Call for Papers
Aims & Scope
Discovery Science 2023 conference provides an open forum for intensive discussions and exchange of new ideas among researchers working in the area of Discovery Science. The conference focus is on the use of artificial intelligence methods in science. Its scope includes the development and analysis of methods for discovering scientific knowledge, coming from machine learning, data mining, intelligent data analysis, and big data analytics, as well as their application in various domains.
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
Abstract submission (deadline): June 3, 2023
Full paper submission (deadline): June 10, 2023
Notification of acceptance: July 21, 2023
Camera-ready version, author registration: August 6, 2023
All dates are specified as 23:59:59 SST (Standard Samoa Time / Anywhere on Earth)
Contributions, written in English, must be formatted according to the guidelines of the Lecture Notes of Computer Science (LNCS) series by Springer-Verlag, which are available together with templates here: https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines. We strongly recommend using the LNCS template for LaTeX. The page limit for any contribution, including figures, title pages, references, and appendices, is 15 pages in the LNCS format. Submission of the camera-ready version of the paper has to include the authors’ consent to publish on the above Springer LNCS website.
Submissions will be reviewed following a single-blind procedure. Therefore, authors should include their names and affiliations in the paper.
Authors may not submit any paper which is under review elsewhere or which has been accepted for publication in a journal or another conference; neither will they submit their papers elsewhere during the review period of DS’ 2023.
Submission System: https://cmt3.research.microsoft.com/DS2023
Copyright Form: https://dei.uc.pt/~pha/CopyrightForm_DS2023_Proceedings.docx
The authors of a number of selected papers presented at DS 2023 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.
Best Paper Award
There will be a Best Student Paper Award in the value of 555 Eur sponsored by Springer.