Difference between revisions of "Template:NextTalk"

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(Created page with "'''May 27 - 11h-13h, SR C''' The Mapper algorithm - Fabian Klos <br> : ''no abstract'' Morse theory and their persistence modules - Peter Albers <br> :''I will present an up...")
 
 
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'''May 27 - 11h-13h, SR C'''
 
  
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The Mapper algorithm - Fabian Klos <br>
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: ''no abstract''
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<h4>
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Seminar
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<h3>
  
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Morse theory and their persistence modules - Peter Albers <br>
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[[Heidelberg TDA Seminar (Summer 2023)]]<br>
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:''I will present an upgrade of classical Morse theory using persistent modules. This seems not (directly) connected to TDA. After a short introduction to the basic ideas of Morse theory I will explain the associated persistence modules. In this situation the corresponding barcode has a very clear meaning and I’ll present a mini-application showing that short bars can be useful. I learned all this from https://arxiv.org/pdf/1904.04044.pdf .''
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Thu 11-13h, Mathematikon 00.200 <br>
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Lukas Hahn, Daniel Spitz
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</h3>
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Journal Club
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<h3>
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[[Heidelberg TDA Seminar (Summer 2022) | Heidelberg TDA Seminar (Summer 2022)]]<br>
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Thu 11h15-12h45, Mathematikon, SR 4 <br>
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Organizers: Lukas Hahn, Maximilian Schmahl, Daniel Spitz
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</h3>
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-- SEMINAR EXAMPLE --
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<h4>
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Seminar
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<h3>
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[https://stat.math.uni-heidelberg.de/courses_detail.php?id=118 Topologische Datenanalyse und persistente Homologie]<br>
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first meeting 13.04. or 21.04. <br>
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organizer: Dr. Johannes Krebs <br>
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please register in advance (until 09.04.2021)
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<p>A model category of tame parametrised chain complexes <br>
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Barbara Giunti (University of Modena) </p>
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<p> Tue, July 7th, 16-18h <br>
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Zoom <small>([mailto:structures-hiwi@mathi.uni-heidelberg.de email us for details])</small><br> </p>
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</h3>
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<div style="text-align:center">
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<i>
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<b>Abstract:</b> Persistent theory is a useful tool to extract information from real-world data sets, and profits of techniques from different mathematical disciplines, such as Morse theory and quiver representation. In this seminar, I am going to present a new approach for studying persistence theory using model categories. I will briefly introduce model categories and then describe how to define a model structure on the category of the tame parametrised chain complexes, which are chain complexes that evolve in time, describing why such an approach can be useful in topological data analysis. In particular, I will use the model category structure to retrieve two invariants that extract homotopical and homological information from any tame parametrised chain complex.
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<p>2nd Workshop on <br>
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Topological Methods in Data Analysis<br>
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October 4th - 6th, Heidelberg University </p>
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<b>Abstract:</b>
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This three-day workshop includes introductions into the powerful data analysis machinery of persistent homology, extensive tutorials on the versatile GUDHI Library, and in particular features invited Colloquium Talks by well-known experts in the field, aimed at a broader audience. In addition, participants will have the opportunity to give a short presentation on their own TDA-related work.
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[https://www.mathi.uni-heidelberg.de/~mbleher/tdaworkshop21.html Further information and registration.]
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<div style="text-align:center; line-height:1.3em; padding: 0px">
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<h3>
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<p>Python Course on <br>
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Topological Methods in Data Analysis<br>
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Heidelberg University </p>
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<p> October 26th - 28th <br>
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Mathematikon and Zoom <br> </p>
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</h3>
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<div style="text-align:center">
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<b>Abstract:</b>
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In this twelve-hour workshop the participants will be introduced to the main techniques utilized in topological data analysis and their implementation provided by the python package scikit-tda. Introductions to the Mapper algorithm and persistent homology will be complemented by respective hands-on tutorial sessions. The workshop will conclude with an exploratory project of these methods on ‘real data’, which may be provided by the participants.
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More information on the [https://micbl.github.io/TDAworkshop/ course website]
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Latest revision as of 18:16, 17 April 2023

Seminar

Heidelberg TDA Seminar (Summer 2023)
Thu 11-13h, Mathematikon 00.200
Lukas Hahn, Daniel Spitz



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