2002 - Fellow of the American Statistical Association (ASA)
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 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.
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.
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.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Saturated Reconstruction of a Volume of Neocortex
Narayanan Kasthuri;Kenneth Jeffrey Hayworth;Daniel Raimund Berger;Daniel Raimund Berger;Richard Lee Schalek.
Scan Statistics on Enron Graphs
Carey E. Priebe;John M. Conroy;David J. Marchette;Youngser Park.
Computational and Mathematical Organization Theory (2005)
The complete connectome of a learning and memory centre in an insect brain
Katharina Eichler;Feng Li;Ashok Litwin-Kumar;Youngser Park.
A Consistent Adjacency Spectral Embedding for Stochastic Blockmodel Graphs
Daniel L. Sussman;Minh Tang;Donniell E. Fishkind;Carey E. Priebe.
Journal of the American Statistical Association (2012)
Discovery of Brainwide Neural-Behavioral Maps via Multiscale Unsupervised Structure Learning
Joshua T. Vogelstein;Youngser Park;Tomoko Ohyama;Rex A. Kerr.
COMPARATIVE EVALUATION OF PATTERN RECOGNITION TECHNIQUES FOR DETECTION OF MICROCALCIFICATIONS IN MAMMOGRAPHY
Kevin S. Woods;Christopher C. Doss;Kevin W. Bowyer;Jeffrey L. Solka.
International Journal of Pattern Recognition and Artificial Intelligence (1993)
FlashGraph: processing billion-node graphs on an array of commodity SSDs
Da Zheng;Disa Mhembere;Randal Burns;Joshua Vogelstein.
file and storage technologies (2015)
Random Forests for Photometric Redshifts
Samuel Carliles;Tamás Budavári;Sébastien Heinis;Carey Priebe.
The Astrophysical Journal (2010)
Statistical inference on random dot product graphs: a survey
Avanti Athreya;Donniell E. Fishkind;Minh Tang;Carey E. Priebe.
Journal of Machine Learning Research (2018)
Fast approximate quadratic programming for graph matching.
Joshua T. Vogelstein;John M. Conroy;Vince Lyzinski;Louis J. Podrazik.
PLOS ONE (2015)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: