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<i style="font-family: Aptos, sans-serif; font-size: 12pt;"><span style="font-family: Helvetica; color: rgb(139, 1, 2);">UNIVERSITY OF CHICAGO</span></i></p>
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<i><span style="font-family: Helvetica; color: rgb(139, 1, 2);">COMPUTER SCIENCE DEPARTMENT</span></i><o:p></o:p></p>
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<i><span style="font-family: Helvetica; color: rgb(139, 1, 2);">PRESENTS</span></i></p>
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<b><span style="font-size: 9pt; font-family: Helvetica;">David Gleich</span></b><span style="font-size: 9pt; font-family: Helvetica;"><o:p></o:p></span></p>
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<b><span style="font-size: 9pt; font-family: Helvetica;">Professor and University Faculty Scholar</span></b><span style="font-size: 9pt; font-family: Helvetica;"><o:p></o:p></span></p>
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<b><span style="font-size: 9pt; font-family: Helvetica;">Department of Computer Science</span></b><span style="font-size: 9pt; font-family: Helvetica;"><o:p></o:p></span></p>
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<b><span style="font-size: 9pt; font-family: Helvetica;">Purdue University</span></b><span style="font-size: 9pt; font-family: Helvetica;"><o:p></o:p></span></p>
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<p class="MsoNormal" style="margin: 0in;"><b><img id="<12240CB9-A198-4E1E-A220-74C326A8EA1B>" src="cid:45CB247F-DC7F-4B0A-8975-F09CB12F182F" alt="david gleich.jpeg" class="Apple-web-attachment" style="opacity: 1;"></b></p>
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<span style="font-size: 9pt; font-family: Helvetica;"> </span></p>
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<b><span style="font-size: 9pt; font-family: Helvetica;">Tuesday, May 7th</span></b><span style="font-size: 9pt; font-family: Helvetica;"><o:p></o:p></span></p>
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<b><span style="font-size: 9pt; font-family: Helvetica;">2:30pm - 3:30pm</span></b><span style="font-size: 9pt; font-family: Helvetica;"><o:p></o:p></span></p>
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<b><span style="font-size: 9pt; font-family: Helvetica;">In Person: John Crerar Library 390</span></b><span style="font-size: 9pt; font-family: Helvetica;"><o:p></o:p></span></p>
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<span style="font-size: 9pt; font-family: Helvetica;"> </span></p>
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<b><span style="font-size: 9pt; font-family: Helvetica;">Title: Topological Structure of Complex Prediction and Graphs</span></b><span style="font-size: 9pt; font-family: Helvetica;"><o:p></o:p></span></p>
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<span style="font-size: 9pt; font-family: Helvetica;"> </span></p>
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<b><span style="font-size: 9pt; font-family: Helvetica;">Abstract:</span></b><span style="font-size: 9pt; font-family: Helvetica;"> It is now standard practice across science to study models that have been trained, fit, or learned based on a set of data. Many
of these models involve a large number of parameters that make direct interpretation of the model challenging and a near black-box model view appropriate. We explore the possibilities of using ideas based on topological analysis methods to understand and evaluate
these complex prediction functions. These show a surprising ability to generate easy to understand insights into these black boxes. In addition to this deep dive on analyzing prediction methods, I’ll discuss recent work on validating a decade-old structural
prediction in terms of the conductance of sets in graphs by Leskovec, Lang, Dasgupta, and Mahoney. This is based on <a href="https://urldefense.com/v3/__https:/arxiv.org/abs/2207.14358__;!!BpyFHLRN4TMTrA!4GP-I3pEEuwamINV8kVIwZzQjqeAytiDNQ0uvcLzeWli8KN7vasZR25Rdl7SuGLVrBq6Tgv-p_clPYOYOzHoGjU$"><span style="color: blue;">https://arxiv.org/abs/2207.14358</span></a> and <a href="https://urldefense.com/v3/__https:/arxiv.org/abs/2303.14550__;!!BpyFHLRN4TMTrA!4GP-I3pEEuwamINV8kVIwZzQjqeAytiDNQ0uvcLzeWli8KN7vasZR25Rdl7SuGLVrBq6Tgv-p_clPYOYRKYAPs0$"><span style="color: blue;">https://arxiv.org/abs/2303.14550</span></a>.
For software, see <a href="https://urldefense.com/v3/__https:/github.com/MengLiuPurdue/Graph-Topological-Data-Analysis__;!!BpyFHLRN4TMTrA!4GP-I3pEEuwamINV8kVIwZzQjqeAytiDNQ0uvcLzeWli8KN7vasZR25Rdl7SuGLVrBq6Tgv-p_clPYOYfqgoNKE$"><span style="color: blue;">https://github.com/MengLiuPurdue/Graph-Topological-Data-Analysis</span></a>, <a href="https://urldefense.com/v3/__https:/mengliupurdue.github.io/Graph-Topological-Data-Analysis/__;!!BpyFHLRN4TMTrA!4GP-I3pEEuwamINV8kVIwZzQjqeAytiDNQ0uvcLzeWli8KN7vasZR25Rdl7SuGLVrBq6Tgv-p_clPYOY0uEgeZA$"><span style="color: blue;">https://mengliupurdue.github.io/Graph-Topological-Data-Analysis/</span></a>, <a href="https://urldefense.com/v3/__https:/github.com/luotuoqingshan/mu-conductance-low-rank-sdp__;!!BpyFHLRN4TMTrA!4GP-I3pEEuwamINV8kVIwZzQjqeAytiDNQ0uvcLzeWli8KN7vasZR25Rdl7SuGLVrBq6Tgv-p_clPYOYnX0o-ho$"><span style="color: blue;">https://github.com/luotuoqingshan/mu-conductance-low-rank-sdp</span></a><o:p></o:p></span></p>
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<span style="font-size: 9pt; font-family: Helvetica;"> </span></p>
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<b><span style="font-size: 9pt; font-family: Helvetica;">Bio:</span></b><span style="font-size: 9pt; font-family: Helvetica;"> David Gleich is a Professor and University Faculty Scholar in the Computer Science Department at Purdue University whose research
is on novel models and fast large-scale algorithms for data-driven computing including network analysis and applications in bioinformatics. He is committed to making software available based on this research and has written software packages such as MatlabBGL
with thousands of users worldwide. Gleich has received a number of awards for his research including a SIAM Outstanding Publication prize (2018), a Sloan Research Fellowship (2016), an NSF CAREER Award (2011), the John von Neumann post-doctoral fellowship
at Sandia National Laboratories in Livermore CA (2009). His research has been funded by the NSF, DOE, DARPA, IARPA, and NASA.<o:p></o:p></span></p>
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<span style="font-size: 9pt; font-family: Helvetica;">For more information, see his website: <a href="https://urldefense.com/v3/__https:/www.cs.purdue.edu/homes/dgleich/__;!!BpyFHLRN4TMTrA!4GP-I3pEEuwamINV8kVIwZzQjqeAytiDNQ0uvcLzeWli8KN7vasZR25Rdl7SuGLVrBq6Tgv-p_clPYOYUOMoDm4$"><span style="color: blue;">https://www.cs.purdue.edu/homes/dgleich/</span></a><o:p></o:p></span></p>
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<span style="font-size: 9pt; font-family: Helvetica;"> </span></p>
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<b><span style="font-size: 9pt; font-family: Helvetica;">Host:</span></b><span style="font-size: 9pt; font-family: Helvetica;"> Michael Maire<o:p></o:p></span></p>
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