Special Issue on Foundations of Data Science - Machine Learning Journal
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.
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Topics of Interest
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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 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
*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.
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Submission Instructions
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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.
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Key Dates
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Continuous submission/review process
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
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Guest Editors
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Alípio Jorge, University of Porto,
João Gama, University of Porto
Salvador García, University of Granada
Carlos Ferreira
ISEP | Instituto Superior de Engenharia do Porto
Rua Dr. António Bernardino de Almeida, 431
4249-015 Porto - PORTUGAL
tel. +351 228 340 500 | fax +351 228 321 159
m...@isep.ipp.pt | www.isep.ipp.pt
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