World's Best Scientists 2026 revealed!
Emmanuel J. Candès

Emmanuel J. Candès

Award Badge
Engineering and Technology
USA
2026
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Mathematics
USA
2026

D-Index & Metrics

Mathematics

D-Index
103
Citations
143959
World Ranking
40
National Ranking
28

Engineering and Technology

D-Index
105
Citations
157138
World Ranking
109
National Ranking
44

Research.com Recognitions

  • 2026 - Research.com Engineering and Technology in United States Leader Award
  • 2026 - Research.com Mathematics in United States Leader Award
  • 2025 - Research.com Engineering and Technology in United States Leader Award
  • 2025 - Research.com Mathematics in United States Leader Award
  • 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 - Wald Memorial Lecturer
  • 2017 - Fellow of the MacArthur Foundation
  • 2014 - Member of the National Academy of Sciences
  • 2014 - Fellow of the American Academy of Arts and Sciences
  • 2010 - George Pólya Prize
  • 2006 - National Science Foundation Alan T. Waterman Award Mathematics
  • 2001 - Fellow of Alfred P. Sloan Foundation

Overview

Emmanuel J. Candès is affiliated with Stanford University in the United States. Their research primarily spans the fields of Mathematics and Computer Science, with significant contributions also in subfields such as Statistics and Probability, Artificial Intelligence, Genetics, Molecular Biology, and areas concerning uncertainty in statistics. The scientist's work covers multiple topics, including Statistical Methods and Inference, Genetic Associations and Epidemiology, Advanced Causal Inference Techniques, Statistical Methods in Clinical Trials, Statistical Methods and Bayesian Inference, Machine Learning and Data Classification, and Machine Learning and Algorithms.

The scientist has published extensively in prominent venues. Frequent publication venues include arXiv (Cornell University) with 42 publications, The Annals of Statistics (6 publications), Proceedings of the National Academy of Sciences (4 publications), bioRxiv (Cold Spring Harbor Laboratory) (4 publications), and the Journal of the Royal Statistical Society Series B (Statistical Methodology) (3 publications).

Recent notable papers authored or coauthored by Emmanuel J. Candès include:

  • Conformal prediction beyond exchangeability, 2023, The Annals of Statistics
  • Conformal Inference of Counterfactuals and Individual Treatment Effects, 2021, Journal of the Royal Statistical Society Series B (Statistical Methodology)
  • Multi-resolution localization of causal variants across the genome, 2020, Nature Communications
  • False discovery rate control in genome-wide association studies with population structure, 2021, Proceedings of the National Academy of Sciences
  • Metropolized Knockoff Sampling, 2020, OPAL (Open@LaTrobe) (La Trobe University)

Collaboration is a key component of the scientist's work. Frequent coauthors include Chiara Sabatti, Rina Foygel Barber, Lihua Lei, Matteo Sesia, and Stephen Bates.

The scientist has been recognized with several awards over the course of their career. These include the Jack S. Kilby Signal Processing Medal in 2021 for contributions to compressed sensing, fellowship awards from the American Mathematical Society and IEEE in 2018 related to work in sparse and low-rank signal and image processing, and the National Science Foundation Alan T. Waterman Award for Mathematics in 2006. Additional honors include a Wald Memorial Lectureship in 2017, fellowship from the MacArthur Foundation in 2017, membership in the National Academy of Sciences and Fellow of the American Academy of Arts and Sciences awarded in 2014, the George Pólya Prize in 2010, and fellowship from the Alfred P. Sloan Foundation in 2001.

Best Publications

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

    E.J. Candes;J. Romberg;T. Tao

  • An Introduction To Compressive Sampling

    E.J. Candes;M.B. Wakin

  • Robust principal component analysis

    Emmanuel J. Candès;Xiaodong Li;Yi Ma;John Wright

  • Decoding by linear programming

    E.J. Candes;T. Tao

  • Stable signal recovery from incomplete and inaccurate measurements

    Emmanuel J. Candès;Justin K. Romberg;Terence Tao

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

    E.J. Candes;T. Tao

  • Exact matrix completion via convex optimization

    Emmanuel Candès;Benjamin Recht

  • A Singular Value Thresholding Algorithm for Matrix Completion

    Jian-Feng Cai;Emmanuel J. Candès;Zuowei Shen

  • Enhancing Sparsity by Reweighted ℓ 1 Minimization

    Emmanuel J. Candès;Michael B. Wakin;Stephen P. Boyd

  • The restricted isometry property and its implications for compressed sensing

    Emmanuel J. Candès

  • The Dantzig selector: Statistical estimation when P is much larger than n

    Emmanuel Candes;Terence Tao

  • Fast Discrete Curvelet Transforms

    Emmanuel J. Candès;Laurent Demanet;David L. Donoho;Lexing Ying

  • The curvelet transform for image denoising

    Jean-Luc Starck;E.J. Candes;D.L. Donoho

  • Sparsity and incoherence in compressive sampling

    Emmanuel Candès;Justin Romberg

  • Enhancing Sparsity by Reweighted L1 Minimization

    Emmanuel J. Candes;Michael B. Wakin;Stephen P. Boyd

  • The Power of Convex Relaxation: Near-Optimal Matrix Completion

    Emmanuel J Candes;Terence Tao

  • Curvelets: A Surprisingly Effective Nonadaptive Representation for Objects with Edges

    Emmanuel J. Candes;David L. Donoho

  • Matrix Completion With Noise

    Emmanuel J Candes;Yaniv Plan

  • New tight frames of curvelets and optimal representations of objects with piecewise C2 singularities

    Emmanuel J. Candès;David L. Donoho

  • Phase Retrieval via Wirtinger Flow: Theory and Algorithms

    Emmanuel J. Candes;Xiaodong Li;Mahdi Soltanolkotabi

  • The curvelet transform for image denoising

    E. Candes

Frequent Co-Authors

David L. Donoho
David L. Donoho Stanford University
Terence Tao
Terence Tao University of California, Los Angeles
Chiara Sabatti
Chiara Sabatti Stanford University
Justin Romberg
Justin Romberg Georgia Institute of Technology
Benjamin Recht
Benjamin Recht University of California, Berkeley
Yi Ma
Yi Ma University of Hong Kong
Lexing Ying
Lexing Ying Stanford University
Michael B. Wakin
Michael B. Wakin Colorado School of Mines
Stephen Boyd
Stephen Boyd Stanford University

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