Technical Work Supporting
my Ambitions.

Real-time computer vision + ML pipeline built from a custom dataset of 2,000+ labeled samples. Focused on reliable live inference, confidence controls, and accessible feedback via text and TTS.

Manual implementation of a feedforward network (MNIST @ 95%). Built to make gradient flow, parameter updates, and model behavior interpretable.

Interactive platform built in Elm, visualizing 20+ algorithmic processes step-by-step. Used by UTA faculty for live lectures and self-paced study.

Full-stack puzzle system with a flood-fill generator that guarantees unique solutions. Emphasizes constraint satisfaction, search, and API design.