In recent times, data analysis has been undergoing a profound transformation due to the rapid expansion of datasets and the proliferation of data sources. From social media networks to public health, bioinformatics to personalized medicine, environmental studies to nanoscience, and even financial analysis, diverse domains are facing new challenges. This surge has not only been confined to academic research; rather, it has permeated the practical spheres of businesses and governmental entities. As a response to this evolving landscape, there is an imperative to craft novel algorithms that can effectively scale with the dimensions of these datasets. In parallel, the development of new theoretical tools is essential to comprehend the statistical properties inherent to these algorithms. Promising breakthroughs in this realm encompass techniques such as variable selection, penalized methods, and variational inference, marking the frontier of advancements in data analysis and interpretation. Since its inception in 2011 at the Fields Institute in Toronto, HDDA gathers leading researchers in the area of high-dimensional statistics and data analysis. The objectives include: (1) to highlight and expand the breadth of existing methods in high-dimensional data analysis and their potential for the advance of both mathematical and statistical sciences, (2) to identify important directions for future research in the theory of regularization methods and variational inference, in algorithmic development, and in methodology for different application areas, facilitate collaboration between theoretical and subject-area researchers (econometrics, finance, social science, biostatistics…), and (3) to provide opportunities for highly qualified personnel to meet and interact with leading researchers in the area.