The cluster focuses on Data Science innovation and rigorous Mathematical Methods. We integrate econometrics, math, stat to tackle complex data challenges, creating new algorithms and theoretical foundations for impactful discoveries.

The Data Science and Mathematical Methods research cluster focuses on the theoretical foundations and methodological aspects essential for the empirical study of complex systems in economics, finance, and management. By bridging the gap between rigorous mathematical theory and cutting-edge data science, the cluster develops the analytical tools necessary to navigate today’s data-driven landscape.

Our research agenda involves both methodology experts and applied scientists, addressing issues at the core of the current scientific debate that affect society, institutions, and global markets.

Its research activities are classified into the following interconnected domains:

• Mathematical and numerical methods (decision theory, risk analysis, optimization) of economics, finance and management science and their theoretical foundations.
• Statistical methods and Econometrics, including computational statistics and Monte-Carlo simulations, data analysis and forecasting methods, big data sets and high-dimensional models, classification and network analysis.
• Algorithmic foundations of data science, leveraging mathematical structures to improve machine learning performance, scalability, and the interpretability of complex models.
• Applied quantitative modeling.