BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:America/Chicago
X-LIC-LOCATION:America/Chicago
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20260522T150127Z
LOCATION:D171
DTSTART;TZID=America/Chicago:20181115T133000
DTEND;TZID=America/Chicago:20181115T141500
UID:submissions.supercomputing.org_SC18_sess482_pec293@linklings.com
SUMMARY:The BP Data Science Sandbox
DESCRIPTION:Max Grossman and Anar Yusifov (BP)\n\nRecent years have seen m
 ajor advances in the state-of-the-art of machine learning, particularly in
  fields such as natural language processing and 2D computer vision.\n\nThe
 se advances have naturally spurred interest in the application of similar 
 techniques to new fields in medicine, science, and engineering. However, t
 he problems in these fields are differentiated from previous machine learn
 ing successes by the level of domain expertise required. While classifying
  an image as a cat, dog, horse, etc is a task that anyone can understand, 
 automatic identification of malignant tumors, subsurface faults, or financ
 ial fraud (for example) often requires far more background in the specific
  domain. Unfortunately, it is rare today for people to have both the skill
 s of a data scientist/statistician and a domain expert (e.g. an oncologist
  or petroleum engineer).\nThis problem can generally be solved in two ways
 : (1) through education (of your data scientists and/or domain experts), o
 r (2) through co-location of these two groups of people such that they can
  work closely together.\n\nThis talk will introduce the BP Data Science Sa
 ndbox (DSS) – an internal environment at BP that supports both of the abov
 e solutions. The sandbox is a platform made up of hardware, software, and 
 people. On the hardware front, the sandbox includes everything from big me
 mory machines to GPU machines to compute clusters, enabling users of the s
 andbox to pick and choose the platform that meets their resource requireme
 nts. On the software front, the sandbox is built on entirely free and open
  source software, including common tools such as Jupyter, JupyterHub, Spar
 k, Dask, Tensorflow, and other packages in the Conda ecosystem. On the peo
 ple front, the sandbox is supported by a team of dedicated data scientists
  and infrastructure engineers who support users and internal customers of 
 the sandbox.\n\nTag: Industry\n\nRegistration Category: Workshop Reg Pass,
  Tutorial Reg Pass, Tech Program Reg Pass, Exhibits Reg Pass, Exhibits - E
 xhibit Hall Only Reg Pass\n\nSession Chairs: Lyle Long (Pennsylvania State
  University) and David Martin (Lawrence Berkeley National Laboratory (LBNL
 ), Energy Sciences Network (ESnet))\n\n
END:VEVENT
END:VCALENDAR