Federated learning

Applying machine learning and artificial intelligence in medicine faces specific obstacles: medical data are hard and costly to obtain, limited in volume, often incomplete, and sensitive enough to require strict privacy protection, while clinicians need to understand and verify why a model reached a decision. This talk presents interpretable and privacy-preserving methods for medical data analysis that offer concise, understandable explanations as an alternative or complement to black-box models. Topics include learning from small medical datasets with abstractions that support rule induction,

…records, with privacy-preserving federated learning at its core, together with digital twin technology and clinician-facing deployment across eight European partner institutions. The postdoctoral research associate takes a leading role in the consortium from Cambridge, in a joi

…and private across distributed and federated settings, from theory through to open-source software. The appointee designs, implements and benchmarks new training algorithms, contributes research software, leads papers at leading machine learning and optimization venues, collaborates with universities and nat

…secure, scalable, continuously learning multimodal AI for biomedical discovery that can learn across distributed data environments without centralising sensitive data. The work spans federated learning, foundation-model adaptation, multimodal biomedical AI across clinical, imaging, omics, text and experimental data, privacy-preserving machine learning, continual learning and agentic AI for orchestrating federated training, with deployment on cloud, secure enclaves, trusted research environments