What is he best known for?
The fields of study he is best known for:
- Algebra
- Mathematical analysis
- Algorithm
Thomas Strohmer spends much of his time researching Algorithm, Compressed sensing, Convex optimization, Artificial intelligence and Phase retrieval.
He has included themes like Transmitter, MIMO, Beamforming and Control theory in his Algorithm study.
While the research belongs to areas of Control theory, Thomas Strohmer spends his time largely on the problem of Rayleigh fading, intersecting his research to questions surrounding Codebook and Grassmannian.
His Compressed sensing research is multidisciplinary, incorporating perspectives in Sparse matrix, Modulo, Signal processing, Perspective and Antenna.
His Artificial intelligence research incorporates elements of Radar, Upper and lower bounds, Computer vision and Pattern recognition.
His Phase retrieval research includes themes of Noise, Unit sphere and Matrix completion.
His most cited work include:
- Grassmannian beamforming for multiple-input multiple-output wireless systems (1167 citations)
- PhaseLift: Exact and Stable Signal Recovery from Magnitude Measurements via Convex Programming (959 citations)
- High-Resolution Radar via Compressed Sensing (907 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Algorithm, Compressed sensing, Discrete mathematics, Pure mathematics and Mathematical optimization.
In his papers, Thomas Strohmer integrates diverse fields, such as Algorithm and Convex optimization.
As a part of the same scientific study, he usually deals with the Compressed sensing, concentrating on Radar and frequently concerns with Upper and lower bounds and Computer vision.
As a member of one scientific family, he mostly works in the field of Discrete mathematics, focusing on Matrix and, on occasion, Linear least squares, Linear subspace, Inverse problem and Least squares.
The study incorporates disciplines such as Zak transform, Gabor transform and Algebra in addition to Pure mathematics.
His Mathematical optimization study integrates concerns from other disciplines, such as Signal reconstruction, Blind deconvolution, Sparse matrix, Sparse approximation and Applied mathematics.
He most often published in these fields:
- Algorithm (42.42%)
- Compressed sensing (15.15%)
- Discrete mathematics (14.55%)
What were the highlights of his more recent work (between 2014-2021)?
- Blind deconvolution (6.06%)
- Algorithm (42.42%)
- Discrete mathematics (14.55%)
In recent papers he was focusing on the following fields of study:
Blind deconvolution, Algorithm, Discrete mathematics, Convex optimization and Calibration are his primary areas of study.
His Blind deconvolution study combines topics from a wide range of disciplines, such as Gradient descent, Mathematical optimization, Robustness and Image processing.
His work carried out in the field of Algorithm brings together such families of science as Algebraic connectivity, Laplacian matrix, Kernel, Spectral clustering and Cut.
His Discrete mathematics research incorporates themes from Connection, Inverse problem, Matrix, Linear least squares and Efficient algorithm.
His Convex optimization research includes elements of Compressed sensing, Mathematical analysis, Astronomical imaging, Matrix completion and Phase problem.
His biological study spans a wide range of topics, including Key and Computer engineering.
Between 2014 and 2021, his most popular works were:
- Phase Retrieval via Matrix Completion (335 citations)
- Self-calibration and biconvex compressive sensing (163 citations)
- Sparse Signal Processing Concepts for Efficient 5G System Design (119 citations)
In his most recent research, the most cited papers focused on:
- Algebra
- Mathematical analysis
- Statistics
His primary areas of study are Blind deconvolution, Algorithm, Discrete mathematics, Convex optimization and Noise.
Thomas Strohmer interconnects Phase problem, Fourier transform and Matrix completion in the investigation of issues within Algorithm.
His work deals with themes such as Image processing and Total least squares, Least squares, Non-linear least squares, which intersect with Discrete mathematics.
His Convex optimization research incorporates elements of Phase retrieval, Mathematical analysis, Degrees of freedom, Limit and Sequence.
His studies deal with areas such as Subspace topology, Maxima and minima, Gradient descent, Convolution and Optimization problem as well as Noise.
He combines subjects such as Wireless and Mathematical optimization with his study of Maxima and minima.
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