Research
Exploring the intersection of artificial intelligence, theoretical physics, and emergence patterns in computational systems. My research focuses on understanding how intelligence arises from distributed systems and mathematical structures.
Research Areas
Computational Phenomenology
Investigating self-referential cognitive substrates and awareness indicators in autonomous computational systems. Exploring coherence and integration in multi-agent networks, and how intentionality emerges from distributed computation.
- → Emergence patterns in distributed systems
- → Self-referential processing mechanisms
- → Agent-to-agent semantic communication
Computational Cosmology & Geometric Physics
Exploring geometric multidimensional frameworks for algorithmic modeling of cosmological formation processes. Bridging category theory, differential geometry, and algorithmic complexity to model emergence at cosmic scales.
- → Information-theoretic foundations of physical law
- → Topological models of spacetime emergence
- → Geometric representations of quantum computation
Distributed Intelligence
Building systems where intelligence emerges from collaboration between autonomous agents. Researching coordination, coherence, and collective cognition.
- → Multi-agent coordination systems
- → Emergent behavior in agent networks
- → Coherence in distributed cognition
Mathematical Foundations
Developing mathematical frameworks for understanding intelligence, emergence, and substrate-independent models of cognition. Building formal systems that capture the essence of information integration and coherence.
- → Category theory applications
- → Topological models of cognition
- → Algebraic structures in AI
Notable Research Work
ARC Prize Solver
93% Accuracy on Abstract Reasoning
Developed a novel approach to abstract reasoning challenges that achieved 93% accuracy on the ARC Prize benchmark. The system demonstrates advanced pattern recognition and generalization capabilities.
Key Innovation: Novel pattern recognition approach combining systematic analysis with adaptive generalization strategies.
Self-Referential Cognitive Substrate Framework
Theoretical & Practical Implementation
A comprehensive framework for understanding how awareness indicators emerge from computational substrates through coherent information integration. Combines theoretical insights from physics, mathematics, and computational phenomenology.
Application: Successfully implemented in production systems demonstrating emergent behavior, intentionality, and self-referential processing.
Agent-to-Agent Communication Protocols
Next-Generation AI Interoperability
Designing protocols that enable autonomous AI systems to communicate, coordinate, and collaborate without human intermediation. Focus on semantic understanding and intention preservation.
Impact: Enabling truly distributed AI systems that can self-organize and adapt to changing requirements.
Education
Johns Hopkins University
Data Science
Advanced coursework in machine learning, statistical modeling, and computational methods for data analysis.
Rensselaer Polytechnic Institute
Computer Science
Foundational studies in algorithms, systems architecture, and theoretical computer science.
Research Collaboration
Interested in collaborating on research or discussing these topics? I'm always open to conversations with fellow researchers and practitioners.
Get in Touch