What is he best known for?
The fields of study he is best known for:
- Computer network
- Artificial intelligence
- The Internet
His primary scientific interests are in Computer network, Distributed computing, Network packet, Quality of service and Wireless network.
His Computer network research incorporates themes from Wireless ad hoc network and Throughput.
His research integrates issues of Decoding methods, Communication channel and Jitter in his study of Throughput.
His Distributed computing research incorporates elements of Mobile ad hoc network, Network topology and Packet forwarding.
Xin Wang has researched Quality of service in several fields, including Network congestion and The Internet.
He studied Wireless network and Antenna diversity that intersect with Distributed algorithm.
His most cited work include:
- Medium access control with a dynamic duty cycle for sensor networks (356 citations)
- Machine Learning for Networking: Workflow, Advances and Opportunities (153 citations)
- Pricing network resources for adaptive applications in a differentiated services network (109 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Computer network, Distributed computing, Artificial intelligence, Wireless and Algorithm.
Xin Wang has included themes like Wireless ad hoc network, Throughput and Communication channel in his Computer network study.
His Distributed computing study also includes fields such as
- Static routing most often made with reference to Link-state routing protocol,
- Wireless Routing Protocol together with Destination-Sequenced Distance Vector routing.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning, Computer vision and Pattern recognition.
His studies deal with areas such as Distributed algorithm, Network topology, Transmission and Backbone network as well as Wireless.
His Algorithm study also includes fields such as
- Anomaly detection which is related to area like Internet traffic and Data modeling,
- Missing data which is related to area like Matrix completion.
He most often published in these fields:
- Computer network (42.68%)
- Distributed computing (20.50%)
- Artificial intelligence (15.06%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (15.06%)
- Image (5.86%)
- Algorithm (11.30%)
In recent papers he was focusing on the following fields of study:
Xin Wang focuses on Artificial intelligence, Image, Algorithm, Computer vision and Missing data.
Xin Wang has researched Artificial intelligence in several fields, including Machine learning and Pattern recognition.
His work carried out in the field of Machine learning brings together such families of science as Adversarial system and Part of speech.
His biological study deals with issues like Matrix decomposition, which deal with fields such as Computation and Sparse matrix.
His Missing data study integrates concerns from other disciplines, such as Optimization problem, Network monitoring and Matrix completion.
In his study, Artificial neural network is inextricably linked to Data mining, which falls within the broad field of Network monitoring.
Between 2018 and 2021, his most popular works were:
- Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting (46 citations)
- A Hybrid Model for Short-Term Traffic Volume Prediction in Massive Transportation Systems (20 citations)
- MillimeTera: Toward A Large-Scale Open-Source mmWave and Terahertz Experimental Testbed (12 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- Artificial intelligence
- Operating system
Artificial intelligence, Algorithm, Computer network, Machine learning and Missing data are his primary areas of study.
His work in the fields of Artificial intelligence, such as Convolutional neural network, Latent variable model and Part of speech, intersects with other areas such as Vocabulary.
Xin Wang works mostly in the field of Algorithm, limiting it down to topics relating to Matrix decomposition and, in certain cases, Curse of dimensionality, Singular value decomposition, Sparse matrix and Computation, as a part of the same area of interest.
His Computer network research includes elements of Schedule, Cloud computing and Approximation algorithm.
His Boosting study in the realm of Machine learning connects with subjects such as Transferability.
His studies in Missing data integrate themes in fields like Anomaly detection, Sensor fusion and Matrix completion.
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