Generative and geometric machine learning — developed rigorously, deployed at scale, for scientific discovery.
The boundary between mathematics, physics, and machine learning
is dissolving. Transformers, diffusion maps, and stochastic dynamics
are not separate tools — they are different regimes of the same geometry.
DAC is built on this unification. We develop the infrastructure
to run these methods at scales that matter to science.
Coifman–Lafon diffusion maps at 100M-sample scale. B300-native sparse eigensolver. Hours, not weeks. Built for MD trajectories, cryo-EM, XFEL, and beyond.
Additional products in development.
The Diffusion–Attention Connection
Transformers, diffusion maps, and magnetic Laplacians are usually treated as separate tools. We show they are all different regimes of a single Markov geometry built from query–key scores — unified through the static Schrödinger bridge framework. DMAP is the equilibrium representative. Attention is NESS.