Michael M. Bronstein

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Imperial College London ∙ USI Università della Svizzera italiana

Michael Bronstein is a professor at Imperial College London, where he holds the Chair in Machine Learning

and Pattern Recognition, and Head of Graph

Learning Research at Twitter. His main research expertise is in theoretical and computational methods for geometric data analysis, a field in which he has published extensively in the leading journals and conferences. He is credited as one of the pioneers of geometric deep learning, generalizing machine learning methods to graph-structured data. Michael received his PhD from the Technion (Israel Institute of Technology) in 2007. He has held visiting appointments at Stanford, MIT, Harvard, and Tel Aviv University, and has also been affiliated with three Institutes for Advanced Study (at TU Munich as Rudolf Diesel Fellow (2017-), at Harvard as Radcliffe fellow (2017-2018), and at Princeton (2020)). Michael is the recipient of five ERC grants, Fellow of IEEE and IAPR, ACM Distinguished Speaker, and World Economic Forum Young Scientist. In addition to his academic career, Michael is a serial entrepreneur and founder of multiple startup companies, including Novafora, Invision (acquired by Intel in 2012), Videocites, and Fabula AI (acquired by Twitter in 2019). He has previously served as Principal Engineer at Intel Perceptual Computing.

Featured Co-authors

Yue Wang
117 publications
Daniel Cremers
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Jian Tang
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Yann LeCun
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Guillermo Sapiro
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Stefanos Zafeiriou
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Joan Bruna
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Leonidas Guibas
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Arthur Szlam
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Ziwei Liu
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Jürgen Schmidhuber
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Utilising Graph Machine Learning within Drug Discovery and Development

Graph Machine Learning (GML) is receiving growing interest within the ph...
12/09/2020 ∙ by Thomas Gaudelet, et al. ∙ 55 ∙ share

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Tuning Word2vec for Large Scale Recommendation Systems

Word2vec is a powerful machine learning tool that emerged from Natural L...
09/24/2020 ∙ by Benjamin P. Chamberlain, et al. ∙ 0 ∙ share

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Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting

While Graph Neural Networks (GNNs) have achieved remarkable results in a...
06/16/2020 ∙ by Giorgos Bouritsas, et al. ∙ 16 ∙ share

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Learning interpretable disease self-representations for drug repositioning

Drug repositioning is an attractive cost-efficient strategy for the deve...
09/14/2019 ∙ by Fabrizio Frasca, et al. ∙ 14 ∙ share

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Transferability of Spectral Graph Convolutional Neural Networks

This paper focuses on spectral graph convolutional neural networks (Conv...
07/30/2019 ∙ by Ron Levie, et al. ∙ 32 ∙ share

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Fake News Detection on Social Media using Geometric Deep Learning

Social media are nowadays one of the main news sources for millions of p...
02/10/2019 ∙ by Federico Monti, et al. ∙ 0 ∙ share

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Isospectralization, or how to hear shape, style, and correspondence

The question whether one can recover the shape of a geometric object fro...
11/28/2018 ∙ by Luca Cosmo, et al. ∙ 4 ∙ share

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Functional Maps Representation on Product Manifolds

We consider the tasks of representing, analyzing and manipulating maps b...
09/28/2018 ∙ by Emanuele Rodolà, et al. ∙ 0 ∙ share

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Nonisometric Surface Registration via Conformal Laplace-Beltrami Basis Pursuit

Surface registration is one of the most fundamental problems in geometry...
09/19/2018 ∙ by Stefan C. Schonsheck, et al. ∙ 4 ∙ share

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Graph Neural Networks for IceCube Signal Classification

Tasks involving the analysis of geometric (graph- and manifold-structure...
09/17/2018 ∙ by Nicholas Choma, et al. ∙ 2 ∙ share

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Dual-Primal Graph Convolutional Networks

In recent years, there has been a surge of interest in developing deep l...
06/03/2018 ∙ by Federico Monti, et al. ∙ 0 ∙ share

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PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks

Deep learning systems have become ubiquitous in many aspects of our live...
05/31/2018 ∙ by Jan Svoboda, et al. ∙ 0 ∙ share

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MotifNet: a motif-based Graph Convolutional Network for directed graphs

Deep learning on graphs and in particular, graph convolutional neural ne...
02/04/2018 ∙ by Federico Monti, et al. ∙ 0 ∙ share

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Dynamic Graph CNN for Learning on Point Clouds

Point clouds provide a flexible and scalable geometric representation su...
01/24/2018 ∙ by Yue Wang, et al. ∙ 0 ∙ share

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Subspace Least Squares Multidimensional Scaling

Multidimensional Scaling (MDS) is one of the most popular methods for di...
09/11/2017 ∙ by Amit Boyarski, et al. ∙ 0 ∙ share

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Localized Manifold Harmonics for Spectral Shape Analysis

The use of Laplacian eigenfunctions is ubiquitous in a wide range of com...
07/09/2017 ∙ by Simone Melzi, et al. ∙ 0 ∙ share

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Generative Convolutional Networks for Latent Fingerprint Reconstruction

