I am interested in interpretable machine learning algorithms for unsupervised anomaly detection.

Interests
  • Interpretable Machine Learning
  • Unsupervised Anomaly Detection
Education
  • Ph.D. Interpretable Machine Learning in Medical Image Analysis, ongoing

    Technical University of Munich

  • M.Sc. in Computer Science, 2018

    FAU Erlangen

  • B.Sc. in Computer Science, 2015

    FAU Erlangen

Research

autoDDPM
Automatic diffusion models for anomaly detection.
PHANES
Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection.
MorphAEus
Novel deformable auto-encoders for unsupervised outlier detection.

Publications

(2023). Bias in Unsupervised Anomaly Detection in Brain MRI. In arXiv.

PDF Cite Dataset Project

(2023). Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection. In arxiv.

PDF Cite Code Project

(2022). Federated Disentangled Representation Learning for Unsupervised Brain Anomaly Detection. In Nature Machine Intelligence.

PDF Cite Code Project Video

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.