DS 2022 : The 25th International Conference on Discovery Science (DS 2022)

The 25th International Conference on Discovery Science (DS 2022)

https://ds2022.sciencesconf.org/

Montpellier, France, October, 10-12, 2022

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::: IMPORTANT DATES :::
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Abstract submission: June 18, 2022 [EXTENDED DEADLINE]
Full paper submission: June 20, 2022 [EXTENDED DEADLINE]
Notification: July 27, 2022
Camera ready version, author registration: August 8, 2022
Conference: October 10-12, 2022


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COVID-19
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We hope that by October the world will have returned to normality and we can welcome you in Montpellier. However, in case the COVID-19 risk persists and traveling is difficult, DS 2022 will take place either as a mixed event by offering both remote and on site presentation options or as a fully online event in the worst case. The accepted papers will still be published by Springer and the special issue will proceed as announced. In these challenging times that the whole of humanity is going through, we hope that all of you are safe and remain healthy.

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::: Scope :::
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The 25th International Conference on Discovery Science (DS 2022) 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.

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::: Keynote Speakers :::
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• Leman Akoglu, Carnegie Mellon University, USA
• Stefan Kramer, University of Mainz, Germany


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::: Submission Topics :::
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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
- Information extraction from scientific literature
- Knowledge discovery from heterogeneous, unstructured and multimedia data
- Data and knowledge visualization
- Planning to Learn
- Knowledge Transfer
- Computational Creativity
- Human-machine interaction for knowledge discovery and management
- Evaluation of models and predictions in discovery setting
- Causality modelling
- AutoML, meta-learning, planning to learn
- Explainable AI, interpretability of machine learning and deep learning models
- Learning from complex data
- Graphs, networks, linked and relational data
- Spatial, temporal and spatiotemporal data
- Unstructured data, including textual and web data
- Multimedia data
- 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 in scientific domains, such as
- Physical sciences (e.g., materials sciences, particle physics)
- Life sciences (e.g., systems biology/systems medicine)
- Environmental sciences
- Life Sciences
- Natural and social sciences



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::: Publishing :::
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Traditionally the proceedings of DS series appear in the Lecture Notes in Artificial Intelligence Series by Springer-Verlag. In addition, authors of best papers will be invited to submit their extended versions to a special issue on Discovery Science of the Machine Learning journal published by Springer. Fast Track Processing will be used to have them reviewed and published.


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::: Submission guidelines :::
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Regular research papers may contain up to fifteen (15) pages and must be formatted according to the layout supplied by Springer-Verlag for the Lecture Notes in Computer Science series. The Program Committee reserves the right to offer acceptance as Short Papers (10 pages in the Proceedings) to some submissions. The reviews are single-blind. You do not need to anonymize your submission.
Submitted papers may not have appeared in or be under consideration for another workshop, conference or a journal, nor may they be under review or submitted to another forum during the DS 2022 review process.
We encourage all authors to include their individual ORCID in their address information.
Authors can submit their regular papers via our submission page through Easychair:

https://easychair.org/my/login_author?sum=073323801fd3b7125c2b6cc57ecf0a6f;conference=267691

Authors of accepted papers must submit along with the final version of their paper a consent to publish, filled and signed. Authors of accepted papers are expected to register to the conference and present their work (see author registration date).

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Special issue and Best Student Paper Award
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The authors of a number of selected papers presented at DS 2022 will be invited to submit extended versions of their papers for possible inclusion in a special issue of Machine Learning journal (published by Springer) on Discovery Science. Fast-track processing will be used to have them reviewed and published.
There will be an award for the Best Student Paper in the value of 555 Eur sponsored by Springer.



Dino Ienco (PC Co-Chairs DS)
Pascal Poncelet (PC Co-Chairs DS)
Sašo Džeroski (General Chair DS)