AI / Machine Learning • Data Science
I build AI systems from start to finish: multi-agent document pipelines, fine-tuned and calibrated transformers, LLM evaluation harnesses, and behavioral prediction models.
Madison, WI · Open to relocation linkedin.com/in/jackjburleson github.com/Exios66
Now
M.S. candidate in Psychology: Data Science in Human Behavior at UW–Madison (expected Dec 2026). My capstone, Digital-Mailroom, is built with industry partner American Family Insurance. It looks for ways to cut the cost of AI inference and improve how documents are read, sorted, and managed.
Featured Projects
Digital-Mailroom
Capstone · American Family Insurance
One LangGraph pipeline that joins legal-document routing, prompt experiments, deterministic scoring, and deployment, in a reproducible Docker environment.
Mailroom-ML
Training, evaluation & calibration
A calibrated ModernBERT classifier that handles routine documents itself and sends uncertain ones to an LLM. Exported to ONNX and served with FastAPI on Modal.
RVL-CDIP-Classifier
Vision LLMs & prompt engineering
Zero-shot document classification with vision LLMs: 29 tracked prompt versions, accuracy checked on larger slices than the tuning set, and the cost of every page measured.
Wave Laboratory
Side project · simulation & graphics
A real-time 3D ocean and ship-motion sandbox that runs in the browser, built on spectral wave physics.
What I’m interested in
I’m interested in AI systems that know when they are unsure. That means calibrated classifiers, evaluation you can trust, and pipelines that pass hard cases to a stronger model or a person instead of guessing. Outside of that, I like turning physics into interactive software, and I’m interested in how people think, which is what my psychology training is about.