​Research

My research sits at the intersection of Network Science, Semantic Web, and key domains such as finance, law, and administration. At its core, my work tackles the fundamental challenge of structuring complex knowledge as graphs—a critical issue across numerous applications. By leveraging graph data, I develop innovative mining algorithms rooted in mathematical principles to extract meaningful insights.

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While graph structures offer powerful tools for representing complexity, maintaining their mathematical rigor remains an ongoing challenge. Much work remains in developing precise, theoretically sound methods for analyzing these intricate systems. At the same time, I am careful to avoid unnecessary complexity or meaningless structures. To mitigate this risk, I collaborate closely with domain experts in finance, law, and administration, ensuring my models and analyses are both practically relevant and theoretically robust. This commitment is further reinforced through extensive international and industry collaborations.

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Through this approach, I strive to enhance decision-making, optimize processes, and uncover meaningful connections within complex systems—ultimately bridging the gap between mathematical theory and real-world applications.