Computer science student at Saint Louis University, building software that's thorough, curious, and quietly useful. Research in high-performance computing, teaching in C and C++, and hackathon projects that actually ship.
I'm a computer science student passionate about turning ideas into reliable software. My work lives at the intersection of research-grade rigor and things people actually use — from HPC libraries to hackathon projects that won first place.
Lately I've been exposing the PaRSEC runtime to Python and Julia, teaching data structures in C and C++, and shipping an OCR-driven pipeline for Canvas course creation. I like tools that are thoughtful under the hood and honest at the surface.
Outside of code: Girls Who Code executive board, SLU Pep Band, and the occasional competitive programming round.
A Python web app that turns uploaded images or text prompts into fully-structured Canvas assignments and quizzes. Under the hood: a three-stage pipeline — Google Cloud Vision extracts text via OCR, Gemini Pro 2.5 structures it into coursework, and the Canvas API publishes it directly to a live course. Cuts manual formatting time dramatically for educators.
An AI-driven web app that analyzes user-provided data to surface meaningful insights and support better decisions. Designed around clean software-engineering principles — modular data processing, intelligent analysis, and a presentation layer that keeps results readable and actionable.
A secure messaging platform for patients and healthcare providers, built in Java with Swing. Implemented end-to-end encryption to guarantee confidentiality and integrity of sensitive health data. Collaborated with Git — branches, merges, and real conflict resolution under tight deadlines.
A modular C++ application for time-series speed data. Built with custom class design and STL containers (vectors, unordered maps) for efficient ingestion and lookup. Applied least-squares regression and standard deviation to detect outliers and improve data integrity.