2021 - IEEE Fellow For contributions to high performance computing in processing, analysis and applications of remote sensing imagery
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 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.
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.
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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Cloud Computing: a Perspective Study
Lizhe Wang;Gregor von Laszewski;Andrew J. Younge;Xi He.
New Generation Computing (2010)
Scientific Cloud Computing: Early Definition and Experience
Lizhe Wang;Jie Tao;M. Kunze;A.C. Castellanos.
high performance computing and communications (2008)
Power-aware scheduling of virtual machines in DVFS-enabled clusters
Gregor von Laszewski;Lizhe Wang;Andrew J. Younge;Xi He.
international conference on cluster computing (2009)
G-Hadoop: MapReduce across distributed data centers for data-intensive computing
Lizhe Wang;Jie Tao;Rajiv Ranjan;Holger Marten.
Future Generation Computer Systems (2013)
Efficient resource management for Cloud computing environments
Andrew J. Younge;Gregor von Laszewski;Lizhe Wang;Sonia Lopez-Alarcon.
international conference on green computing (2010)
Remote sensing big data computing
Yan Ma;Haiping Wu;Lizhe Wang;Bormin Huang.
Future Generation Computer Systems (2015)
Towards Energy Aware Scheduling for Precedence Constrained Parallel Tasks in a Cluster with DVFS
Lizhe Wang;Gregor von Laszewski;Jay Dayal;Fugang Wang.
grid computing (2010)
An overview of energy efficiency techniques in cluster computing systems
Giorgio Luigi Valentini;Walter Lassonde;Samee Ullah Khan;Nasro Min-Allah.
Cluster Computing (2013)
Review of performance metrics for green data centers: a taxonomy study
Lizhe Wang;Samee U. Khan.
The Journal of Supercomputing (2013)
Energy-aware parallel task scheduling in a cluster
Lizhe Wang;Lizhe Wang;Samee U. Khan;Dan Chen;Joanna KołOdziej.
Future Generation Computer Systems (2013)
Profile was last updated on December 6th, 2021.
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