BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:America/New_York
X-LIC-LOCATION:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20210402T160549Z
LOCATION:Track 10
DTSTART;TZID=America/New_York:20201119T100000
DTEND;TZID=America/New_York:20201119T120000
UID:submissions.supercomputing.org_SC20_sess388@linklings.com
SUMMARY:Gordon Bell COVID-19 Prize Finalist Session 1
DESCRIPTION:ACM Gordon Bell COVID Finalist, Awards Presentation\n\nHigh-Th
 roughput Virtual Laboratory for Drug Discovery Using Massive Datasets\n\nG
 laser, Vermaas, Rogers, Larkin, LeGrand...\n\nTime-to-solution for structu
 re-based screening of massive chemical databases for COVID-19 drug discove
 ry has been decreased by an order of magnitude, and a virtual laboratory h
 as been deployed at scale on up to 27,612 GPUs on the Summit supercomputer
 , allowing an average molecular docking of 19,028 ...\n\n-----------------
 ----\nAI-Driven Multiscale Simulations Illuminate Mechanisms of SARS-CoV-2
  Spike Dynamics\n\nCasalino, Dommer, Gaieb, Barros, Stzain...\n\nWe develo
 p a generalizable AI-driven workflow that leverages heterogeneous HPC reso
 urces to explore the time-dependent dynamics of molecular systems.  We use
  this workflow to investigate the mechanisms of infectivity of the SARS-Co
 V-2 spike protein, the main viral infection machinery. Our workflow e...\n
 \n---------------------\nEnabling Rapid COVID-19 Small Molecule Drug Desig
 n Through Scalable Deep Learning of Generative Models\n\nJacobs, Moon, McL
 oughlin, Jones, Hysom...\n\nWe improved the quality and reduced the time t
 o produce machine-learned models for use in small molecule antiviral desig
 n. Our globally asynchronous multi-level parallel training approach strong
  scales to all of Sierra with up to 97.7% efficiency. We trained a novel, 
 character-based Wasserstein auto...\n\n---------------------\nA Population
  Data-Driven Workflow for COVID-19 Modeling and Learning\n\nOzik, Wozniak,
  Collier, Macal, Binois\n\nCityCOVID is a detailed agent-based model (ABM)
  that represents the behaviors and social interactions of 2.7 million resi
 dents of Chicago as they move between and colocate in 1.2 million distinct
  places, including households, schools, workplaces and hospitals, as deter
 mined by individual hourly acti...\n\n\nRegistration Category: Tech Progra
 m Reg Pass
END:VEVENT
END:VCALENDAR