Dynamic Formal Systems Research Laboratory
Advancing theoretical foundations of computational intelligence
The DFS Lab explores the intersection of formal logic, type theory, and artificial intelligence. We develop mathematically rigorous AI systems using advanced constraint generation and formal verification methods.
Research Focus
Formal Methods for Intelligent Systems
Our research combines theoretical foundations with practical applications, creating AI systems that are both powerful and mathematically verifiable.
Type-Theoretic AI
Developing AI systems based on System F and polymorphic lambda calculus, enabling reasoning about abstract mathematical structures and proofs.
Recursive Function Theory
Implementing Gödel's System T to create AI that can reason about computation, recursion, and mathematical induction.
Dynamic Constraint Systems
Building adaptive constraint generation frameworks that guide AI reasoning through mathematically sound logical structures.
Research Areas
Core Research Domains
Our interdisciplinary approach spans theoretical computer science, mathematical logic, and artificial intelligence
Constraint-Based Reasoning
Developing AI systems that generate, manipulate, and solve complex logical constraints in real-time applications.
Formal Verification
Creating AI systems whose reasoning processes can be formally verified and mathematically proven correct.
Proof Synthesis
Building AI that can construct mathematical proofs and verify their correctness using automated theorem proving.
Type System Design
Advancing dependent types and higher-order logic to create more expressive AI reasoning capabilities.
Meta-Mathematical Reasoning
Developing systems that can reason about mathematical structures, theories, and their relationships.
Adaptive Logic Systems
Creating dynamic logical frameworks that can evolve and optimize their reasoning strategies.
Current Metrics
Research Impact
Measuring our contributions to formal AI research
Current Projects
Next-Generation Mathematical AI
Our flagship research combines polymorphic type systems with dynamic constraint generation to create AI systems capable of sophisticated mathematical reasoning. We're building AI that doesn't just process information, but truly understands mathematical structure and logical relationships.
