Full Professor, California Institute of Technology
3 papers at NeurIPS 2025
We present a general framework to solve inverse PDE problems through function space diffusion models in a plug-and-play way.
A neural operator framework that maps biologically interpretable embeddings of neuron models to realistic neuronal responses, thereby enabling the fast generation of ensembles of neuron models that capture experimental variability.
