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. Theoretically well-founded contributions and their real-world applications in laying new foundations for machine learning and data science are welcome.

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.

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

• Auto-ML
• 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 casual 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
• Reproductibility, replicability and retractability
• Green Data sciences

Infrastructures 

• IoT data analytics and Big Data
• Large-scale processing and distributed/parallel computing
• Cloud computing

Data Science for the Next Digital Frontier in

• Telecommunications and 5G
• Retail
• 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.

Submission Instructions
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/10994Authors 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.

Key Dates

Continuous submission/review process 
Starting dissemination of the CFP: April 2020
Cutoff dates: 30 September, 30 December and 1st March
Last paper submission deadline: 1 March 2021
Paper acceptance: 1 June 2021
Camera-ready: 15 June 2021

Guest Editors
Alípio Jorge, University of Porto

João Gama, University of Porto

Salvador García, University of Granada