Seven years shipping production mobile apps. Now building products that think, with ML, LLMs, and the instinct to ship that only comes from doing it for real._
Multi-agent LLM web app that helps newcomers find the right Sydney suburb using AI, combining Reddit sentiment, crime statistics, transport data, and civic amenities into a conversational assistant. Won the Best AI LLM Project Award at UTS 2026.
Interactive data narrative examining the growing tension between housing affordability and public transport access across Australia's 8 capital cities from 2020 to 2025, built for policy makers using real ABS and BITRE data.
End-to-end NLP analysis of 299 public submissions to the Australian parliamentary inquiry into the 2025 federal election, investigating the perceived relationship between digital misinformation and electoral intimidation across different submitter types.
Two PyTorch image captioning architectures trained on the VizWiz dataset, comparing a lightweight MobileNetV3 + LSTM baseline against a GoogLeNet + LSTM with Luong spatial attention, evaluated with BLEU-1/2/3/4 and ROUGE-L metrics.
Comparative study of CNN transfer learning on 101-class food image classification, with MobileNetV3 achieving 72.44% Top-1 accuracy after deep fine-tuning across GoogLeNet, MobileNetV3, and ResNet50.
Perceptron built from scratch with NumPy and custom neural network trained on Japanese MNIST (70k Hiragana characters) using PyTorch, achieving 91.60% test accuracy through systematic hyperparameter tuning and L2 regularisation.
End-to-end ML data product predicting next-day high prices for four cryptocurrencies, with an LSTM model for Ethereum achieving a test MAE of 70.8, served via FastAPI and visualised in a Streamlit dashboard with live market data.
Machine learning service predicting rain in Sydney 7 days ahead (binary classification) and cumulative precipitation over 3 days (regression), trained on 34 years of Open-Meteo historical data and deployed as a REST API on Render.
Full-stack geospatial platform built for the Honduran Ministry of Agriculture, enabling government professionals to publish agricultural map layers and citizens to explore agri-food production data across the country.
Complete rebuild of a Honduran on-demand delivery and ride-hailing iOS app in Swift 5, achieving a 90% reduction in bugs and a 40% improvement in performance.
iOS application for safety training in the petrochemical industry, built from scratch in Swift 5 and used by 20+ enterprise clients to certify employees in oil and gas safety standards.
A reusable Swift UI framework for displaying standardised alert messages (success, danger, warning, info) as a clean alternative to UIAlertController in UIKit applications.
Open to AI engineering and full-stack roles where production instincts matter. Remote, hybrid, or Sydney in person.
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