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DTSTART;TZID=America/New_York:20181025T103000
DTEND;TZID=America/New_York:20181025T114500
DTSTAMP:20260422T132157
CREATED:20210629T210716Z
LAST-MODIFIED:20210629T210716Z
UID:1962180-1540463400-1540467900@www.cs.jhu.edu
SUMMARY:Lecturer: Ben Moseley\, Carnegie Mellon – “Algorithmic Methods for Massively Parallel Data Science”
DESCRIPTION:LocationHackerman Hall B-17AbstractThis talk is concerned with designing algorithms for large scale data science using massively parallel computation. The talk will discuss theoretical models and algorithms for massively parallel frameworks such as MapReduce and Spark. The constraints of the models are well connected to practice\, but pose algorithmic challenges.This talk introduces recent developments that overcome these challenges\, widely applicable massively parallel algorithmic techniques\, and key questions on the theoretical foundations of massively parallel computation. The methods introduced will be applied to large data problems that are central to operations research\, theoretical computer science and machine learning\, clustering and submodular function optimization.The work in this talk has been supported by Google\, Yahoo and the NSF.VideoWatch seminar video.
URL:https://www.cs.jhu.edu/event/lecturer-ben-moseley-carnegie-mellon-algorithmic-methods-for-massively-parallel-data-science/
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