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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link="#467886" vlink="#96607D" 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:.5104in' id=logo src="cid:image001.jpg@01DB7D20.39A10140" alt="Department of Statistics and Data Science
"></span></a></span><span style='font-family:"Arial",sans-serif;color:black;mso-ligatures:none'> </span><span style='font-size:12.0pt;color:black'><a href="https://statistics.yale.edu/" title=Home><b><span style='font-size:22.0pt;font-family:"Arial",sans-serif;color:#286DC0;mso-ligatures:none'>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><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:12.0pt;font-family:"Arial",sans-serif;mso-ligatures:none'>Allen Liu, MIT<o:p></o:p></span></p><p class=MsoNormal><!--[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=108 height=129 style='width:1.125in;height:1.3437in' src="cid:image004.jpg@01DB7D28.43439CF0" align=left hspace=12 v:shapes="Picture_x0020_2"><![endif]><span style='font-size:12.0pt;font-family:"Arial",sans-serif;mso-ligatures:none'>Date: Monday, February 17, 2025<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Arial",sans-serif;mso-ligatures:none'>Time: 12:00PM to 1:00PM<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Arial",sans-serif;mso-ligatures:none'>Location: Kline Tower, 13th Floor, Rm. 1327 <a href="http://maps.google.com/?q=219+Prospect+Street%2C+New+Haven%2C+CT%2C+06511%2C+us">See map</a> <o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Arial",sans-serif;mso-ligatures:none'>219 Prospect Street<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Arial",sans-serif;mso-ligatures:none'>New Haven, CT 06511<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Arial",sans-serif;mso-ligatures:none'>Webcas: <a href="https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=dca6587d-e389-4dbe-b000-b266011a6bf1">https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=dca6587d-e389-4dbe-b000-b266011a6bf1</a> <o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Arial",sans-serif;mso-ligatures:none'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Arial",sans-serif;mso-ligatures:none'>Learning Theoretic Foundations for Modern (Data) Science<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Arial",sans-serif;mso-ligatures:none'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Arial",sans-serif;mso-ligatures:none'>Information and Abstract: In this talk, I will explain how fundamental problems in computational learning theory are at the heart of modern problems in machine learning and scientific applications and how algorithmic insights in mathematically tractable models can inspire new solutions in a wide variety of domains. I will explore two directions. First, I will explore algorithmic foundations for model stealing of language models. Model stealing, where a learner tries to recover an unknown model through query access, is a critical problem in machine learning. Here, I will aim to build a theoretical foundation for designing model stealing algorithms. Second, I will introduce Hamiltonian learning, a central computational task towards understanding and benchmarking quantum systems. I will highlight how the lens of learning theory plays a key role in identifying and circumventing previous barriers and allows us to give efficient algorithms in settings that were previously conjectured to be intractable.<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Arial",sans-serif;mso-ligatures:none'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Arial",sans-serif;mso-ligatures:none'>Speaker bio: Allen Liu is currently a fifth-year graduate student in EECS at MIT, advised by Ankur Moitra. His research is in learning theory, broadly defined, encompassing classical learning theory and statistics, as well as problems in modern machine learning and scientific applications such as quantum information. His work has been awarded Best Student Paper at QIP in 2024 and featured in popular science media such as Quanta Magazine’s Biggest Breakthroughs in Computer Science for 2024. <o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Arial",sans-serif;mso-ligatures:none'><o:p> </o:p></span></p><p class=MsoNormal><b><span style='font-size:12.0pt;font-family:"Arial",sans-serif;mso-ligatures:none'>Lunch at 11:30am in room 1307<br>Talk at 12:00-1:00pm in room 1327A<o:p></o:p></span></b></p><p class=MsoNormal><i><span style='font-size:12.0pt;font-family:"Calibri",sans-serif;mso-ligatures:none'><o:p> </o:p></span></i></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 </span><span style='font-size:12.0pt'><a href="https://statistics.yale.edu/calendar"><span style='font-family:"Arial",sans-serif;color:#467886;mso-ligatures:none'>https://statistics.yale.edu/calendar</span></a></span><span style='font-size:12.0pt;font-family:"Arial",sans-serif;mso-ligatures:none'>. </span><span style='font-size:12.0pt;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<br>Kline Tower<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'>219 Prospect Street<br>New Haven, CT 06511<o:p></o:p></span></p><p class=MsoNormal><a href="https://statistics.yale.edu/"><span style='color:#467886'>https://statistics.yale.edu/</span></a><o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p></div></body></html>