I build LLM systems and applied ML, from research pipelines that hold up to peer review to products people actually use. Recent Computer Software Engineering grad, based in Chicago.
An end-to-end LLM pipeline analyzing an electrical-construction media corpus: data collection, GPT-based classification, human expert annotation, and rigorous evaluation, plus a deployed retrieval-augmented chatbot. Flagship research, journal manuscript in progress.
Ensemble-based uncertainty quantification for red-tide bloom prediction on Florida's coast. Four UQ methods (Bootstrap, GLUE, EnKF from scratch, LPU) propagate input uncertainty through a Random Forest classifier to produce calibrated probabilistic forecasts. Grew from notebooks into a tested, packaged Python library.
A multi-service LLM platform that grades uploaded pitch decks across six categories and persists a history view. React frontend, FastAPI/PostgreSQL persistence service, and a Node/TypeScript grading service with structured JSON-schema output, adaptive retry, and non-blocking DB writes so the API stays up if the database is down.
LLM programming tutor that teaches through Socratic hints, not solutions. Modular system-prompt architecture adapts to skill level.
Python · FastAPI · React · PostgreSQL14-model scikit-learn benchmark on 32,416 loans. Full preprocessing, tuning, and evaluation pipeline; 0.95 test ROC-AUC.
Python · scikit-learn · pandasNormalized five-table schema modeling fighters, fights, and stats. Aggregation queries for win/loss ratios and outcomes by class.
PostgreSQL · SQLEight Selenium + TestNG UI test classes driving a live browser through login, posting, search, and recovery paths.
Java · Selenium · TestNG · MavenFull Tetris recreation from scratch: rendering, collision, line-clearing, and scoring in C++ with SFML.
C++ · SFMLArduino security sensor written in AVR assembly on the ATmega328p: ultrasonic ranging and alarm logic.
Assembly · ATmega328pI'm a software engineer who leans hard into AI/ML. My work spans the full arc: collecting and cleaning messy real-world data, building and evaluating LLM and ML systems, and shipping them as real products.
My flagship is ElectriAI, an independent research project I built end to end and am publishing, advised at FGCU and funded by the Head Start Grant. Alongside it I've shipped full-stack LLM apps and a tested scientific-computing package, with a research engineer's habits: reproducible, evaluated, and honest about what the numbers say.
Comfortable across the stack, from PostgreSQL and REST APIs to systems-level C++ and assembly, with strong fundamentals in data structures, algorithms, and operating systems.