RAG pipelines, secure LLM tooling, and machine learning systems that turn messy information into fast, reliable software.
About Me
CS major and Math minor in the FSU Honors Program. Right now I'm building a production RAG pipeline at Access to Arabia and researching differentially private graph neural networks at FSU. Most of my projects exist because I wanted to know whether something actually worked: how well an LLM resists 200+ injection attacks, whether a cheaper model can serve the same prompt, what a retrieval stack scores when you measure it honestly.
Teams beat in the programming contest held by FSU ACM in Spring '26.
Passing tests backing Lodestone, my hybrid search engine.
Portfolio projects across AI, security, and data.
Experience
Portfolio
AI academic planner that reads deadlines from uploaded files and plain-English notes, then generates conflict-free schedules with one-click calendar export.
Installable Claude Code plugin that makes any AI subagent fleet 2.5–3× more token-efficient and 30–50% faster, applying four dispatch rules with zero config, benchmarked on production agent sessions.
Claude Code skill that grounds AI coding agents in live OSV/CVE data, flagging known-vulnerable dependency versions at 100% precision and recall before they ship.
Claude Code skill that scores every AI change from 0 to 100 by running your tests, lints, and risk-weighted diff analysis, so you know whether to accept it before it touches your files.
Hybrid search engine that fuses dense-vector and BM25 retrieval with reranking for sharp, relevant results, backed by 262 passing tests.
RAG app that turns any course syllabus into a question-answering bot, pulling policies, deadlines, and grading rules straight from the document.
Red-team tool that fires 200+ prompt-injection and jailbreak attacks at any LLM, scoring how well it resists and ranking models on a live leaderboard.
LLM router that sends each prompt to the cheapest capable model, then shows the real cost and latency saved versus always calling the top model.
Reproducible statistics dashboard that runs real OLS regression on public data, reporting p-values and 95% confidence intervals while filters recompute the inference live in the browser.
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