Applied Research Services
Collaborative Research Programs & Technology Transfer
Sometimes, research finds.
Custom Research Solutions
Symbolic AI with Formally Constrained Machine Learning
Integrating dependent type systems with probabilistic inference engines to enable provably correct neural architectures. Our framework combines refinement types, liquid types, and proof-carrying code to constrain ML training within formally verified specifications. Applications include verified theorem proving, correct-by-construction code generation, and decidable constraint satisfaction in high-dimensional latent spaces.
Financial Mathematics Research
Quantitative Analysis & Algorithmic Trading Research
Applying stochastic optimal control and measure-theoretic probability to derivative pricing models. Research includes computational complexity of arbitrage detection (proven NP-hard in certain market microstructures), martingale methods under jump-diffusion processes, and quantum-inspired optimization for portfolio allocation. Collaboration with academic institutions and quantitative hedge funds.
Analog Neuromorphic Hardware
Interaction Net Processors for Biologically-Inspired Computation
Designing analog VLSI circuits that implement interaction combinators and symmetric interaction calculus at the hardware level. Our architecture enables massively parallel graph rewriting with constant-time β-reduction through asynchronous, event-driven computation. The happy fly™ project aims to simulate 10¹⁴ Drosophila neurons (complete connectome) using interaction net substrates, achieving neuromorphic efficiency while maintaining formal correctness guarantees.
To build machines that think, we must first build machines that compute what cannot be thought.