• Engineered a machine learning pipeline to predict pressure ulcer occurrence in 53,494 spinal cord injury patients using neural networks and logistic regressions with PyTorch and scikit-learn• Aided clinical decision-making by deriving 30 cutpoints for pressure ulcer risk factors using decision tree classifiers• Streamlined model accessibility by developing a RESTful API with Flask and containerizing models with Docker• Improved model accuracy by 18% through collaboration with clinicians and domain-specific feature engineering

• Created a comprehensive database of U.S. mass shootings spanning 39 years (1980-2018) on a $289k grant• Architected a multi-threaded C++ application using Tesseract OCR and regex for extracting and categorizing shooting incidents from historical newspaper images, boosting research speed by 200%• Performed statistical analysis revealing stable trends in mass shootings and a 9% presence of assault weapons

• Optimized foot patrol distribution in Las Vegas by implementing Dijkstra’s algorithm in C++ using hotspot data• Wrote the 2020 Annual Report and compiled numerous literature reviews for Dr. George Pesta

• Managed instruction for 75 students, including lecture delivery, office hours, and assessment development