Chief Supervisor

Prof. HE Mingguang

Project Title

Synopsis

Aging is a complex and heterogeneous process that cannot be fully described by chronological age alone. Biological markers provide a more accurate window into this variability and hold promise for redefining how aging is measured.

This project aims to design mathematical algorithms and apply artificial intelligence (AI) tools to integrate aging biomarkers into robust indicators of biological age. Leveraging large-scale population data, the study will explore potential triggers that may initiate or accelerate the aging process, as well as examine whether aging tends to follow specific sequences across biological systems.

The broader goal is to develop computational frameworks to better quantify aging, improve the prediction of health outcomes, and generate actionable evidence to guide intervention priorities for promoting healthy ageing.