The rapid development of Artificial Intelligence (AI) and machine learning has generated powerful tools for extracting patterns from complex data. However, many AI techniques lack integration with rigorous statistical methodology — particularly in inference, uncertainty quantification, interpretability, and principled modeling.

This special issue aims to advance the interface between AI-inspired techniques and statistical methodology by focusing on statistically principled methods that enhance data-analytic insight, support scientific inference, and demonstrate impact on real data problems. We invite contributions that develop innovative statistical approaches motivated by AI ideas, or that apply such approaches to extract interpretable and reproducible results from complex datasets.

Contributions may emphasize methodological development, data-driven applications, or both, provided statistical reasoning and innovation are central. Preference will be given to work that includes thorough evaluation on real data, meaningful comparison with existing statistical approaches, and careful discussion of uncertainty, interpretability, and reproducibility.

Topics of Interest

Topics of interest include, but are not limited to:

· Statistically principled model adaptation of AI/ML representations for structured data (e.g., high-dimensional, longitudinal, network, spatial).
· Novel statistical methodologies inspired by AI techniques that preserve interpretability and inference (e.g., regularized models integrating learned features with statistical frameworks).
· Uncertainty quantification and validation for AI-enhanced models in statistical settings.
· Hybrid statistical learning pipelines combining AI components with classical statistical estimation and testing.
· Applications of AI-driven statistical methods to real scientific problems with substantive interpretation (e.g., bioinformatics, environmental science, social science, imaging).

Guest Editors
Dr. Tianchen Gao (Beijing International Center for Mathematical Research (BICMR), Peking University, China)
Dr. Ao Sun (Marshall School of Business, University of Southern California, USA)

Deadline
The deadline for manuscript submissions is January 31st, 2027, but we can accommodate extensions on a case-by-case basis. All papers will be published as open-access articles upon acceptance.

Submission Instructions
Please submit the full manuscript to Statistics Innovation via our Submission System. Additionally, please choose a topic of this special issue when submitting and mention it in your cover letter. For further inquiries, please contact Guest Editors and Statistics Innovation Editorial Office(stati@maxapress.com).