Keywords: Machine Learning, Optimization, Signal Processing, Information Theory, and Statistics.

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.

Our mathematical premise spans across different disciplines and leverages a diverse background. The theoretical tools that we use come from approximation theory, discrete mathematics, optimization theory, statistics, coding theory, graph theory, algorithms, and information theory.

Our work is complemented and guided by technological implementations and tests with real data.

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 Past Projects