Data Science aims at gaining insights about complex real-world effects through information from existing datasets. Modern data-centric approaches combine deep foundations in Statistics and Applied Mathematics with state-of-the-art algorithms and provide a basis for Computer Science, Artificial Intelligence (AI), and Machine Learning. Data-enabled discoveries, permitted only due to recent methods and the advent of modern computing power, accelerate innovation across the Sciences and Engineering and bridge together distant fields giving rise to Information Engineering and Bioinformatics.

As the editorial (Jasra, Law and Maroulas,  2019) noted, the recent trend driving the enormous interest in the Data Science field is the explosion of available data, which has led to the emerging 4th paradigm of data-intensive science. This 4th paradigm activity includes the wealth of interesting data-driven problems arising from machine learning and AI communities. Complex data problems require a mathematical disciplinary interplay to develop new theories and address interdisciplinary questions. For example, in recent years, there have been many new theoretical developments combining ideas from topology and geometry with statistical and machine learning methods, for data analysis, visualization, and dimensionality reduction. Applications range from classification and clustering in fields such as action recognition, handwriting analysis, and natural language processing, and biology, to the analysis of complex systems, for example, related to national defense and energy networks.

Faculty in the Statistics and Data Analysis group are leaders in cutting-edge research at the frontier of Science and Engineering. Our interests cover both foundational research in Statistics and Computation, Topological Data Analysis, Statistical Learning, Bayesian Nonparametrics as well as numerous cross-disciplinary applications in Engineering and the Sciences.

Statistics and Data Analysis

• Vasileios Maroulas
• Jan Rosinski
• Ioannis Sgouralis