2020 - Member of the National Academy of Engineering For contributions to the design of scientific software, including tensor decompositions and multilinear algebra.
2019 - ACM Fellow For innovations in algorithms for tensor decompositions, contributions to data science, and community leadership
2015 - SIAM Fellow For contributions to numerical algorithms and software in multi-linear algebra, optimization, and graph analysis.
2011 - ACM Distinguished Member
2009 - ACM Senior Member
Her primary scientific interests are in Matrix, Theoretical computer science, Algorithm, Tensor product and Multilinear map. Her Matrix research integrates issues from Tensor, Probability and statistics, Singular value decomposition, Link and Bipartite graph. Her Computation study in the realm of Algorithm interacts with subjects such as Signal processing.
Her Tensor product research incorporates elements of Matricization and Cartesian tensor. Her Matricization research focuses on subjects like Combinatorics, which are linked to Discrete mathematics. Her research investigates the link between Tensor product of Hilbert spaces and topics such as Invariants of tensors that cross with problems in Algebra.
Her main research concerns Theoretical computer science, Algorithm, Matrix, Mathematical optimization and Combinatorics. She works mostly in the field of Theoretical computer science, limiting it down to topics relating to Graph and, in certain cases, Degree distribution. Her biological study spans a wide range of topics, including Singular value decomposition, Computation, Bipartite graph and Tensor product.
As a part of the same scientific study, Tamara G. Kolda usually deals with the Singular value decomposition, concentrating on Tensor and frequently concerns with Symmetric tensor and Tensor contraction. Tensor product is the subject of her research, which falls under Algebra. As a part of the same scientific family, Tamara G. Kolda mostly works in the field of Mathematical optimization, focusing on Nonlinear programming and, on occasion, Pattern search and Optimization problem.
The scientist’s investigation covers issues in Algorithm, Theoretical computer science, Applied mathematics, Symmetric tensor and Combinatorics. Her research integrates issues of Overdetermined system, Landmark, Probabilistic logic, Divergence and Count data in her study of Algorithm. Her work deals with themes such as Linear programming, Graph, Community structure and Integer programming, which intersect with Theoretical computer science.
Her Symmetric tensor study combines topics in areas such as Tensor contraction and Decomposition. Her work deals with themes such as Nearest neighbor search, Dot product and Tensor product, which intersect with Combinatorics. Her Tensor product of Hilbert spaces research incorporates themes from Cartesian tensor and Tensor density.
Her primary areas of investigation include Theoretical computer science, Cluster analysis, Multilinear algebra, Clustering coefficient and Algorithm. Her research integrates issues of Unsupervised learning, Community structure and Missing data in her study of Theoretical computer science. Her study in Cluster analysis is interdisciplinary in nature, drawing from both Triangle counting and Graph.
Her research investigates the connection between Triangle counting and topics such as Computation that intersect with problems in Symmetric tensor. Her research in Clustering coefficient tackles topics such as Degree distribution which are related to areas like Parallelizable manifold and Bipartite graph. Her work on Optimization problem as part of her general Algorithm study is frequently connected to Kernel, thereby bridging the divide between different branches of science.
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.
Tensor Decompositions and Applications
Tamara G. Kolda;Brett W. Bader.
Siam Review (2009)
Optimization by Direct Search: New Perspectives on Some Classical and Modern Methods ∗
Tamara G. Kolda;Robert Michael Lewis;Virginia Torczon.
Siam Review (2003)
An overview of the Trilinos project
Michael A. Heroux;Roscoe A. Bartlett;Vicki E. Howle;Robert J. Hoekstra.
ACM Transactions on Mathematical Software (2005)
Scalable tensor factorizations for incomplete data
Evrim Acar;Daniel M. Dunlavy;Tamara G. Kolda;Morten Mørup.
Chemometrics and Intelligent Laboratory Systems (2011)
Graph partitioning models for parallel computing
Bruce Hendrickson;Tamara G. Kolda.
parallel computing (2000)
Algorithm 862: MATLAB tensor classes for fast algorithm prototyping
Brett W. Bader;Tamara G. Kolda.
ACM Transactions on Mathematical Software (2006)
Temporal Link Prediction Using Matrix and Tensor Factorizations
Daniel M. Dunlavy;Tamara G. Kolda;Evrim Acar.
ACM Transactions on Knowledge Discovery From Data (2011)
Efficient MATLAB Computations with Sparse and Factored Tensors
Brett W. Bader;Tamara G. Kolda.
SIAM Journal on Scientific Computing (2007)
An overview of Trilinos.
Kevin R. Long;Raymond Stephen Tuminaro;Roscoe Ainsworth Bartlett;Robert John Hoekstra.
Orthogonal Tensor Decompositions
Tamara G. Kolda.
SIAM Journal on Matrix Analysis and Applications (2001)
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