NullDeltaQ
Physics-grounded AI — where the Laplacian is stationary.
What I’m building: AI architectures where physics is the inductive bias — energy-based models, symmetry-aware architectures, geometric deep learning, dynamical systems. Not physics as metaphor. Physics as the actual structure of the model.
NullDeltaQ is the null space of the Laplacian of Q — the harmonic regime where Dirichlet energy vanishes. That’s the mathematical condition we’re building toward.
Background: I spent 16 years leading data science and ML organizations across fintechs financial services, online and data platforms — close enough to production to know what breaks, senior enough to decide what gets built. In 2024 I stepped back from operating roles to focus entirely on the research question I couldn’t stop thinking about: what happens when you build AI from physical first principles rather than statistical ones?
Research focus:
- Energy-based models and their physical interpretation
- Symmetry-aware neural architectures (geometric deep learning)
- Dirichlet energy as structural prior in learning systems
- Dynamical systems as foundation for inference