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   At LIONS, we are concerned with optimized information extraction from signals or data volumes. We therefore develop mathematical theory and computational methods for information recovery from highly incomplete data.

We divide our research into two synergistic theory thrusts: information scalable optimization and data acquisition, and learning theory and methods for low-dimensional signal models. These research directions dovetail in order to develop a unified theory and practical toolset for adaptive representations, sampling and computational methods for high-dimensional data that feature structured geometric and combinatorial foundations.

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