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Biography
Postgraduate Researcher (PhD candidate) at the 糖心原创, with a background in industrial engineering, specifically in reliability engineering and system improvement. Current research focuses on integrating artificial intelligence, human factors, and system modelling to improve the reliability of medical processes, mainly in the Newborn Life Support (NLS) procedures.
In addition to my current doctoral research, I have several years of experience working in higher education institutions, alongside consultancy involvement in manufacturing and service companies. My broader research interests also include the application of reliability engineering in the manufacturing sector, mainly in failure modelling, and maintenance and warranty policy design and optimisation.
Expertise Summary
- Reliability engineering
- System modelling and simulation
- Artificial intelligence and computer vision
- Failure modelling
- Maintenance and warranty policy design and optimisation
- Quality and process improvement
- Data analysis
Research Summary
My current research develops a novel framework that integrates system modelling and artificial intelligence (AI) to identify, map, and analyse variation in medical processes and to explore how such… read more
Current Research
My current research develops a novel framework that integrates system modelling and artificial intelligence (AI) to identify, map, and analyse variation in medical processes and to explore how such variation may influence patient outcomes. The research focuses on Newborn Life Support (NLS) procedures, with the potential for future extension to other medical procedures. A Coloured Petri Net (CPN) is used to represent the structure and behaviour of the NLS process, incorporating both deterministic and probabilistic rules to reflect the complexity of real practice. In parallel, machine learning and deep learning models are developed to automatically detect procedural deviations and provide inputs to the simulation. The research also investigates human factors, such as clinicians' cognitive load and attention level, as variables that may affect process performance. Finally, this work aims to provide deeper insight into how variations in clinical procedures arise and how they may affect reliability, while also supporting the future development of more reproducible and practical assessment tools.
Past Research
My previous research focused on reliability engineering in the manufacturing sector, mainly in warranty cost analysis, maintenance and warranty policy optimisation, and predictive maintenance. These studies mostly employed mathematical modeling approaches to represent failure behaviour, evaluate warranty risks and cost, identify optimal maintenance strategies for companies. Through this work, I developed a strong interest in the use of quantitative methods to support reliability improvement. This background now informs my current research, where similar analytical thinking is extended into healthcare through the integration of AI and system modelling.
Future Research
Future research will extend this framework beyond Newborn Life Support (NLS) to other medical procedures, especially those that are time-critical and require high reliability. It may also support practical applications, such as augmented reality tools that provide real-time guidance, feedback, or assessment for clinicians.
Furthermore, future research may also apply similar modelling and AI-based approaches to manufactured products, particularly for analysing failure behaviour, predicting reliability, and supporting maintenance decision-making. This could further contribute to establishing optimal maintenance, repair, and replacement policies for improved performance and cost efficiency.