Georgios Kaissis
Technical University of Munich - Helmholtz Munich - Imperial College London
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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