What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Operating system
- The Internet
Lizhe Wang mainly investigates Distributed computing, Cloud computing, Big data, Scheduling and Remote sensing.
His Distributed computing research incorporates themes from Virtual machine and Genetic algorithm.
His studies deal with areas such as The Internet, World Wide Web and Server as well as Cloud computing.
His biological study spans a wide range of topics, including Data-intensive computing, Sparse approximation, K-SVD and Data modeling.
The various areas that Lizhe Wang examines in his Scheduling study include Schedule, Real-time computing and Resource allocation.
His Remote sensing research is multidisciplinary, relying on both Wavelet transform, Artificial intelligence and Pattern recognition.
His most cited work include:
- Cloud Computing: a Perspective Study (518 citations)
- Scientific Cloud Computing: Early Definition and Experience (451 citations)
- Remote sensing big data computing (280 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Distributed computing, Artificial intelligence, Cloud computing, Remote sensing and Big data.
The study incorporates disciplines such as Virtual machine, Utility computing, Grid computing, Scheduling and Workflow in addition to Distributed computing.
His primary area of study in Scheduling is in the field of Fair-share scheduling.
Lizhe Wang has included themes like Machine learning, Computer vision and Pattern recognition in his Artificial intelligence study.
His Cloud computing research includes elements of World Wide Web, Server, Quality of service and Data science.
His research integrates issues of Feature extraction, Earth observation and Convolutional neural network in his study of Remote sensing.
He most often published in these fields:
- Distributed computing (23.87%)
- Artificial intelligence (20.16%)
- Cloud computing (19.36%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (20.16%)
- Remote sensing (16.45%)
- Pattern recognition (11.67%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Remote sensing, Pattern recognition, Feature extraction and Remote sensing.
Lizhe Wang has researched Artificial intelligence in several fields, including Machine learning and Computer vision.
His work carried out in the field of Remote sensing brings together such families of science as Elevation, Earth observation, Convolutional neural network and Bathymetry.
The concepts of his Pattern recognition study are interwoven with issues in Image resolution and Detector.
Lizhe Wang combines subjects such as Self attention, Change detection and Real-time computing with his study of Remote sensing.
His Big data study combines topics in areas such as Quality of service, Cloud computing and Distributed computing.
Between 2019 and 2021, his most popular works were:
- A Multi-Order Distributed HOSVD with Its Incremental Computing for Big Services in Cyber-Physical-Social Systems (37 citations)
- Incremental Factorization of Big Time Series Data with Blind Factor Approximation (24 citations)
- Orchestrating the Development Lifecycle of Machine Learning-based IoT Applications: A Taxonomy and Survey (22 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- The Internet
Lizhe Wang mostly deals with Artificial intelligence, Remote sensing, Convolutional neural network, Feature and Feature extraction.
His Artificial intelligence study integrates concerns from other disciplines, such as Data modeling, Machine learning and Pattern recognition.
His studies in Pattern recognition integrate themes in fields like Pixel, Spectral signature and Data set.
His research in Remote sensing intersects with topics in Classifier and Image, Generative adversarial network.
As a part of the same scientific family, Lizhe Wang mostly works in the field of Feature, focusing on Discriminative model and, on occasion, Feature vector.
His Feature extraction research is multidisciplinary, incorporating perspectives in Algorithm, Representation, Principal component analysis and Dimensionality reduction.
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