Call for Papers: Special Issue on Foundations of Data Science
Data science is currently a very active topic with an extensive scope, both in terms of theory and applications. Machine Learning is one of its core foundational pillars. Simultaneously, Data Science applications provide important challenges that can often be addressed only with innovative Machine Learning algorithms and methodologies. This special issue focuses on the latest developments in Machine Learning foundations of data science, as well as on the synergy between data science and machine learning. We welcome new developments in statistics, mathematics and computing that are relevant for data science from a machine learning perspective, including foundations, systems, innovative applications and other research contributions related to the overall design of machine learning and models and algorithms that are relevant for data science. We welcome theoretically well-founded contributions and their real-world applications in laying new foundations for machine learning and data science.
This special issue solicits the attention of a broad research audience. Since it brings together a variety of foundational issues and real-world best practices, it is also relevant to practitioners and engineers interested in machine learning and data science.
Accepted papers will be presented at the IEEE DSAA conference in Porto, October 2021.
The paper will be published in the Machine Learning journal and a two-page extended abstract will be published in the DSAA’2021 conference proceedings.
Topics of Interest
We welcome original research papers on all aspects of data science in relation to machine learning, including the following topics:
- Machine Learning Foundations of Data Science
- Fusion of information from disparate sources
- Feature engineering, Feature embedding and data preprocessing
- Learning from network data
- Learning from data with domain knowledge
- Reinforcement learning
- Evaluation of Data Science systems
- Risk analysis
- Causality, learning causal models
- Multiple inputs and outputs: multi-instance, multi-label, multi-target
- Semi-supervised and weakly supervised learning
- Data streaming and online learning
- Deep Learning
- Emerging Applications
- Autonomous systems
- Analysis of Evolving Social Networks
- Embedding methods for Graph Mining
- Online Recommender Systems
- Augmented Reality, Computer Vision
- Real-Time Anomaly, Failure, image manipulation and fake detection
- Human Centric Data Science
- Privacy preserving, Ethics, Transparency
- Fairness, Explainability, and Algorithm Bias
- Accountability and responsibility
- Reproducibility, replicability and retractability
- Green Data Sciences
- IoT data analytics and Big Data
- Large-scale processing and distributed/parallel computing
- Cloud computing
- Data Science for the Next Digital Frontier
- Telecommunications and 5G
- Green Transportation
- Finance, Blockchains, Cryptocurrencies
- Manufacturing, Predictive Maintenance, Industry 4.0
- Energy, Smart Grids, Renewable energies
- Climate change and sustainable environment
Contributions must contain new, unpublished, original and fundamental work relating to the Machine Learning journal’s mission. All submissions will be reviewed using rigorous scientific criteria whereby the novelty of the contribution will be crucial.
Submit manuscripts to http://MACH.edmgr.com. Select “SI: Foundations of Data Science” as the article type. Papers must be prepared in accordance with the Journal guidelines: https://www.springer.com/journal/10994
Authors are encouraged to submit high-quality, original work that has neither appeared in nor is under consideration by other journals.
All papers will be reviewed following standard reviewing procedures for the Journal.
Journal Track - Key Dates
Continuous submission/review process
|Starting dissemination of the CFP||April, 2020|
|Cutoff dates||30 September
|Last paper submission deadline||March 1, 2021|
|Paper acceptance||June 1, 2021|
|June 15, 2021|
Alípio Jorge, University of Porto,
João Gama, University of Porto
Salvador García, University of Granada