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DTSTART;TZID=America/Denver:20231113T143900
DTEND;TZID=America/Denver:20231113T144700
UID:submissions.supercomputing.org_SC23_sess454_ws_eduhpcp115@linklings.co
 m
SUMMARY:AutoLearn:  Learning in the Edge to Cloud Continuum
DESCRIPTION:Alicia Esquivel Morel (University of Missouri - Columbia), Wil
 liam Fowler (Tufts University), Kate Keahey (Argonne National Laboratory (
 ANL)), Kyle Zheng (Modesto Junior College), Michael Sherman (University of
  Chicago), and Richard Anderson (Rutgers University)\n\nTechnological adva
 ncements have led to an increase in teaching the fundamentals of robotics 
 and autonomous systems and their importance, relying on strong hands-on pr
 actical experimentation. National Science Foundation (NSF)-supported testb
 eds have opened the doors for experimentation and support in the next era 
 of computing platforms and large-scale cloud research. \n\nWe present an o
 pen-source educational module that conveys accessibility to education, aim
 ing to prepare learners for technological career paths. Our educational mo
 dule is developed with the motivation to bring hands-on sessions and allow
  students to attain knowledge in a comprehensive manner. Specifically, we 
 present AutoLearn: Learning in the Edge to Cloud Continuum, an educational
  module that integrates a collection of educational artifacts, based on an
  open-source self-driving platform for small scale that leverages the Cham
 eleon Cloud testbed to teach cloud computing concepts, edge devices techno
 logy, and artificial intelligence driven applications.\n\nTag: Education, 
 Heterogeneous Computing, Reproducibility, State of the Practice, Sustainab
 ility\n\nRegistration Category: Workshop Reg Pass\n\nSession Chairs: David
  P. Bunde (Knox College); Sushil K. Prasad (University of Texas at San Ant
 onio); Satish Puri (Missouri University of Science and Technology); Apan Q
 asem (Texas State University); and George K. Thiruvathukal (Loyola Univers
 ity, Chicago; Argonne National Laboratory (ANL))\n\n
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