What is he best known for?
The fields of study he is best known for:
- Operating system
- Computer network
- Artificial intelligence
His scientific interests lie mostly in Distributed computing, Computer network, Cloud computing, Real-time computing and Server.
His studies deal with areas such as Virtualization, Computational complexity theory, Approximation algorithm, Complex system and Randomized algorithm as well as Distributed computing.
His study in the field of Thin provisioning is also linked to topics like Power management.
In most of his Computer network studies, his work intersects topics such as Instrumentation.
His Real-time computing research is multidisciplinary, relying on both Fault management, Fault detection and isolation, OpenFlow, Wildcard and Key.
His Server research includes themes of Workload and Virtual machine.
His most cited work include:
- Power and performance management of virtualized computing environments via lookahead control (587 citations)
- A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction (241 citations)
- FlowSense: monitoring network utilization with zero measurement cost (207 citations)
What are the main themes of his work throughout his whole career to date?
Guofei Jiang mainly investigates Data mining, Distributed computing, Real-time computing, Artificial intelligence and Computer network.
Guofei Jiang focuses mostly in the field of Data mining, narrowing it down to matters related to Systems management and, in some cases, Complex system.
His Distributed computing research integrates issues from Virtual machine, Virtualization, Cloud computing, Network topology and Server.
His Real-time computing research focuses on subjects like Data center, which are linked to Workload.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning, Time series and Pattern recognition.
His study in Computer network focuses on Network simulation, OpenFlow, Network packet, Network management and Testbed.
He most often published in these fields:
- Data mining (30.10%)
- Distributed computing (28.57%)
- Real-time computing (15.82%)
What were the highlights of his more recent work (between 2016-2018)?
- Data mining (30.10%)
- Set (8.16%)
- Anomaly detection (8.16%)
In recent papers he was focusing on the following fields of study:
His main research concerns Data mining, Set, Anomaly detection, Artificial intelligence and Graph.
His research on Data mining focuses in particular on Analytics.
Guofei Jiang has researched Set in several fields, including Timestamp, Test data and Host.
His work deals with themes such as Content, Leverage and Nonlinear system, which intersect with Anomaly detection.
His studies examine the connections between Artificial intelligence and genetics, as well as such issues in Pattern recognition, with regards to Malware, Control flow and Constant.
His work is dedicated to discovering how Domain knowledge, Troubleshooting are connected with Computer network and other disciplines.
Between 2016 and 2018, his most popular works were:
- A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction (241 citations)
- A dual-stage attention-based recurrent neural network for time series prediction (158 citations)
- Magneto: Unified Fine-grained Path Control in Legacy and OpenFlow Hybrid Networks (19 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- Computer network
His primary areas of study are Data mining, Graph, Process graph, Trustworthiness and Graph based.
His Data mining research incorporates themes from Leverage and Search engine indexing.
His studies in Graph integrate themes in fields like Port, Connectivity and Network connection.
His work carried out in the field of Process graph brings together such families of science as Theoretical computer science and Cluster analysis.
The various areas that Guofei Jiang examines in his Graph based study include Event correlation, Security management and Enterprise information security architecture.
The study incorporates disciplines such as Encoder, Time series and Nonlinear system in addition to Artificial intelligence.
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