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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link="#0563C1" vlink="#954F72"><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:.5069in' id=logo src="cid:image001.jpg@01D945E7.BD52EA70" alt="Department of Statistics and Data Science
"></span></a></span><span style='font-family:"Arial",sans-serif;color:black'> <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'> <o:p></o:p></span></u></i></b></p><p class=MsoNormal><span style='font-family:"Arial",sans-serif'>In-Person seminars will be held at Mason Lab 211, 9 Hillhouse Avenue with the option of virtual participation (<a href="https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f8b73c34-a27b-42a7-a073-af2d00f90ffa"><span style='color:windowtext;text-decoration:none'>https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f8b73c34-a27b-42a7-a073-af2d00f90ffa</span></a>)<o:p></o:p></span></p><p class=MsoNormal><b><span style='font-family:"Arial",sans-serif'><a href="https://0.0.0.10/"><span style='color:windowtext;text-decoration:none'>3:30pm</span></a> - Pre-talk meet and greet teatime - Dana House, 24 Hillhouse Avenue </span></b><b><o:p></o:p></b></p><p class=MsoNormal><span style='font-family:"Arial",sans-serif'><o:p> </o:p></span></p><h1 style='mso-margin-top-alt:.1in;margin-right:0in;margin-bottom:0in;margin-left:0in'><span style='font-size:14.0pt;font-family:"Arial",sans-serif;text-transform:uppercase'>TAILIN WU</span><span style='font-size:14.0pt;font-family:"Arial",sans-serif'>, </span><span class=odd><span style='font-size:14.0pt;font-family:"Arial",sans-serif'>Stanford University</span></span><span style='font-size:14.0pt;font-family:"Arial",sans-serif'><o:p></o:p></span></h1><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=146 height=175 style='width:1.5208in;height:1.8263in' src="cid:image004.jpg@01D945E7.C7018DB0" align=left hspace=12 v:shapes="Picture_x0020_2"><![endif]><o:p></o:p></p><p class=MsoNormal><span class=date-display-single><span style='font-size:13.5pt'>Date; Wednesday, February 22, 2023<o:p></o:p></span></span></p><p class=MsoNormal><span class=date-display-single><span style='font-size:13.5pt'>Time: </span></span><span class=date-display-start><span style='font-size:13.5pt'>4:00PM</span></span><span class=date-display-range><span style='font-size:13.5pt'> to </span></span><span class=date-display-end><span style='font-size:13.5pt'>5:00PM</span></span><span style='font-size:13.5pt'><o:p></o:p></span></p><p class=MsoNormal><span class=fn>Mason Lab 211</span> <span class=map-icon><span style='font-size:10.0pt;font-family:"Verdana",sans-serif;letter-spacing:.6pt'><a href="http://maps.google.com/?q=9+Hillhouse+Ave%2C+New+Haven%2C+CT%2C+06511%2C+us"><span style='color:windowtext'>see map</span></a> </span></span><o:p></o:p></p><p class=MsoNormal>9 Hillhouse Ave<o:p></o:p></p><p class=MsoNormal><span class=locality>New Haven</span>, <span class=region>CT</span> <span class=postal-code>06511</span><o:p></o:p></p><p class=MsoNormal><a href="https://tailin.org/"><span style='font-size:12.0pt;color:windowtext'>Website</span></a><o:p></o:p></p><p class=MsoNormal><b><o:p> </o:p></b></p><p class=MsoNormal><b><o:p> </o:p></b></p><p class=MsoNormal><b><span style='font-size:12.0pt;font-family:"Arial",sans-serif'>Title: Learning structured representations for accelerating scientific discovery and simulation<o:p></o:p></span></b></p><p class=MsoNormal><b><o:p> </o:p></b></p><p class=MsoNormal><b>Information and Abstract: <o:p></o:p></b></p><p style='mso-margin-top-alt:0in;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;box-sizing: inherit'>Across most disciplines of science, e.g., physics, chemistry, biomedicine, materials, mechanical engineering, and energy, a most critical challenge is that their simulations and discoveries are typically slow due to the large-scale, complex and multi-scale nature of the system. In this talk, I will introduce my research that tackles this challenge by developing machine learning models with structured and efficient representations for accelerating scientific discovery and simulation. To accelerate scientific discovery, I developed neuro-symbolic methods which can distill the data into human-interpretable symbolic knowledge (governing equations and relational structures) and generalize to more complex data in inference. To accelerate large-scale scientific simulations, I developed structured representations to accelerate critical scientific simulations for fluid dynamics, plasma science, and generic partial differential equations (PDEs). For example, I developed a hybrid particle-fluid representation for simulating a large-scale laser-plasma interaction in a national lab facility that has important applications in physics, materials, and biomedical science. Our model is able to simulate millions of particles per time step, orders of magnitude faster than the classical solver, and significantly reduce long-term prediction error compared to strong deep learning baselines.<o:p></o:p></p><p style='mso-margin-top-alt:0in;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;box-sizing: inherit'><strong><span style='font-family:"Calibri",sans-serif'>BIO: </span></strong>Tailin Wu is a postdoctoral scholar in the Computer Science Department at Stanford University, working with Prof. Jure Leskovec. He received his Ph.D. from MIT Physics, where his thesis focused on AI for Physics and Physics for AI. His research interests include developing machine learning methods for large-scale scientific simulations, neuro-symbolic methods for scientific discovery, and representation learning, using tools of graph neural networks, information theory, and physics. His work has been published in top machine learning conferences and leading physics journals, and featured in MIT Technology Review. He also serves as a reviewer for high-impact journals such as PNAS, Nature Communications, Nature Machine Intelligence, and Science Advances. <o:p></o:p></p><p style='mso-margin-top-alt:0in;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;box-sizing: inherit'><strong><i><span style='font-family:"Calibri",sans-serif'>In-Person seminars will be held at Mason Lab 211, 9 Hillhouse Avenue with the option of virtual participation (</span></i></strong><a href="https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f8b73c34-a27b-42a7-a073-af2d00f90ffa"><span style='color:#286DC0'>https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f8b73c34-a27b-42a7-a073-af2d00f90ffa</span></a>)<o:p></o:p></p><p style='mso-margin-top-alt:0in;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;box-sizing: inherit'><strong><i><span style='font-family:"Calibri",sans-serif'><a href="https://0.0.0.10/"><span style='color:#286DC0'>3:30pm</span></a> - Pre-talk meet and greet teatime - Dana House, 24 Hillhouse Avenue </span></i></strong><o:p></o:p></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Arial",sans-serif;color:black'>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><o:p></o:p></p><p class=MsoNormal><span style='font-family:"Arial",sans-serif'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:18.0pt;font-family:"Arial",sans-serif'>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'>Yale University<br>24 Hillhouse Avenue<br>New Haven, CT 06511<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:9.0pt;font-family:"Arial",sans-serif;color:black'>t 203.432.0666<br>f 203.432.0633<o:p></o:p></span></p><p class=MsoNormal><o:p> </o:p></p></div></body></html>