What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Statistics
- Quantum mechanics
Her scientific interests lie mostly in Algorithm, Compressed sensing, Mathematical optimization, Signal processing and Signal reconstruction.
Yonina C. Eldar has included themes like Subspace topology, Sampling, Signal, Phase retrieval and Convex optimization in her Algorithm study.
Her Compressed sensing research is multidisciplinary, incorporating perspectives in Sampling, Sparse matrix, Sparse approximation, Bandwidth and Iterative reconstruction.
Her Bandwidth research includes elements of Nyquist rate, Wideband and Nyquist–Shannon sampling theorem.
She studied Mathematical optimization and Mean squared error that intersect with Estimation of covariance matrices.
Yonina C. Eldar has researched Signal processing in several fields, including Continuous signal, Analog signal, Robustness, Artificial intelligence and Pattern recognition.
Her most cited work include:
- Compressed sensing : theory and applications (1555 citations)
- From Theory to Practice: Sub-Nyquist Sampling of Sparse Wideband Analog Signals (953 citations)
- Block-Sparse Signals: Uncertainty Relations and Efficient Recovery (920 citations)
What are the main themes of her work throughout her whole career to date?
Her primary areas of investigation include Algorithm, Compressed sensing, Mathematical optimization, Artificial intelligence and Electronic engineering.
While the research belongs to areas of Algorithm, Yonina C. Eldar spends her time largely on the problem of Sampling, intersecting her research to questions surrounding Nyquist–Shannon sampling theorem.
The concepts of her Compressed sensing study are interwoven with issues in Matrix, Beamforming, Sparse approximation and Analog signal.
Her biological study spans a wide range of topics, including Mean squared error, Estimator, Applied mathematics and Convex optimization.
Her Artificial intelligence study integrates concerns from other disciplines, such as Ultrasound, Computer vision and Pattern recognition.
Her research integrates issues of Radar and MIMO in her study of Electronic engineering.
She most often published in these fields:
- Algorithm (37.39%)
- Compressed sensing (18.89%)
- Mathematical optimization (17.80%)
What were the highlights of her more recent work (between 2019-2021)?
- Algorithm (37.39%)
- Artificial intelligence (16.02%)
- Deep learning (5.64%)
In recent papers she was focusing on the following fields of study:
Her scientific interests lie mostly in Algorithm, Artificial intelligence, Deep learning, Communication channel and Radar.
Her Algorithm research focuses on Compressed sensing in particular.
Her study in Artificial intelligence is interdisciplinary in nature, drawing from both Beamforming, Computer vision and Pattern recognition.
Her Deep learning study also includes fields such as
- Temporal resolution which connect with Super-resolution microscopy,
- Microscopy, which have a strong connection to Ultrasound.
Her research in Communication channel intersects with topics in Wireless, Telecommunications link and Detector.
Her Radar research integrates issues from Computer hardware, Modulation, Waveform, Communications system and Electronic engineering.
Between 2019 and 2021, her most popular works were:
- Deep Learning in Ultrasound Imaging (59 citations)
- Deep Unfolded Robust PCA With Application to Clutter Suppression in Ultrasound (49 citations)
- ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection (36 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Quantum mechanics
Yonina C. Eldar spends much of her time researching Algorithm, Artificial intelligence, MIMO, Deep learning and Communication channel.
Her Algorithm study combines topics from a wide range of disciplines, such as Graphical model, Graph, Graph and Signal processing.
Her Artificial intelligence research is multidisciplinary, relying on both Ultrasound and Ultrasound imaging.
Her studies deal with areas such as Electronic circuit, Analog signal, Reduction, Baseband and Electronic engineering as well as MIMO.
Her Electronic engineering research includes elements of Radar, Wireless network, Spectral bands and Compressed sensing.
Her Compressed sensing research is multidisciplinary, incorporating elements of Waveform, Narrowband and Antenna.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
