H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Mathematics H-index 74 Citations 101,196 116 World Ranking 82 National Ranking 48
Engineering and Technology H-index 84 Citations 131,994 147 World Ranking 95 National Ranking 45

Research.com Recognitions

Awards & Achievements

2021 - Jack S. Kilby Signal Processing Medal For groundbreaking contributions to compressed sensing.

2018 - Fellow of the American Mathematical Society For contributions to the field of compressed sensing, and to multiscale analysis, statistics, and matrix completion.

2018 - IEEE Fellow For contributions to sparse and low-rank signal and image processing

2017 - Fellow of the MacArthur Foundation

2017 - Wald Memorial Lecturer

2014 - Fellow of the American Academy of Arts and Sciences

2014 - Member of the National Academy of Sciences

2010 - George Pólya Prize

2006 - National Science Foundation Alan T. Waterman Award Mathematics

2001 - Fellow of Alfred P. Sloan Foundation


What is he best known for?

The fields of study he is best known for:

  • Statistics
  • Mathematical analysis
  • Algorithm

The scientist’s investigation covers issues in Algorithm, Combinatorics, Compressed sensing, Convex optimization and Matrix completion. His Algorithm study integrates concerns from other disciplines, such as Dimension, Discontinuity, Object, Sparse matrix and Fourier transform. His Combinatorics research is multidisciplinary, relying on both Discrete mathematics, Inverse problem and Restricted isometry property.

His Compressed sensing study combines topics from a wide range of disciplines, such as Sense, Mathematical optimization, Minification, Image processing and Lasso. Emmanuel J. Candès interconnects Phase retrieval, Mathematical analysis, Ode and Square root in the investigation of issues within Convex optimization. Emmanuel J. Candès has researched Matrix completion in several fields, including Low-rank approximation and Matrix norm.

His most cited work include:

  • Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information (12980 citations)
  • An Introduction To Compressive Sampling (7569 citations)
  • Decoding by linear programming (6013 citations)

What are the main themes of his work throughout his whole career to date?

Emmanuel J. Candès focuses on Algorithm, Convex optimization, Compressed sensing, Combinatorics and Mathematical optimization. His work deals with themes such as Matrix, Sparse matrix, Phase retrieval and Linear model, which intersect with Algorithm. The various areas that Emmanuel J. Candès examines in his Matrix study include Discrete mathematics, Complement and Rank.

His study looks at the relationship between Convex optimization and topics such as Fourier transform, which overlap with Fast Fourier transform. His biological study spans a wide range of topics, including Smoothing, Bandwidth and Signal processing. Within one scientific family, Emmanuel J. Candès focuses on topics pertaining to Linear regression under Combinatorics, and may sometimes address concerns connected to Lasso.

He most often published in these fields:

  • Algorithm (35.16%)
  • Convex optimization (17.58%)
  • Compressed sensing (16.41%)

What were the highlights of his more recent work (between 2018-2021)?

  • Inference (8.20%)
  • False discovery rate (9.38%)
  • Algorithm (35.16%)

In recent papers he was focusing on the following fields of study:

Emmanuel J. Candès spends much of his time researching Inference, False discovery rate, Algorithm, Leverage and Conformal map. His research in Inference intersects with topics in Categorical variable, Distribution, Sample size determination and Distribution free. His research investigates the link between Sample size determination and topics such as Combinatorics that cross with problems in Asymptotic distribution.

Borrowing concepts from Reference design, he weaves in ideas under Algorithm. As part of one scientific family, Emmanuel J. Candès deals mainly with the area of Leverage, narrowing it down to issues related to the Statistical hypothesis testing, and often Differentiable function and Test. In his work, Quantile regression, Missing data, Extension and Covariate shift is strongly intertwined with Prediction interval, which is a subfield of Conformal map.

Between 2018 and 2021, his most popular works were:

  • A modern maximum-likelihood theory for high-dimensional logistic regression (68 citations)
  • Gene hunting with hidden Markov model knockoffs (51 citations)
  • A knockoff filter for high-dimensional selective inference (47 citations)

In his most recent research, the most cited papers focused on:

  • Statistics
  • Mathematical analysis
  • Algebra

His scientific interests lie mostly in Inference, Covariate, False discovery rate, Linear regression and Prediction interval. His research integrates issues of Machine learning, Categorical variable and Sample size determination in his study of Inference. His Covariate research is multidisciplinary, incorporating elements of Data mining, Identification and Hidden Markov model.

His Linear regression research includes themes of Logistic regression, Parameterized complexity, Combinatorics, Kappa and Limit. His Combinatorics research includes elements of Sequence and Robustness. His Prediction interval study also includes

  • Conformal map that connect with fields like Constant, Space, Quantile regression, Regression and Heteroscedasticity,
  • Extension together with Large set.

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.

Top Publications

Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information

E.J. Candes;J. Romberg;T. Tao.
IEEE Transactions on Information Theory (2006)

16660 Citations

An Introduction To Compressive Sampling

E.J. Candes;M.B. Wakin.
IEEE Signal Processing Magazine (2008)

10173 Citations

Decoding by linear programming

E.J. Candes;T. Tao.
IEEE Transactions on Information Theory (2005)

7501 Citations

Stable signal recovery from incomplete and inaccurate measurements

Emmanuel J. Candès;Justin K. Romberg;Terence Tao.
Communications on Pure and Applied Mathematics (2006)

7450 Citations

Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?

E.J. Candes;T. Tao.
IEEE Transactions on Information Theory (2006)

7405 Citations

Robust principal component analysis

Emmanuel J. Candès;Xiaodong Li;Yi Ma;John Wright.
Journal of the ACM (2011)

6697 Citations

Enhancing Sparsity by Reweighted ℓ 1 Minimization

Emmanuel J. Candès;Michael B. Wakin;Stephen P. Boyd.
Journal of Fourier Analysis and Applications (2008)

4375 Citations

A Singular Value Thresholding Algorithm for Matrix Completion

Jian-Feng Cai;Emmanuel J. Candès;Zuowei Shen.
Siam Journal on Optimization (2010)

4347 Citations

Exact Matrix Completion via Convex Optimization

Emmanuel Candès;Benjamin Recht.
Foundations of Computational Mathematics (2009)

4304 Citations

The restricted isometry property and its implications for compressed sensing

Emmanuel J. Candès.
Comptes Rendus Mathematique (2008)

3917 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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