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.
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