Focus and Scope

Saintis: Journal of Mathematical Data Science focuses on high-quality, theory-oriented research at the intersection of mathematics, statistics, computation, and data science. The journal prioritizes mathematically rigorous contributions that strengthen the foundations of modern data-driven methodologies, including machine learning, artificial intelligence, and advanced data analytics.

Focus

• Mathematical and statistical foundations that underpin data science and machine learning, emphasizing rigor, reproducibility, and theoretical contribution.
• Development of new mathematical models, optimization methods, and algorithmic analyses for data-driven inference, learning, and prediction.
• Research that demonstrates both scientific novelty and mathematical precision, with practical relevance when supported by substantial theoretical justification.

Scope

The journal welcomes original research articles, comprehensive reviews, and methodological papers within (but not limited to) the following areas:

• Mathematical Foundations of Data Science: mathematical analysis of machine learning models, statistical learning theory, generalization and convergence analysis, representation learning theory.
• Optimization and Numerical Methods: convex and non-convex optimization, stochastic optimization, gradient-based and gradient-free algorithms, numerical linear algebra for data science.
• Probability, Statistics, and Stochastic Processes: probabilistic modeling, Bayesian inference and variational methods, high-dimensional statistics, stochastic processes, random matrix theory.
• Mathematical Modelling for AI and Machine Learning: differential-equation-based learning (e.g., Neural ODEs, PDE-based models), dynamical systems and learning, mathematical analysis of neural networks, operator learning, kernel methods.
• Data-Driven Modeling and Simulation: surrogate modeling, sparse modeling, physics-informed machine learning (PINNs), data assimilation, inverse problems.
• Algorithms and Computational Mathematics: efficient algorithms for large-scale data, graph theory and network science, combinatorial optimization, computational statistics.
• Applications with Mathematical Rigor: application-driven studies are considered only when the mathematical contribution is substantial (e.g., new theory, provable guarantees, or novel mathematically grounded methodology). Example domains include epidemiology, finance and risk modelling, climate and environmental modelling, and engineering systems.

Out of Scope

To maintain a clear scholarly identity and ensure rigorous standards, the journal does not consider:

• Purely experimental machine learning studies without meaningful mathematical grounding or theory.
• Application-only papers that do not introduce or justify a significant methodological or theoretical contribution.
• Descriptive data analytics or simple prediction studies without mathematically substantive models.
• Data science studies that primarily report empirical performance without theoretical analysis, guarantees, or rigorous validation.

Section Policies

Matematika Terapan

Open Submissions

Indexed

Peer Reviewed

Aljabar

Open Submissions

Indexed

Peer Reviewed

Analisis

Open Submissions

Indexed

Peer Reviewed

Statistik

Open Submissions

Indexed

Peer Reviewed

Komputasi

Open Submissions

Indexed

Peer Reviewed

Peer Review Process

Review Policy

Saintis: Journal of Mathematical Data Science applies a rigorous double-blind peer review process to ensure the quality, originality, and scientific integrity of all published articles. In this review model, the identities of both authors and reviewers are concealed throughout the review process.

Every manuscript submitted to the journal is first evaluated by the editorial team to assess its suitability with respect to the journal’s focus, scope, and basic scholarly standards. Manuscripts that pass this initial screening are then sent for external peer review.

Review Process

• Each manuscript is reviewed by at least two independent reviewers who possess relevant expertise in the subject area of the submission.
• Reviewers are asked to provide objective, constructive, and confidential evaluations of the manuscript.
• Based on the reviewers’ reports, the editor will make one of the following decisions: accept, minor revision, major revision, or reject.

Review Criteria

Reviewers are requested to assess submissions based on the following criteria:

• Originality and novelty of the research contribution.
• Mathematical rigor, theoretical soundness, and methodological correctness.
• Relevance to the journal’s aims and scope.
• Clarity of problem formulation, analysis, and presentation.
• Adequacy of references and positioning within existing literature.
• Significance of results for mathematical data science and related fields.

Review Timeline

• The initial editorial screening is typically completed within 1–2 weeks after submission.
• The peer review process generally takes 4–6 weeks, depending on reviewer availability and the complexity of the manuscript.
• Authors are promptly informed of editorial decisions and reviewer comments through the journal’s online submission system.

Reviewer Selection and Ethics

• Reviewers are selected based on their academic qualifications, publication record, and expertise relevant to the manuscript topic.
• The journal avoids conflicts of interest by ensuring that reviewers have no personal, professional, or financial relationships with the authors.
• All reviewers are expected to adhere to principles of confidentiality, objectivity, and academic integrity throughout the review process.

Through this peer review policy, the journal is committed to maintaining high academic standards and ensuring that all published articles contribute meaningfully to the advancement of mathematical data science.

Publication Frequency

Saintis: Journal of Mathematical Data Science is published on a regular issue-based schedule. Articles are published collectively as part of a complete issue after completing the peer review and editorial process.

The journal is published four times a year (quarterly), with issues released in February, May, August, and November. Each calendar year constitutes one volume consisting of four numbered issues.

Open Access Policy

This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.

Archiving

This journal utilizes the LOCKSS system to create a distributed archiving system among participating libraries and permits those libraries to create permanent archives of the journal for purposes of preservation and restoration. More...

Publication Ethics and Malpractice Statement

Publication Ethics and Malpractice Statement

Saintis: Journal of Mathematical Data Science is committed to upholding the highest standards of publication ethics and takes all possible measures against publication malpractice. This statement follows the principles and best practices recommended by the Committee on Publication Ethics (COPE).

Duties of Authors

• Authors must ensure that their manuscripts are original works and have not been published or submitted elsewhere.
• All data, results, and sources must be presented accurately and cited appropriately.
• Plagiarism, data fabrication, data falsification, and redundant publication are strictly prohibited.
• All authors listed must have made a significant contribution to the research.
• Any potential conflicts of interest must be clearly disclosed.

Duties of Editors

• Editors are responsible for deciding which articles submitted to the journal should be published, based solely on academic merit and relevance to the journal’s scope.
• Editors must evaluate manuscripts without regard to race, gender, religious belief, citizenship, or political philosophy of the authors.
• Editors and editorial staff must not disclose any information about a submitted manuscript to anyone other than the corresponding author, reviewers, or publisher.

Duties of Reviewers

• Reviewers assist editors in making editorial decisions and help authors improve the quality of their manuscripts.
• Reviewers must treat manuscripts as confidential documents.
• Reviews should be conducted objectively, with clear and supported arguments.
• Reviewers should identify relevant published work that has not been cited.

Ethical Oversight

• The journal will investigate allegations of misconduct in accordance with COPE guidelines.
• Articles found to involve unethical behavior may be rejected, retracted, or corrected.

Open Access Policy

Saintis: Journal of Mathematical Data Science provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge. All articles are published under an open access license without subscription or paywall barriers.

Copyright and Licensing

Authors retain copyright of their work and grant the journal the right of first publication. Articles are published under a Creative Commons Attribution (CC BY) license unless otherwise stated.