Independent AI Researcher focused on developing architectures that are transparent and aligned by design, rather than attempting to fix black-box systems after the fact.

I design and build advanced AI architectures for real-world impact through direct human-AI collaboration, leveraging AI agent tools to accelerate research and implementation. I focus on conceptual design while overseeing the technical workflow.

My research centers on two primary areas: Deterministic Hierarchical Causal State Space Models (DHC-SSM) for interpretable reasoning, and State Space Meta-Reinforcement Learning (SSM-MetaRL) for safe continual adaptation. These approaches challenge the current paradigm of scaling opaque transformer architectures without addressing fundamental safety concerns.

Working outside traditional academic or corporate constraints allows me to pursue genuinely safety-first research directions that might be overlooked by institutions focused on capability advancement. This unique position enables me to explore novel architectural approaches through intensive human-AI collaboration, using AI agents as research accelerators while maintaining conceptual oversight.

I believe the AI safety community needs diverse perspectives and innovative development methodologies to address existential risks from advanced AI systems. My work emphasizes open-source development, rigorous benchmarking, and clear documentation to enable broader adoption of safety-oriented AI architectures.

I'm committed to making my research accessible to the technical safety community and contributing to the development of aligned AI systems through this collaborative research approach.