Projects
Selected academic and technical projects
Concise project previews across medical imaging, biomedical machine learning, quantitative finance, and regional data analytics.
Medical Imaging / Deep Learning
Brain Tumor Segmentation using 3D Deep Learning
This research project investigates automated brain tumour segmentation from multimodal MRI scans using 3D deep learning models. Experiments compare multiple U-Net variants under a consistent training and evaluation workflow, with results interpreted through Dice-based region metrics, HD95, and qualitative prediction examples.
- Built 3D medical imaging pipelines using PyTorch and MONAI.
- Evaluated segmentation quality using Dice Score and Hausdorff Distance.
Biomedical Machine Learning / Shiny App
Autoimmune Disease Classification using Machine Learning
AutoimmuneMap is a machine learning research prototype and Shiny application for exploring autoimmune disease classification using whole-blood gene expression data. The project combines model benchmarking, reduced-gene analysis, and an interactive interface for sample selection, prediction visualisation, and model interpretation.
- Developed an interactive Shiny interface for upload, prediction, and model interpretation.
- Benchmarked multiple machine learning models on transcriptomic data.
This is a research prototype and not a clinical diagnostic tool.
Quantitative Finance / Portfolio Research
CSI500 Index Enhancement Framework
An execution-aware CSI500 index enhancement research framework combining alpha signal modelling, constrained portfolio optimization, backtesting, and execution diagnostics. The project focuses on translating model signals into tradable portfolios under practical constraints such as turnover limits, lot-size rounding, minimum trade thresholds, and capital budgets.
- Built an execution-aware research pipeline for CSI500 index enhancement.
- Evaluated strategy quality using risk, robustness, and execution diagnostics.
Regional Socioeconomic Analysis / Data Analytics
Sydney SA2 Analysis
A regional socioeconomic analysis project using PostgreSQL, Python, and Tableau to study Sydney SA2 regions and construct interpretable indicators of regional prosperity.
- Integrated demographic, business, transport, and education datasets across Sydney SA2 regions.
- Developed Tableau dashboards to communicate findings to non-technical audiences.