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SUMMARY:IC Colloquium : Transportation Techniques for Geometric Data Proce
 ssing
DTSTART:20150305T101500
DTEND:20150305T113000
DTSTAMP:20260408T085709Z
UID:d113c084a8e4e94143b8594f9e2698ccf6bb25c2fc0e993a5cb9a6de
CATEGORIES:Conferences - Seminars
DESCRIPTION:By : Justin Solomon - Stanford University\nIC Faculty candidat
 eAbstract :\nModeling and understanding low- and high-dimensional data is 
 a recurring theme in graphics\, optimization\, learning\, and vision.  Ab
 stracting away application domains reveals common threads using geometric 
 constructs like distances\, similarities\, and curvatures.  This shared s
 tructure suggests the possibility of developing geometric data processing 
 as a discipline in itself.\nTo this end\, I will introduce optimal transpo
 rtation (OT) as a versatile component of the geometric data processing too
 lkit.  Originally proposed for minimizing the cost of shipping products f
 rom producers to consumers\, OT links probability and geometry using distr
 ibutions to encode geometric features and developing metric machinery to q
 uantify their relationships. \nTo transition OT from theory to practice\,
  I will show how to solve previously intractable OT problems efficiently o
 n discretized domains and demonstrate a wide range of applications enabled
  by this new machinery.  I will illustrate the advantages and challenges 
 of OT for geometric data processing by outlining my recent work in geometr
 y processing\, computer graphics\, and machine learning.  In each case\, 
 I will consider optimization aspects of the OT problem for relevant geomet
 ric domains---including triangulated surfaces\, graphs\, and subsets of Eu
 clidean space---and then show how the resulting machinery can be used to a
 pproach outstanding problems in surface correspondence\, modeling\, and se
 mi-supervised learning.Bio :\nJustin Solomon is a PhD candidate and teachi
 ng fellow in the Geometric Computing Group at Stanford University studying
  problems in shape analysis\, machine learning\, and graphics from a geome
 tric perspective.  His work is supported by the Hertz Foundation Fellowsh
 ip\, the NSF Graduate Research Fellowship\, and the NDSEG Fellowship.  Ju
 stin holds bachelors degrees in mathematics and computer science and an MS
  in computer science from Stanford.  He has served as the lecturer for co
 urses in graphics\, differential geometry\, and numerical methods\; his fo
 rthcoming textbook entitled Numerical Algorithms focuses on applications o
 f numerical methods across modern computer science.  Before his graduate 
 studies\, Justin was a member of Pixar's Tools Research group.  He is a p
 ianist\, cellist\, and amateur musicologist with award-winning research on
  early recordings of the Elgar Cello Concerto.More information
LOCATION:BC 420 https://plan.epfl.ch/?room==BC%20420
STATUS:CONFIRMED
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