معرفی
Rémi Flamary is a Monge Assistant Professor at the Applied Mathematics department and CMAP Laboratory of École Polytechnique. His research focuses on Optimal Transport for machine learning, with applications in transfer learning for biomedical and astronomical data. He investigates Gromov-Wasserstein distances for structured data and cross-architecture model transfers in deep learning.
Research Interests:
- Optimal Transport theory and applications
- Machine learning with structured data
- Transfer learning across domains
- Deep learning model interoperability
Associated with the CMAP Laboratory (Centre de Mathématiques Appliquées), his work bridges theoretical mathematics and practical machine learning challenges. No specific grants or awards are listed in the provided information.
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