Independent researcher working at the intersection of human capability taxonomy and AI cognitive architecture. I study how intelligence is structured in humans, and how to build AI systems that mirror that structure rather than merely approximate its outputs.

I'm developing HCQM (Human Capability Quotient Map) — an integrated, hierarchical taxonomy that synthesizes existing capability research from cognitive science, psychology, and intelligence studies into a unified framework spanning eight domains: cognitive, executive, emotional/social, creative, motivational, learning, digital, and systems intelligence.

HCQM is designed for two purposes:

1. Human development — multidimensional capability assessment for targeted growth planning
2. Synthetic cognitive architecture — a prescriptive engineering blueprint for AI systems grounded in the full range of human capabilities, not just cognitive ability

The framework began as a tool I built to assess and develop my daughter's capabilities holistically. It became clear the same structure could serve as an architectural blueprint for the next generation of AI systems — ones designed to mirror the structure of human cognition rather than only its surface behavior.

Long-term direction: advancing toward more general and capable AI systems through cognitive architectures grounded in human capability research.

📄 HCQM Project: github.com/hgenix20/hcqm Status: v0.1 working draft published. v1.0 with full literature review in progress. Architecture whitepaper planned as follow-up publication.

• Master of Science in Computer Science, concentration in Artificial Intelligence — University of Nebraska at Omaha (in progress)
• PhD in Artificial Intelligence (planned, post-Master's)
• Bachelor of Science in Business Administration, concentration in Economics

10 years of professional experience spanning:

• Enterprise automation & RPA — production-grade workflow systems
• AI systems engineering — bridging classical automation with modern LLM-based agent design
• Multi-agent architectures — orchestration, verification, and compounding agent work
• API integration & orchestration — resilient connective tissue between complex systems

Microsoft AI Agents Hackathon 2025 — Best JavaScript/TypeScript Agent Winner out of 18,000+ registered developers and 570 project submissions across seven categories. Built ModelProof: Sentinel AI Chat — a dual-LLM consistency verification system that cross-checks AI outputs in real time for hallucinations, bias, and intent alignment. Treats AI responses with a sentinel guard pattern, providing users with confidence reports alongside answers.

🔗 ModelProof repository · Microsoft category winners showcase

• Cognitive architecture for LLM-based agents
• Capability taxonomies grounded in human intelligence research
• Long-horizon agent systems and memory architectures
• Pathways toward more general and capable AI through architectural design
• The gap between descriptive capability frameworks and prescriptive engineering blueprints

I welcome substantive engagement from researchers and practitioners working on adjacent problems — particularly cognitive architecture, capability frameworks, agent systems, and human-AI alignment.

• 🔗 HCQM Project: github.com/hgenix20/hcqm
• 🆔 ORCID: 0009-0002-8350-3641
• 💼 LinkedIn: linkedin.com/in/kameronmgreen
• 𝕏 X: @KameronMGreen
• 📧 Academic email: kgreen@unomaha.edu

Building in public. Researching how intelligence actually works — in humans, and in the systems we design to think.