2023 - Research.com Computer Science in United Kingdom Leader Award
2020 - Member of Academia Europaea
2020 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to 3D data acquisition, processing, representation and analysis
2019 - IEEE Fellow For contributions to acquisition, processing, and analysis of geometric data
His primary scientific interests are in Artificial intelligence, Pattern recognition, Invariant, Computer graphics and Convolutional neural network. Many of his studies involve connections with topics such as Computer vision and Artificial intelligence. The study incorporates disciplines such as Machine learning and Face in addition to Pattern recognition.
His research integrates issues of Embedding, Geodesic, Topology, Principal geodesic analysis and Facial recognition system in his study of Invariant. His Computer graphics research includes themes of Anisotropic diffusion, Point cloud, Geometry processing and Euclidean distance. His studies deal with areas such as Artificial neural network, Segmentation, Theoretical computer science and Generalization as well as Convolutional neural network.
Michael M. Bronstein mostly deals with Artificial intelligence, Computer vision, Algorithm, Pattern recognition and Shape analysis. His study in Artificial intelligence focuses on Deep learning in particular. His Deep learning research incorporates elements of Artificial neural network, Theoretical computer science and Convolutional neural network.
In his research, Dimensionality reduction is intimately related to Multidimensional scaling, which falls under the overarching field of Algorithm. His biological study spans a wide range of topics, including Representation, Face, Blind signal separation and Biometrics. His Shape analysis research includes elements of Heat kernel, Mathematical analysis, Active shape model, Heat kernel signature and Computer graphics.
Artificial intelligence, Deep learning, Graph, Graph and Machine learning are his primary areas of study. He combines subjects such as Theoretical computer science, Polygon mesh, Computer vision and Pattern recognition with his study of Artificial intelligence. His study in Theoretical computer science is interdisciplinary in nature, drawing from both Recommender system, Convolutional neural network and Graphics.
As a member of one scientific family, Michael M. Bronstein mostly works in the field of Pattern recognition, focusing on Biometrics and, on occasion, Metric. His Deep learning research focuses on Matrix completion and how it relates to Parametric statistics and Laplace operator. The concepts of his Graph study are interwoven with issues in Adversarial system, Autoencoder, Representation, Inference and Computer graphics.
His primary scientific interests are in Artificial intelligence, Deep learning, Theoretical computer science, Graph and Graph. His Artificial intelligence research incorporates elements of Algorithm and Pattern recognition. The various areas that he examines in his Deep learning study include Variety, Eigenvalues and eigenvectors, Forward propagation and SAFER.
His Theoretical computer science research incorporates themes from Recommender system, Graph neural networks, Network science, Graphics and Convolutional neural network. Michael M. Bronstein has researched Convolutional neural network in several fields, including Point cloud, Segmentation, Training set, Computer graphics and Feature vector. His Graph research integrates issues from Adversarial system, Distributed computing, Artificial neural network, Inference and Euclidean geometry.
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Geometric Deep Learning: Going beyond Euclidean data
Michael M. Bronstein;Joan Bruna;Yann LeCun;Arthur Szlam.
IEEE Signal Processing Magazine (2017)
Dynamic Graph CNN for Learning on Point Clouds
Yue Wang;Yongbin Sun;Ziwei Liu;Sanjay E. Sarma.
ACM Transactions on Graphics (2019)
Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs
Federico Monti;Davide Boscaini;Jonathan Masci;Emanuele Rodola.
computer vision and pattern recognition (2017)
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.
ACM Transactions on Graphics (2011)
Three-Dimensional Face Recognition
Alexander M. Bronstein;Michael M. Bronstein;Ron Kimmel.
International Journal of Computer Vision (2005)
LDAHash: Improved Matching with Smaller Descriptors
C. Strecha;A. M. Bronstein;M. M. Bronstein;P. Fua.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2012)
Generalized multidimensional scaling: A framework for isometry-invariant partial surface matching
Alexander M. Bronstein;Michael M. Bronstein;Ron Kimmel.
Proceedings of the National Academy of Sciences of the United States of America (2006)
Geodesic Convolutional Neural Networks on Riemannian Manifolds
Jonathan Masci;Davide Boscaini;Michael M. Bronstein;Pierre Vandergheynst.
international conference on computer vision (2015)
Scale-invariant heat kernel signatures for non-rigid shape recognition
Michael M. Bronstein;Iasonas Kokkinos.
computer vision and pattern recognition (2010)
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