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SUMMARY:What Can a Spectrum Learn? AI Perspectives on the Kohn Laplacian o
 f Sphere Quotients - Yunus Zeytuncu (University of Michigan)
DTSTART:20260420T143000Z
DTEND:20260420T144500Z
UID:TALK246205@talks.cam.ac.uk
DESCRIPTION:Spectral data of the Kohn Laplacian on sphere quotients is ric
 h\, structured\, and explicitly computable&acirc\;&euro\;&rdquo\;yet its g
 eometric implications are not always transparent. In this expository talk\
 , I will revisit earlier work on computing spectra of the Kohn Laplacian o
 n spheres and lens spaces\, emphasizing how symmetry and group actions sha
 pe eigenvalues and multiplicities. I will then outline a new direction: us
 ing machine learning tools to identify patterns and invariants in these sp
 ectra\, with the aim of better understanding how geometry\, arithmetic\, a
 nd symmetry are reflected in spectral signatures. This perspective positio
 ns AI as a hypothesis-generating tool for classical problems in analysis a
 nd geometry.
LOCATION:Seminar Room 1\, Newton Institute
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