The physical world
is not a dataset.

The dominant paradigm of modern AI has achieved remarkable things by treating intelligence as a function from observations to outputs, refined through oceans of data. But the physical world is causal. It is dynamic. It resists manipulation, conserves energy, flows according to differential equations older than any neural network. And crucially: it acts back.

"True intelligence requires not just pattern recognition, but an understanding of why things happen, what causes what, and what would happen if."

I believe the next frontier of AI is not wider models but deeper understanding — machines that model cause and effect, that embed governing physical laws into their architecture, that build compressed, interpretable representations of how the world actually works. Algorithms that are not only accurate but controllable, auditable, and safe.

This is Physical AI: not AI applied to physical problems, but AI that is physically grounded — in causality, in mechanism, in the language of dynamics. My research advances this vision through four interlocking programs, each essential to the larger whole: