2022 - Special Issue on Foundations of Data Science with Machine Learning Journal for Journal Track at DSAA’2022

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.
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
João Gama, University of Porto, Portugal
Nitesh Chawla, University of Notre Dame, United States
Joshua Huang, Shenzhen University, China