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<div class="" style="word-wrap:break-word"><span class="" style="font-size:14.666666984558105px">This is an announcement of Jafar Jafarov's Dissertation Defense.</span><br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">===============================================</span><br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">Candidate: Jafar Jafarov</span><br class="" style="font-size:14.666666984558105px">
<br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">Date: Wednesday, June 08, 2022</span><br class="" style="font-size:14.666666984558105px">
<br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">Time: 12 pm CST</span><br class="" style="font-size:14.666666984558105px">
<br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">Remote Location: </span><a href="https://urldefense.com/v3/__https://uchicago.zoom.us/j/93270019541?pwd=SVJwVkcwZHdpb1hOQml6WVYzYTNxZz09__;!!BpyFHLRN4TMTrA!5Kdvs3tkNF_M5DxDbaJCfRMLlJK5kG9u_LN99GW3MA1FyjmbZiLP4Gvw4B-u95QFMf1N1KpAfFMgvlV20ejYB0i8IxzNmJN5ipAN$" class="" style="font-size:14.666666984558105px">https://uchicago.zoom.us/j/93270019541?pwd=SVJwVkcwZHdpb1hOQml6WVYzYTNxZz09</a><span class="" style="font-size:14.666666984558105px">
Meeting ID: 932 7001 9541 Passcode: 305048</span><br class="" style="font-size:14.666666984558105px">
<br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">Location: JCL 298</span><br class="" style="font-size:14.666666984558105px">
<br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">Title: Correlation Clustering with Local and Global Objectives</span><br class="" style="font-size:14.666666984558105px">
<br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">Abstract: In the Correlation Clustering problem, we are given a graph with its edges labeled as "similar"; and "dissimilar" by a noisy binary classifier, and the goal is to produce a clustering of the vertex
set which matches with the edge labels as much as possible. Correlation Clustering has been mainly studied under two models where the input graph is (i) complete and unweighted, and (ii) arbitrary and weighted.</span><br class="" style="font-size:14.666666984558105px">
<br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">In this thesis, we introduce a new model of Correlation Clustering that better captures real life instances. In this model the input graph is complete with bounded edge weights. We give an approximation
algorithm and give a matching integrality gap instance. Moreover, we examine the model under an l_p objective which is a generalization of the standard Correlation Clustering objective, MinDisagree. We give an approximation algorithm and show an almost matching
integrality gap for this objective.</span><br class="" style="font-size:14.666666984558105px">
<br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">Advisors: Yury Makarychev and Janos Simon</span><br class="" style="font-size:14.666666984558105px">
<br class="" style="font-size:14.666666984558105px">
<span class="" style="font-size:14.666666984558105px">Committee Members: Yury Makarychev, Janos Simon, Konstantin Makarychev and Aaron Potechin</span></div>
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