What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
Random graph, Combinatorics, Adjacency matrix, Artificial intelligence and Adjacency list are his primary areas of study.
His Random graph research is multidisciplinary, relying on both Random regular graph, Graph, Spectral clustering, 3-dimensional matching and Dot product.
His research in Combinatorics intersects with topics in Discrete mathematics and Test statistic.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Theoretical computer science and Pattern recognition.
Carey E. Priebe interconnects Embedding, Upper and lower bounds, Stochastic block model and Graph in the investigation of issues within Adjacency list.
He focuses mostly in the field of Stochastic block model, narrowing it down to matters related to Robustness and, in some cases, Algorithm.
His most cited work include:
- Saturated Reconstruction of a Volume of Neocortex (605 citations)
- Scan Statistics on Enron Graphs (283 citations)
- The complete connectome of a learning and memory centre in an insect brain (264 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Algorithm, Random graph, Combinatorics and Discrete mathematics.
He has researched Artificial intelligence in several fields, including Machine learning, Computer vision and Pattern recognition.
His work carried out in the field of Algorithm brings together such families of science as Mathematical optimization, Cluster analysis and Graph.
Carey E. Priebe combines subjects such as Embedding, Graph theory and Theoretical computer science with his study of Graph.
His Random graph research incorporates themes from Statistical hypothesis testing, Test statistic, Inference, Adjacency matrix and Asymptotic distribution.
His Discrete mathematics research is multidisciplinary, incorporating elements of Central limit theorem, Dot product and Adjacency list.
He most often published in these fields:
- Artificial intelligence (23.96%)
- Algorithm (21.52%)
- Random graph (16.14%)
What were the highlights of his more recent work (between 2018-2021)?
- Graph (13.45%)
- Algorithm (21.52%)
- Vertex (13.94%)
In recent papers he was focusing on the following fields of study:
Carey E. Priebe mostly deals with Graph, Algorithm, Vertex, Inference and Artificial intelligence.
His Graph research includes themes of Theoretical computer science, Embedding, Stochastic block model, Adjacency list and Dot product.
His study explores the link between Algorithm and topics such as Nonlinear dimensionality reduction that cross with problems in Euclidean space.
His Vertex research integrates issues from Adjacency matrix, Vertex and Random graph.
His Random graph study is related to the wider topic of Discrete mathematics.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Pattern recognition.
Between 2018 and 2021, his most popular works were:
- The two-to-infinity norm and singular subspace geometry with applications to high-dimensional statistics (37 citations)
- Seeded graph matching (33 citations)
- From Distance Correlation to Multiscale Graph Correlation (31 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
His primary areas of investigation include Graph, Vertex, Algorithm, Artificial intelligence and Stochastic block model.
His study in the field of Random graph is also linked to topics like Scalability.
His Vertex research is within the category of Combinatorics.
The concepts of his Algorithm study are interwoven with issues in Change detection, Diffusion map, Graph embedding and Network science.
Carey E. Priebe has included themes like Univariate, Machine learning and Computer vision in his Artificial intelligence study.
His study looks at the relationship between Adjacency matrix and topics such as Adjacency list, which overlap with Statistical inference, Laplacian matrix, Pattern recognition and Mixture model.
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