AI Research & Publications
Geometric reasoning over brute-force scaling. 93% on ARC Prize without transformers. Interdisciplinary research combining AI, physics, mathematics, and distributed systems. Pattern recognition at the foundation of intelligence.
Research Areas
Geometric AI & Abstract Reasoning
Geometric approaches to artificial intelligence without transformers. Pattern recognition through spatial reasoning rather than language models. Abstract reasoning as geometry, not tokens. 93% accuracy on ARC Prize benchmark.
- → Pattern recognition without neural networks
- → Geometric reasoning systems
- → Spatial intelligence models
Physics-Informed AI Methods
Applying principles from theoretical physics to artificial intelligence. Energy minimization, conservation laws, and geometric optimization in learning systems. Treating intelligence as physics rather than statistics.
- → Energy-based models for reasoning
- → Topological data analysis
- → Geometric deep learning foundations
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: 93% Accuracy on Abstract Reasoning Challenge
Geometric Reasoning Without Transformers
Achieved 93% accuracy on the Abstraction and Reasoning Corpus (ARC) Prize benchmark using geometric reasoning rather than neural networks or transformers. Treats patterns as spatial structures, not language tokens. Demonstrates that abstract reasoning can be achieved through geometric principles without brute-force scaling or large language models.
Approach
- → Geometric pattern decomposition
- → Spatial transformation analysis
- → No neural networks required
Impact
- → Alternative to transformer scaling
- → Interpretable reasoning process
- → Efficient computation
Key Innovation: Geometric reasoning framework that operates on spatial structures rather than learned representations. Pattern recognition through explicit shape analysis rather than statistical correlation.
Mathematical Pattern Recognition
Cross-Domain Pattern Analysis
Research into pattern recognition across multiple domains: visual, mathematical, temporal, and structural. Mathematical frameworks for identifying isomorphisms and transformations. Combines insights from topology, category theory, and computational geometry.
Application: Pattern detection systems deployed in production demonstrating superior generalization compared to learned approaches.
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.
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