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DS 2019 : Discovery Science


Conference Series : Discovery Science
When Oct 28, 2019 - Oct 30, 2019
Where Split, Croatia
Submission Deadline Jun 25, 2019
Notification Due Jul 29, 2019
Final Version Due Aug 20, 2019
Categories    machine learning   knowledge discovery   scientific discovery   artificial intelligence

Call For Papers

The 22nd International Conference on Discovery Science (DS 2019) provides an open forum for intensive discussions and exchange of new ideas among researchers working in the area of Discovery Science.

The scope of the conference includes the development and analysis of methods for discovering scientific knowledge, coming from machine learning, data mining, intelligent data analysis, big data analysis as well as their application in various scientific domains.

We invite submissions of research papers addressing all aspects of discovery science: papers that focus on the analysis of different types of massive and complex data, including structured, spatio-temporal and network data. We would also like to encourage contributions from the areas of computational scientific discovery, mining scientific data, computational creativity and discovery informatics. We particularly welcome papers addressing applications from different domains of science including biomedicine and life sciences, astronomy, physics, chemistry, as well as social sciences.

Applications to massive, heterogeneous, continuous or imprecise data sets are of particular interests. Possible topics include, but are not limited to:

-Knowledge discovery, machine learning and statistical methods
-Ubiquitous Knowledge Discovery
-Data Streams, Evolving Data and Models
-Change Detection and Model Maintenance
-Active Knowledge Discovery
-Learning from Text and web mining
-Information extraction from scientific literature
-Knowledge discovery from heterogeneous, unstructured and multimedia data
-Knowledge discovery in network and link data
-Knowledge discovery in social networks
-Data and knowledge visualization
-Spatial/Temporal Data
-Mining graphs and structured data
-Planning to Learn
-Knowledge Transfer
-Computational Creativity
-Human-machine interaction for knowledge discovery and management
-Evaluation of models and predictions in discovery setting
-Causality modeling
-Interpretability of machine learning and deep learning models
-AI frameworks for discovery in scientific domains
-Biomedical knowledge discovery, analysis of (multi)omics, micro-array, gene deletion, gene set enrichment data
-Machine Learning for High-Performance Computing, Grid and Cloud Computing
-Applications of the above techniques to other natural or social sciences

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