The Journal of Statistical Planning and Inference invites submissions for a special issue featuring selected papers to be presented at the Statistical Learning and Data Science (SLDS 2026) conference with a particular focus on inference and intelligence. The special issue will highlight recent advances in statistical methodology, theory, computation, and applications in statistical learning and data science.
We welcome high-quality contributions on topics including, but not limited to:
Statistical learning theory and methodology, including high-dimensional, nonparametric, semiparametric, and modern statistical learning methods;
Machine learning and artificial intelligence, including deep learning, generative models, reinforcement learning, and statistically principled approaches to AI;
Statistical inference and uncertainty quantification, including selective inference, conformal inference, and inference for complex learning procedures;
Causal inference and decision-making, including causal discovery, treatment effect estimation, policy learning, and individualized decision rules;
Modern computational and data-driven methods, including distributed and federated learning, online learning, active learning, adaptive sampling, and scalable statistical computation; and
Applications of statistical learning and data science in science, engineering, medicine, economics, and other fields.
We particularly encourage papers that develop rigorous statistical foundations for emerging data science methodologies or bridge theoretical and methodological advances with important applications.
The special issue will feature selected contributions from SLDS 2026. Participation in the conference does not guarantee acceptance. All manuscripts must contain original, unpublished work that is not under consideration elsewhere and will undergo the journal's regular peer-review process. Submissions should follow the author guidelines of the Journal of Statistical Planning and Inference and indicate the special issue in the cover letter.
We look forward to receiving contributions that advance statistical learning and data science and foster connections among statistical theory, methodology, computation, and applications.
Yang Ning, Department of Statistics and Data Science, Cornell University — yn265@cornell.edu
Joshua Cape, Department of Statistics, University of Wisconsin–Madison — jrcape@wisc.edu
Lucas Mentch, Department of Statistics, University of Pittsburgh — lucasmentch@gmail.com
Boxiang Wang, Department of Statistics and Actuarial Science, University of Iowa — boxiang-wang@uiowa.edu