Over the past two decades, the field of high-dimensional statistics has experienced substantial progress, driven largely by technological advances that have dramatically reduced the cost and effort for data collection and storage across a broad range of domains, including biology, medicine, astronomy, and the social and environmental sciences. Modern datasets are increasingly complex, often exhibiting rich dependency, heterogeneity, and other features that challenge traditional statistical methods. In response, high-dimensional statistics has evolved to address more sophisticated estimation and inference problems. This evolution has, in turn, fostered deep connections with and contributions to a wide range of research areas, including optimization, concentration of measure, random matrix theory, information theory, and theoretical computer science. Given the rapid pace of recent developments in high-dimensional statistics, our goal is to synthesize representative advances, highlight common themes and open problems, and point to important works that offer entry points into the field.

翻译：过去二十年间，高维统计学领域取得了显著进展，这一进步主要得益于技术革新大幅降低了多个领域（包括生物学、医学、天文学、社会科学及环境科学）中数据采集与存储的成本和难度。现代数据集日益复杂，常呈现丰富的相关性、异质性及其他挑战传统统计方法的特征。为此，高维统计学不断演进以应对更复杂的估计与推断问题。这一演进过程又进一步促进了与优化理论、测度集中、随机矩阵理论、信息论及理论计算机科学等广泛研究领域的深度联系与贡献。鉴于高维统计学近年来的快速发展，本文旨在综合代表性进展，突出共性主题与开放问题，并指出为该领域研究提供切入点的关键文献。