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DTSTART;TZID=America/Chicago:20230727T183000
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DESCRIPTION:This is a weekly session of the CIT Summer Series\, with David 
 A. Bader presenting Solving Global Grand Challenges with High Performance 
 Data Analytics :\n\nData science aims to solve grand global challenges suc
 h as: detecting and preventing disease in human populations\; revealing co
 mmunity structure in large social networks\; protecting our elections from
  cyber-threats\, and improving the resilience of the electric power grid. 
 Unlike traditional applications in computational science and engineering\,
  solving these social problems at scale often raises new challenges becaus
 e of the sparsity and lack of locality in the data\, the need for research
  on scalable algorithms and architectures\, and development of frameworks 
 for solving these real-world problems on high performance computers\, and 
 for improved models that capture the noise and bias inherent in the torren
 tial data streams. In this talk\, Bader will discuss the opportunities and
  challenges in massive data science for applications in social sciences\, 
 physical sciences\, and engineering.\n\nSpeaker(s): David A Bader\, \n\nVi
 rtual: https://events.vtools.ieee.org/m/364003
LOCATION:Virtual: https://events.vtools.ieee.org/m/364003
ORGANIZER:mviron@findaschool.net
SEQUENCE:34
SUMMARY:CIT Summer Series - David A. Bader - Solving Global Grand Challenge
 s with High Performance Data Analytics
URL;VALUE=URI:https://events.vtools.ieee.org/m/364003
X-ALT-DESC:Description: <br /><p>This is a weekly session of the CIT Summer
  Series\, with David A. Bader presenting <strong>Solving Global Grand Chal
 lenges with High Performance Data Analytics</strong>&nbsp\;:</p>\n<p>Data 
 science aims to solve grand global challenges such as: detecting and preve
 nting disease in human populations\; revealing community structure in larg
 e social networks\; protecting our elections from cyber-threats\, and impr
 oving the resilience of the electric power grid. Unlike traditional applic
 ations in computational science and engineering\, solving these social pro
 blems at scale often raises new challenges because of the sparsity and lac
 k of locality in the data\, the need for research on scalable algorithms a
 nd architectures\, and development of frameworks for solving these real-wo
 rld problems on high performance computers\, and for improved models that 
 capture the noise and bias inherent in the torrential data streams. In thi
 s talk\, Bader will discuss the opportunities and challenges in massive da
 ta science for applications in social sciences\, physical sciences\, and e
 ngineering.</p>
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