From Zero to Hero: Federated AI in Healthcare Systems Workshop
Federated learning is reshaping how we build machine learning models on sensitive clinical data, letting institutions collaborate and learn from one another without ever moving a patient's records. We think it's one of the most important directions in healthcare ML, and we'd love for you to join us in Cambridge to dig into it together.
This two-day workshop is designed to take you from zero to hero, whatever your starting point. Across the two days you'll hear keynotes and research talks from people working at the frontier of the field, take part in hands-on tutorial sessions with prepared notebooks you can work through alongside us, and join panel discussions and live demos exploring where the field is heading. Everyone who participates will receive a certificate of attendance.
Whether you're a clinician curious about the methods, a researcher already deploying federated systems, or someone who has only just heard the term, there's a place for you here. Come for the tutorials, stay for the conversations!
Spaces are very limited. Register, and tell us a little about what you're hoping to get out of the event. We can't wait to see you in Cambridge.
(If you registered and can no longer attend, please indicate this on Luma such that we can release the space to guests on the waiting list)
PROGRAMME:
https://cambridgecia.github.io/zero-to-hero-fl.github.io/
THE EVENT WILL BE HELD IN-PERSON ONLY.
LOCATION:
St John's College, Divinity School, Main Lecture Theatre
St John's College, founded in 1511, is one of the oldest and largest of the University of Cambridge's 31 colleges, with alumni ranging from Nobel laureates and prime ministers to writers and scientists. The workshop will be held in the college's conference facilities, set among its historic courts and gardens beside the River Cam, a short walk from the city centre.
SPEAKERS:
Organisers:
Fan Zhang, Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK
Daniel Kreuter, Precision Healthcare University Research Institute, Queen Mary University of London, UK
Shiqiang Wang, CORE-AIx, Department of Computer Science, University of Exeter, UK
Gregory Verghese, PharosAI, UK
Nicholas Lane, Flower Labs & Department of Computer Science and Technology, University of Cambridge, UK
Kapal Dev, Department of Computer Science, Munster Technological University, Ireland.
Javier Fernandez-Marques, Flower Labs, UK
Carola-Bibiane Schönlieb, Cambridge Image Analysis, Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK
Michael Roberts, BloodCounts! & Department of Applied Mathematics and Theoretical Physics, University of Cambridge, UK
Exclusive Sponsor:
Flower Labs