Statistical Learning and Data Science: Methods, Theory, and Applications

Dear Colleagues,

This Special Issue, “Statistical Learning and Data Science: Methods, Theory, and Applications”, aims to showcase recent advances at the intersection of statistical methodology, machine learning, and data-driven discovery. This Issue brings together contributions that develop new theoretical foundations, methodological innovations, and practical tools for statistical learning and data science. Topics of interest include, but are not limited to, supervised and unsupervised learning, high-dimensional inference, regularization and sparsity, deep and ensemble learning, explainable and interpretable models, and scalable algorithms for large and complex data. 

In addition to methodological and theoretical contributions, the Issue emphasizes real-world applications of statistical learning and data science across diverse domains such as healthcare, finance, engineering, environmental science, economics, and social sciences. These applications demonstrate how modern data-driven approaches can extract meaningful insights, enhance decision-making, and address complex scientific and societal challenges. Overall, this Special Issue aims to provide a comprehensive forum for researchers and practitioners to exchange ideas, highlight emerging trends, and advance the theory and practice of statistical learning and data science.

We welcome your contributions and hope that this collection will serve as a valuable resource for both authors and readers.

Dr. Laleh Tafakori
Prof. Dr. Marco Bee
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the Special Issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.