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SUMMARY:Machine Learning for Spectral Geometry and Vice Versa - Justin Sol
 omon (Massachusetts Institute of Technology)
DTSTART:20260421T090000Z
DTEND:20260421T094500Z
UID:TALK244921@talks.cam.ac.uk
DESCRIPTION:In this speculative and informal talk\, I will share some rese
 arch progress and open problems at the intersection of machine learning an
 d spectral geometry.&nbsp\; My talk will consider two problems:\n\nHow can
  the theory of and algorithms for spectral geometry benefit applications i
 n machine learning?\nHow can machine learning tools accelerate numerical s
 olution of spectral geometry problems?\n\nIn particular\, we will see how 
 ideas from spectral geometry can help featurize 3D shapes and entire datas
 ets\, as well as how neural networks\, neural ODEs\, and other function re
 presentations suggest new approaches to solving spectral geometry problems
  in practice.\nJoint work with Ana Dodik\, Hamid Kamkari\, Mohammad Sina N
 abizadeh\, David Palmer\, Dmitriy Smirnov\, Oded Stein\, Yu Wang\, and oth
 er members of the MIT Geometric Data Processing group.
LOCATION:Seminar Room 1\, Newton Institute
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