The Geometric Deep Learning core technology cluster consists of PhD students interested in the intersection between geometry and machine learning. Subtopics include Group equivariant neural networks, Graph neural networks, Topological data analysis and more.
Members Page
Link to members page. This is a separate page containing information for cluster members. Feel free to have a look if you are interested in joining the cluster.
Cluster Activities
Currently, we meet on zoom approximately once a month. A precise schedule and list of past meetings can be found on the members page.
Cluster Leader
Current Members
Below is a list of some of the Geometric Deep Learning cluster members along with short descriptions of their research interests. If you are a cluster member and would like to add your name to the list, please contact the cluster leader. Similarly, if you would like your name to be removed from the list or your info to be edited, also contact the cluster leader.
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My research is focused on investigating and extending the mathematical foundations of equivariant neural networks, with emphasis on Group-equivariant Convolutional Neural Networks (GCNN). Equivariant neural networks are designed to utilize symmetry, when present, to facilitate learning. |
| Affiliation | Mathematical Sciences at Chalmers University of Technology |
| Main Supervisor | Daniel Persson |
| Contact | jimmyar@chalmers.se |
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Equivariant neural networks and their application to computer vision problems.
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| Affiliation | Electrical Engineering, Chalmers |
| Main Supervisor | Fredrik Kahl |
| Contact | bokman@chalmers.se, https://www.chalmers.se/en/Staff/Pages/bokman.aspx |
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Conformal embedding and neural networks, Models equivariant to geometric transformations
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| Affiliation | Computer Vision Laboratory, Department of Electrical Engineering, Linköping University |
| Main Supervisor | Michael Felsberg |
| Contact | pavlo.melnyk@liu.se |
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I’m a Ph.D. student in the division of Biomedical Imaging with affiliation to the WASP-AI program. My research’s primary interest is the development and investigation of novel Machine-Learning techniques in biomedical image processing. Specifically, I want to exploit geometrical properties and symmetries of the given image-signals to tackle data-sparsity problems with domain-specific model-design (e.g., Geometrical Deep learning, Graph-CNN, Topology). |
| Affiliation | KTH, Biomedical Imaging |
| Main Supervisor | Rodrigo Moreno |
| Contact | fabiansi@kth.se |