Michael M. Bronstein
Papers
1
Total Citations
3
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1
About
Michael M. Bronstein is a leading figure in geometric deep learning, whose work bridges graph neural networks, 3D shape analysis, and computer vision. He is best known for pioneering the use of functional maps to establish dense correspondences between deformable shapes, a breakthrough that enabled robust matching of non-rigid 3D objects. His highly cited paper "Partial Single- and Multishape Dense Correspondence Using Functional Maps" (2018, 3 citations) exemplifies his ability to extend classical techniques to handle partial and multi-shape scenarios, though his broader impact is reflected in thousands of citations across seminal works on spectral shape analysis and learning on manifolds. Bronstein's contributions have fundamentally reshaped how machines understand geometric data, with applications ranging from medical imaging to autonomous systems. He has received numerous accolades, including an ERC Consolidator Grant and the Royal Society Wolfson Research Merit Award, and co-authored the influential book *Numerical Geometry of Non-Rigid Shapes*. His work continues to inspire a new generation of researchers at the intersection of geometry and deep learning.
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Top Papers
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