CFP: Special Issue on Foundations of Data Science - Machine Learning Journal

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Carlos Ferreira

Sep 5, 2020, 1:55:41 PM9/5/20
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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.


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

    in: 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.


Submission Instructions


Submit manuscripts to: Select “SI: Foundations of Data Science” as the article type.
Papers must be prepared in accordance with the Journal guidelines:

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.


Key Dates


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


Guest Editors


Alípio Jorge, University of Porto,

João Gama, University of Porto

Salvador García, University of Granada

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