What is he best known for?
The fields of study he is best known for:
- Optics
- Quantum mechanics
- Artificial intelligence
His primary areas of study are Optics, Holography, Nanotechnology, Phase retrieval and Image processing.
The study of Optics is intertwined with the study of Conical surface in a number of ways.
His Diffraction efficiency, Volume hologram and Reference beam study in the realm of Holography interacts with subjects such as Volume.
His Nanotechnology study combines topics in areas such as Optoelectronics and Electrode.
His Phase retrieval research is multidisciplinary, incorporating elements of Artificial neural network, Optical path length, Noise and Computer vision.
His Image processing study combines topics in areas such as Image quality, Deep learning and Microscopy.
His most cited work include:
- Macroscopic invisibility cloak for visible light (333 citations)
- Macroscopic invisibility cloak for visible light (333 citations)
- Nanotextured Silica Surfaces with Robust Superhydrophobicity and Omnidirectional Broadband Supertransmissivity (310 citations)
What are the main themes of his work throughout his whole career to date?
George Barbastathis mostly deals with Optics, Holography, Phase retrieval, Optoelectronics and Artificial intelligence.
Optics is closely attributed to Phase in his work.
His work focuses on many connections between Holography and other disciplines, such as Microscope, that overlap with his field of interest in Microscopy.
His Phase retrieval research integrates issues from Image processing, Phase imaging and Intensity.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning, Inverse problem and Computer vision.
His Inverse problem study incorporates themes from Algorithm and Tomography.
He most often published in these fields:
- Optics (68.63%)
- Holography (29.47%)
- Phase retrieval (16.73%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (15.40%)
- Artificial neural network (8.56%)
- Phase retrieval (16.73%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Artificial neural network, Phase retrieval, Optics and Deep learning.
His Artificial intelligence research includes elements of Phase, Inverse problem, Computer vision, Machine learning and Pattern recognition.
His Artificial neural network study combines topics from a wide range of disciplines, such as Tomographic reconstruction, Iterative reconstruction and Convolutional neural network.
His studies deal with areas such as Phase modulation, Deep neural networks, Pixel, Modulation and Photon as well as Phase retrieval.
Optics is often connected to Image processing in his work.
He has included themes like Image quality, Stochastic gradient descent and Training set in his Deep learning study.
Between 2017 and 2021, his most popular works were:
- On the use of deep learning for computational imaging (210 citations)
- Imaging through glass diffusers using densely connected convolutional networks (137 citations)
- Low Photon Count Phase Retrieval Using Deep Learning (85 citations)
In his most recent research, the most cited papers focused on:
- Optics
- Quantum mechanics
- Artificial intelligence
The scientist’s investigation covers issues in Artificial neural network, Artificial intelligence, Deep learning, Phase retrieval and Phase.
His work carried out in the field of Artificial neural network brings together such families of science as Algorithm, Control and Operations research.
His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning and Inverse problem.
In Deep learning, George Barbastathis works on issues like Computer vision, which are connected to Tomography and Synthetic data.
George Barbastathis has researched Phase retrieval in several fields, including Optics and Pattern recognition.
The various areas that George Barbastathis examines in his Optics study include Integrated circuit, Convolutional neural network and Artifact.
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