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Volume 3, 2020

Volume 2, 2019

Volume 1, 2018

Mathematical Foundations of Computing

Call for papers

Call for Papers:  Special Issue on Analysis in Data Science: Methods and Applications

Guest Editors:  Xin Guo (Hong Kong Polytechnic University),  Lei Shi (Fudan University)

The fast development of information technology in the last twenty years has provided us with a solid foundation of data collection, storage, transmission, and computation. The productive interactions between industry and academia have been generating many successful algorithms and cultivating significant theoretical developments. Data science, a new yet fast-developing multi-disciplinary field across mathematics, statistics, and computer science, has well become an active research area. On the one hand, the broad adoption of data-driven decision making stimulates more applications of data science and more extensive collection of data. Many new challenges are proposed, including the analytics of data with large size (the "big data" phenomenon), the methodology and theory of privacy protection, the design and analysis of distributed learning schemes, the renaissance of deep learning and artificial intelligence, and so on. On the other hand, the paradigm shift of embracing more data-driven and quantitative methodologies is happening in many branches of physical and sociological sciences. New algorithms are desired to efficiently analyze data with different forms, for example, text data in different languages, count and grouped data in social science, genome and protein data, network data, and so on. Mathematics, as the cornerstone of modern science, would provide more methodologies and insights for the further development of data science.

The objective of this special issue is to provide new insights, analysis, and methodologies for the emerging demand in data analytics. In particular, we aim to explore the recent progress of mathematical and statistical analysis of algorithms in data analysis, to identify new methods for data mining, to provide insights and deepen the theoretical understanding of both the data-related problems, and the methodologies, and to nurture and promote the research of data science.

Topics included:

This special issue of Mathematical Foundations of Computing would cover several related topics, including but not limited to:

1. Mathematical foundations of data science
2. Learning theory
3. Approximation theory for data science
4. Non-uniform sampling
5. Kernel-based approximation methods
6. deep learning
7. High-dimensional data analysis
8. Data analytics in social science

Important dates:

  • Manuscript submission deadline: February 01, 2020
  • Completion of peer reviews: June 01, 2020
  • Tentative publication date: August 01, 2020



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