens of chemistry\, 
 focusing on how prior chemical knowledge\, physical constraints\, and da
 ta can be combined to make meaningful probabilistic statements about mod
 el parameters and predictions. Particular emphasis is placed on physics-
 informed Gaussian process priors and their application to experimental d
 ata analysis and surrogate modeling\, with examples drawn from neutron a
 nd X-ray scattering and molecular force-field development. Bio: Brennons
 ' research focuses on developing probabilistic machine learning methods 
 and Bayesian inference techniques to study the thermodynamics and atomic
  structure of liquid-state materials. He works at the intersection of mo
 lecular simulation\, statistical mechanics\, and data-driven modeling\, 
 with an emphasis on uncertainty quantification and interpretable predict
 ions. A core aspect of his work involves using Gaussian Process models a
 nd Bayesian approaches to improve the accuracy and reliability of molecu
 lar dynamics simulations\, particularly in modeling many-body interactio
 ns and complex fluid behavior. More broadly\, his research aims to integ
 rate physics-based simulation with machine learning to develop flexible\
 , data-efficient models for predicting the structure and thermodynamics 
 of condensed-phase systems.
LOCATION:Lecture Theatre 0.04 IMC
CATEGORIES:WCPM
LAST-MODIFIED:20260121T133208Z
ORGANIZER;CN=Jin Kang:
END:VEVENT
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