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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link="#0563C1" vlink="#954F72" style='word-wrap:break-word'><div class=WordSection1><p class=MsoNormal style='background:white'><span style='color:black'><a href="https://statistics.yale.edu/" title="Department of Statistics and Data Science
"><span style='font-size:22.0pt;font-family:"Arial",sans-serif;color:#286DC0;text-decoration:none'><img border=0 width=150 height=49 style='width:1.5625in;height:.5138in' id=logo src="cid:image001.jpg@01DA6B04.C8C0F120" alt="Department of Statistics and Data Science
"></span></a></span><span style='font-family:"Arial",sans-serif;color:black;mso-ligatures:none'> <a href="https://statistics.yale.edu/" title=Home><b><span style='font-size:22.0pt;color:#286DC0'>Department of Statistics and Data Science </span></b></a></span><b><i><u><span style='font-size:22.0pt;font-family:"Arial",sans-serif;color:#286DC0;mso-ligatures:none'> <o:p></o:p></span></u></i></b></p><h1 style='mso-margin-top-alt:.1in;margin-right:0in;margin-bottom:0in;margin-left:0in;background:white'><span style='font-size:23.0pt;font-family:Mallory;color:black;text-transform:uppercase;font-weight:normal'>SETH FLAXMAN</span><span style='font-size:13.0pt;font-family:Mallory;color:black'>, </span><span class=odd><span style='font-size:13.0pt;font-family:Mallory;color:black'>University of Oxford</span></span><span style='font-size:13.0pt;font-family:Mallory;font-weight:normal'><o:p></o:p></span></h1><p class=MsoNormal style='background:white'><!--[if gte vml 1]><v:shapetype id="_x0000_t75" coordsize="21600,21600" o:spt="75" o:preferrelative="t" path="m@4@5l@4@11@9@11@9@5xe" filled="f" stroked="f">
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</v:shape><![endif]--><![if !vml]><img width=147 height=176 style='width:1.5277in;height:1.8333in' src="cid:image003.jpg@01DA6B04.C8C0F120" align=left hspace=12 v:shapes="Picture_x0020_2"><![endif]><span class=date-display-single><span style='font-size:13.5pt;font-family:Mallory;color:black'>Date: Monday, March 04, 2024</span></span><span class=date-display-single><span style='font-size:13.5pt;font-family:Mallory'><o:p></o:p></span></span></p><p class=MsoNormal style='background:white'><span class=date-display-single><span style='font-size:13.5pt;font-family:Mallory;color:black'>Time: </span></span><span class=date-display-start><span style='font-size:13.5pt;font-family:Mallory;color:black'>4:00PM</span></span><span class=date-display-range><span style='font-size:13.5pt;font-family:Mallory;color:black'> to </span></span><span class=date-display-end><span style='font-size:13.5pt;font-family:Mallory;color:black'>5:00PM</span></span><span style='font-size:13.5pt;font-family:Mallory'><o:p></o:p></span></p><p class=MsoNormal style='background:white'><span class=fn><span style='font-size:13.0pt;font-family:Mallory;color:#222222'>Kline Tower</span></span><span style='font-size:13.0pt;font-family:Mallory;color:#222222'> </span><span class=map-icon><span style='font-size:12.0pt;font-family:Mallory;color:#222222;letter-spacing:.6pt'><a href="http://maps.google.com/?q=219+Prospect+Street%2C+13+Floor%2C+Rm+1327%2C+New+Haven%2C+CT%2C+06511%2C+us"><span style='color:#286DC0'>see map</span></a> </span></span><span style='font-size:13.0pt;font-family:Mallory;color:#222222'><o:p></o:p></span></p><p class=MsoNormal style='background:white'><span style='font-size:13.0pt;font-family:Mallory;color:#222222'>Location: 219 Prospect Street, 13 Floor, Rm 1327<o:p></o:p></span></p><p class=MsoNormal style='background:white'><span class=locality><span style='font-size:13.0pt;font-family:Mallory;color:#222222'>New Haven</span></span><span style='font-size:13.0pt;font-family:Mallory;color:#222222'>, <span class=region>CT</span> <span class=postal-code>06511</span><o:p></o:p></span></p><p class=MsoNormal style='background:white'><span style='font-size:12.0pt;font-family:Mallory;color:#222222'>Zoom Link: <a href="https://yale.zoom.us/j/94223816617">https://yale.zoom.us/j/94223816617</a> Meeting ID: 942 2381 6617<o:p></o:p></span></p><p class=MsoNormal style='background:white'><span style='font-size:13.0pt;font-family:Mallory;color:#222222'><a href="http://www.sethrf.com/"><span style='font-size:12.0pt;color:#003C76'>Website</span></a><o:p></o:p></span></p><p class=MsoNormal style='background:white'><b><span style='font-size:15.0pt;font-family:Mallory;color:#222222'><o:p> </o:p></span></b></p><p class=MsoNormal style='background:white'><b><span style='font-size:15.0pt;font-family:Mallory;color:#222222'>Inferential Machine Learning: Statistics, Data Science, and Public Policy<o:p></o:p></span></b></p><p class=MsoNormal style='background:white'><b><span style='font-size:13.0pt;font-family:Mallory;color:#222222'><o:p> </o:p></span></b></p><p class=MsoNormal