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DTSTART:19700308T020000
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DTSTAMP:20181221T160728Z
LOCATION:D161
DTSTART;TZID=America/Chicago:20181112T144000
DTEND;TZID=America/Chicago:20181112T150000
UID:submissions.supercomputing.org_SC18_sess158_ws_lasalss102@linklings.co
 m
SUMMARY:Machine Learning-Aided Numerical Linear Algebra:  Convolutional Ne
 ural Networks for the Efficient Preconditioner Generation
DESCRIPTION:Workshop\nAlgorithms, Heterogeneous Systems, Resiliency, Works
 hop Reg Pass\n\nMachine Learning-Aided Numerical Linear Algebra:  Convolut
 ional Neural Networks for the Efficient Preconditioner Generation\n\nGötz\
 n\nMarkus Götz received his Bachelors and Masters degree in Software Engin
 eering from the University of Potsdam in 2010 and 2014 respectively. After
 wards, he has been with the Research Center Jülich and the University of I
 celand, from which Markus obtained his PhD degree in Computational Enginee
 ring for his works on parallel data-analysis algorithms on high-performanc
 e computing (HPC) systems. Since the beginning of 2018 Markus is with the 
 Steinbuch Centre for Computing (SCC) at the Karlsruhe Institute of Technol
 ogy (KIT). There, he manages the Helmholtz Analytics Framework project, a 
 german-wide initiative with the aim of developing the data sciences in the
  Helmholtz Association. His research topics include applied machine learni
 ng, scalable data analysis frameworks and parallel algorithms.
URL:https://sc18.supercomputing.org/presentation/?id=ws_lasalss102&sess=se
 ss158
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