We are happy to welcome Majid Mohammadi to our team. His research focuses on interpretable and trustworthy machine learning, including game-theoretic explanations (e.g., Shapley-value methods), uncertainty-aware modelling, and kernel/Gaussian-process approaches.

In our group, Majid will work on multiple applications of modern generative machine-learning methods for mass spectrometry and broader life-science data.