Call for Papers: Special Issue on Foundations of Data Science with Machine Learning Journal

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 on Foundations of Data Science with Machine Learning Journal (MLJ) will highlight the latest development of the Machine Learning foundations of data science and on the synergy of data science and machine learning. 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. 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. 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.

Topics of Interest 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.

Submission Instructions 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.

Key Dates We will have a continuous submission/review process starting in Oct. 2021. Last paper submission deadline: 1 March 2022 Paper acceptance: 1 June 2022 Camera-ready: 15 June 2022

Guest Editors Longbing Cao, University of Technology Sydney, Australia Joao Gama, University of Porto, Portugal Nitesh Chawla, University of Notre Dame, United States Joshua Huang, Shenzhen University, China