AI-driven, sensor-based, non-imaging systems for patient monitoring

 Xiao Gu is a Senior Research Associate in Medical AI at the University of Oxford and a Junior Research Fellow at Kellogg College. He also holds an honorary research position with the Oxford University Hospitals, supporting close collaboration between algorithm development and clinical practice. He obtained his PhD and Master’s degrees from Imperial College London, and his undergraduate degree from Fudan University, at both of which he received several competitive awards, including the Dean’s Prize and National Scholarship. His research focuses on AI for medical sensing, with particular emphasis on wearable and ambient sensing intelligence for digital health, aiming to develop scalable, accessible, and generalisable solutions for real-world healthcare applications.

Dr Gu’s research has been recognised through over 50 peer-reviewed publications in leading venues across AI, robotics, biomedical engineering, and computer vision, including Nature Machine Intelligence, Communications Engineering, IEEE Transactions on Image Processing, IEEE Transactions on Neural Networks and Learning Systems, IEEE Journal of Biomedical and Health Informatics, Medical Image Analysis, ECCV, BMVC, ICRA, and IROS. His work is reflected by a Google Scholar h-index of 21. As an early-career researcher, he has secured competitive research funding totalling around £250K as Principal Investigator. He has also actively served as a lead organiser for international workshops and journal special issues, including an ICRA 2024 workshop on wearable intelligence for robotics and a special issue of IEEE Journal of Biomedical and Health Informatics on biomedical sensing.
 

Himaya is a sixth form student with a strong interest in pursuing a career in medicine. Alongside her academic studies, she is actively engaged in tap dance and music, reflecting a well-rounded skill set.

Her Extended Project Qualification (EPQ) explores the role of emerging biomarkers in predicting cardiovascular events, with a particular focus on the potential integration of Lipoprotein(a) [Lp(a)] testing into UK screening pathways. Her work evaluates current evidence, risk prediction models, and the clinical and public health implications of introducing Lp(a) into routine practice.