I'm a high school researcher and developer focused on Applied Mathematics, Quantitative Finance, and Computational Science. I write software to model complex systems, simulate stochastic processes, and apply mathematical frameworks to real-world data.
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Applied & Computational Mathematics: Numerical methods, linear algebra engines, and infinite series convergence (
$C++$ ). - Quantitative Research & Modeling: Stochastic processes, options pricing, time-series forecasting, and Monte Carlo probability simulations.
- Applied Machine Learning & AI: Predictive modeling pipelines, feature importance analysis, and local-first AI applications.
spacex-ipo-time-series-analysis— Deep learning framework for time-series forecasting and pattern recognition in financial evaluation models.black-scholes-model— Financial options pricing engine implementing stochastic calculus and partial differential equations.oil-feature-importance— Machine learning pipeline isolating key macroeconomic and market variables driving oil price volatility.
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ramanujan-sato-series— High-performance$C++$ implementation computing infinite series convergence for$\pi$ with low-level memory efficiency. -
gamblers-ruin-sim/blackjack-odds-simulation— Large-scale Monte Carlo engines running$1,000,000+$ iterations to simulate discrete stochastic processes and probability boundaries.
d4c-core— Local-first voice assistant architecture integrating speech recognition, command routing, and automated task workflows.heart-pal— Cardiovascular risk assessment pipeline using interactive ML visualization for predictive health metrics.
- Languages: Python, C++, SQL
- Math & Scientific Computing: NumPy, SciPy, Pandas, Matplotlib, SymPy
- Machine Learning & Frameworks: PyTorch, TensorFlow, Scikit-learn, XGBoost
- Developer Tools: Git/GitHub, Linux CLI, CMake