Higher Degree by Research Application Portal

Machine learning models, including linear regression, ensemble methods like random forests, and deep neural networks, have transformed medical research by enabling breakthroughs in diagnostics, drug discovery, and personalized treatment. However, these models often function as "black boxes," offering little insight into how they arrive at predictions. This lack of interpretability hinders trust and adoption in healthcare, where understanding the reasoning behind decisions is critical for clinical validation and patient safety.

This PhD project aims to address the urgent need for explainable AI (XAI) in medical research by developing algorithms that combine predictive accuracy with clear, interpretable outputs. Emerging approaches like Kolmogorov-Arnold Networks (KANs) and techniques such as SHAP or LIME provide pathways to make AI decisions transparent. By creating models that explain their processes and results, this research will empower medical professionals to validate AI outputs, uncover biological insights, and ensure ethical, equitable healthcare solutions.

As a PhD candidate, you will design and implement novel XAI models tailored for medical datasets, including epidemiology, genomics, and epigenetics data. Using Python and frameworks like PyTorch or scikit-learn, you will prototype algorithms, test them on real-world medical benchmarks, and evaluate both accuracy and interpretability. You will collaborate with AI experts and clinicians, contributing to publications in top journals and conferences.

Your work will advance the field of medical AI by creating trustworthy, interpretable models that could improve disease detection or treatment planning. We welcome candidates with a background in computer science or related fields, proficiency in programming, and a passion for impactful research.

Professor Andreas Wicenec | andreas.wicenec@uwa.edu.au

Director, Data Intensive Astronomy

Dr. Fuling Chen | fuling.chen@uwa.edu.au

Research Fellow, Data Intensive Astronomy

The Doctor of Philosophy (PhD) is a program of independent, supervised research that is assessed solely on the basis of a thesis, sometimes including a creative work component, that is examined externally. The work presented for a PhD must be a substantial and original contribution to scholarship, demonstrating mastery of the subject of interest as well as an advance in that field of knowledge. 

Visit the course webpage for full details of this course including admission requirements, course rules and the relevant CRICOS code/s.