How I approach difficult technical problems.

Some valuable problems arrive without a clear playbook. I’m Brian Theory, an independent Applied AI and R&D consultant, helping ambitious teams turn unusual ideas into useful systems.

Through August 2025, I led software engineering work supporting NASA Ames Thermophysics Research. Before that, I worked in computational neuroscience and contributed to several publications, including a 2024 study I led using explainable machine learning to develop shorter psychometric measures.

I developed correspondence matrices, an original structural approach to Boolean computation, and actively maintain its public GitHub repository. It includes the implementation, reproducible comparisons with established algorithms, and detailed explorations of potential applications, from formal verification and rule systems to symbolic AI. If you’re technically curious, you can examine the code and evidence directly.

Codex and Claude Code are part of my development process, helping me explore alternatives and implement faster. I frame the problem, choose the approach, connect the pieces, and test whether the system behaves as intended.

That approach applies to multi-agent automation, evidence-grounded research tools, and scientific or creative software, especially where requirements evolve as the work reveals new information.

I take on focused consulting and fractional R&D engagements for technically difficult work. I’m drawn to learning new domains, investigating open problems, and adapting as research moves forward.

If you’re building something unusual or unproven, visit CorrespondenceMatrices.com and tell me what you’re working on.

Current website: CorrespondenceMatrices.com. Implementation, papers and limitations are linked in Selected Technical Work and the CV.