Mati Wax

What is he best known for?

The fields of study he is best known for:

• Statistics
• Algorithm
• Machine learning

His primary areas of study are Algorithm, Signal processing, Covariance matrix, Multipath propagation and Electronic engineering. Within one scientific family, Mati Wax focuses on topics pertaining to Mathematical optimization under Algorithm, and may sometimes address concerns connected to Estimator, Maximum likelihood and Shaping. His Signal processing study frequently draws connections to adjacent fields such as Topology.

His research integrates issues of Covariance, Narrowband, Control theory and Rank in his study of Covariance matrix. His Multipath propagation study incorporates themes from Signature and Signal. His Electronic engineering research incorporates themes from Information theory, Sensor array and Model selection.

His most cited work include:

• Detection of signals by information theoretic criteria (2862 citations)
• On spatial smoothing for direction-of-arrival estimation of coherent signals (1390 citations)
• Maximum likelihood localization of multiple sources by alternating projection (1098 citations)

What are the main themes of his work throughout his whole career to date?

Mati Wax mainly investigates Algorithm, Signal processing, Mathematical optimization, Covariance matrix and Multipath propagation. Mati Wax has researched Algorithm in several fields, including Detection theory, Covariance, Maximum likelihood, Estimator and Sensor array. His Sensor array study combines topics in areas such as Preprocessor and Electronic engineering.

He interconnects Signal and Direction of arrival in the investigation of issues within Electronic engineering. His Signal processing research is multidisciplinary, relying on both Estimation theory, Information theory, Statistics and Minimum-variance unbiased estimator. His Covariance matrix study integrates concerns from other disciplines, such as Topology and Rank.

He most often published in these fields:

• Algorithm (56.67%)
• Signal processing (30.00%)
• Mathematical optimization (22.22%)

What were the highlights of his more recent work (between 2016-2021)?

• Algorithm (56.67%)
• Maximum likelihood (11.11%)
• Signal subspace (6.67%)

In recent papers he was focusing on the following fields of study:

His main research concerns Algorithm, Maximum likelihood, Signal subspace, Constant and Beamforming. In his works, Mati Wax undertakes multidisciplinary study on Algorithm and Single sample. The Maximum likelihood study combines topics in areas such as Sensor array, Linear subspace and Total least squares.

His Signal subspace study also includes fields such as

• Matching which intersects with area such as Colors of noise, Eigenvalues and eigenvectors, White noise, Metric and Matrix,
• Signal-to-noise ratio which connect with Noise and Estimator. His studies deal with areas such as Modulus and Global optimality as well as Beamforming. His Calibration study combines topics from a wide range of disciplines, such as Covariance matrix, Maximum likelihood solution, Likelihood function and Maximization.

Between 2016 and 2021, his most popular works were:

• Direct Localization by Partly Calibrated Arrays: A Relaxed Maximum Likelihood Solution (3 citations)
• Constant modulus algorithms via low-rank approximation (3 citations)
• Constant Modulus Beamforming Via Low-Rank Approximation (2 citations)

In his most recent research, the most cited papers focused on:

• Statistics
• Algorithm
• Telecommunications

His primary areas of investigation include Algorithm, Low-rank approximation, Beamforming, Trace norm and Constant. Mati Wax works on Algorithm which deals in particular with Likelihood function. His Low-rank approximation research is multidisciplinary, incorporating perspectives in Modulus, Convergence, Global optimality and Applied mathematics.

His Beamforming research encompasses a variety of disciplines, including Polynomial time complexity and Convex optimization.