​Research

Our research focuses on the mathematical modeling and analysis of complex knowledge structures, drawing on network science, semantic web technologies, and graph-based data mining. At its core, our work addresses a fundamental challenge across many fields, which is how to represent fragmented, dynamic, and often ambiguous social knowledge in a way that is both theoretically rigorous and practically relevant. We develop algorithms based on network mining and machine learning that identify meaningful patterns while preserving the underlying semantics of the data.

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Graph structures provide powerful tools for representing complexity, but maintaining their mathematical rigor remains a persistent challenge. There is still much to be done in developing precise, well-founded methods for analyzing these intricate systems. At the same time, we avoid introducing unnecessary complexity or structures that lack interpretability. To address this, we collaborate closely with experts in finance, law, and public administration, ensuring that our models and analyses remain both practically useful and theoretically robust. This commitment is supported by a wide network of international and industry collaborations.

 

In parallel, we also explore the use of alternative data in finance and the structure of financial networks. This includes the analysis of bank transfers and cryptocurrency systems, with particular attention to micro-level dynamics, transient relationships between flows and stocks, and arbitrage opportunities. This research has led to a range of collaborations with both industry partners and international universities.

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Through these efforts, we aim to support better decision-making, deepen scientific understanding, and uncover meaningful relationships within complex systems. Our goal is to bridge the gap between mathematical theory and real-world applications.