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Applied Mathematics (Topological Data Analysis) Lab (Yasuaki Hiraoka)
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A lab that studies the shape of complex data through topology.
AboutAbout the lab
Reading the "shape" of data through mathematics.
The Hiraoka Lab studies topological data analysis within the field of applied mathematics.
Using persistent homology, representation theory of algebras, random topology, and machine learning, the lab develops new descriptors for complex data.
In applied research, the lab focuses mainly on problems in materials science and life sciences.
Key terms
- Topological data analysis
- An analytical approach that captures the shape of data through geometric properties such as holes and connectivity
- Persistent homology
- A method that records how long the holes and connections in data persist as the scale is varied
- Descriptor
- A compact representation of the features of complex data, expressed as numbers or diagrams
ResearchResearch themes
The lab conducts research on topological data analysis. Using mathematical tools, it builds new descriptors of complex data and applies them to materials science and life sciences.
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MembersFaculty
- ProfessorYasuaki Hiraoka
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Persistence Diagram” Rrrlw / CC BY-SA 4.0 (cropped)
- New Descriptors for Complex Data: “Crystalline and glassy carbon” Samsiq / CC BY 4.0 (cropped)
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