About

 To uncover the fundamental principles of representation that connect physical reality to cognitive experience. DataQualia Lab conducts foundational research at the intersection of physics and cognitive science, developing AI architectures that learn to encode the laws of the universe. Our mission is to bridge the gap between raw physical information and human-like understanding, creating the next generation of world models for scientific discovery.

Research Focus Areas: 

1. Neuro-Physical Representation Learning

How can neural networks move beyond mere pattern matching to learn the underlying symmetries and conservation laws of physics? This involves research into Equivariant Neural Networks and Manifold Learning.

2. The Information Bottleneck of Perception

Investigating how biological and artificial systems filter a nearly infinite stream of physical data into a finite, actionable "mental" representation.

3. Latent World Models

Building generative environments that don't just mimic pixels, but simulate the causal structure of reality (gravity, collisions, thermodynamics) as perceived by human observers.