Exploring Fundamental Questions in Computational Intelligence
Our research addresses core limitations in current AI architectures, investigates quantum-inspired classical systems, and conducts foundational work in computational mathematics.
Post-Transformer Computational Models
Current artificial intelligence systems rely heavily on transformer-based architectures with inherent computational limitations. Our research investigates alternative mathematical frameworks that challenge fundamental assumptions about neural computation and information flow.
Classical Implementations of Quantum Principles
While quantum computing promises revolutionary capabilities, practical quantum systems remain years from deployment. Our work explores whether quantum computational advantages can be achieved on classical hardware through novel mathematical approaches.
Fundamental Mathematical Frameworks
Advances in practical computing often require breakthroughs in underlying mathematics. We conduct foundational research in areas where improved mathematical frameworks could enable new computational capabilities and efficiencies.
Implications Across Multiple Domains
Enabling advanced AI capabilities on resource-constrained devices
Reducing infrastructure costs and deployment complexity
Computational independence and supply chain resilience
Accelerating research across disciplines through improved efficiency
Collaboration Opportunities
We are interested in discussing research partnerships, government funding opportunities (DARPA, NSA, SBIR), and strategic collaborations.
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