Bin Yu is currently working on statistical machine learning theory, methodologies, and algorithms for solving high-dimensional data problems. The current research topics of her group cover sparse modeling (e.g. Lasso), structured sparsity (e.g. hierarchical and group and graph path), and analysis and methods for spectral clustering for undirected and directed graphs. Her data problems come from diverse interdisciplinary areas, including remote sensing, neuroscience, document summarization, and social networks. Her past research areas have included empirical processes, Markov Chain Monte Carlo, signal processing, the minimum description length principle (MDL), and information theory.