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，高维统计学领域取得了显著进展，这主要得益于技术发展极大降低了生物学、医学、天文学以及社会科学与环境科学等广泛领域的数据采集与存储成本与工作量。现代数据集日益复杂，常呈现丰富的依赖关系、异质性及其他挑战传统统计方法的特征。为此，高维统计学已发展为处理更复杂的估计与推断问题。这一演变进而促进了与优化、测度集中性、随机矩阵理论、信息论及理论计算机科学等众多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，我们旨在综合代表性进展、突出共同主题与开放问题，并指引入门该领域的重要文献。