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.
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.
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.
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
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Model-Based Compressive Sensing
R.G. Baraniuk;V. Cevher;M.F. Duarte;C. Hegde.
IEEE Transactions on Information Theory (2010)
Compressive Sensing for Background Subtraction
Volkan Cevher;Aswin Sankaranarayanan;Marco F. Duarte;Dikpal Reddy.
european conference on computer vision (2008)
Ultrasensitive hyperspectral imaging and biodetection enabled by dielectric metasurfaces
Filiz Yesilkoy;Eduardo R. Arvelo;Yasaman Jahani;Mingkai Liu.
Nature Photonics (2019)
Convex Optimization for Big Data: Scalable, randomized, and parallel algorithms for big data analytics
Volkan Cevher;Stephen Becker;Mark W. Schmidt.
IEEE Signal Processing Magazine (2014)
Sparse Signal Recovery Using Markov Random Fields
Volkan Cevher;Marco F. Duarte;Chinmay Hegde;Richard Baraniuk.
neural information processing systems (2008)
Distributed target localization via spatial sparsity
Volkan Cevher;Marco F. Duarte;Richard G. Baraniuk.
european signal processing conference (2008)
A compressive beamforming method
A.C. Gurbuz;J.H. McClellan;V. Cevher.
international conference on acoustics, speech, and signal processing (2008)
Bilinear Generalized Approximate Message Passing—Part I: Derivation
Jason T. Parker;Philip Schniter;Volkan Cevher.
IEEE Transactions on Signal Processing (2014)
Low-Dimensional Models for Dimensionality Reduction and Signal Recovery: A Geometric Perspective
Richard G Baraniuk;Volkan Cevher;Michael B Wakin.
Proceedings of the IEEE (2010)
Convex Optimization for Big Data
Volkan Cevher;Stephen Becker;Mark Schmidt.
arXiv: Optimization and Control (2014)
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