Explore the cutting edge of sparse modeling with ‘Practical Applications of Sparse Modeling’. This essential book dives into key approaches in this rapidly evolving field, bridging statistical learning and signal processing. Discover how sparse modeling tackles challenges in high-dimensional data, enhancing model interpretability and recovering hidden signals from limited measurements. Focusing on real-world applications, this MIT Press publication delves into crucial areas like neuroscience, computational biology, and computer vision. It addresses practical challenges and presents state-of-the-art solutions for genomics, protein mass spectrometry, brain imaging stability, sequential testing, algorithmic recovery, and learning sparse latent models. An indispensable resource for researchers and practitioners in advanced data science.