HDDA-XVI

HDDA-XVI
16th Workshop on High-Dimensional Data Analysis
Padova, Italy · August 31 – September 3, 2027

The 16th Workshop on High-Dimensional Data Analysis (HDDA-XVI) will take place in Padova, Italy, from August 31 to September 3, 2027.

The increasing complexity and heterogeneity of contemporary data pose major challenges for statistical modelling, inference and computation. High-dimensional and complex data arise in genomics and medicine, economics and finance, environmental and social sciences, and many other areas of data science. These settings require methods combining rigorous statistical foundations with computational scalability, robustness, and interpretability.

Founded by Professor S. Ejaz Ahmed in 2011 at the Fields Institute in Toronto, the HDDA workshop series has become an international forum for advancing high-dimensional statistics and data science. Following the 15th edition held in Istanbul in 2026, HDDA-XVI aims to bring together researchers working on the theoretical, methodological and computational foundations of high-dimensional statistics and data analysis, as well as scholars addressing substantive applications.

The workshop seeks in particular to:
– highlight and extend the range of methods available for high-dimensional data analysis and clarify their statistical foundations;
– identify open problems and emerging research directions in high-dimensional inference, regularization, computation and learning;
– strengthen collaboration between theoretical researchers and scholars working in application areas;
– promote interaction among established researchers, early-career scholars and doctoral students from different countries and disciplinary backgrounds.

Participants will be invited to contribute oral presentations and posters and to propose special sessions on emerging or interdisciplinary topics. HDDA-XVI will offer a forum for presenting recent advances, discussing unresolved problems and identifying promising directions for future research.

Topics of interest include, but are not limited to:

* regularization and sparsity;
* high-dimensional estimation and inference;
* variational and approximate Bayesian methods;
* tensor and functional data analysis;
* causality in large-scale settings;
* statistical learning and data science methods;
* text mining and high-dimensional text data;
* graphical models and network data analysis;
* imprecise, fuzzy and uncertain data analysis;
* high-dimensional applications in omics, biostatistics, economics, finance, environmental and social sciences