Jules [ʒyl]

I am a PhD candidate in bioinformatics and machine learning at the University of Tübingen. My research focuses on developing robust machine learning methods for medicine and genomics. I have a particular interest in genomic foundation models, tabular machine learning for high-dimensional biological data, and privacy-preserving federated analytics. My recent work includes extending tabular foundation models to extreme feature counts, analysing privacy vulnerabilities in DNA embeddings against model inversion attacks and developing federated learning infrastructure as part of the PrivateAIM consortium.

On this page, you’ll find public project data, slides, tools, and more. Some materials, like lecture slides, are accessible only within the university network.

You might also find me online under the alias not_a_feature.

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