I am an M.S. Physics candidate at the University of Central Florida, specializing in computational modeling and experimental data systems. My background combines rigorous physics training with practical experience building machine learning pipelines, predictive models, and data visualization tools.
I have worked on production deployments that processed more than 300,000 records and have contributed to peer-reviewed research across astrophysics, spintronics, and quantitative finance. I enjoy turning raw, complex data into structured, actionable insights that support better decisions.
My experience includes designing Python-based machine learning workflows, automating measurement protocols, and developing predictive data products for research teams. I have also built preprocessing pipelines for unstructured simulation outputs and improved model performance through targeted methodology refinements.
In quantitative finance, I designed an evaluation framework for options data, applied stochastic modeling and backtesting, and delivered production-ready predictive models. I value analytical discipline, statistical rigor, and the ability to validate ideas with real data.
I also built an end-to-end public analytics tool for U.S. gun violence intelligence, integrating CDC records, statistical modeling, and interactive visualization into a deployed platform. This project reflects my ability to manage the full data lifecycle from ingestion to delivery.
Overall, I bring a cross-domain perspective that blends physics, machine learning, and data analytics. I am motivated by challenging problems, collaborative research, and building systems that are both technically sound and practically useful.
Designed and executed Python-based machine learning pipelines across 10+ material configurations, accelerating data pipeline development by an estimated 35% using LLM-assisted development. Built automated measurement protocols and predictive data products for a 5-person research team, reducing measurement uncertainty by an estimated 20%.
Executed 50+ first-principles DFT simulations in Python and Fortran, identifying where computational models failed to predict observed physical behavior across 3 defect configurations. Developed automated preprocessing pipelines to normalize unstructured simulation outputs, improving model accuracy by an estimated 15%.
Designed a quantitative evaluation framework analyzing 250+ trading days of options data, surfacing a systematic IV vs. RV performance gap and earning Top Project recognition. Applied stochastic modeling and backtesting techniques using Python to build production-ready predictive models.
Architected an end-to-end data pipeline ingesting 300,000+ CDC records spanning 25 years, integrating statistical modeling and interactive Tableau-style visualization into a deployed analytics platform. Deployed a production-ready machine learning analytics tool serving users across all 50 states.
Queried and normalized auction and logistics data across multiple SQL source systems, producing analytics-ready datasets for executive dashboards and regional expansion strategy. Built Tableau dashboards joining U.S. Census demographic data with internal logistics records to surface regional cost patterns and growth signals.
M.S. Physics, Concentration in Spintronics, Materials Science and Electrical Engineering
M.S. Physics candidate specializing in spintronics, materials science, and electrical engineering.
B.S. Physics, Minors in Computer Science and Applied Mathematics
Undergraduate degree in physics with minors in computer science and applied mathematics.
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