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  <title><![CDATA[ARC Colloquium:  Amin Coja-Oghlan (Goethe University, Frankfurt)]]></title>
  <body><![CDATA[<p align = "center"><strong>Algorithms &amp; Randomness Center (ARC) </strong></p>

<p align = "center"><strong>Amin Coja-Oghlan (Goethe University, Frankfurt)</strong></p>

<p align = "center"><strong>Monday, April 12, 2021</strong></p>

<p align = "center"><strong>Virtual via Bluejeans - 11:00 am</strong></p>

<p>&nbsp;</p>

<p><strong>Title:&nbsp; </strong>Group Testing</p>

<p><strong>Abstract:&nbsp; </strong>In the group testing problem the goal is to identify a small number of infected individuals within a large population by conducting as small a number of tests as possible. The tests are assumed to accept pools of individuals rather than just single individuals, with a test returning a positive result iff at least one individual in the pool is actually infected. Mathematically we ask for information-theoretic lower bounds showing that a certain number of tests is necessary to correctly learn the set of infected individuals as well as algorithmic upper bounds showing that a certain number of tests suffice. In this talk I am going to present some mathematical results, some experimental results and some open problems.</p>

<p>----------------------------------</p>

<p><a href="https://simons.berkeley.edu/people/amin-coja-oghlan">Speaker&#39;s Webpage</a></p>

<p><em>Videos of recent talks are available at: </em><a href="http://arc.gatech.edu/node/121">http://arc.gatech.edu/node/121</a></p>

<p><a href="https://mailman.cc.gatech.edu/mailman/listinfo/arc-colloq">Click here to subscribe to the seminar email list: arc-colloq@Klauscc.gatech.edu </a></p>
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