Our SCML 2026 paper, Cluster-Weighted EDMD, jointly learns a soft partition of the state space and local Koopman operators to capture dynamics that vary across regions.
What interests me more broadly about Koopman theory is the possibility that complex dynamics may admit simpler descriptions through the right observables and spectral modes.
I am a research lead at Fractal,
@AEStudioLA’s skunkworks research division focused on neglected approaches in AI and alignment. We are interested in exploring whether Koopman-inspired and related operator-theoretic methods can be useful for studying language and reasoning.
If you are working on related ideas, let’s talk.
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