This page highlights applied analytical work across statistical learning, prediction, financial modelling, and robust risk analysis. The emphasis is on how I approach data problems, compare methods, and communicate results in a practical setting.
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This group project develops a generalized principal component regression approach for stock price modelling and stock selection. The focus was on handling high-dimensional financial factors through PCA-based compression before prediction.
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This group project analyzes Hong Kong horse racing data and compares several machine learning methods for predicting race outcomes. The work combines data preparation, feature engineering, model comparison, and result interpretation.
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This project implements a robust risk assessment toolkit in R based on F-divergences. It reproduces and extends ideas from the literature and applies them in both simulation-based and practical examples.
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This project studies diffusion probabilistic models under Gaussian mixture models and compares several major samplers under the same target distribution. The work focuses on both theoretical understanding and practical performance across dimensions.