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DSAA 2021 : CFP: Special Issue on Foundations of Data Science - Machine Learning Journal

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Submission Deadline Sep 30, 2020
Notification Due Jun 1, 2021
Final Version Due Jun 15, 2021
 

Call For Papers

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


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