Information technology has enabled collection of massive amounts of data in science, engineering, social science, finance and beyond.  Extracting useful information from massive and high-dimensional data is the focus of today’s statistical research and practice. After broad success of statistical machine learning on prediction through regularization, interpretability is gaining attention and sparsity is being used as its proxy. With the virtues of both regularization and sparsity, sparse modeling methods (e.g., Lasso) has attracted much attention for theoretial research and for data modeling.