CV

Education

• Ph.D in Deep Learning, Le Cnam, 2021-2024 (expected)
• Engineering degree, CentraleSupelec, 2016-2020
• Preparatory classes, Lycée Lakanal, 2014-2016

Work experience

• 2021-present: Deep learning PhD student - Le Cnam
  • Image retrieval
  • Direct optimization of metrics
• Mai to October 2020: Research intern in deep learning - Le Cnam, Paris
  • Semantic segmentation of medical 3D volumes, using a multi-view approach
  • Implementation of a 2D fully convolutional segmentation network: U-Net
  • Research on a confidence based fusion with a confidence measure learned by an auxiliary neural network based on ConfidNet
• January to July 2019: Data engineer and data scientist - Sancare, Paris
  • Data scientist (2 months): Implementing methods for the interpretability of a deep neural network.
  • Data engineer (4 months): Developing data pipelines to extract data from hospitals’ IT system and structure them to Sancare’s data model.
• July to December 2018: Data engineer - Balto, Sydney

Publications

Robust and Decomposable Average Precision for Image Retrieval

Ramzi, E., Thome, N., Rambour, C., Audebert, N., Bitot, X.: Robust and decomposable average precision for image retrieval. Advances in Neural Information Processing Systems 35 (2021).

Hierarchical Average Precision Training for Pertinent Image Retrieval

Ramzi, E., Audebert, N., Thome, N., Rambour, C., Bitot, X.: RHierarchical Average Precision Training for Pertinent Image Retrieval. In: European Conference on Computer Vision. Springer (2022).