I鈥檓 driven by a simple question: how can noisy market data be transformed into decisions that are rigorous, rational, and investable? I am interested in quantitative research, systematic trading, and machine learning applications in finance. At Emory University, where I studied applied mathematics, statistics, and philosophy, I built a foundation that combines mathematical modeling with structured reasoning. In my quant research work, I have developed high-frequency alpha factors from minute-level market data, using market microstructure, volatility, trade-intensity, and tail-risk signals to identify patterns that support investment decisions. I have also built machine learning pipelines for cross-sectional return forecasting, evaluating models through information coefficient, quantile performance, neutralization, and portfolio attribution. What makes my perspective distinct is that I approach markets both technically and conceptually. My philosophy training strengthened how I define problems, challenge assumptions, and communicate complex ideas clearly, while my quantitative work taught me how to test those ideas with data. I would welcome any opportunity to connect virtually or in person. 听
