Geometry, Algebra and Physics in Deep Neural Networks (GAPinDNNs)

We explore the interface of Geometry, Algebra, and Physics with Deep Neural Networks. Specifically, we use techniques and ideas from pure mathematics and theoretical physics to develop the foundations of deep learning and deep neural networks.

You can read more about our research on our external website.

Visit the GAPinDNNs website

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Seminar

We organise the seminar in Geometry, Algebra and Physics in Deep Neural Networks (GAPinDNNs).

Members

Faculty

Jan Gerken

  • Assistant Professor, Algebra and Geometry, Math­e­mat­i­cal Sciences
To personal page

Daniel Persson

  • Head of Division, Algebra and Geometry, Math­e­mat­i­cal Sciences
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Postdocs

Max Guillen

  • Postdoc, Algebra and Geometry, Math­e­mat­i­cal Sciences
To personal page

PhD students

Miaowen Dong

  • Doctoral Student, Algebra and Geometry, Math­e­mat­i­cal Sciences
To personal page

Longde Huang

  • Doctoral Student, Algebra and Geometry, Math­e­mat­i­cal Sciences
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Philipp Misof

  • Doctoral Student, Algebra and Geometry, Math­e­mat­i­cal Sciences
To personal page

Elias Nyholm

  • Doctoral Student, Algebra and Geometry, Math­e­mat­i­cal Sciences
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Affiliated

Hampus Linander

  • Researcher, Algebra and Geometry, Math­e­mat­i­cal Sciences
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Christoffer Petersson

  • Researcher, Chalmers Next Labs AB
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Contact

Jan Gerken

  • Assistant Professor, Algebra and Geometry, Math­e­mat­i­cal Sciences
To personal page