Hi! I am a researcher at IGN, the French National 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 (a.k.a. the Graph Reduction Spring Tour!):
– 16/05/18 : FOSS-4G, ENSG: presentation of the SuperPointGraph repository
– 04/06/18 : NoMADS, Politecnico di Milano, Nonlocal Methods for Imaging
– 06/06/18 : SIAM symposium in Bologne, Graph Techniques for Image Processing
– 18/06/18: CVPR2018, Salt Lake City : Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs (Poster)
– 25/06/18 : RFIAP2018, ENSG: Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs
– 03/07/2018: ICML2018, Stockholm: Cut-pursuit algorithm for Regularizing Non smooth Functionals with Graph Total Variation
– 10-08/08/2018: Optimization in Image Analysis Summer School, Copenhagen
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 review for ICML, NIPS, ICCV, Mathematical Reviews, IJDSA, ANR, ISPRS among many others.