Building production-grade web apps and AI-powered systems with FastAPI, Next.js, and a focus on performance, scale, and real-world usability — from RAG assistants to ATS scoring engines that turn 10 minutes of review into 3 seconds.
I build scalable web applications and AI-powered systems. I'm currently finishing my B.S. in Computer & Information Science at the University of Wisconsin–Eau Claire, with a certificate in Information Systems. My focus is on API design, system architecture, and database integration — using tools like FastAPI, Next.js, PostgreSQL, and modern LLM workflows to ship things that actually work in production.
LLM-powered resume scoring with job-matching APIs. Cuts review time from 10+ min to under 3 seconds.
RAG assistant that helps devs navigate 5,000+ line codebases through semantic search and live explanations.
ML model trained on 20+ seasons and 50,000+ race records. Reaches 70–75% accuracy on outcomes.
WebSocket chat with millisecond AI message filtering. Tested with 50+ concurrent users without latency drops.
REST APIs powering a dynamic frontend. Cut SQL query times from 400ms to under 250ms via indexing.
Processed 100K+ records, engineered 15+ features, improved fraud detection by 20–25% over baseline.
A semester-long research project investigating fraudulent traffic patterns in advertising data. I processed and cleaned datasets of over 100,000 records using Pandas, engineered 15+ features, and trained classification and anomaly detection models that improved detection performance by 20–25%. I evaluated models with precision and recall, then generated visual reports in Matplotlib to surface fraud trends and inform future work.
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