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About Me
I am now a Senior Research Associate at the Computational Health Informatics Lab, University of Oxford, working with Prof David Clifton. I am also an honorary researcher at Oxford University Hospital, and a Junior Research Fellow with Kellogg College, Oxford. Previously, I obtained my PhD degree in Computing and MRes degree (Distinction & Dean's Prize) in Medical Robotics and Image Guided Intervention, supervised by Dr Benny Lo and Prof Guang-Zhong Yang CBE FREng. Prior to that, I obtained BEng degree (Honors Student & National Scholarship) in Electronic Engineering from Fudan University in 2018, supervised by Prof Wei Chen. My research interests focus on integrating pervasive sensing (wearable and ambient sensors) and deep learning for healthcare, to address pressing generalization issues in real-world applications.
[2025-12-17]
   Awarded a SDR UK Fellowship (PI, £200K) on foundation AI and wearables!
[2025-12-04]
   Secured a MLSTF-ETI award (PI, £50K) on translational study of biosignal foundation models!
[2025-11-19]
   Our cardiac sensing foundation model is accepted to Nature Machine Intelligence!
[2025-10-01]
   Officially started my fellowship in Kellogg.
[2025-08-24]
   Our survey paper on recent advances of foundation models for biosignals is online.
[2025-06-10]
   Our paper on SSL on wearables is accepted to Communications Engineering.
[2025-03-27]
   Selected as Junior Research Fellow of Kellogg College (starting Michaelmas 2025).
[2025-01-02]
   Our CFP for JBHI SI on biomedical sensing frontiers is live, welcome submissions!
[2024-12-02]
   One paper on depth camera based pose estimation got accepted to IEEE TII.
[2024-11-04]
   Selected as outstanding reviewer at BMVC24.
[2024-11-02]
   Two papers on LLM and time series foundation models got accepted to ML4H24.
[2024-10-28]
   One paper on semi-supervised medical image segmentation got accepted to WACV25.
[2024-10-28]
   Gave a talk to HKUMed on GenAI in Medicine.
My research interests are mainly focused on three tracks in Wearable/Ambient Intelligence for Digital Health
Generalization-Issue-Oriented   review paper
To examine and address real-world data learning issues, prompting generalizable and trustworthy solutions for digital health.
Dataset Scarcity (label scarcity / imbalanced /...),
Source Heterogeneity (cross-subject / position / modality / dataset /...),
Data&Annotation Quaility (noisy sensor data / annotation bias /...)
Sensing-Modality-Oriented
To harness the full potential of wearable and ambient sensing technologies for the development of healthcare solutions.
Ambient Camera / Egocentric Camera / EEG / EMG / IMU / ECG / Novel Sensing /...
Healthcare-Application-Oriented
To ultimately translate research advancements into tangible benefits for real-world health and wellbeing applications.
Clinical Outcome Prediction / Rehabilitation Monitoring / Human Behaviour Understanding / ...
Last Updated : Dec 17th 2025 by Xiao (Shawn)