style='background:white'><b><span style='font-size:13.0pt;font-family:Mallory;color:#222222'>Information and Abstract: </span></b><span style='font-size:13.0pt;font-family:Mallory;color:#222222'>Machine learning is the computational beating heart of the modern AI renaissance. Behind the hype, a range of machine learning and computational statistical methods are quietly revolutionizing our approach to difficult statistical and scientific inference problems. I will present my perspective on the emerging field of “inferential Machine Learning” (iML) through a series of case studies on important public policy challenges. I conceive of iML as a big tent, encompassing modern probabilistic programming, replicable data scientific workflows, methods for assessing Big Data quality, uncertainty quantification, active learning, and a range of computational and deep learning approaches to transform applied statistical analyses. I will discuss iML in the context of my work during the COVID-19 pandemic as part of the Imperial College COVID-19 Response Team and the collaborations I am now leading through the Machine Learning & Global Health Network (<a href="http://www.mlgh.net/"><span style='color:#286DC0'>www.MLGH.net</span></a>).<o:p></o:p></span></p><p style='margin:0in;background:white;box-sizing: inherit'><span style='font-size:13.0pt;font-family:Mallory;color:#222222'><o:p> </o:p></span></p><p style='margin:0in;background:white'><span style='font-size:13.0pt;font-family:Mallory;color:#222222'>Speaker: Seth Flaxman, Associate Professor, Department of Computer Science, University of Oxford (<a href="http://www.sethrf.com/"><span style='color:#286DC0'>www.sethrf.com</span></a>)<o:p></o:p></span></p><p style='margin:0in;background:white;box-sizing: inherit'><b><span style='font-size:13.0pt;font-family:Mallory;color:#222222'><o:p> </o:p></span></b></p><p style='margin:0in;background:white'><b><span style='font-size:13.0pt;font-family:Mallory;color:#222222'>Bio</span></b><span style='font-size:13.0pt;font-family:Mallory;color:#222222'>: Seth Flaxman is an associate professor in the Department of Computer Science at Oxford. Originally from the Chicago area, he received his PhD in 2015 from Carnegie Mellon University in machine learning and public policy (School of Computer Science and Heinz College of Information Systems and Public Policy) and has worked for the World Health Organization in Geneva. Seth’s research is on spatiotemporal statistics and Bayesian machine learning, applied to public policy, global health and social science. He was part of the Imperial College COVID-19 Response Team, leading a number of publications on non-pharmaceutical interventions, computational epidemiology, and COVID-19 orphanhood. He has published on filter bubbles / echo chambers in media, the Big Data paradox, and the regulation of machine learning algorithms. He is the statistical lead for the Global Reference Group on Children Affected by Crisis. Seth won the Samsung AI Researcher of the Year Award (2020) and the SPI-M-O Award for Modelling and Data Support (2022) for modeling advice provided to the UK government during the COVID-19 pandemic. In 2022, he co-founded the Machine Learning & Global Health network (<a href="http://www.mlgh.net/"><span style='color:#286DC0'>www.MLGH.net</span></a>) of researchers spanning three continents with a kickoff workshop held in Kigali, Rwanda at ICLR in 2023.<o:p></o:p></span></p><p style='margin:0in;background:white;box-sizing: inherit'><strong><span style='font-size:13.0pt;font-family:Mallory;color:#222222'><o:p> </o:p></span></strong></p><p style='margin:0in;background:white'><strong><span style='font-size:13.0pt;font-family:Mallory;color:#222222;font-weight:normal'>3:30pm - Pre-talk meet and greet teatime - 219 Prospect Street, 13 floor, there will be light snacks and beverages in the kitchen area.</span></strong><span style='font-size:13.0pt;font-family:Mallory;color:#222222'><o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none'>For more details and upcoming events visit our website at <a href="http://statistics.yale.edu/"><span style='color:black'>http://statistics.yale.edu/</span></a></span><span style='mso-ligatures:none'><o:p></o:p></span></p><p class=MsoNormal><span style='font-family:"Arial",sans-serif;mso-ligatures:none'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:18.0pt;font-family:"Arial",sans-serif;mso-ligatures:none'>Department of Statistics and Data Science<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:9.0pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none'>Yale University<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:9.0pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none'>Kline Tower<br>219 Prospect Street<br>New Haven, CT 06511<o:p></o:p></span></p><p class=MsoNormal><o:p> </o:p></p></div></body></html>