What is he best known for?
The fields of study he is best known for:
- Operating system
- Computer network
- Microsoft Windows
His scientific interests lie mostly in Computer network, Wireless network, Wireless, Operating system and Code.
His is involved in several facets of Wireless network study, as is seen by his studies on Wireless WAN and Wi-Fi array.
His Wireless WAN research is multidisciplinary, incorporating elements of Distributed computing, Service set, Wireless ad hoc network and Wireless mesh network, Switched mesh.
His Hybrid Wireless Mesh Protocol study in the realm of Wireless mesh network interacts with subjects such as Network interface.
His studies in Wireless integrate themes in fields like Testbed, Node and Handover.
His Wireless lan research integrates issues from Access network and Throughput.
His most cited work include:
- Architecture and algorithms for an IEEE 802.11-based multi-channel wireless mesh network (1456 citations)
- Centralized channel assignment and routing algorithms for multi-channel wireless mesh networks (1222 citations)
- Large-scale malware indexing using function-call graphs (253 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Computer network, Operating system, Distributed computing, Embedded system and Quality of service.
The Computer network study combines topics in areas such as Wireless network, Wi-Fi array and The Internet.
His study in Wireless WAN and Wireless mesh network falls within the category of Wireless network.
His studies link Service set with Wireless mesh network.
His study in Distributed computing is interdisciplinary in nature, drawing from both Scheduling, Overhead, Scalability and Resource allocation.
His Network packet research incorporates themes from Buffer overflow and Router.
He most often published in these fields:
- Computer network (39.84%)
- Operating system (28.46%)
- Distributed computing (15.85%)
What were the highlights of his more recent work (between 2009-2016)?
- Operating system (28.46%)
- Virtual machine (7.72%)
- Virtualization (6.50%)
In recent papers he was focusing on the following fields of study:
Tzi-cker Chiueh mainly investigates Operating system, Virtual machine, Virtualization, Hypervisor and Embedded system.
His biological study spans a wide range of topics, including Reference counting and Garbage collection.
His Virtual machine research is multidisciplinary, relying on both False positive paradox, Access control, Intrusion detection system, Full virtualization and Malware.
Tzi-cker Chiueh combines subjects such as Workspace, Overhead, Distributed computing and Commit with his study of Virtualization.
In his study, Web server, The Internet, Redundancy, Unix and Synchronization is strongly linked to Fault tolerance, which falls under the umbrella field of Embedded system.
His Server study introduces a deeper knowledge of Computer network.
Between 2009 and 2016, his most popular works were:
- Scalable segment-based data de-duplication system and method for incremental backups (53 citations)
- Random write optimization techniques for flash disks (44 citations)
- Facilitating inter-application interactions for OS-level virtualization (19 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Computer network
- Microsoft Windows
His main research concerns Operating system, Virtual machine, Incremental backup, Virtualization and Parallel computing.
His research integrates issues of Intrusion detection system, Full virtualization and Malware in his study of Virtual machine.
His research in Virtualization intersects with topics in False positive paradox, Software, Microsoft Windows, Commit and Workspace.
His work deals with themes such as File system, Block and Erasure, which intersect with Parallel computing.
To a larger extent, Tzi-cker Chiueh studies Computer network with the aim of understanding Node.
His specific area of interest is Computer network, where Tzi-cker Chiueh studies Network architecture.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
