What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Algorithm
Algorithm, Mathematical optimization, Compressed sensing, Artificial intelligence and Convex optimization are his primary areas of study.
His work on Approximation algorithm as part of his general Algorithm study is frequently connected to Message passing, thereby bridging the divide between different branches of science.
His Mathematical optimization research integrates issues from Probability distribution, Order statistic, Probabilistic logic and Convex function, Proximal Gradient Methods.
His Compressed sensing research is multidisciplinary, incorporating elements of Wavelet, Signal processing, Combinatorics and Nyquist–Shannon sampling theorem.
Volkan Cevher combines subjects such as Computer vision and Pattern recognition with his study of Artificial intelligence.
His work carried out in the field of Convex optimization brings together such families of science as Computation, Computer engineering, Basis pursuit and Big data.
His most cited work include:
- Model-Based Compressive Sensing (1446 citations)
- Compressive Sensing for Background Subtraction (268 citations)
- Convex Optimization for Big Data: Scalable, randomized, and parallel algorithms for big data analytics (257 citations)
What are the main themes of his work throughout his whole career to date?
Volkan Cevher mainly focuses on Mathematical optimization, Algorithm, Convex optimization, Compressed sensing and Artificial intelligence.
His Mathematical optimization research incorporates themes from Selection, Convex function, Proximal Gradient Methods, Function and Robustness.
The study incorporates disciplines such as Sampling, Matrix, Sparse matrix and Reproducing kernel Hilbert space in addition to Algorithm.
His Convex optimization study combines topics from a wide range of disciplines, such as Smoothing, Convergence, Rate of convergence, Residual and Augmented Lagrangian method.
His Compressed sensing research includes elements of Signal reconstruction, Approximation algorithm and Greedy algorithm.
Volkan Cevher studied Artificial intelligence and Pattern recognition that intersect with Speech recognition.
He most often published in these fields:
- Mathematical optimization (33.42%)
- Algorithm (31.36%)
- Convex optimization (21.34%)
What were the highlights of his more recent work (between 2017-2021)?
- Mathematical optimization (33.42%)
- Algorithm (31.36%)
- Convex optimization (21.34%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Mathematical optimization, Algorithm, Convex optimization, Applied mathematics and Rate of convergence.
His studies in Mathematical optimization integrate themes in fields like Artificial neural network, Gradient descent, Constraint, Variance reduction and Robustness.
His Algorithm study incorporates themes from Reproducing kernel Hilbert space, Sampling, Kernel method, Constant and Function.
He interconnects Langevin dynamics, Probability distribution, Greedy algorithm and Compressed sensing in the investigation of issues within Sampling.
Volkan Cevher has researched Convex optimization in several fields, including Smoothing, Semidefinite programming, Range and Augmented Lagrangian method.
The various areas that Volkan Cevher examines in his Applied mathematics study include Convergence, Iterated function, Bounded function and Sequence.
Between 2017 and 2021, his most popular works were:
- Ultrasensitive hyperspectral imaging and biodetection enabled by dielectric metasurfaces (134 citations)
- Learning-Based Compressive MRI (59 citations)
- A Smooth Primal-Dual Optimization Framework for Nonsmooth Composite Convex Minimization (44 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Algorithm
His main research concerns Mathematical optimization, Algorithm, Rate of convergence, Applied mathematics and Convergence.
His work deals with themes such as Strategy, Generative grammar and Constraint, which intersect with Mathematical optimization.
Algorithm is a component of his Greedy algorithm and Compressed sensing studies.
His study with Compressed sensing involves better knowledge in Artificial intelligence.
His Rate of convergence study also includes
- Homotopy which is related to area like Convex optimization, Convex function, Almost surely, Discrete mathematics and Basis pursuit,
- Smoothing that connect with fields like Variational inequality and Acceleration.
His Applied mathematics study integrates concerns from other disciplines, such as Iterated function, Differential, Path, Estimator and Augmented Lagrangian method.
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