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  <title><![CDATA[Detecting Gerrymandering with Mathematical Rigor Lecture Video]]></title>
  <body><![CDATA[<p><strong>Series</strong>&nbsp;<a href="https://math.gatech.edu/seminars-colloquia/series/joint-school-mathematics-and-aco-colloquium/wesley-pegden-20200206">Joint School of Mathematics and ACO Colloquium</a></p>

<p><strong>Time</strong>&nbsp;Thursday, February 6, 2020 - 1:30pm&nbsp;for 1 hour (actually 50 minutes)</p>

<p><strong>Location</strong>&nbsp;Skiles 005</p>

<p><strong>Speaker</strong>&nbsp;Wesley Pegden&nbsp;&ndash; Mathematics, Carnegie Mellon University</p>

<p><strong>Organizer</strong>&nbsp;Prasad Tetali</p>

<p>In recent years political parties have more and more expertly crafted political districtings to favor one side or another, while at&nbsp;the same time, entirely new techniques to detect and measure these&nbsp;efforts are being developed.<br />
<br />
The speaker will discuss a rigorous method which uses Markov chains---random&nbsp;walks---to statistically assess gerrymandering of political districts&nbsp;without requiring heuristic validation of the structures of the Markov&nbsp;chains which arise in the redistricting context.&nbsp;In particular, we will&nbsp;see two examples where this methodology was applied in successful&nbsp;<br />
lawsuits which overturned district maps in Pennsylvania and North Carolina.</p>

<p><strong><em>The <a href="https://smartech.gatech.edu/handle/1853/62457">lecture video</a> is now available in Georgia Tech&#39;s Institutional Repository,&nbsp;SMARTech.</em></strong></p>

<p>&nbsp;</p>
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      <value><![CDATA[A link to the video of the excellent lecture by Wes Pegden on "Detecting Gerrymandering with Mathematical Rigor", hosted by SoM]]></value>
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