Cooper Morgan

An online literary journal: my wife is the editor-in-chief, and I build and run the platform solo.

It covers everything from the public submission funnel to the editorial back office: a review queue, plagiarism search, team accounts, a site CMS, and a live workbench for typesetting pieces. The backend is Go with HTMX and Templ, and the frontend is strict TypeScript. The ops are the fun part: Litestream replicates SQLite to Cloudflare R2 from inside the server binary.

The submission funnel and decision emails have shipped. The road to 1.0 is a public redesign, a submitter portal, and migrating the first issue in.

As a part of my graduate research, I'm replacing the traditional terrain rendering pipeline with neural networks. Instead of decoding massive elevation tiles at runtime, TINRS trains compact models (~0.5-1MB) that the GPU evaluates directly via Vulkan compute shaders. I went through about 6+ architectures before landing on multi-resolution feature grids: 30x compression over raw data, with accuracy within the source data's own measurement noise. The Zig viewer hits 20,000+ FPS on desktop and 300 FPS on a Raspberry Pi 5. The code is public.

My main graduate research: a modular framework for DNS exfiltration research. It includes an RL agent that learns encoding strategies, an LSTM timing model that mimics real traffic patterns, and Stegatetra, a steganographic encoding I designed that hit 0% detection across every detector we threw at it. Stegatetra encodes data through which queries get sent, rather than what's in them. Presented at ACNS 2026.

For my undergraduate research, I spearheaded the development of a high-performance visualization system built for Raspberry Pi 5 using Go and Vulkan. I created a concurrent chunk system to handle large NASA datasets. The system is designed to be modular, allowing for easy integration of new data sources and visualization techniques. We are hoping to have this implemented in hobbyist aircrafts to save some lives.

A web application to streamline grading workflows for instructors and TAs, featuring backend integration with Canvas LMS using Django and a responsive React frontend. Developed based on qualitative research with faculty about their grading processes. We deployed and used it internally on a DigitalOcean droplet, but it never reached a production release. Either way, this extensively strengthened my skills by using Python and Django for backend development.