Dr Shing Chan
Shing Chan
BEng, PhD
Postdoctoral Research Scientist in Statistical Machine Learning for Cardiovascular Medicine
Shing is a researcher in the Big Data Institute and Oxford Population Health working with Aiden Doherty. His research applies machine learning techniques to extract useful information from wearable devices such as wrist accelerometers and body cameras to better understand the links between physical activity and cardiovascular health.
Shing obtained his PhD in petroleum engineering from Heriot-Watt in Edinburgh, where he applied generative modelling techniques from computer vision to the problem of subsurface modelling. Prior to that, he obtained his BEng in aeronautical engineering from Instituto Universitario Aeronáutico in Argentina, where he implemented GPU-accelerated codes for airflow simulations for his final year project.
Recent publications
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Self-Supervised Machine Learning to Characterise Step Counts from Wrist-Worn Accelerometers in the UK Biobank.
Journal article
Small SR. et al, (2024), Medicine and science in sports and exercise
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Self-supervised learning for human activity recognition using 700,000 person-days of wearable data.
Journal article
Yuan H. et al, (2024), NPJ digital medicine, 7
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Capture-24: Activity tracker dataset for human activity recognition - Temperature & light sensor data
Dataset
CHAN CHANG S. and DOHERTY A., (2023)