I get a kick out of turning research ideas into fast, testable code — usually withRust orC/C++ for performance andPython for experimentation/flexibility.

• Reinforcement- & deep-learning algorithms — from classic q-learning/policy-gradients to autoencoder/transformer-based processes
• Distributed RL frameworks for HPC and robotics
• Experimental OS research ranging from the kernel to interactions with userland
• Neurosymbolic SAT engines that blend logic with learning
• Generative UX tools — e.g., GAN-driven mouse-trajectory synthesis, adaptive song equalizer pipeline, etc.
• Computer-vision apps that leverage CV models for real-time use

LinkedIn • open to collaboration and feedback

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1. RelayRL-prototypeRelayRL-prototypePublic

   Unstable Single-Agent Distributed Reinforcement Learning Framework written in Rust & exported to Python

   Rust 1

2. knowleJ-graphknowleJ-graphPublic

   Propositional logic SAT solver for deterministic/stochastic expressions using Neo4J graph database & machine learning system optimization

   Java 1

3. chess-cv-recognitionchess-cv-recognitionPublic

   Real-time chess recognition with Stockfish GUI suggestions

   Jupyter Notebook 1 1

4. generative-mouse-trajectoriesgenerative-mouse-trajectoriesPublic

   GAN-based imitation learning of user mouse movements

   Rust 2