The problems I care about share a common bottleneck: the speed at which we can traverse design space.

Every project I've worked on (batteries, robotics, medical devices) has been constrained not by a lack of ideas, but by the cost of testing them. Physical iteration is slow and expensive. Computational iteration is not nearly as fast and cheap as it should be.

In physical design, CAD formats don't talk to each other, simulations require manual setup, and manufacturing constraints live in tribal knowledge rather than computable rules. In science, experimental data is siloed, hypotheses aren't machine-readable, and the path from observation to insight is rarely automated.

The result is that most of the design space, and most of the hypothesis space, remains unexplored.

I'm interested in latent spaces for 3D geometry and scientific knowledge. Representations that let us search, interpolate, and optimize across designs and experiments.

If we can make design and discovery navigable, we can solve harder problems faster.