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<oembed><version>1.0</version><provider_name>Department of Computer Science</provider_name><provider_url>https://www.cs.jhu.edu</provider_url><title>Lecturer: Ben Moseley, Carnegie Mellon &#x2013; &#x201C;Algorithmic Methods for Massively Parallel Data Science&#x201D; - Department of Computer Science</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content"&gt;&lt;a href="https://www.cs.jhu.edu/event/lecturer-ben-moseley-carnegie-mellon-algorithmic-methods-for-massively-parallel-data-science/"&gt;Lecturer: Ben Moseley, Carnegie Mellon &#x2013; &#x201C;Algorithmic Methods for Massively Parallel Data Science&#x201D;&lt;/a&gt;&lt;/blockquote&gt;
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&lt;/script&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.cs.jhu.edu/event/lecturer-ben-moseley-carnegie-mellon-algorithmic-methods-for-massively-parallel-data-science/embed/" width="600" height="338" title="&#x201C;Lecturer: Ben Moseley, Carnegie Mellon &#x2013; &#x201C;Algorithmic Methods for Massively Parallel Data Science&#x201D;&#x201D; &#x2014; Department of Computer Science" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;</html><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&hellip;</description></oembed>
