Time-series machine learning of wearable sleep datasets

Hang is a final-year DPhil student supervised by Aiden Doherty and Simon Kyle. He joined the NDPH as part of the inaugural class of the CDT for Health Data Science in 2019. Hang’s work has been focused on developing machine-learning methods for wearable sensing data. In particular, with coworkers from the Wearables group, Hang led the development of the first foundation model for wearable sensing data. Hang has also had a keen interest in refining the measurement of sleep phenotypes from wearable devices for large-scale epidemiological studies. Hang has worked extensively with coworkers in the UK, USA, Australia and China. His foundation model work won the best poster award in the first CDT symposium. 

Refreshments will be available for those attending in the Atrium from 10:00am in readiness for the seminar to start at 10:30.