Performance of fingerprint recognition depends heavily on the extraction...
05/04/2017 ∙ by Jan Svoboda, et al. ∙ 0 ∙ share

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Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks

Matrix completion models are among the most common formulations of recom...
04/22/2017 ∙ by Federico Monti, et al. ∙ 0 ∙ share

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Geometric deep learning on graphs and manifolds using mixture model CNNs

Deep learning has achieved a remarkable performance breakthrough in seve...
11/25/2016 ∙ by Federico Monti, et al. ∙ 0 ∙ share

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Geometric deep learning: going beyond Euclidean data

Many scientific fields study data with an underlying structure that is a...
11/24/2016 ∙ by Michael M. Bronstein, et al. ∙ 0 ∙ share

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Learning shape correspondence with anisotropic convolutional neural networks

Establishing correspondence between shapes is a fundamental problem in g...
05/20/2016 ∙ by Davide Boscaini, et al. ∙ 0 ∙ share

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Efficient Globally Optimal 2D-to-3D Deformable Shape Matching

We propose the first algorithm for non-rigid 2D-to-3D shape matching, wh...
01/22/2016 ∙ by Zorah Lähner, et al. ∙ 0 ∙ share

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Partial Functional Correspondence

In this paper, we propose a method for computing partial functional corr...
06/17/2015 ∙ by Emanuele Rodolà, et al. ∙ 0 ∙ share

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Geodesic convolutional neural networks on Riemannian manifolds

Feature descriptors play a crucial role in a wide range of geometry anal...
01/26/2015 ∙ by Jonathan Masci, et al. ∙ 0 ∙ share

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Functional correspondence by matrix completion

In this paper, we consider the problem of finding dense intrinsic corres...
12/27/2014 ∙ by Artiom Kovnatsky, et al. ∙ 0 ∙ share

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Shape-from-intrinsic operator

Shape-from-X is an important class of problems in the fields of geometry...
06/07/2014 ∙ by Davide Boscaini, et al. ∙ 0 ∙ share

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Sparse similarity-preserving hashing

In recent years, a lot of attention has been devoted to efficient neares...
12/19/2013 ∙ by Jonathan Masci, et al. ∙ 0 ∙ share

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Heat kernel coupling for multiple graph analysis

In this paper, we introduce heat kernel coupling (HKC) as a method of co...
12/11/2013 ∙ by Michael M. Bronstein, et al. ∙ 0 ∙ share

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Structure-preserving color transformations using Laplacian commutativity

Mappings between color spaces are ubiquitous in image processing problem...
11/01/2013 ∙ by Davide Eynard, et al. ∙ 0 ∙ share

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Making Laplacians commute

In this paper, we construct multimodal spectral geometry by finding a pa...
07/19/2013 ∙ by Michael M. Bronstein, et al. ∙ 0 ∙ share

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Multimodal diffusion geometry by joint diagonalization of Laplacians

We construct an extension of diffusion geometry to multiple modalities t...
09/11/2012 ∙ by Davide Eynard, et al. ∙ 0 ∙ share

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Multimodal similarity-preserving hashing

We introduce an efficient computational framework for hashing data belon...
07/06/2012 ∙ by Jonathan Masci, et al. ∙ 0 ∙ share

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Descriptor learning for omnidirectional image matching

Feature matching in omnidirectional vision systems is a challenging prob...
12/29/2011 ∙ by Jonathan Masci, et al. ∙ 0 ∙ share

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Multimodal diff-hash

Many applications require comparing multimodal data with different struc...
11/07/2011 ∙ by Michael M. Bronstein, et al. ∙ 0 ∙ share

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Kernel diff-hash

This paper presents a kernel formulation of the recently introduced diff...
11/02/2011 ∙ by Michael M. Bronstein, et al. ∙ 0 ∙ share

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A correspondence-less approach to matching of deformable shapes

Finding a match between partially available deformable shapes is a chall...
01/29/2011 ∙ by Jonathan Pokrass, et al. ∙ 0 ∙ share

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Diffusion framework for geometric and photometric data fusion in non-rigid shape analysis

In this paper, we explore the use of the diffusion geometry framework fo...
01/22/2011 ∙ by Artiom Kovnatsky, et al. ∙ 0 ∙ share

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Affine-invariant geodesic geometry of deformable 3D shapes

Natural objects can be subject to various transformations yet still pres...
12/29/2010 ∙ by Dan Raviv, et al. ∙ 0 ∙ share

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Affine-invariant diffusion geometry for the analysis of deformable 3D shapes

We introduce an (equi-)affine invariant diffusion geometry by which surf...
12/29/2010 ∙ by Dan Raviv, et al. ∙ 0 ∙ share

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Diffusion-geometric maximally stable component detection in deformable shapes

Maximally stable component detection is a very popular method for featur...
12/17/2010 ∙ by Roee Litman, et al. ∙ 0 ∙ share

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The Video Genome

Fast evolution of Internet technologies has led to an explosive growth o...
03/27/2010 ∙ by Alexander M. Bronstein, et al. ∙ 0 ∙ share

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