Rémi Giraud

Rémi Giraud is an Associate Professor at ENSEIRB-MATMECA (Bordeaux INP) in the Electronic department, conducting research at the IMS laboratory within the Signal and Image Processing group (MOTIVE team). He is also a member of the In2Brain research group. Dr. Giraud received his M.Sc. in telecommunications from ENSEIRB-MATMECA and a Master's in signal and image processing from the University of Bordeaux in 2014, graduating with honors as top of his class. He completed his Ph.D. in computer science at the University of Bordeaux in 2017, followed by a year as Assistant Professor before becoming Associate Professor in 2018.

• Current position: Associate Professor at ENSEIRB-MATMECA (Bordeaux INP), Electronic department
• Research affiliation: IMS laboratory, Signal and Image Processing group, MOTIVE team
• Additional affiliation: In2Brain research group
• Education: PhD in Computer Science (2017, University of Bordeaux), M.Sc. in Telecommunications and Signal/Image Processing (2014, ENSEIRB-MATMECA and University of Bordeaux)

His research focuses on image processing and analysis, deep learning, and computer vision, with particular expertise in (un)supervised image segmentation, colorization, matching techniques, irregular under-representations (superpixels), spatial relations, and medical imaging (3D MRI applications). His work bridges theoretical computer vision with practical medical applications, developing algorithms that enhance image understanding in both general and specialized contexts.

Dr. Giraud has developed several significant methodologies including SCALP (Superpixels with Contour Adherence using Linear Path), TASP (Texture-Aware SuperPixel), DSP (Dual Superpixel Descriptors), and NNSC (Nearest Neighbor-based Superpixel Clustering). His publications demonstrate consistent advancement in superpixel segmentation techniques with increasing focus on medical imaging applications, particularly brain MRI analysis.

He currently supervises multiple PhD students including Julien Walther (working on Deep Learning Models from Structural Image Representations), Eloi Navet (An AI Assembly for Neurological Disease Prediction), Edern Le Bot (Holistic Brain MRI Segmentation), and Matthieu Vilain (Semi-supervised Deep Learning for image sequences). His research has resulted in numerous publications in top-tier conferences and journals, with a clear trajectory from theoretical algorithm development to practical implementation in medical contexts.