First-Class Honours Statistics graduate (UNSW) with a strong focus on applied data science, machine learning and quantitative modelling.
I enjoy building end-to-end projects from data ingestion and feature engineering to modelling, evaluation and deployment with an emphasis on clarity, robustness, and real-world impact.
- 📊 Applied machine learning projects (tabular ML, forecasting, imputation)
- 📈 Quantitative finance & systematic trading research
- 🧠 Bridging academic methods with production-style pipelines
- Car Prediction Price Challenge - End-to-end tabular ML project focused on data cleaning, feature engineering, modelling and evaluation
- Landmark Image Classification - Computer vision pipeline for large-scale image classification, covering data preprocessing, model training, evaluation and inference.
- Machine Learning Driven Imputation Methods - Practical study of imputation methods for tabular ML, focusing on downstream model performance robustness and reproducibility.
- Systematic Equity Research Project (in progress) - Production-oriented ML pipeline for equity signal development, backtesting, and deployment using structured Australian financial data.
- Statistical Modelling and Machine learning for both structured and unstructured data
- Time series, forecasting, risk modelling
- Backtesting, evaluation, and model diagnostics
- AWS and Docker (learning and experimenting)
I value simple, explainable models that perform well in practice.
Clear assumptions, good validation, and reproducibility matter more than unnecessary complexity.
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