Data science is a hot topic with an extensive scope, both in terms of  theory and applications. Machine Learning forms 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 will highlight the latest development of the Machine Learning foundations of data science and on the synergy of data science and machine learning. We welcome new developments in statistics, mathematics, informatics and computing-driven machine learning for data science, including foundations, algorithms and models, systems, innovative applications and other research contributions.

Following the great success of the 2021 MLJ special issue with DSAA'2021, this
2022 special issue will further capture the state-of-the-art machine learning advances for data science. Accepted papers will be published in MLJ and presented at a journal track of the 2022 IEEE International Conference on Data Science and Advanced Analytics (DSAA'2022) in Shenzhen, October 2022.

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Topics of Interest
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We welcome original and well-grounded research papers on all aspects of foundations of data science including but not limited to the following topics:

Machine Learning Foundations for Data Science
* Auto-ML
* Information fusion from disparate sources
* Feature engineering, embedding, mining and representation
* Learning from network and graph data
* Learning from data with domain knowledge
* Reinforcement learning
* Non-IID learning, nonstationary, coupled and entangled learning
* Heterogeneous, mixed, multimodal, multi-view and multi-distributional learning
* Online, streaming, dynamic and real-time learning
* Causality and learning causal models
* Multi-instance, multi-label, multi-class and multi-target learning
* Semi-supervised and weakly supervised learning
* Representation learning of complex interactions, couplings, relations
* Deep learning theories and models
* Evaluation of data science systems
* Open domain/set learning

Emerging Impactful Machine Learning Applications
* Data preprocessing, manipulation and augmentation
* Autonomous learning and optimization systems
* Digital, social, economic and financial (finance, FinTech, blockchains and
cryptocurrencies) analytics
* Graph and network embedding and mining
* Machine learning for recommender systems, marketing, online and e-commerce
* Augmented reality, computer vision and image processing
* Risk, compliance, regulation, anomaly, debt, failure and crisis
* Cybersecurity and information disorder, misinformation/fake detection
* Human-centered and domain-driven data science and learning
* Privacy, ethics, transparency, accountability, responsibility, trust, reproducibility and retractability
* Fairness, explainability and algorithm bias
* Green and energy-efficient, scalable, cloud/distributed and parallel analytics and infrastructures
* IoT, smart city, smart home, telecommunications, 5G and mobile data science and learning
* Government and enterprise data science
* Transportation, manufacturing, procurement, and Industry 4.0
* Energy, smart grids and renewable energies
* Agricultural, environmental and spatio-temporal analytics and climate change

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 this special issue as the article type. Papers must be prepared in accordance with the Journal guidelines:
https://www.springer.com/journal/10994

All papers will be reviewed following standard reviewing procedures for the Journal.

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Key Dates
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We will have a continuous submission/review process starting in Oct. 2021.

Last paper submission deadline: 1 April 2022

Paper acceptance: 1 June 2022

Camera-ready: 15 June 2022

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Guest Editors
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Longbing Cao, University of Technology Sydney, Australia

João Gama, University of Porto, Portugal

Nitesh Chawla, University of Notre Dame, United States

Joshua Huang, Shenzhen University, China

Carlos Ferreira

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