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PRODID:IEEE vTools.Events//EN
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TZID:Europe/Copenhagen
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DTSTART:20260329T030000
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DTSTART:20251026T020000
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BEGIN:VEVENT
DTSTAMP:20251205T145004Z
UID:4BF97A86-1047-4CA0-BEA6-E839A62A6046
DTSTART;TZID=Europe/Copenhagen:20251215T090000
DTEND;TZID=Europe/Copenhagen:20251215T150000
DESCRIPTION:The widespread availability of digital devices enables the coll
 ection of data from multiple environments\, from simple tracking of physic
 al activities to the monitoring of industrial processes. When analyzing th
 is time-series data\, the use of machine learning models is continually gr
 owing\, as they often provide better insights\, enabled by their large siz
 e and complexity. However\, deploying these models at the edge\, closer to
  where data is collected\, poses a challenge\, as they struggle in computa
 tionally constrained environments.\n\nCo-sponsored by: Sean Bin Yang\n\nSp
 eaker(s): David Campos\n\nRoom: 0.2.13\, Selma Lagerløfs Vej 300\, Aalbor
 g\, Arhus Amt\, Denmark\, 9220
LOCATION:Room: 0.2.13\, Selma Lagerløfs Vej 300\, Aalborg\, Arhus Amt\, De
 nmark\, 9220
ORGANIZER:seany@cs.aau.dk
SEQUENCE:9
SUMMARY:PhD. defense at the Department of Computer Science\, Aalborg Univer
 sity
URL;VALUE=URI:https://events.vtools.ieee.org/m/520154
X-ALT-DESC:Description: <br /><p>The widespread availability of digital dev
 ices enables the collection of data from multiple environments\, from simp
 le tracking of physical activities to the monitoring of industrial process
 es. When analyzing this time-series data\, the use of machine learning mod
 els is continually growing\, as they often provide better insights\, enabl
 ed by their large size and complexity. However\, deploying these models at
  the edge\, closer to where data is collected\, poses a challenge\, as the
 y struggle in computationally constrained environments.</p>
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