Candidate ID: DC5
Topic: Super-resolution techniques to enhance low-resolution metering and inspection data
Beneficiary: UFR
I completed my Master’s degree at Université Paris-Est Créteil (UPEC), France, specializing in data engineering and information systems. During my studies, I developed a strong interest in data processing, machine learning, and AI-based analytical systems, which led me toward research in intelligent monitoring and digital inspection.
Within the FOURIER MSCA Doctoral Network, my research focuses on AI-based super-resolution methods to enhance low-quality monitoring and inspection data. Many infrastructure monitoring applications rely on UAV imagery, open spatial data, and low-cost sensors, where limited resolution can affect automated defect detection and asset assessment. My work aims to reconstruct higher-resolution images, videos, and time-series data to reduce uncertainty and improve analysis reliability.
I joined FOURIER for its strong link between advanced AI research and real-world applications. The network’s interdisciplinary approach and close collaboration between academia and industry align well with my goal of contributing to smarter and more resilient infrastructure systems.
