2026 Michael M. Bronstein: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	82	941	910	46	46	349	36130

Michael M. Bronstein publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Michael M. Bronstein sits on this spectrum.

32 publications 991+

This scientist: 349 publications — 82nd percentile

82% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Michael M. Bronstein D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Michael M. Bronstein sits on this spectrum.

30 D-Index 131+

This scientist: 82 D-Index — 94th percentile

94% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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

6447
Citations
Geometric Deep Learning: Going beyond Euclidean data

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

3663
Citations
Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs

Federico Monti;Davide Boscaini;Jonathan Masci;Emanuele Rodola

1902
Citations
Numerical geometry of non-rigid shapes

Alexander Bronstein;Michael Bronstein;Ron Kimmel

937
Citations
Shape google: Geometric words and expressions for invariant shape retrieval

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

828
Citations
Geodesic Convolutional Neural Networks on Riemannian Manifolds

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

778
Citations
Three-Dimensional Face Recognition

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

774
Citations
Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning.

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

765
Citations
LDAHash: Improved Matching with Smaller Descriptors

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

725
Citations
Generalized multidimensional scaling: A framework for isometry-invariant partial surface matching

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

721
Citations
Scale-invariant heat kernel signatures for non-rigid shape recognition

Michael M. Bronstein;Iasonas Kokkinos

720
Citations
CayleyNets: Graph Convolutional Neural Networks With Complex Rational Spectral Filters

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

669
Citations
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges

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

578
Citations
Data fusion through cross-modality metric learning using similarity-sensitive hashing

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

528
Citations
Expression-invariant 3D face recognition

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

435
Citations
Learning shape correspondence with anisotropic convolutional neural networks

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

418
Citations
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks

Federico Monti;Michael M. Bronstein;Xavier Bresson

417
Citations
A Gromov-Hausdorff Framework with Diffusion Geometry for Topologically-Robust Non-rigid Shape Matching

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

346
Citations
Efficient Computation of Isometry-Invariant Distances Between Surfaces

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

308
Citations
Deep Functional Maps: Structured Prediction for Dense Shape Correspondence

Or Litany;Tal Remez;Emanuele Rodola;Alex Bronstein

282
Citations
Fake News Detection on Social Media using Geometric Deep Learning

Federico Monti;Fabrizio Frasca;Davide Eynard;Damon Mannion

207
Citations
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting

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

192
Citations
Temporal Graph Networks for Deep Learning on Dynamic Graphs.

Emanuele Rossi;Ben Chamberlain;Fabrizio Frasca;Davide Eynard

78
Citations

Frequent Co-Authors

Alexander M. Bronstein Technion – Israel Institute of Technology

Ron Kimmel Technion – Israel Institute of Technology

Emanuele Rodolà Sapienza University of Rome

Michael Zibulevsky Technion – Israel Institute of Technology

Umberto Castellani University of Verona

Yehoshua Y. Zeevi Technion – Israel Institute of Technology

Xavier Bresson National University of Singapore

Stefanos Zafeiriou Imperial College London

Pierre Vandergheynst École Polytechnique Fédérale de Lausanne

Daniel Cremers Technical University of Munich

External Links

Wikipedia of Michael M. Bronstein Personal website of Michael M. Bronstein Google Scholar page

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