
Fourier Shuffle for Semantic Vision
A frequency–spatial upsampling method for medical image segmentation that improves validation performance while reducing memory use.
+1.77% validation · −13.8% GPU memoryBrisbane, Australia
Machine learning researcher working on frequency-domain methods for computer vision. I build the systems I study, and teach the mathematics behind them.
Open to research collaboration
Selected work
Research prototypes and developer tools across computer vision, explainable AI, and NLP. Each links to methods, results, and source.

A frequency–spatial upsampling method for medical image segmentation that improves validation performance while reducing memory use.
+1.77% validation · −13.8% GPU memory
An explainable detector for AI-generated code, combining stylometric AST features, random forests, SHAP, and developer tooling.
94% accuracy · 1.5K+ extension downloads
An experimental pipeline for extracting, visualising, and modifying internal attention distributions in pretrained vision transformers.

A comparison of full fine-tuning, LoRA, and evolution strategies for translating specialist biomedical reports into accessible summaries.
>70% ROUGE · 2–3% trainable parameters with LoRALatest talk
A presentation on frequency-domain image segmentation research completed through UQ’s Summer Research Program.
27 March 2026

About
I am a research assistant and Master of Data Science student at The University of Queensland, working on computer vision, deep learning, and the mathematical structure behind model behaviour.
Alongside research I teach capstone computing build, pattern recognition, and introductory software engineering; work part-time as an AI engineer at Apex Fincap; and lead industry partnerships for UQ Computing Society.