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Michael M. Bronstein

Michael M. Bronstein

Award Badge
Computer Science
UK
2025

D-Index & Metrics

Computer Science

D-Index
82
Citations
36130
World Ranking
941
National Ranking
46

Research.com Recognitions

  • 2025 - Research.com Computer Science in United Kingdom Leader Award
  • 2023 - Research.com Computer Science in United Kingdom Leader Award
  • 2022 - Research.com Computer Science in United Kingdom Leader Award
  • 2020 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to 3D data acquisition, processing, representation and analysis
  • 2020 - Member of Academia Europaea
  • 2019 - IEEE Fellow For contributions to acquisition, processing, and analysis of geometric data

Overview

Michael M. Bronstein is affiliated with the University of Oxford in the United Kingdom. Their research primarily falls within the broad field of Computer Science, with a focus on Artificial Intelligence, Molecular Biology, Computer Vision and Pattern Recognition, Computational Theory and Mathematics, and Computational Mechanics.

Their work spans several main topics, including:

  • Advanced Graph Neural Networks
  • Computational Drug Discovery Methods
  • Machine Learning in Materials Science
  • Bioinformatics and Genomic Networks
  • Graph Theory and Algorithms
  • 3D Shape Modeling and Analysis
  • Complex Network Analysis Techniques

Among their recent publications are:

  • "Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges" (2021), published in arXiv (Cornell University)
  • "Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting" (2022), published in IEEE Transactions on Pattern Analysis and Machine Intelligence
  • "Utilizing graph machine learning within drug discovery and development" (2021), published in Briefings in Bioinformatics
  • "SIGN: Scalable Inception Graph Neural Networks" (2020), published in arXiv (Cornell University)
  • "De novo design of protein interactions with learned surface fingerprints" (2023), published in Nature

Frequent co-authors in Bronstein's research include:

  • Bruno E. Correia
  • Francesco Di Giovanni
  • Fabrizio Frasca
  • Píetro Lió
  • Xiaowen Dong

Michael M. Bronstein's publications are often featured in venues such as:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Nature
  • Cell Systems

In addition to articles, Bronstein has contributed to book publications, including a work published by Springer Science+Business Media titled "Imaging Systems for GI Endoscopy, and Graphs in Biomedical Image Analysis" (2022).

Their academic contributions have been recognized by several awards:

  • Member of Academia Europaea (2020)
  • Fellow of the International Association for Pattern Recognition (IAPR) (2020), for contributions to 3D data acquisition, processing, representation and analysis
  • IEEE Fellow (2019), for contributions to acquisition, processing, and analysis of geometric data

Best Publications

  • Dynamic Graph CNN for Learning on Point Clouds

    Yue Wang;Yongbin Sun;Ziwei Liu;Sanjay E. Sarma

  • Geometric Deep Learning: Going beyond Euclidean data

    Michael M. Bronstein;Joan Bruna;Yann LeCun;Arthur Szlam

  • Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs

    Federico Monti;Davide Boscaini;Jonathan Masci;Emanuele Rodola

  • Numerical geometry of non-rigid shapes

    Alexander Bronstein;Michael Bronstein;Ron Kimmel

  • Shape google: Geometric words and expressions for invariant shape retrieval

    Alexander M. Bronstein;Michael M. Bronstein;Leonidas J. Guibas;Maks Ovsjanikov

  • Geodesic Convolutional Neural Networks on Riemannian Manifolds

    Jonathan Masci;Davide Boscaini;Michael M. Bronstein;Pierre Vandergheynst

  • Three-Dimensional Face Recognition

    Alexander M. Bronstein;Michael M. Bronstein;Ron Kimmel

  • Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning.

    P. Gainza;F. Sverrisson;F. Monti;E. Rodolà

  • LDAHash: Improved Matching with Smaller Descriptors

    C. Strecha;A. M. Bronstein;M. M. Bronstein;P. Fua

  • Generalized multidimensional scaling: A framework for isometry-invariant partial surface matching

    Alexander M. Bronstein;Michael M. Bronstein;Ron Kimmel

  • Scale-invariant heat kernel signatures for non-rigid shape recognition

    Michael M. Bronstein;Iasonas Kokkinos

  • CayleyNets: Graph Convolutional Neural Networks With Complex Rational Spectral Filters

    Ron Levie;Federico Monti;Xavier Bresson;Michael M. Bronstein

  • Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

    Michael M. Bronstein;Joan Bruna;Taco Cohen;Petar Veličković

  • Data fusion through cross-modality metric learning using similarity-sensitive hashing

    Michael M. Bronstein;Alexander M. Bronstein;Fabrice Michel;Nikos Paragios

  • Expression-invariant 3D face recognition

    Alexander M. Bronstein;Michael M. Bronstein;Ron Kimmel

  • Learning shape correspondence with anisotropic convolutional neural networks

    Davide Boscaini;Jonathan Masci;Emanuele Rodolà;Michael M. Bronstein

  • Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks

    Federico Monti;Michael M. Bronstein;Xavier Bresson

  • A Gromov-Hausdorff Framework with Diffusion Geometry for Topologically-Robust Non-rigid Shape Matching

    Alexander M. Bronstein;Michael M. Bronstein;Ron Kimmel;Mona Mahmoudi

  • Efficient Computation of Isometry-Invariant Distances Between Surfaces

    Alexander M. Bronstein;Michael M. Bronstein;Ron Kimmel

  • Deep Functional Maps: Structured Prediction for Dense Shape Correspondence

    Or Litany;Tal Remez;Emanuele Rodola;Alex Bronstein

  • Fake News Detection on Social Media using Geometric Deep Learning

    Federico Monti;Fabrizio Frasca;Davide Eynard;Damon Mannion

  • Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting

    Giorgos Bouritsas;Fabrizio Frasca;Stefanos Zafeiriou;Michael M. Bronstein

  • Temporal Graph Networks for Deep Learning on Dynamic Graphs.

    Emanuele Rossi;Ben Chamberlain;Fabrizio Frasca;Davide Eynard

Frequent Co-Authors

Alexander M. Bronstein
Alexander M. Bronstein Technion – Israel Institute of Technology
Ron Kimmel
Ron Kimmel Technion – Israel Institute of Technology
Emanuele Rodolà
Emanuele Rodolà Sapienza University of Rome
Michael Zibulevsky
Michael Zibulevsky Technion – Israel Institute of Technology
Umberto Castellani
Umberto Castellani University of Verona
Yehoshua Y. Zeevi
Yehoshua Y. Zeevi Technion – Israel Institute of Technology
Xavier Bresson
Xavier Bresson National University of Singapore
Stefanos Zafeiriou
Stefanos Zafeiriou Imperial College London
Pierre Vandergheynst
Pierre Vandergheynst École Polytechnique Fédérale de Lausanne
Daniel Cremers
Daniel Cremers Technical University of Munich

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