What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Computer vision
Artificial intelligence, Algorithm, Theoretical computer science, Wavelet and Spectral graph theory are his primary areas of study.
His Artificial intelligence research incorporates elements of Computational complexity theory, Computer vision and Pattern recognition.
He has researched Algorithm in several fields, including Discrete mathematics, Imaging phantom, Excitation and Mathematical optimization.
The Theoretical computer science study combines topics in areas such as Graph theory, Deep learning, Topological graph theory and Graph.
His research integrates issues of Mathematical analysis and Stereographic projection in his study of Wavelet.
His work in Spectral graph theory covers topics such as 1-planar graph which are related to areas like Leverage, Spectral density, Wiener filter and Stationary process.
His most cited work include:
- The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains (2237 citations)
- Convolutional neural networks on graphs with fast localized spectral filtering (1765 citations)
- FREAK: Fast Retina Keypoint (1338 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Algorithm, Computer vision, Wavelet and Pattern recognition.
Matching pursuit, Iterative reconstruction, Compressed sensing, Sparse approximation and Image processing are subfields of Artificial intelligence in which his conducts study.
His research in Compressed sensing intersects with topics in Spread spectrum, Electronic engineering, Fourier transform and Convex optimization.
His Algorithm research integrates issues from Theoretical computer science, Mathematical optimization, Graph and Signal processing.
His biological study spans a wide range of topics, including Inverse problem and Omnidirectional antenna.
His Wavelet study combines topics in areas such as Mathematical analysis and Cosmic microwave background.
He most often published in these fields:
- Artificial intelligence (44.98%)
- Algorithm (28.71%)
- Computer vision (26.10%)
What were the highlights of his more recent work (between 2015-2020)?
- Algorithm (28.71%)
- Artificial intelligence (44.98%)
- Graph (7.83%)
In recent papers he was focusing on the following fields of study:
His main research concerns Algorithm, Artificial intelligence, Graph, Graph and Theoretical computer science.
His Algorithm research integrates issues from Discrete mathematics, Matrix, Spectral graph theory, Graph based and Signal processing.
His research in Artificial intelligence intersects with topics in Computer vision and Pattern recognition.
His studies deal with areas such as Matrix decomposition, Spectral clustering, Eigenvalues and eigenvectors and Data mining as well as Graph.
His biological study spans a wide range of topics, including Embedding, Recommender system, Deep learning and Generalization.
His Embedding research is multidisciplinary, relying on both Computational complexity theory and Connectome.
Between 2015 and 2020, his most popular works were:
- Convolutional neural networks on graphs with fast localized spectral filtering (1765 citations)
- Geometric Deep Learning: Going beyond Euclidean data (1319 citations)
- Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering (623 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Computer vision
Pierre Vandergheynst mainly investigates Graph, Theoretical computer science, Artificial intelligence, Algorithm and Spectral graph theory.
Within one scientific family, he focuses on topics pertaining to Euclidean geometry under Graph, and may sometimes address concerns connected to Upsampling, Data domain, Graph bandwidth and Graph reduction.
He interconnects Matrix decomposition, Non-negative matrix factorization, Recommender system, Collaborative filtering and Matrix completion in the investigation of issues within Theoretical computer science.
His Artificial intelligence research includes elements of Computational complexity theory and Pattern recognition.
His work deals with themes such as Discrete mathematics, Time–frequency analysis and Signal processing, which intersect with Algorithm.
His Spectral graph theory research incorporates elements of 1-planar graph, Bandlimiting and Random graph.
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