Primary supervisor
Sicily Fung Fung TingCo-supervisors
- Ting Chee Ming
- Dr. Chong Lor Huai
- Assoc. Prof. Anne Yee
- Assoc. Prof. Satoshi Ogawa
- Prof. Jiangning Song
- Prof. Terence O'Brien
- Prof. Patrick Kwan
- Dr Shahid Javaid
- Dr Ana Antonic-Baker
Research area
Machine LearningUsing the Project-1 hiPSC platform, this project builds AI pipelines to learn disease-relevant representations from cellular images, fused with multi-omics. Models will classify diagnosis and predict treatment response with strict donor-level splits, cross-regional external validation, and fairness audits (sex/ethnicity stratification). Interpretable AI (e.g., attribution maps, SHAP) will nominate mechanism-anchored biomarkers and candidate interventions. Tooling will be containerised and open to support reproducibility and clinical translation. Outputs include validated models, ranked biomarkers, and decision support for dosing/regimen choices that directly guide Project-3 therapeutic testing.
Required knowledge
Preferred candidates for having prior experience in AI model development, biomedical image analysis, or multi-omics integration, with strong competence in deep learning frameworks (e.g., PyTorch/TensorFlow) and data engineering for reproducible research. Familiarity with cloud/HPC workflows, containerisation (Docker), and basic bioinformatics pipelines will be preferred. Cross-disciplinary training and co-supervision with computational and neuroscience partners under GEMS 2026 will be provided.
