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SUMMARY:Bayesian Joint Inversions for the Exploration and Characterization
  of Geothermal Energy Targets 
DTSTART:20131003T161500
DTEND:20131003T171500
DTSTAMP:20260408T035040Z
UID:a1d45b0f82113461afdb6542fd69c6e126f247436f3b0fe8cbdf1ed4
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dr. Edwin Bonilla\, Machine Learning group at NICTA\, Australi
 a\nBio: I am a senior researcher at NICTA and an adjunct research fellow a
 t the Australian National University.\nI work on machine learning\, develo
 ping probabilistic models and efficient inference methods for structured p
 rediction problems.\nA non-exhaustive list of my research interests is:\n
     Transfer learning\n    Graphical models\n    Bayesian statist
 ics\n    Gaussian processes\n    learning from structured data\nWe p
 ropose a machine learning approach to geophysical inversion problems for t
 he exploration and characterization of geothermal energy targets. Our appr
 oach is based on Bayesian methods\, and provides a full distribution over 
 the predicted geophysical properties whilst enabling the incorporation of 
 data from different modalities. We assess our method qualitatively and qua
 ntitatively using simulated experiments and a real dataset from South Aust
 ralia with the goal of characterizing rock properties. The significance of
  our probabilistic inversion extends to general exploration problems.
LOCATION:INR113 http://plan.epfl.ch/?zoom=20&recenter_y=5863814.94355&rece
 nter_x=730548.85489&layerNodes=fonds\,batiments\,labels\,information\,park
 ings_publics\,arrets_metro&floor=1&q=INR113
STATUS:CONFIRMED
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