Advancing AI Through
Self-Organizing Systems
Research & development at the intersection of artificial intelligence,
self-organization, and critical systems
Research Focus
Cadenzai conducts research in artificial intelligence with work in self-organizing memory and recursive model optimization. Our approach leverages principles of criticality and emergent behavior to develop robust and adaptive AI systems.
Applications
Our research findings drive innovation across multiple domains, creating practical solutions for complex real-world challenges.
Biotechnology
Advanced AI systems for drug discovery, protein folding prediction, and genomic analysis using self-organizing principles.
Medicine
Adaptive diagnostic systems and personalized treatment optimization through recursive learning algorithms.
Finance
Self-organizing market analysis and risk assessment systems that adapt to changing financial landscapes.
Infrastructure
Critical systems monitoring and optimization for smart cities and autonomous infrastructure management.
AI Tooling
Next-generation development frameworks and tools that leverage self-organizing capabilities for enhanced performance.
Leadership
Anthony Martin
Founder
Contributed to research in self-organizing systems, cognitive architectures and AI optimization. Author of papers exploring recursive model improvement and memory organization in artificial intelligence systems.
Continues to pursue applied research in self-organizing systems and criticality, working to advance understanding in artificial intelligence.
Dr. Marcus Badgeley
M.D., Ph.D.
Top 1% board-ranked physician-scientist and highly cited in medical AI.
Broad research background spanning epidemiology, molecular biology, nanotech, neuroscience, genetics, and extensive CNN work with Google. Work includes generalizability studies, translating image recognition to language models, and navigating complex legal and ethical research challenges.
Alex Isaev
Research Coordinator
AI Engineer • Physics & Data Science
Previously at AWS, holding a bachelor's degree in Physics and Data Science from Queen Mary University of London. Key contributor to the SOMA framework development with expertise in agentic systems and quantitative finance.
Passionate problem-solver with experience in leadership roles and collaborative research, bringing hands-on technical skills from game development to network validation and financial technologies.
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