Specializing in Machine Learning, Financial Economics, and Information Fusion
I'm a quantitative researcher and data scientist with a Ph.D. in Financial Economics from Fordham University. My work focuses on developing machine learning models and trading strategies using information fusion techniques, combining academic research with practical applications in quantitative finance.
At OBEX Securities, I design and implement algorithmic trading strategies. My research spans machine learning, information fusion, ensemble learning, and applied econometrics. I'm particularly interested in how different models can be combined to improve prediction accuracy and portfolio performance.
Years Experience
Years in Quant Research & Trading
Strategy Return (2024-2025)
Using information fusion of ML models and three drug encoding schemes to predict ADMET properties. Achieved #1 ranking in 4 out of 22 datasets on TDC leaderboards.
Information fusion of ML models with nonparametric stochastic dominance for stock ranking. Portfolio outperformed S&P 500 with 89.5% vs 26.5% cumulative returns and Sharpe ratio of 1.4 vs 0.7.
Extension of the multitasking model examining how career concerns affect employer incentive structures in different labor market conditions.
G.P.A: 3.97/4.0
G.P.A: 3.68/4.0