What is he best known for?
The fields of study he is best known for:
- Statistics
- Computer network
- Artificial intelligence
Constantine Caramanis mainly investigates Mathematical optimization, Algorithm, Matrix completion, Principal component analysis and Matrix decomposition.
Constantine Caramanis is involved in the study of Mathematical optimization that focuses on Robust optimization in particular.
The study incorporates disciplines such as Overfitting and Industrial engineering in addition to Robust optimization.
His work carried out in the field of Algorithm brings together such families of science as Matrix, Minification, Regression and Maxima and minima.
Constantine Caramanis combines subjects such as Subspace topology, Standard algorithms and Dimensionality reduction with his study of Principal component analysis.
His Matrix decomposition research includes themes of Sparse matrix and Single-entry matrix.
His most cited work include:
- Theory and Applications of Robust Optimization (1484 citations)
- User Association for Load Balancing in Heterogeneous Cellular Networks (971 citations)
- Robust PCA via Outlier Pursuit (345 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Algorithm, Mathematical optimization, Artificial intelligence, Robust optimization and Combinatorics.
His research on Algorithm also deals with topics like
- Matrix completion together with Matrix decomposition,
- Outlier which is related to area like Dimensionality reduction.
His Mathematical optimization study combines topics from a wide range of disciplines, such as Scheduling, Regression and Convex optimization.
His Artificial intelligence research includes elements of Machine learning and Pattern recognition.
His Robust optimization research integrates issues from Optimization problem and Robustness.
His Combinatorics study integrates concerns from other disciplines, such as Matching, Matrix, Upper and lower bounds and Discrete mathematics.
He most often published in these fields:
- Algorithm (27.85%)
- Mathematical optimization (26.48%)
- Artificial intelligence (10.50%)
What were the highlights of his more recent work (between 2017-2021)?
- Algorithm (27.85%)
- Combinatorics (9.59%)
- Linear regression (6.39%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Algorithm, Combinatorics, Linear regression, Graph and Convergence.
Constantine Caramanis has researched Algorithm in several fields, including Statistical inference, Random variable and Time series.
His research integrates issues of Matching, Matrix, Upper and lower bounds and Generalization in his study of Combinatorics.
His work in Linear regression covers topics such as Applied mathematics which are related to areas like Local convergence.
Constantine Caramanis works mostly in the field of Convergence, limiting it down to topics relating to Minimax and, in certain cases, Iterated function, Rate of convergence, Convex optimization and Signal-to-noise ratio.
Constantine Caramanis integrates Initialization and Mathematical optimization in his research.
Between 2017 and 2021, his most popular works were:
- Finding Low-Rank Solutions via Nonconvex Matrix Factorization, Efficiently and Provably (25 citations)
- High Dimensional Robust Sparse Regression (25 citations)
- Global Convergence of the EM Algorithm for Mixtures of Two Component Linear Regression (15 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Computer network
- Artificial intelligence
Constantine Caramanis mainly investigates Algorithm, Convergence, Linear regression, Applied mathematics and Thresholding.
His work on Compressed sensing as part of general Algorithm study is frequently linked to Ellipsoid method, bridging the gap between disciplines.
Constantine Caramanis combines subjects such as Matching, Quantum tomography, Computational complexity theory and Minimax with his study of Convergence.
The Matching study combines topics in areas such as Signal-to-noise ratio, Information theory, Mathematical optimization and Convex optimization.
As a part of the same scientific study, he usually deals with the Applied mathematics, concentrating on Local convergence and frequently concerns with Contrast, Regression, Mixture model and Inference.
His work deals with themes such as Covariance matrix, Estimator and Constant, which intersect with Thresholding.
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