>_ ML Engineer & AI Systems Builder
I build production-grade machine learning systems at the intersection of quantitative finance and deep learning — from reinforcement learning market makers to physics-informed neural networks.
I'm a machine learning engineer with a focus on production ML systems, quantitative finance, and applied deep learning. I graduated Magna Cum Laude from Auburn University with a BS in Computer Science and an undergraduate certificate in AI Engineering.
My work spans the full ML lifecycle — from research and model architecture to deployment, drift detection, and monitoring. I care about building systems that don't just work in a notebook, but hold up in production.
Physics-Informed Neural Network with Black-Scholes PDE constraint trained on real AAPL options data. Computes five Greeks via PyTorch autograd, models the full volatility surface, prices American options via LSM, and uses a PPO agent for dynamic hedging.
Production-grade reinforcement learning market maker for high-frequency trading. Gymnasium environment with 12-dimensional order book observations, PPO agent trained with Avellaneda-Stoikov reward, Poisson order flow, VPIN toxicity tracking, and square-root market impact modeling.
End-to-end MLOps platform for telecom customer churn prediction. Features model versioning, statistical drift detection, Optuna-powered hyperparameter optimization across XGBoost, LightGBM, and Random Forest ensembles, plus real-time inference monitoring and cost tracking.
Production-grade portfolio optimization combining Modern Portfolio Theory, the Black-Litterman model, and XGBoost-driven return forecasting. Includes dynamic rebalancing, risk metrics computation, and an interactive dashboard for live portfolio analysis.