I am interested in interpretable machine learning algorithms for unsupervised anomaly detection.
Interests
- Interpretable Machine Learning
- Unsupervised Anomaly Detection
Education
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Ph.D. Interpretable Machine Learning in Medical Image Analysis, ongoing
Technical University of Munich
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M.Sc. in Computer Science, 2018
FAU Erlangen
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B.Sc. in Computer Science, 2015
FAU Erlangen
Research
Novel deformable auto-encoders for unsupervised outlier detection.
Implicit disentanglement of shape and appearance with federated learning for unsupervised brain pathology segmentation.
Publications
(2022).
Federated Disentangled Representation Learning for Unsupervised Brain Anomaly Detection.
In Nature Machine Intelligence.
(2022).
What do we learn? Debunking the Myth of Unsupervised Outlier Detection.
In arxiv.
(2020).
FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation.
In arxiv.
(2019).
SHAMANN: Shared Memory Augmented Neural Networks.
In International Conference on Information Processing in Medical Imaging (IPMI).
(2016).
Confidence-aware Levenberg-Marquardt optimization for joint motion estimation and super-resolution.
In International Conference on Image Processing (ICIP).
Teaching
If you are interested in BSc/MSc projects in unsupervised anomaly detection please contact me and attach a motivation letter, transcript of academic records and CV.
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Deep Medical Anomaly Segmentation
Winter semester 2022. TUM Informatics. Master Seminar.
Unsupervised Anomaly Detection for Medical Imaging (f/m/x)
Bachelor/Master Thesis)