Michael Glöckler M. Sc.

PhD Project:

Explainable 3D Deep Learning for medical data

Michael Glöckler joined the Visual Computing research group in 2025. He received his M.Sc. from the Institute of Media Informatics at Ulm University in 2025, where he specialized in 3D Contrastive Learning. Currently, he is pursuing his PhD under the supervision of Prof. Dr. Timo Ropinski as part of the interdisciplinary KEMAI project team. His work focuses on Explainable 3D Deep Learning for medical data. His broader research explores methods for transferring knowledge from the 2D domain to 3D medical data. By leveraging the rich information available in 2D datasets, he aims to tackle the data bottleneck in medical AI and develop transparent, high-dimensional models that provide interpretable and valuable insights for clinical decision-making.

Research interests

• Machine Learning
• 3D Deep Learning for Medical Images
• Contrastive Learning
• Cross-Modal Representation Learning

Publications

Peer-Reviewed

Rixen, Jan Ole, Luca-Maxim Meinhardt, Michael Glöckler, Marius-Lukas Ziegenbein, Anna Schlothauer, Mark Colley, Enrico Rukzio, and Jan Gugenheimer. “The loop and reasons to break it: Investigating infinite scrolling behaviour in social media applications and reasons to stop.” Proceedings of the ACM on Human-Computer Interaction 7, no. MHCI (2023): 1-22.

Hartwig, Sebastian, Dominik Engel, Leon Sick, Hannah Kniesel, Tristan Payer, Poonam Poonam, Michael Glockler, Alex Bauerle, and Timo Ropinski. “A survey on quality metrics for text-to-image generation.” IEEE Transactions on Visualization and Computer Graphics (2025).