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DTSTART;TZID=America/Chicago:20251116T110000
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UID:submissions.supercomputing.org_SC25_sess220_ws_eduhpcp104@linklings.co
 m
SUMMARY:A Model for Teaching Machine Learning, Deep Learning, and Research
  Computing to Domain Scientists on HPC Resources
DESCRIPTION:William J. Allen, Kelsey M. Beavers, Erik Ferlanti, Lorenzo Co
 ncia, Joshua Urrutia, Ernesto A. B. F. Lima, John M. Fonner, Felix Zuo, H.
  E. Duplechin Seymour, Ari B. Kahn, Joe Stubbs, Anagha Jamthe, Stephanie N
 . Baker, Tabish Khan, and James P. Carson (Texas Advanced Computing Center
 )\n\nThis paper presents outcomes and insights from a one-week workshop de
 signed to teach biologists essential skills in high-performance computing 
 (HPC), machine learning (ML), and deep learning (DL). Participants with li
 ttle or no prior experience with HPC learned how to navigate file systems 
 via a command-line interface, launch jobs with SLURM, and apply ML and DL 
 techniques to real-world biological datasets. Hands-on activities were del
 ivered with accessible technologies such as Jupyter Notebooks, graphical d
 esktop interfaces (DCV), and software containers, all deployed on HPC syst
 ems with minimal user setup required. We propose this workshop model as an
  adaptable framework for training domain scientists how to effectively use
  HPC resources to advance scientific discovery, and we present survey data
  demonstrating its effectiveness in improving participant skills.\n\nRecor
 ding: Livestreamed, Recorded\n\nRegistration Category: Technical Program R
 eg Pass, Workshop Reg Pass\n\nSession Chairs: Erik Saule (University of No
 rth Carolina at Charlotte), Sushil K. Prasad (University of Texas at San A
 ntonio), David P. Bunde (Knox College), and Suzanne J. Matthews (United St
 ates Military Academy)\n\n
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