Interfacing Statistics, Machine Learning and Data Science from a Probabilistic Modelling Viewpoint

Dear Colleagues,

Modern statistics is the science of learning from data. As a discipline, it is concerned with the collection, analysis, and interpretation of data, as well as the effective communication and presentation of results relying on data. Statistics is a highly interdisciplinary field; in developing methods and studying the theory that underlies the methods, statisticians draw on a great variety of mathematical and computational tools.

Today, vast amounts of data are transforming the world and the way we live in it. Statistical methods and theories are used everywhere, from health, science and business to managing traffic and studying sustainability and climate change. This, in turn, will create the need for a much closer collaboration between statisticians, mathematicians, computer scientists and domain scientists. The call for a new generation of data scientists working at this interface is becoming louder and louder; there is a strong need to develop data-science university curricula.

Undoubtedly, fundamental statistical research has laid important foundations upon which Data Science approaches have been established. Conversely, modern (applied) statistics is continuing to pave a broad road to its data-science future.

Machine Learning has substantially advanced through statistical learning. Two fundamental ideas in the field of statistical learning are uncertainty and variation. The common basis for dealing with these complex issues is probabilistic modelling of the problems at hand.

The aim of this Topic is to encourage interested researchers in applied mathematics and statistics, engineering science disciplines, and bio-, geo- and environmental sciences to present original and recent developments on interfacing statistical inference with advanced machine learning and data science concepts and approaches for model selection, data analysis, estimation and prediction, uncertainty quantification and risk analysis in their research work. We particularly welcome novel applications of these concepts for the following:

• Statistical process control in industrial manufacturing;
• Predicting natural hazards and climate change processes;
• Graph modelling for energy, telecommunication and environmental monitoring;
• Development of efficient numerical algorithms for big data analysis;
• Model estimation (including variable selection) and validation;
• Regularisation methods;
• Causal inference and targeted learning;
• Ensemble learning methods.

Note that submission is still possible until 31 December 2024, despite of missed abstract submission deadline.

Prof. Dr. Jürgen Pilz
Dr. Noelle I. Samia
Prof. Dr. Dirk Husmeier
Topic Editors