What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Algorithm
His scientific interests lie mostly in Compressed sensing, Artificial intelligence, Algorithm, Computer vision and Signal.
His Compressed sensing research integrates issues from Theoretical computer science, Signal reconstruction, Speech recognition, Greedy algorithm and Computation.
His Pixel, Iterative reconstruction and Data compression study in the realm of Artificial intelligence connects with subjects such as Field.
His work on Signal processing expands to the thematically related Algorithm.
His study explores the link between Signal processing and topics such as Mathematical optimization that cross with problems in Error detection and correction, Sparse matrix, Sequence and Series.
His Image processing study combines topics in areas such as Sampling, Coherent sampling, Nyquist frequency and Nyquist–Shannon sampling theorem.
His most cited work include:
- An Introduction To Compressive Sampling (7569 citations)
- Enhancing Sparsity by Reweighted ℓ 1 Minimization (3695 citations)
- A Simple Proof of the Restricted Isometry Property for Random Matrices (2130 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Algorithm, Compressed sensing, Artificial intelligence, Signal and Matrix.
He combines subjects such as Basis, Subspace topology, Sparse matrix, Mathematical optimization and Signal processing with his study of Algorithm.
Michael B. Wakin studies Compressed sensing, focusing on Restricted isometry property in particular.
The Artificial intelligence study combines topics in areas such as Computer vision and Pattern recognition.
His study in Signal is interdisciplinary in nature, drawing from both Probabilistic logic, Representation, Iterative reconstruction and Compression.
His Matrix study combines topics from a wide range of disciplines, such as Geometry and Eigenvalues and eigenvectors.
He most often published in these fields:
- Algorithm (40.47%)
- Compressed sensing (35.35%)
- Artificial intelligence (20.47%)
What were the highlights of his more recent work (between 2016-2021)?
- Algorithm (40.47%)
- Matrix (17.21%)
- Subspace topology (9.30%)
In recent papers he was focusing on the following fields of study:
Michael B. Wakin mostly deals with Algorithm, Matrix, Subspace topology, Eigenvalues and eigenvectors and Geometry.
The concepts of his Algorithm study are interwoven with issues in Signal, Signal processing, Time–frequency analysis, Radar imaging and Exponential function.
His work carried out in the field of Subspace topology brings together such families of science as Sparse matrix, Solver and Demodulation.
His studies deal with areas such as Sampling, Energy, Structural health monitoring and Compressed sensing as well as Demodulation.
His Compressed sensing research incorporates themes from Image, Mutual coherence, Dimension, Linear operators and Manifold.
His Artificial intelligence research is multidisciplinary, incorporating elements of Nyquist rate, Bandwidth and Nyquist–Shannon sampling theorem.
Between 2016 and 2021, his most popular works were:
- Global optimality in low-rank matrix optimization (64 citations)
- Compressive Video Sensing: Algorithms, architectures, and applications (62 citations)
- Approximating Sampled Sinusoids and Multiband Signals Using Multiband Modulated DPSS Dictionaries (42 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Algorithm
Michael B. Wakin mainly focuses on Algorithm, Global optimization, Time–frequency analysis, Signal processing and Gradient descent.
His study on Algorithm also encompasses disciplines like
- Matrix which connect with Distribution,
- Matrix completion which is related to area like Phase.
His research in Time–frequency analysis intersects with topics in Sampling, Bandlimiting and Structural health monitoring.
His biological study spans a wide range of topics, including Clutter, Sampling, Constant false alarm rate and Continuous-wave radar.
His work deals with themes such as Oversampling, Bandwidth and Computer vision, Nyquist–Shannon sampling theorem, which intersect with Sampling.
His research on Gradient descent also deals with topics like
- Factorization, Function and Product most often made with reference to Low-rank approximation,
- Maxima and minima and related Mean squared error,
- Rank which intersects with area such as Euclidean space, Dynamical system and Dimension.
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