<div dir="ltr"><br>Caros redistas,<div><br></div><div>Gostaria de convidar a todos para o seminário da Escola de Matemática Aplicada (EMAp) da Fundação Getúlio Vargas desta semana, que tratará de machine learning e otimização aplicadas à análise de crimes. O seminário acontece nesta quinta-feira, às 16h horário de Brasília. O link encontra-se abaixo.</div><div><br></div><div>Cordialmente,</div><div><br></div><div>Luiz<br><div class="gmail_quote"><div dir="ltr" class="gmail_attr"><br></div><br>





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<b><span lang="EN-US" style="font-size:13.0pt;line-height:115%;font-family:"Arial",sans-serif;color:#1f497d">EXPLORING COUNTERFACTUAL ANTECEDENTS ON CRIME ANALYSIS</span></b><span lang="EN-US"><u></u><u></u></span></p>
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<span lang="EN-US" style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#767171">Mining counterfactual antecedents became a valuable tool to mine knowledge and explain machine learning models. It consists of generating synthetic samples
 from an original sample to achieve the desired outcome in a machine learning model. It can be used to analyze the impact of socioeconomic and urban variables in crime by understanding which changes in a set of variables could alter a region’s definition from
 unsafe to safe. To help decision-makers interpret crime (and other tasks) define public policies (and other decisions), we propose two methodologies: MAPOCAM and CounterCrime. (1) MAPOCAM is a tree-based search that generates a diverse set of Pareto-optimal
 counterfactual antecedents by relying on multi-objective concepts. (2) CounterCrime uses various interactive visual metaphors on crime analysis using counterfactual antecedents. We organize our analysis at three levels, whole city, region group, and at the
 region level to facilitate exploration. Both methodologies empower users to inspect multiple counterfactual antecedents for the best interventions and have a broader understanding of the prediction.</span><span lang="EN-US" style="color:#767171"><u></u><u></u></span></p>
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^{<span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#3b3838">Texto informado pelo autor.
</span>}<u></u><u></u></p>
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<b><span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#1f497d"> Quando e Onde:</span></b><u></u><u></u></p>
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<span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#767171">20</span><span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#595959"> de Maio de 2021, às
<b>16h</b></span><u></u><u></u></p>
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<span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#595959">Via Zoom</span><u></u><u></u></p>
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<span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#595959">Registre-se com antecedência para este seminário:
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<span style="font-size:3.0pt;font-family:"Calibri",sans-serif;color:#1f497d"> </span><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:#595959">Após o registro, você receberá um e-mail de confirmação contendo informações sobre conexão no seminário.</span><u></u><u></u></p>
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<b><span style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#1f4e79">Palestrante:</span></b><u></u><u></u></p>
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<b><span lang="EN-US" style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#767171">Marcos M. Raimundo:</span></b><span lang="EN-US" style="color:#767171">
</span><span lang="EN-US" style="font-size:10.5pt;line-height:115%;font-family:"Arial",sans-serif;color:#767171">Marcos M. Raimundo received the B.S. degree in computer engineering, the M.S. and Ph.D. degree in electrical engineering from the University of
 Campinas, Campinas, Brazil, in 2011, 2014, and 2018 respectively. He is a post-doctoral researcher at the School of Applied Mathematics at Fundação Getulio Vargas. His research interests include machine learning, multiobjective optimization, mathematical programming,
 and operations research.</span><span lang="EN-US"><u></u><u></u></span></p>
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</div><br clear="all"><div><br></div>-- <br><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div dir="ltr" style="color:rgb(80,0,80)"><div dir="ltr">Luiz Max Fagundes de Carvalho</div><div dir="ltr">Lecturer, School of Applied Mathematics (EMAp), Getúlio Vargas Foundation (FGV), Brazil.</div><div dir="ltr">Praia de Botafogo, 190, Sala 511, Rio de Janeiro, RJ 22250-900.</div><div dir="ltr">Brazil</div></div></div></div></div></div></div></div>