Passionate AI Engineer, Researcher, and Consultant architecting next-generation systems that leverage Large Language Models (LLMs), Knowledge Graphs, High-Performance Computing (HPC), and Decentralized Technologies. I thrive on tackling complex challenges, building innovative open-source tools, and sharing insights on the future of AI and its impact.
- 🧠 Core Focus: Designing and implementing advanced AI systems for knowledge representation, model optimization, code understanding, and autonomous agent collaboration.
- 🌱 Always Exploring: Next-gen AI architectures, neuro-symbolic integration, bio-inspired AI (Self-Conceptualizing KG), model compression techniques (NeuralShrink), graph database optimization (Neo4j), ethical AGI development, and quantum-ready AI systems.
- 🛠️ Key Methodologies: SPARC-Omega Vibe Coding, Knowledge Graph-Based Memory Encoding, Behavior Ontology Modeling.
- 🤝 Open to Collaborate: Eager to connect on projects involving large-scale knowledge graph systems, LLM compression/optimization, decentralized AI infrastructure, advanced web crawling/automation, and applied AI for business strategy.
- 💬 Ask me about: LLM Optimization & Memory Encoding, Knowledge Graph Design & Implementation (Neo4j), Decentralized System Architecture, AI-driven Code Analysis (CodeGraph), Model Compression Techniques (NeuralShrink), AI Ethics & Safety, Prompt Engineering, Multi-Agent Systems (MCP), Neuro-Symbolic AI.
- ⚡ Fun fact: I believe AI, augmented with structured knowledge and ethical oversight, holds the key to solving many of the world's most complex, knowledge-intensive problems.
(ts-morph, MCP, P2P, Web Scraping/Automation, CI/CD, HPC/Quantum Concepts)
(A selection of my core open-source work)
| Project | Description | Key Technologies |
|---|---|---|
| 🧠 NeuralShrink | State-of-the-art LLM compression framework achieving up to 60%+ size reduction via tensor-specific techniques (SVD, Wavelets, Sparsity, etc.) with multi-GPU support. | Python, PyTorch, CUDA, Model Compression |
| 🧬 CogniGraph | Bio-inspired framework enabling LLMs to autonomously build and evolve dynamic knowledge graphs in Neo4j, mimicking biological adaptation. | Python, Neo4j, GDS, LLMs, Bio-Inspired AI |
| 💻 CodeGraph | Transforms TS/JS codebases into queryable Neo4j graphs, enabling deep structural analysis, visualization, and MCP-powered natural language interaction. | TypeScript, Node.js, Neo4j, ts-morph, MCP |
| 🤖 SPARC-Omega | Knowledge-guided multi-agent AI framework integrating Perplexity, GitHub, and Hyperbrowser via MCP for proactive, research-backed software development. | AI Agents, MCP, Prompt Engineering, Automation |
See repositories for more projects spanning web automation, data analysis, neuro-symbolic AI, and more.
- Royse, C. (2025). Knowledge Graph-Based Memory Encoding for Large Language Models. ResearchGate.
- Royse, C. (2025). Cognitive Mirror: Full Architecture and Implementation Blueprint. ResearchGate.
- Royse, C. (2025). Modular AGI System Architecture Blueprint. ResearchGate.
- Royse, C., Skumanich, A., Kim, H. K., & Küçük McGinty, H. (2024). Examining Misogyny in Twitch Chat Logs: A Quantitative Analysis and Comparative Study. ResearchGate.
- Royse, C. (2025). Gender, Cognition, and STEM: Leveraging Neuroscience, Education, and AI to Empower Diverse Innovation. ResearchGate.
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