I'm a Master's student in Applied Statistics at NYU, with a Bachelor's in Statistics from Baylor University.
Passionate about leveraging data and AI to solve real-world problems, I specialize in machine learning, financial modeling, and NLP.
My work spans across healthcare, finance, and quantitative research, with expertise in building production-ready ML systems and conducting independent AI research.
Currently publishing research in peer-reviewed journals and working on cutting-edge projects in personalized medicine and financial technology.
Masters of Science in Applied Statistics
September 2024 - Present
New York City, New York
Bachelors of Science in Statistics, Minor in Biology
August 2020 - August 2024
Waco, Texas
Engineered real-time fraud detection system using Graph Neural Networks (GNN) and XGBoost in Python, processing transaction networks with 15% accuracy improvement.
Optimized cloud inference pipeline on AWS Lambda, achieving 20% reduction in response time for low-latency financial surveillance applications.
Built a production-grade options analytics platform integrating Random Forest volatility modeling with Black Scholes theory, delivering R² of 0.97 for highly liquid equity options.
Achieved 68% accuracy in predicting five-day volatility regimes and produced Sharpe ratios of 0.84–1.47 through automated signal generation.
Extended the Mehra–Prescott model to include a disaster state via a three-state Markov chain, improving historical U.S. equity market fit by 18%.
Calibrated disaster probabilities using GDP and asset return data from 1929–2020, aligning equity premia with the historical 5–7% range.
Built a tennis analytics platform integrating surface-specific Elo ratings and interpretable ML models, achieving AUC = 0.81 and 74% match prediction accuracy across 36,342 ATP/WTA matches.
Developed computer vision serve analysis with serve speed estimates within 8.2 km/h MAE of Hawk-Eye benchmarks.
Architected unified traffic optimization framework fusing Neural ODE forecasting, PDE-based flow modeling, and graph-theoretic routing, enabling 85% accurate 30-minute congestion predictions.
Implemented adaptive shortest-path routing driving 15.9% reduction in average travel time and 23.1% cut in total vehicle delay.
September 2024
The Oncologist Journal
Published research on characterization of driver mutations and gene signatures predictive of prognosis in multiple myeloma. This study identified novel biomarkers for improved patient stratification and treatment selection.
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