World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
47
Citations
32520
World Ranking
6275
National Ranking
2805

Overview

Joan Bruna is affiliated with New York University in the United States. Their primary field of study is Computer Science, with a specialized focus on several subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Statistics and Probability, and Computational Mechanics.

The scientist's research covers a variety of topics, including:

  • Model Reduction and Neural Networks
  • Neural Networks and Applications
  • Generative Adversarial Networks and Image Synthesis
  • Stochastic Gradient Optimization Techniques
  • Machine Learning and Algorithms
  • Advanced Graph Neural Networks
  • Gaussian Processes and Bayesian Inference

Joan Bruna has contributed to numerous scientific publications, frequently publishing in venues such as:

  • arXiv (Cornell University)
  • Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • IEEE Transactions on Signal Processing
  • Monthly Notices of the Royal Astronomical Society
  • 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Some recent papers authored or co-authored by Joan Bruna include:

  • Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges, 2021, arXiv (Cornell University)
  • Can We Trust AI-Powered Real-Time Embedded Systems? (Invited Paper), 2022, Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • Stability Properties of Graph Neural Networks, 2020, IEEE Transactions on Signal Processing
  • A new approach to observational cosmology using the scattering transform, 2020, Monthly Notices of the Royal Astronomical Society
  • Neural Fields as Learnable Kernels for 3D Reconstruction, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

The scientist has collaborated frequently with several co-authors, including:

  • Alberto Bietti
  • Eric Vanden-Eijnden
  • Aaron Zweig
  • Gitta Kutyniok
  • Carles Domingo-Enrich

Joan Bruna has also contributed to academic literature as an author of books, notably publishing a book with Cambridge University Press titled Mathematical Aspects of Deep Learning in 2022.

Best Publications

  • Intriguing properties of neural networks

    Christian Szegedy;Wojciech Zaremba;Ilya Sutskever;Joan Bruna

  • Spectral Networks and Locally Connected Networks on Graphs

    Joan Bruna;Wojciech Zaremba;Arthur Szlam;Yann LeCun

  • Geometric Deep Learning: Going beyond Euclidean data

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

  • Invariant Scattering Convolution Networks

    J. Bruna;S. Mallat

  • Deep Convolutional Networks on Graph-Structured Data.

    Mikael Henaff;Joan Bruna;Yann LeCun

  • Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation

    Emily L Denton;Wojciech Zaremba;Joan Bruna;Yann LeCun

  • Few-Shot Learning with Graph Neural Networks

    Victor Garcia;Joan Bruna

  • Training Convolutional Networks with Noisy Labels

    Sainbayar Sukhbaatar;Joan Bruna;Manohar Paluri;Lubomir Bourdev

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

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

  • Video (language) modeling: a baseline for generative models of natural videos.

    Marc'Aurelio Ranzato;Arthur Szlam;Joan Bruna;Michaël Mathieu

  • Super-Resolution with Deep Convolutional Sufficient Statistics

    Joan Bruna;Pablo Sprechmann;Yann LeCun;Yann LeCun

  • Stability Properties of Graph Neural Networks

    Fernando Gama;Joan Bruna;Alejandro Ribeiro

  • Deep Geometric Prior for Surface Reconstruction

    Francis Williams;Teseo Schneider;Claudio Silva;Denis Zorin

  • Classification with scattering operators

    Joan Bruna;Stephane Mallat

  • Unsupervised Learning of Spatiotemporally Coherent Metrics

    Ross Goroshin;Joan Bruna;Jonathan Tompson;David Eigen

  • Supervised community detection with line graph neural networks

    Zhengdao Chen;Lisha Li;Joan Bruna

  • On the equivalence between graph isomorphism testing and function approximation with GNNs

    Zhengdao Chen;Soledad Villar;Lei Chen;Joan Bruna

  • Mathematics of Deep Learning

    Rene Vidal;Joan Bruna;Raja Giryes;Stefano Soatto

  • A Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks.

    Alex Nowak;Soledad Villar;Afonso S. Bandeira;Joan Bruna

  • A new approach to observational cosmology using the scattering transform

    Sihao Cheng;Yuan Sen Ting;Brice Menard;Joan Bruna;Joan Bruna;Joan Bruna

  • Can Graph Neural Networks Count Substructures

    Zhengdao Chen;Lei Chen;Soledad Villar;Joan Bruna

  • Kymatio: Scattering Transforms in Python

    Mathieu Andreux;Tomás Angles;Georgios Exarchakis;Roberto Leonarduzzi

  • Supervised Community Detection with Line Graph Neural Networks

    Zhengdao Chen;Xiang Li;Joan Bruna

Frequent Co-Authors

Yann LeCun
Yann LeCun Facebook (United States)
Stéphane Mallat
Stéphane Mallat École Normale Supérieure
Arthur Szlam
Arthur Szlam DeepMind (United Kingdom)
Denis Zorin
Denis Zorin New York University
Rob Fergus
Rob Fergus New York University
Daniele Panozzo
Daniele Panozzo New York University
Alejandro Ribeiro
Alejandro Ribeiro University of Pennsylvania
Kyunghyun Cho
Kyunghyun Cho New York University
Rajesh Ranganath
Rajesh Ranganath New York University

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