High-Dimensional Data Analysis (HDDA) emerged in response to modern scientific and technological advances that generate data with a very large number of variables relative to sample size. Breakthroughs in genomics, bioinformatics, finance, image processing, and machine learning highlighted limitations of classical methods and motivated new theory, models, and computation for high-dimensional settings, including sparsity-based methods, regularization, dimension reduction, and modern regression and inference theory. IMS Representative(s) on Program Committees: S. Ejaz Ahmed and Yi Li