SI-FDS-MLJ 2021 : 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.