Centre for Biomedical AI

Primary Supervisor: Associate Professor Daniel Hesselson
Supervisory Team: Lise Hunault
Centre: Biomedical AI

Type 1 diabetes (T1D) results from the autoimmune destruction of insulin-producing beta cells, requiring continuous management of blood sugar levels. A lifelong cure for T1D will require regeneration or replacement of these cells. This project focuses on the transcription factor PDX1, which is a master regulator of pancreatic cell fate and is also essential for promoting optimal beta cell function. Excitingly, PDX1 both promotes regeneration and is critical for converting other pancreatic cell-types into beta cells. Our team uses directed evolution to engineer novel proteins in cells that model human disease. We have developed PDX1 variants with new properties that could enhance the function of stem cell-derived islets, which are the only renewable source of beta cells for curing T1D. The goal of this project is to determine which variant has the most potential for increasing the durability and function of transplanted islets, setting the stage for clinical translation.

Primary Supervisor: Associate Professor Daniel Hesselson
Supervisory Team: Dr Alex Cole
Centre: Biomedical AI

Solid tumours create an immunosuppressive microenvironment that limits the efficacy and safety of current immunotherapies, often leading to poor efficacy and off-target toxicity. This project aims to engineer next-generation protein and cellular therapies that are triggered by tumour-restricted signals such as hypoxia, acidic pH, or matrix metalloproteinase activity. Using our directed evolution and rational protein design platform, you will develop and test novel therapies in cell-based models, then evaluate them for safety and therapeutic benefit in mouse models of solid cancer. The goal of this research is to identify the most promising tumour-activated immunotherapeutics, laying the groundwork for more potent and less toxic cancer treatments.

Primary Supervisor: Associate Professor Daniel Hesselson
Supervisory Team: Manuela Florido
Centre: Biomedical AI

Myocardial infarction (MI) results from the sudden loss of blood flow to cardiac tissue, causing massive cardiomyocyte death and subsequent scar formation that impairs pump function. Current clinical interventions stabilise patients and limit further damage but cannot regenerate lost myocardium. This project targets that unmet need by engineering protein therapeutics and delivering them at the optimal time and place to maximise cardiomyocyte survival, proliferation, and neovascularisation. Using our directed evolution and rational design platform, we have already generated lead protein candidates. The aim of this research is to deliver these candidates in in vitro human cardiomyocyte cultures and in vivo rodent MI models to identify the most potent regenerators, setting the stage for future preclinical and clinical translation.

Primary Supervisor: Dr Yagiz Aksoy
Supervisory Team: Associate Professor Daniel Hesselson
Centre: Biomedical AI

Australia has the highest melanoma incidence in the world, yet risk is still assessed with short questionnaires, and no one can yet say at what risk score screening should begin. This project spans both halves of that problem. Students build and validate melanoma risk calculators using ACEMID, a national collaboration that has captured whole-body 3D photographs of thousands of Australians, testing whether measurable image phenotypes — naevus counts, freckling, ultraviolet damage, objective skin colour — outperform the questions currently asked. Those calculators then feed adaptive screening thresholds: decision-analytic models that weigh clinical benefit, overdiagnosis and real workforce capacity, region by region, for a national screening programme under active design. The work sits inside national collaborations with a direct route into policy. Suits students with a computational, statistical, epidemiological or engineering background; clinical knowledge welcome but not required.

Primary Supervisor: Dr Yagiz Aksoy
Supervisory Team: Associate Professor Daniel Hesselson
Centre: Biomedical AI

A single retinal photograph carries signal about the eye and about the body behind it. Oculomics is the emerging discipline of reading that signal at scale, and this laboratory sits on one of the strongest platforms in the country to pursue it: more than 300,000 retinal scans within the Global RETFound consortium, and a macular degeneration biobank linking blood biomarkers — plasma proteomics across 9,500 proteins, targeted lipidomics, combined nuclear and mitochondrial genetic risk scores — to imaging. Students build AI models that connect what the eye shows to what the blood measures, asking how much earlier eye disease can be flagged and how far systemic signals can responsibly be read from the retina. Suits students with a machine learning, biostatistics, bioinformatics or vision-science background.

Primary Supervisor: Dr Yagiz Aksoy
Supervisory Team: Associate Professor Daniel Hesselson
Centre: Biomedical AI

One in three Australian children is now myopic, and early onset carries lifelong risk of retinal detachment, glaucoma and myopic maculopathy — yet treatment options have multiplied faster than the evidence to choose between them. Students design explainable predictive tools: models that forecast how fast a child’s eye will grow, translate that trajectory into lifetime risk of high myopia and its complications including glaucoma, and recommend when to escalate treatment — built on an eight-year Australian paediatric cohort, and designed so every recommendation can be explained to the clinician making the call and the parent hearing it. Suits students with a machine learning, engineering or optometry/orthoptics background