The Machine Learning journal (Springer) is pleased to announce a special issue dedicated to Discovery Science 2024. We invite submissions of high-quality research papers exploring methods and applications of machine learning, data mining, artificial intelligence, and big data analytics for scientific knowledge discovery.
Topics of Interest
We welcome contributions addressing various aspects of discovery science, including but not limited to:
Core AI & Machine Learning Techniques
- Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning
- Transfer Learning, Online Learning, and Active Learning
- Explainable AI and Interpretable Machine Learning
- Anomaly and Outlier Detection
- AutoML, Meta-Learning, and Planning to Learn
- AI for High-Performance, Grid, and Cloud Computing
Data Science & Knowledge Discovery
- Knowledge Discovery and Data Mining
- Time-Series Analysis and Change Detection
- Spatiotemporal, Text, and Unstructured Data Analysis
- Complex Network Analysis and Causal Modeling
- Data and Knowledge Visualization
Ethical & Trustworthy AI
- Fairness, Bias, Privacy, and Accountability in AI
- Trustworthy and Responsible AI
- Human-Machine Interaction for Knowledge Discovery
Application Domains
- Physical Sciences: Materials Science, Particle Physics
- Life Sciences: Biomedicine, Systems Biology, Medicine
- Social & Environmental Sciences: Economics, Sociology, Climate Science
Important Dates
- Paper Submission Deadline: March 3, 2025
- First Notification of Acceptance: May 26, 2025
- Revised Submissions Deadline: July 14, 2025
- Final Notification of Acceptance: September 15, 2025
- Expected Online Publication: November/December 2025
Submission Guidelines
- Submissions should be made via the Springer Machine Learning journal.
- Select the special issue type: “S.I.: Discovery Science 2024”.
- Recommended paper length: up to 20 pages (including references).
- Papers should be formatted using Springer Nature’s LaTeX template.
- Authors must include a supplementary information sheet summarizing their contributions.
- Extended versions of conference papers must contain at least 30% new content beyond the original publication.
Guest Editors
- Riccardo Guidotti (University of Pisa, Italy)
- Anna Monreale (University of Pisa, Italy)
- Dino Pedreschi (University of Pisa, Italy)
For queries regarding submissions, contact:
riccardo.guidotti@unipi.it | anna.monreale@unipi.it | dino.pedereschi@unipi.it
More details & submission link: Springer Machine Learning Journal
We look forward to your contributions!