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rafaelmiguez/README.md

Rafael Miguez 👋

Computer Engineer | Software Developer | XR Researcher 🥽

I develop software and interactive systems, with experience in AR/VR prototypes, instructional interfaces, and computer-vision workflows using Unity and Unreal Engine. My work connects software development with research in human–computer interaction and immersive technologies.

Development & research interests 🔬

  • Software engineering, software architecture, and maintainable systems
  • Extended reality (XR), augmented reality (AR), and virtual reality (VR)
  • Human–computer interaction (HCI) and usability evaluation
  • Eye tracking and XR analytics
  • Unity, Unreal Engine, and computer vision
  • Immersive technologies for industrial and educational applications

Selected projects 🚀

  • AR Interactive Instructions — a Unity/C# augmented-reality prototype for step-by-step equipment guidance on Meta Quest. The public case study includes demonstration videos and selected core scripts for spatial anchors and instructional workflows.
  • Fashion-MNIST Classification — an academic TensorFlow/Keras experiment using a dense neural network, with training, prediction, and classification metrics in a Jupyter notebook.
  • Gen Ragnarok Online (GenRO) — private software-engineering project involving the development and maintenance of a Ragnarok Online server emulator with rAthena, C++, MySQL, and web tooling. Work included QA, debugging core mechanics, database and account-management integration, server administration, and user-driven improvements. The repository and implementation details are intentionally private.

Academic coursework

  • CNN Classifier — a coursework fork of RafaSantos484/cnn-classifier, used to explore image-classification workflows with Python, TensorFlow/Keras, and Poetry. The original project provides the foundation; this repository is presented as an academic exercise.

Profiles & contact 🔗

I am interested in software development, research, and collaboration in XR, HCI, and intelligent interactive systems.

Pinned Loading

  1. AR-LabFactory-Interactive-Instructions AR-LabFactory-Interactive-Instructions Public

    Unity/C# augmented-reality instructions for Meta Quest: spatial anchors, contextual UI, and equipment training. Includes demos and selected core scripts.

    C#

  2. TensorFlow-test TensorFlow-test Public

    Fashion-MNIST classification with a dense neural network in TensorFlow/Keras. Academic notebook covering training, inference, and evaluation.

    Jupyter Notebook