Résumé :

Blockchain, a decentralized peer-to-peer network storing append-only transaction data, has emerged as a transformative technology with potential applications beyond cryptocurrencies like Bitcoin and Ethereum. Despite the wealth of available data, its practical applications are still evolving and not fully understood. My research adopts an interdisciplinary approach, integrating visual analytics, network science, and data mining, aiming to understand emergent user behaviors and mechanisms in the blockchain system. In my doctoral thesis, I focused on Bitcoin mining, a critical activity to validate transactions and ensure network stability. My work included a systematic review of blockchain data visualization, algorithm development for miner identification, and empirical analyses of mining pool behaviors. I developed a visual analytics tool, MiningVis, to explore historical mining concentration and identify factors influencing miners’ behaviors. My ongoing research addresses transaction network analysis and Decentralized Finance (DeFi) risk management. I investigate Bitcoin money flows using graph embedding techniques to differentiate user behaviors. Collaborating with economists, I develop simulations to assess systemic risks in decentralized lending protocols, aiming to create a visual analytics tool for risk detection and trend analysis. Furthermore, I obtained a grant from CHIST-ERA to work on the FairOnChain project (Fair and Modular Blockchain Data Infrastructure for Open Science and Society). The project aims to develop a FAIR-compliant infrastructure to standardize cross-platform data APIs, facilitating effective querying, annotating, and referencing of blockchain data. The collaboration comprises four consortium partners: École Polytechnique, EPFL, HEG Genève, and Imperial College London.