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High Dimensional Data Analysis

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High Dimensional Data Analysis

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Research Frontiers in Compressed Sensing and Sparse Recovery

Study of signal reconstruction from undersampled measurements leveraging sparsity assumptions and convex optimization techniques.

Sublinear Sampling Regimes Beyond the Nyquist Barrier
Coherence-Free Recovery in Highly Structured Signals
Nonconvex Optimization Landscapes in Sparse Reconstruction
Measurement-Adaptive Sensing for Dynamic Sparse Signals
Phase Transitions in Underdetermined Linear Systems
Computational Complexity of Sparse Recovery Algorithms
Sparsity Patterns in Noisy and Adversarial Settings
Matrix Completion via Low-Rank Structure Exploitation
Sampling Efficiency in High-Dimensional Geometric Recovery
Implicit Bias of Iterative Algorithms in Sparse Inference

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