
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 memoryProject archive
Research prototypes, developer tools, and applied analyses across computer vision, explainable AI, NLP, topology, and statistics. Please note that this is not updated. Please visit my github to see all personal and academic projects.

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 LoRA
A pipeline that augments conventional machine-learning models with topological descriptors extracted through persistent homology.

A supervised-learning study using racket kinematics to classify combined demographic labels across several model families.

A statistical analysis of biomedical variables associated with gallstone status, using covariance structure and multivariate inference.