Hi! I am a researcher at IGN, the French Nation Geographical Institute, in the computer science department MATIS. The focus of my research is to exploit the graph structure of spatial data to improve the precision and speed of learning methods.

During my PhD I studied learning and optimization structured by large graphs (total variation, graphical models, etc). I now work on remote sensing application such as  semantic semantization of LiDAR point clouds, satellite hyperspectral imagery and surface reconstruction. I have also developed an interest for deep models structured by graphs such as recurrent networks (GRU, LSTM, ECC).

UPCOMING PRESENTATIONS:
– 18/06/18: CVPR2018, Salt Lake City : Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs (Poster)
– 06/06/18 : SIAM symposium in Bologne, Graph Techniques for Image Processing
– 04/06/18 : Politecnico di Milano, Nonlocal Methods for Imaging: Theory and Optimisation
– 16/05/18 : FOSS-4G, ENSG: presentation of the SuperPointGraph open-source repository

NEW: the new version of cut pursuit can now handle nonsmooth terms, and is considerably optimized (speed x1000 compared to state-of-the art for EEG inverse problems). The preprint is ready, the code as well!

NEW: our new framework superpoint graph, with Martin Simonovsky, is first in the Semantic3D challenge by 11.8 and 8.8 mIoU points. We also lead the S3DIS challenge by 12.4 mIoU  points. Check out the code here.

I teach machine learning at master DesiGéo and master PPMD at ENSG as a teaching assistant.

I review for ICML, NIPS, ICCV, Mathematical Reviews, IJDSA, ANR, ISPRS among many others.