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- Joel A. Tropp

Discipline name
H-index
Citations
Publications
World Ranking
National Ranking

Computer Science
D-index
50
Citations
38,304
82
World Ranking
2838
National Ranking
1501

Mathematics
D-index
50
Citations
24,894
94
World Ranking
549
National Ranking
278

2020 - IEEE Fellow For contributions to sparse signal processing

2019 - SIAM Fellow For contributions to signal processing, data analysis, and randomized linear algebra.

2010 - Fellow of Alfred P. Sloan Foundation

- Statistics
- Algorithm
- Algebra

His scientific interests lie mostly in Algorithm, Sparse approximation, Mathematical optimization, Compressed sensing and Matching pursuit. His Algorithm research is multidisciplinary, incorporating perspectives in Block matrix, LU decomposition and Signal processing. The study incorporates disciplines such as Dimension, Linear combination, Sparse matrix, Equiangular polygon and Eigenvalues and eigenvectors in addition to Sparse approximation.

His Mathematical optimization research integrates issues from Time complexity, Orthonormal basis and Kaczmarz method. His Compressed sensing study combines topics in areas such as Sampling, Convolution and Signal reconstruction. His Matching pursuit research is multidisciplinary, incorporating perspectives in Approximation algorithm, Greedy algorithm and Approximation theory.

- Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit (7158 citations)
- CoSaMP: Iterative signal recovery from incomplete and inaccurate samples (3431 citations)
- Greed is good: algorithmic results for sparse approximation (3136 citations)

Joel A. Tropp spends much of his time researching Algorithm, Matrix, Random matrix, Combinatorics and Convex optimization. Joel A. Tropp combines subjects such as Dimension, Sampling and Mathematical optimization with his study of Algorithm. Joel A. Tropp interconnects Factorization, Semidefinite programming and Rank in the investigation of issues within Matrix.

His Random matrix research includes themes of Discrete mathematics, Matrix norm, Noncommutative geometry, Pure mathematics and Applied mathematics. His studies examine the connections between Combinatorics and genetics, as well as such issues in Norm, with regards to Subspace topology. The concepts of his Sparse approximation study are interwoven with issues in Linear combination, Greedy algorithm, Sparse matrix, Matching pursuit and Approximation theory.

- Algorithm (36.63%)
- Matrix (27.91%)
- Random matrix (18.60%)

- Matrix (27.91%)
- Random matrix (18.60%)
- Pure mathematics (9.30%)

His primary areas of investigation include Matrix, Random matrix, Pure mathematics, Applied mathematics and Dimensionality reduction. In the field of Matrix, his study on Positive-definite matrix overlaps with subjects such as Decomposition. His Random matrix course of study focuses on Concentration inequality and Mutually unbiased bases and Pauli exclusion principle.

His Dimensionality reduction study also includes fields such as

- Linear map together with Singular value, Embedding, Stochastic geometry and Universality,
- Randomized algorithm that intertwine with fields like Theoretical computer science, Singular value decomposition, Principal component analysis and Range,
- Numerical linear algebra which is related to area like Linear regression and Linear algebra. With his scientific publications, his incorporates both A priori and a posteriori and Algorithm. The study incorporates disciplines such as Dimension and Tensor in addition to Algorithm.

- Universality laws for randomized dimension reduction, with applications (41 citations)
- Streaming Low-Rank Matrix Approximation With An Application To Scientific Simulation (22 citations)
- Randomized numerical linear algebra: Foundations and algorithms (20 citations)

- Statistics
- Algebra
- Mathematical analysis

Joel A. Tropp mostly deals with Matrix, Dimensionality reduction, Numerical linear algebra, Randomized algorithm and Semidefinite programming. His Matrix research incorporates themes from Trace, Work, Pure mathematics, Smoothness and Product. His research investigates the connection between Dimensionality reduction and topics such as Linear map that intersect with problems in Singular value, Embedding, Stochastic geometry and Universality.

His research integrates issues of Theoretical computer science, Data collection, Singular value decomposition, Compression and Low-rank approximation in his study of Numerical linear algebra. His Semidefinite programming study is concerned with the field of Mathematical optimization as a whole. His Mathematical optimization research integrates issues from Range, Quadratic equation, Phase retrieval and Convex optimization.

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.

Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit

J.A. Tropp;A.C. Gilbert.

IEEE Transactions on Information Theory **(2007)**

9982 Citations

CoSaMP: Iterative signal recovery from incomplete and inaccurate samples

Deanna Needell;Joel A. Tropp.

Applied and Computational Harmonic Analysis **(2009)**

4423 Citations

Greed is good: algorithmic results for sparse approximation

J.A. Tropp.

IEEE Transactions on Information Theory **(2004)**

4028 Citations

Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions

N. Halko;P. G. Martinsson;J. A. Tropp.

Siam Review **(2011)**

3101 Citations

Algorithms for simultaneous sparse approximation: part I: Greedy pursuit

Joel A. Tropp;Anna C. Gilbert;Martin J. Strauss.

Signal Processing **(2006)**

1381 Citations

User-Friendly Tail Bounds for Sums of Random Matrices

Joel A. Tropp.

Foundations of Computational Mathematics **(2012)**

1288 Citations

Just relax: convex programming methods for identifying sparse signals in noise

J.A. Tropp.

IEEE Transactions on Information Theory **(2006)**

1268 Citations

Computational Methods for Sparse Solution of Linear Inverse Problems

Joel A Tropp;Stephen J Wright.

Proceedings of the IEEE **(2010)**

1224 Citations

Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals

J.A. Tropp;J.N. Laska;M.F. Duarte;J.K. Romberg.

IEEE Transactions on Information Theory **(2010)**

1169 Citations

Algorithms for simultaneous sparse approximation: part II: Convex relaxation

Joel A. Tropp.

Signal Processing **(2006)**

739 Citations

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