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SUMMARY:Agnese Barbensi (Queensland)
DTSTART:20231122T100000Z
DTEND:20231122T110000Z
DTSTAMP:20260423T021047Z
UID:CompAlg/29
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/CompAlg/29/"
 >Persistent homology\, hypergraphs and geometric cycle matching</a>\nby Ag
 nese Barbensi (Queensland) as part of Machine Learning Seminar\n\n\nAbstra
 ct\nTopological data analysis has been demonstrated to be a powerful tool 
 to describe topological signatures in real-life data\, and to extract comp
 lex patterns arising in natural systems. An important challenge in topolog
 ical data analysis is to find robust ways of computing and analysing persi
 stent generators\, and to match significant topological signals across dis
 tinct systems. In this talk\, I will present some recent work dealing with
  these problems. Our method is based on an interpretation of persistent ho
 mology summaries with network theoretical tools\, combined with statistica
 l and optimal transport techniques.\n
LOCATION:https://researchseminars.org/talk/CompAlg/29/
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