I'm a full stack developer, with a bias towards back-end distributed computing.

A native iOS metroidvania-style platformer built entirely from scratch using Objective-C and the MVC architectural pattern. I implemented the animation system, collision detection, character physics, scene management, boss mechanics, and gameplay logic while adapting and integrating custom-edited game assets for modern iPhone devices.

TheList is a cross-platform desktop application for organizing and searching multiple collections of user-defined lists. Built with the Fyne toolkit, it emphasizes fast in-memory performance and powerful regular-expression based searching to efficiently filter large collections of entries.

This environment consists of a distributed Apache Airflow Cluster backed by Celery, that utilizes an array of custom docker containers to fetch and process public IoT data from webpages, third party systems, and data buckets. This data is processed and sent to the Ingestion API. This system was designed around fast and reliable setup, deployment, testing, tear down, and failure recovery. It fetched and standardized high throughput data ranging from broad spectrum satellite and ocean data, all the way to traffic counters, solar meters, and vehicle speed sensors.

The Ingestion API is designed to function as the middle man between independent user programs orchestrated by Apache Airflow that collect IoT data, and multiple postgres database connections. This API was built with golang, and utilizes GORM and Gorilla Mux for database operations and routing, and can concurrently process multipart forms in bulk for data ingestion through http requests.

Re-integrated RoboVM into the Quorum programming language toolchain after the original compiler became unavailable. The project enables Quorum applications to be transpiled into native iOS binaries using the open-source MobiVM implementation and was ultimately incorporated into official Quorum Studio releases.

Developed interactive geospatial visualizations for large-scale IoT datasets using Python, JavaScript, and Kepler.gl. The project focuses on transforming raw sensor and telemetry data into intuitive, map-based visualizations for exploratory analysis and presentation.

Built an automated geospatial mapping pipeline that collected, processed, and visualized publicly available datasets using Python, Selenium, and R. The project automated data acquisition and generated interactive world maps for exploring environmental and IoT-related information.