Topology, Geometry and Data Mini Course - Yasu Hiraoka

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Yasu Hiraoka
November 15, 2017
11:00AM - 12:00PM
Location
Enarson Classroom Bldg 222

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Add to Calendar 2017-11-15 11:00:00 2017-11-15 12:00:00 Topology, Geometry and Data Mini Course - Yasu Hiraoka Title: TGDA Mini-Course 3: Machine learnings on persistent homology and its applications to materials scienceSpeaker: Yasu Hiraoka (Tohoku University, Advanced Institute for Materials Research, Sendai Japan)Abstract: In this series of lectures, I present several research topics on random topology and machine learnings on persistent homology. Persistent homology is one of the important tools in topological data analysis and it characterizes shapes of data. In particular, it provides a tool called the persistence diagram that extracts multiscale topological features such as rings and cavities in data (e.g. atomic configurations, high dimensional digital images etc).In talk #3, I switch to the application of persistent homology to materials science. Here, I will summarize several recent activities about machine learnings on persistent homology. In particular, I would like to emphasize that, by combining with inverse problems of persistent homology, a new concept called sparse persistence diagram, which is obtained as a result of plays an important role for studying materials properties (e.g., conductivity of battery materials).Seminar URL: https://research.math.osu.edu/tgda/tgda-seminar.html Enarson Classroom Bldg 222 Department of Mathematics math@osu.edu America/New_York public
Description

Title: TGDA Mini-Course 3: Machine learnings on persistent homology and its applications to materials science

SpeakerYasu Hiraoka (Tohoku University, Advanced Institute for Materials Research, Sendai Japan)

Abstract: In this series of lectures, I present several research topics on random topology and machine learnings on persistent homology. Persistent homology is one of the important tools in topological data analysis and it characterizes shapes of data. In particular, it provides a tool called the persistence diagram that extracts multiscale topological features such as rings and cavities in data (e.g. atomic configurations, high dimensional digital images etc).

In talk #3, I switch to the application of persistent homology to materials science. Here, I will summarize several recent activities about machine learnings on persistent homology. In particular, I would like to emphasize that, by combining with inverse problems of persistent homology, a new concept called sparse persistence diagram, which is obtained as a result of plays an important role for studying materials properties (e.g., conductivity of battery materials).

Seminar URLhttps://research.math.osu.edu/tgda/tgda-seminar.html

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