Advances in Data Visualization and Analysis
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 November 2026 | Viewed by 213
Editors
Interests: visualization; rendering; interaction; visual analytics; dimension reduction; machine learning; kernel density estimation; time-series data; clustering; Monte-Carlo techniques; distance measures; Lloyd’s relaxation; Voronoi diagrams; vector/chamfer distances; volume rendering; data structures (kd-trees); GPU programming
Interests: visual analytics; machine learning; digital geometry processing; pattern recognition and vision; multi-dimensional data analysis; information retrieval and indexing
Special Issues, Collections and Topics in MDPI journals
Interests: volume rendering; ray tracing; 3D medical image visualization; lossless medical image compression; computer graphics algorithms; explainable AI in medical analytics; high‑performance visualization; implicit surface modelling; machine learning for imaging; scientific visual analytics
Special Issue Information
Dear Colleagues,
In recent years, machine learning has emerged as a powerful enabler for advancing visualization capabilities. Neural‑network‑based dimensionality reduction, such as autoencoders, variational frameworks, deep manifold learning, and contrastive representation techniques, offers new pathways for rendering complex, high‑dimensional datasets into intuitive 2D and 3D visual interfaces. These approaches not only improve the scalability and expressiveness of visual analysis but also enable interactive exploration of structures that were previously difficult or impossible to perceive.
This Special Issue invites contributions that push the boundaries of how data can be represented, explored, and understood. Our aim is to bring together innovative methodologies, theoretical insights, and practical applications that demonstrate the next generation of visual analytics.
This Special Issue is particularly interested in research directions including, but not limited to, the following:
- Novel visual encoding and interaction techniques for large, dynamic, or multi‑modal datasets.
Explainable visual analytics, integrating model interpretability with visualization to help users understand complex decisions made by AI systems. - Scalable and real‑time visualization systems, including GPU‑accelerated pipelines and edge‑based rendering approaches.
- Human‑centred and perceptually grounded design, leveraging cognitive science to improve clarity, accessibility, and insight generation.
- Automated and AI‑assisted visualization design, enabling systems to recommend or generate effective visual representations based on data characteristics or analytical goals.
- Temporal and event‑based visualization, especially for high‑frequency, irregular, or streaming data sources.
- Multivariate and multi‑dimensional statistical visualization, combining classical and modern approaches for insight extraction.
- Topology‑aware and structure‑preserving visualization, including graph, manifold, and geometric data techniques.
Prof. Dr. Mark Jones
Dr. Gary K. L. Tam
Dr. Benjamin Mora
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. Applied Sciences 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 2400 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
- data visualization
- visual analytics
- machine learning
- dimensionality reduction
- high‑dimensional data
- explainable AI
- interactive visualization
- AI‑assisted visualization design
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