Team
I am privileged to work with an amazing and diverse team of talented researchers
Current supervisions
- Alex Ziller (PhD): Differentially private deep learning, privacy auditing, federated learning
- Tamara Müller (PhD): Differentially private graph neural networks
- Florian Hölzl (PhD): Equivariant CNNs for differential privacy, memorisation and generalisation
- Jonas Kuntzer (PhD): Mechanistic interpretability, AI alignment, federated learning
- Leonhard Feiner (PhD): Bayesian deep learning
- Moritz Knolle (PhD): Training dynamics of differentially private neural networks, fairness
- Johannes Kaiser (PhD): Individual privacy accounting, federated learning
- Dmitrii Usynin (PhD): Attacks against privacy-preserving machine learning models (red-teaming)
- Florent Dufour (PhD): Machine learning with trusted execution environments
- Reihaneh Torkzadehmahani (PhD): Label-noise resistant learning, differentially private synthetic data generation, machine unlearning
- Reza Nasirigerdeh (PhD): Federated learning, architecture design for differential privacy
- Friederike Jungmann (MD): Human-in-the-loop machine learning
- Johannes Brandt (MD): Medical imaging analysis with deep learning techniques
- Philip Müller (PhD): Natural Language Processing, Multimodal (image/text) AI
- Felix Meissen (PhD): Anomaly detection
- Anneliese Riess (PhD): Mathematical foundations of differential privacy
- Vasiliki Sideri-Lampretsa (PhD): Deep learning-assisted image registration
Collaborators
- Rickmer Braren (TUM): Machine learning in Radiology
- Johannes Paetzold (TUM, Imperial College): Machine learning on graphs
- Ben Glocker (Imperial College): Fairness
- Jamie Hayes (Google DeepMind): Differentially private machine learning
- Borja Balle (Google DeepMind): Differentially private machine learning
- Eleni Triantafilou (Google DeepMind): Machine unlearning
- Stefan Kolek (LMU Munich): Mathematical foundations of AI and differential privacy
- Gitta Kutyniok (LMU Munich): Mathematical foundations of AI and differential privacy
- Dimitris Karampinos (TUM): Deep learning for magnetic resonance imaging
- Kerstin Hammernik (TUM, NVidia): Complex-valued deep learning
- Jan Böttcher (TUM): Deep learning in immunology
- Daniel Truhn (UK Aachen): Privacy-preserving machine learning in radiology