Final-year Data Science student @ Deakin University · Melbourne 🇦🇺
Applied AI/ML systems that connect models to real products. I'm comfortable across the stack — from data pipelines and ML models to FastAPI backends and Vue frontends.
My work tends to combine: data → ML model → API → frontend, end-to-end.
🤖 Skedy — Multi-tenant AI customer-service platform. RAG pipeline with embeddings + Supabase pgvector + OpenAI, integrated with WhatsApp Business API.
📊 Mood Predictor — Full-stack ML web app. Multivariable linear regression from scratch in NumPy, FastAPI backend, Vue frontend.
🌲 IoT Capstone (DataBytes) — Junior Lead, Models team T1 2026. Anomaly detection on time-series sensor data (Isolation Forest, PCA, z-score).
Languages: Python, JavaScript, SQL, C#, C++ ML/Data: NumPy, pandas, scikit-learn, XGBoost, RAG/embeddings Backend: FastAPI, Node.js, REST APIs, PostgreSQL, Supabase Frontend: Vue.js, React, Tailwind, map-based viz
- Junior Lead of Models team on IoT data analytics capstone (Deakin DataBytes)
- Available for T2 2026 internships / part-time data science roles
- Working on a productivity app called TaskMaster