About

I am a computer vision and machine-learning engineer based in Île-de-France, France.

My work sits at the intersection of image processing, computer vision, machine learning, and software engineering. I enjoy turning imaging and ML ideas into systems that can be evaluated, integrated, and used under real-world constraints.

What I work on

My experience includes:

• Image processing, enhancement, restoration, and analysis
• Object detection, recognition, segmentation, and tracking
• Deep-learning model training, evaluation, and optimisation
• Vision systems for edge devices and latency-constrained applications
• Optical and medical-imaging algorithms
• Camera, projector, and depth-sensor calibration workflows
• Learning-based image and light-field compression

Research background

I received a PhD in Signal and Image Processing from Université Paris-Saclay and CentraleSupélec. My doctoral research, conducted within the RealVision Marie Skłodowska-Curie project, focused on learning-based coding for light-field imaging.

This work included hybrid learning-based and HEVC-based coding, lossless light-field compression, and distributed coding approaches. It resulted in peer-reviewed conference and journal publications, a book chapter, and a doctoral dissertation.

Engineering approach

I work best when a problem can be expressed clearly and assessed through meaningful measurements. In practice, that means understanding the data, defining appropriate evaluation criteria, comparing alternatives carefully, and designing software that other people can run and maintain.

I am comfortable moving between research prototypes and production-oriented engineering: Python notebooks and model training on one side; C++, integration, optimisation, hardware constraints, and deployment on the other.