DS 2020 : 23rd International Conference on Discovery Science

We invite submissions of research papers addressing all aspects of discovery science. We encourage papers that focus on the analysis of different types of massive and complex data, including structured, spatio-temporal and network data, as well as heterogeneous, continuous or imprecise data. We also encourage papers in the fields of computational scientific discovery, mining scientific data, computational creativity and discovery informatics. We welcome papers addressing applications of artificial intelligence in different domains of science, including biomedicine and life sciences, materials science, astronomy, physics, chemistry, as well as social sciences.

Possible topics include, but are not limited to:

• Artificial intelligence (machine learning, knowledge representation and reasoning, natural language processing, statistical methods, etc.) applied to science
• Machine learning: supervised learning (including ranking, multi-target prediction and structured prediction), unsupervised learning, semi-supervised learning, active learning, reinforcement learning, online learning, transfer learning, etc.
• Knowledge discovery and data mining 
• Causal modelling
• AutoML, meta-learning, planning to learn
• Machine learning and high-performance computing, grid and cloud computing
• Literature-based discovery
• Ontologies for science, including  the representation and annotation of datasets and domain knowledge
• Explainable AI, interpretability of machine learning and deep learning models
• Process discovery and analysis
• Computational creativity
• Anomaly detection and outlier detection
• Data streams, evolving data, change detection, concept drift, model maintenance
• Network analysis
• Time-series analysis
• Learning from complex data
  • Graphs, networks, linked and relational data
  • Spatial, temporal and spatiotemporal data
  • Unstructured data, including textual and web data 
  • Multimedia data
• Data and knowledge visualization
• Human-machine interaction for knowledge discovery and management
• Evaluation of models and predictions in discovery setting
• Machine learning and cybersecurity 
• Applications of the above techniques in scientific domains, such as
  • Physical sciences (e.g., materials sciences, particle physics)
  • Life sciences (e.g., systems biology/systems medicine)
  • Environmental sciences
  • Natural and social sciences