2026 Joel A. Tropp: Mathematics Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Mathematics	69	284	240	157	125	146	47982
Computer Science	67	2131	2065	1074	1034	132	47418

Joel A. Tropp publication distribution in Mathematics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Mathematics in 2026. The highlighted bar marks where Joel A. Tropp sits on this spectrum.

42 publications 537+

This scientist: 146 publications — 36th percentile

36% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 537 publications or more.

Joel A. Tropp D-index placement in Mathematics in 2026

The chart shows the D-index (discipline H-index) distribution of Mathematics scientists ranked by Research.com in 2026. The highlighted bar marks where Joel A. Tropp sits on this spectrum.

30 D-Index 86+

This scientist: 69 D-Index — 92nd percentile

92% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 86 D-Index or more.

Research.com Recognitions

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

Overview

Joel A. Tropp is affiliated with the California Institute of Technology in the United States. Their research primarily spans the fields of Computer Science, Mathematics, and Engineering, with a strong focus on areas including Artificial Intelligence, Computational Mechanics, Computational Theory and Mathematics, Statistics and Probability, and Applied Mathematics.

The topics frequently addressed in their work include Sparse and Compressive Sensing Techniques, Stochastic Gradient Optimization Techniques, Matrix Theory and Algorithms, Random Matrices and Applications, Point Processes and Geometric Inequalities, Quantum Computing Algorithms and Architecture, and Markov Chains and Monte Carlo Methods.

Joel A. Tropp has contributed to numerous research publications across notable venues. The most common publication outlets include:

arXiv (Cornell University)
SIAM Journal on Matrix Analysis and Applications
SIAM Journal on Mathematics of Data Science
Acta Numerica
The Caltech Institute Archives (California Institute of Technology)

Some of their recent papers are:

Randomized numerical linear algebra: Foundations and algorithms (2020) published in Acta Numerica
Fast state tomography with optimal error bounds (2020) published in The Caltech Institute Archives (California Institute of Technology)
Low-Rank Tucker Approximation of a Tensor from Streaming Data (2020) published in SIAM Journal on Mathematics of Data Science
Fast and Accurate Randomized Algorithms for Linear Systems and Eigenvalue Problems (2024) published in SIAM Journal on Matrix Analysis and Applications
Randomized Nyström Preconditioning (2023) published in SIAM Journal on Matrix Analysis and Applications

Joel A. Tropp has collaborated frequently with several researchers throughout their career. Notable coauthors include:

Ethan N. Epperly
Robert J. Webber
De Huang
Madeleine Udell
Richard Kueng

Their professional recognition includes being named an IEEE Fellow in 2020 for contributions to sparse signal processing, a SIAM Fellow in 2019 for contributions to signal processing, data analysis, and randomized linear algebra, and receiving a fellowship from the Alfred P. Sloan Foundation in 2010.

Best Publications

Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit

J.A. Tropp;A.C. Gilbert

12102
Citations
CoSaMP: iterative signal recovery from incomplete and inaccurate samples

Deanna Needell;Joel A. Tropp

5773
Citations
Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions

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

4645
Citations
Greed is good: algorithmic results for sparse approximation

J.A. Tropp

4557
Citations
User-Friendly Tail Bounds for Sums of Random Matrices

Joel A. Tropp

1777
Citations
Algorithms for simultaneous sparse approximation: part I: Greedy pursuit

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

1597
Citations
Just relax: convex programming methods for identifying sparse signals in noise

J.A. Tropp

1424
Citations
Beyond Nyquist: Efficient Sampling of Sparse Bandlimited Signals

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

1397
Citations
Computational Methods for Sparse Solution of Linear Inverse Problems

Joel A Tropp;Stephen J Wright

1356
Citations
An Introduction to Matrix Concentration Inequalities

Joel A. Tropp

839
Citations
Algorithms for simultaneous sparse approximation: part II: Convex relaxation

Joel A. Tropp

825
Citations
Designing structured tight frames via an alternating projection method

J.A. Tropp;I.S. Dhillon;R.W. Heath;T. Strohmer

595
Citations
Living on the edge: phase transitions in convex programs with random data

Dennis Amelunxen;Martin Lotz;Michael B. McCoy;Joel A. Tropp

501
Citations
Random Filters for Compressive Sampling and Reconstruction

J.A. Tropp;M.B. Wakin;M.F. Duarte;D. Baron

397
Citations
Simultaneous sparse approximation via greedy pursuit

J.A. Tropp;A.C. Gilbert;M.J. Strauss

382
Citations
One sketch for all: fast algorithms for compressed sensing

A. C. Gilbert;M. J. Strauss;J. A. Tropp;R. Vershynin

375
Citations
IMPROVED ANALYSIS OF THE SUBSAMPLED RANDOMIZED HADAMARD TRANSFORM

Joel A. Tropp

352
Citations
ALGORITHMS FOR SIMULTANEOUS SPARSE APPROXIMATION

J A Tropp

331
Citations
Paved with good intentions: Analysis of a randomized block Kaczmarz method

Deanna Needell;Joel A. Tropp

322
Citations
On the existence of equiangular tight frames

Mátyás A. Sustik;Joel A. Tropp;Inderjit S. Dhillon;Robert W. Heath

311
Citations
Signal Recovery from Random Measurements Via Orthogonal Matching Pursuit: The Gaussian Case

Joel A. Tropp;Anna C. Gilbert

21
Citations
Computational Methods for Sparse Solution of Linear Inverse Problems In many engineering areas, such as signal processing, practical results can be obtained by identifying approaches that yield the greatest quality improvement, or by selecting the most suitable computation methods.

Joel A. Tropp;Stephen J. Wright

0
Citations

Frequent Co-Authors

Anna C. Gilbert Yale University

Inderjit S. Dhillon Google (United States)

Volkan Cevher École Polytechnique Fédérale de Lausanne

Robert W. Heath University of California, San Diego

Beverley McKeon California Institute of Technology

Martin J. Strauss University of Michigan–Ann Arbor

Thomas Strohmer University of California, Davis

Deanna Needell University of California, Los Angeles

Roman Vershynin University of California, Irvine

Richard G. Baraniuk Rice University

External Links

Wikipedia of Joel A. Tropp Personal website of Joel A. Tropp Google Scholar page

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