Advances in Machine Learning and Mathematical Modelling for Data-Driven Discovery
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 1099
Editor
Interests: data science; machine learning; natural language processing; data visualization
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid expansion of machine learning and data science continues to transform research and practice across multiple scientific domains. This Special Issue, “Advances in Machine Learning and Mathematical Modelling for Data-Driven Discovery”, invites contributions that explore innovative methods, models, and applications that bridge mathematical foundations with real-world data challenges.
We welcome studies addressing predictive and generative modelling, representation learning, and explainable approaches that enhance the reliability, interpretability, and scalability of data-driven systems. Particular emphasis is placed on mathematical and statistical frameworks that strengthen model generalization, support uncertainty quantification, and enable the integration of domain knowledge into learning processes.
Applications in health, biology, social sciences, and engineering are especially encouraged, as they illustrate how mathematical modelling and machine learning jointly contribute to solving complex problems in diagnosis, forecasting, decision support, and scientific discovery. We also welcome contributions on visual analytics and interpretable visualization, which remain central to transforming data into insight and supporting evidence-based reasoning.
The aim of this Special Issue is to highlight recent advances that connect methodological innovation with impactful applications, reinforcing the role of machine learning and mathematical modelling in driving the next generation of data-driven discovery.
Dr. Alvaro Figueira
Guest Editor
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.
Keywords
- machine learning
- mathematical modelling
- predictive modelling
- explainable artificial intelligence (XAI)
- uncertainty quantification
- data visualization
- interpretability
- health, social media, engineering and biological data
- data-driven discovery
- deep-learning
- large language models
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