Michael Bronstein

Rudolf Diesel Industry Fellow

University of Lugano/ Tel-Aviv University/ Intel

Computational Science

Daniel Cremers

Focus Group
Computer Vision and Machine Learning

Short CV

Michael Bronstein is an associate professor of Informatics at USI Lugano in Switzerland, associate professor of Applied Mathematics at Tel Aviv University in Israel, and a Principal Engineer at the Intel Perceptual Computing. Michael got his Ph.D. with distinction in Computer Science from the Technion in 2007. He is a Senior Member of the IEEE, alumnus of the Technion Excellence Program and the Academy of Achievement, ACM Distinguished Speaker, and a member of the Young Academy of Europe. His research appeared in the international media such as CNN and was recognized by numerous prestigious awards, including several best paper awards, three ERC grants (Starting Grant 2012, Proof of Concept Grant 2016, and Consolidator Grant 2016), Google Faculty Research Award (2016), Radcliffe Fellowship from the Institute for Advanced Study at Harvard University (2017), and Rudolf Diesel Industrial Fellowship from TU Munich (2017). In 2014, he was invited as a Young Scientist to the World Economic Forum, an honor bestowed on forty world's leading scientists under the age of forty. He was a guest speaker at the World Economic Forum meeting in Dalian, China in 2015. Michael is the author of the first book on deformable 3D shape analysis, editor of four books, over 100 papers in top scientific journals and conferences, and inventor of over 25 granted patents. He has chaired over a dozen of conferences and workshops in his field, and has served as area chair at ECCV 2016 and ICCV 2017 and as associate editor of the Computer Vision and Image Understanding journal. Besides academic work, Michael is actively involved in the industry. He has co-founded and served in leading technical and management positions at several startup companies, including Invision, an Israeli startup developing 3D sensing technology acquired by Intel in 2012.


2017 Radcliffe fellowship, Harvard University

2017 Rudolf Diesel industrial fellowship, TU Munich

2016 ERC Consolidator Grant 2016 ERC Proof of Concept Grant

2016 Best Paper Award, Symposium on Geometry Processing

2016 Google Faculty Research Award

2015 Intel High Five Award

2015 ACM Distinguished Speaker

2015 Elected member, Global Young Academy

2015 Elected member, Young Academy of Europe

2014 World Economic Forum Young Scientist

2012 EUROGRAPHICS Service Award

2012 ERC Starting Grant

2005 Adams Fellowship

2005 Best Paper Award, Copper Mountain Conference on Multigrid Methods

2003 Hershel Rich Technion Innovation Award

2003 Gensler Prize

2003 Honorary Student Delegate, International Achievement Summit

2002 Alumnus, Technion Excellence Program

2002 Kasher Prize for best undergraduate project

2002 Thomas Schwartz Award for best undergraduate project

2001 Technion Humanities and Arts Department prize

Research Interests

• Theoretical and computational methods in spectral and metric geometry and their application to problems in computer vision, pattern recognition, shape analysis, computer graphics, image processing, and machine learning

• Deep learning on non-Euclidean structured data (graphs and manifolds)

Selected Publications

  • F. Monti, D. Boscaini, J. Masci, E. Rodolà, M. M. Bronstein: Geometric deep learning on graphs and manifolds using mixture model CNNs. Proc. Computer Vision and Pattern Recognition (CVPR), 2017 mehr… BibTeX
  • Litany, O.; Rodolà, E.; Bronstein, A. M.; Bronstein, M. M.; Cremers, D.: Non-Rigid Puzzles. Computer Graphics Forum 35 (5), 2016, 135-143 mehr… BibTeX Volltext ( DOI )
  • Boscaini, D.; Masci, J.; Rodolà, E.; Bronstein, M. M.; Cremers, D.: Anisotropic Diffusion Descriptors. Computer Graphics Forum 35 (2), 2016, 431-441 mehr… BibTeX Volltext ( DOI )
  • Rodolà, E.; Cosmo, L.; Bronstein, M. M.; Torsello, A.; Cremers, D.: Partial Functional Correspondence. Computer Graphics Forum 36 (1), 2016, 222-236 mehr… BibTeX Volltext ( DOI )
  • D. Boscaini, J. Masci, E. Rodolà, M. M. Bronstein: Learning shape correspondence with anisotropic convolutional neural networks. Proc. Neural Information Processing Systems (NIPS), 2016 mehr… BibTeX
  • A. Kovnatsky, K. Glashoff, M. M. Bronstein: MADMM: a generic algorithm for non-smooth optimization on manifolds. Proc. European Conf. Computer Vision (ECCV), 2016 mehr… BibTeX
  • Boscaini, Davide; Eynard, Davide; Kourounis, Drosos; Bronstein, Michael M.: Shape-from-Operator: Recovering Shapes from Intrinsic Operators. Computer Graphics Forum 34 (2), 2015, 265-274 mehr… BibTeX Volltext ( DOI )
  • D. Eynard, A. Kovnatsky, M. M. Bronstein, K. Glashoff, A. M. Bronstein: Multimodal manifold analysis using simultaneous diagonalization of Laplacians. IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), 2015, 2505-2517 mehr… BibTeX
  • D. Boscaini, J. Masci, S. Melzi, M. M. Bronstein, U. Castellani, P. Vandergheynst: Learning class-specific descriptors for deformable shapes using localized spectral convolutional networks. Computer Graphics Forum, 2015, 13-23 mehr… BibTeX
  • J. Masci, D. Boscaini, M. M. Bronstein, P. Vandergheynst: Geodesic convolutional neural networks on Riemannian manifolds. Proc. Workshop on 3D Representation and Recognition (3dRR), 2015 mehr… BibTeX