Hong Kong, Hong Kong SAR
I am a Physics undergraduate at CUHK (graduating 2027) with a strong background in computational methods, statistical modelling, and machine learning. Across 30+ independent projects and 15,000+ lines of Python, I have built and validated quantitative pipelines — from 7-method ODE solver frameworks to end-to-end ML pipelines achieving 98.9% cross-validated accuracy. My work spans numerical methods (RK4, Verlet, BDF/Radau), deep learning (PyTorch CNNs, LSTMs), and data analysis (pandas, scikit-learn, Matplotlib). I am comfortable moving from mathematical theory to working code, and I prioritise rigorous validation — every result is checked against analytical solutions or held-out data. I am seeking opportunities in quantitative research where I can apply physical intuition and data-driven thinking to financial modelling problems. I am particularly drawn to environments that value intellectual curiosity, precise reasoning, and the full cycle from hypothesis to production.