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Python, ML and Deep Learning
PyTorch deep-learning models for medical imaging and image synthesis on NCI GADI HPC, a sparse-projection feature pipeline, and customer segmentation with K-means.
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AI-Driven Melanoma Detection
A PyTorch deep-learning model for classifying dermoscopic melanoma images, built for early-detection use in healthcare.
+12% accuracy on imbalanced data
93% precision25% faster convergence15,000+ images, 88% GPU utilisation
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Generative Adversarial Network for Image Synthesis
A Wasserstein GAN that generates high-resolution synthetic faces to address data scarcity.
15% FID stability gain vs baseline
40% faster training50,000+ images90% model efficiency in constrained settings
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Sparse Random Projection for Medical Image Analysis
A feature-extraction pipeline that compresses high-dimensional medical imaging while keeping diagnostic signal.
65% dimensionality reduction
35% faster than PCA10,000+ images standardisedScales to 3D with under 10% code change
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Customer Segmentation for Retail Strategy
A segmentation analysis for a national supermarket chain to guide targeted marketing.
2,000 customer profiles analysed
Segments delivered in a 1,000-word non-technical report
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