Application of Mathematical Theory in Data Science
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D: Statistics and Operational Research".
Deadline for manuscript submissions: 31 May 2026 | Viewed by 659
Special Issue Editors
Interests: data science; privacy preserving; game theory
Special Issue Information
Dear Colleagues,
The rapid development of data science has led to an increasing need for robust mathematical frameworks to tackle the complex challenges posed by large-scale data analysis. This Special Issue aims to explore how mathematical theories can be applied to enhance various aspects of data science, including machine learning, statistical modeling, data mining, and artificial intelligence (AI). We seek contributions that demonstrate how mathematical approaches can improve key processes such as data preprocessing, feature extraction, model development, and performance evaluation.
The Special Issue will also explore the role of mathematics in ensuring the security, privacy, and robustness of data-driven systems. As AI models and data applications are increasingly deployed in sensitive areas, ensuring the integrity and reliability of these systems is crucial. Contributions that address the mathematical foundations behind secure data processing, model transparency, and the mitigation of adversarial risks are highly encouraged.
By bringing together insights from mathematics and data science, this Special Issue will provide a platform for advancing the state of the art in developing more accurate, efficient, and secure data-driven technologies. We invite researchers to share innovative solutions that tackle real-world challenges in both academic and industrial settings.
Dr. Lefeng Zhang
Dr. Minfeng Qi
Guest Editors
Manuscript Submission Information
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Keywords
- big data analytics
- computational statistics
- statistical data analysis
- data science
- statistical learning
- advanced data analytics
- data preprocessing
- feature extraction
- data-driven systems
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