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Hui Guo

Reader in Biostatistics


What are your research interests?

My research focuses on statistical causal inference from observational studies, with the aim to improve accuracy of disease diagnosis and help make informed decisions on targets for treatments.

What is the focus of your current research?

I am developing algorithms which take forward strengths of both classic statistical methods and machine learning to pinpoint environmental and biological causal risk factors, and to gain insights into multimorbidity from large-scale high dimensional data.

What are some projects or breakthroughs you wish to highlight?

A Bayesian approach to Mendelian randomisation which uses genetic instruments to flexibly model causal relationships between risk factors and diseases. Also developing fast machine learning algorithms in combination with Mendelian randomisation to learn complex biological causal mechanisms of clinical outcomes by utilising multi-omics data.

What memberships and awards do you hold/have you held in the past?

Turing Fellowship, QResearch Scientific Committee, Editorial Board of BMC Medical Research Methodology.

What is the biggest challenge in Data Science and AI right now?

Improving Data Science and AI literacy in wider communities.

What real world challenges do you see Data Science and AI meeting in the next 25 years?

Optimising the way of AI interacting with human beings to assist in healthcare especially in ageing populations.


Find out more about Hui’s research at Research Explorer.