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
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.
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.
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
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.
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Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
J.A. Tropp;A.C. Gilbert.
IEEE Transactions on Information Theory (2007)
CoSaMP: iterative signal recovery from incomplete and inaccurate samples
Deanna Needell;Joel A. Tropp.
Communications of The ACM (2010)
Greed is good: algorithmic results for sparse approximation
IEEE Transactions on Information Theory (2004)
Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions
N. Halko;P. G. Martinsson;J. A. Tropp.
Siam Review (2011)
User-Friendly Tail Bounds for Sums of Random Matrices
Joel A. Tropp.
Foundations of Computational Mathematics (2012)
Algorithms for simultaneous sparse approximation: part I: Greedy pursuit
Joel A. Tropp;Anna C. Gilbert;Martin J. Strauss.
Signal Processing (2006)
Just relax: convex programming methods for identifying sparse signals in noise
IEEE Transactions on Information Theory (2006)
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)
Computational Methods for Sparse Solution of Linear Inverse Problems
Joel A Tropp;Stephen J Wright.
Proceedings of the IEEE (2010)
Algorithms for simultaneous sparse approximation: part II: Convex relaxation
Joel A. Tropp.
Signal Processing (2006)
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