What is he best known for?
The fields of study he is best known for:
- Operating system
- Artificial intelligence
- Programming language
Wentong Cai mainly investigates Distributed computing, High-level architecture, Grid computing, Artificial intelligence and Discrete event simulation.
His studies deal with areas such as Software architecture, Virtual machine, Scalability and The Internet as well as Distributed computing.
His High-level architecture research is multidisciplinary, relying on both Object-oriented programming, Virtual reality and Reusability.
His Grid computing research includes elements of Distributed algorithm, Distributed design patterns, Middleware and Semantic grid.
His research integrates issues of Crowd simulation, Machine learning, Process and Symbiotic simulation in his study of Artificial intelligence.
Wentong Cai has researched Discrete event simulation in several fields, including Systems engineering and Parallel computing.
His most cited work include:
- Transparent adaptation of single-user applications for multi-user real-time collaboration (164 citations)
- Crowd modeling and simulation technologies (127 citations)
- An auto-adaptive dead reckoning algorithm for distributed interactive simulation (108 citations)
What are the main themes of his work throughout his whole career to date?
Wentong Cai spends much of his time researching Distributed computing, Grid computing, Scalability, High-level architecture and Parallel computing.
He does research in Distributed computing, focusing on Distributed algorithm specifically.
His work deals with themes such as Web service, Workflow and Service, which intersect with Grid computing.
His High-level architecture study is concerned with the larger field of Interoperability.
He studies Speedup which is a part of Parallel computing.
The concepts of his Server study are interwoven with issues in Quality of service, Cloud computing and The Internet.
He most often published in these fields:
- Distributed computing (43.08%)
- Grid computing (11.75%)
- Scalability (10.18%)
What were the highlights of his more recent work (between 2015-2021)?
- Distributed computing (43.08%)
- Traffic simulation (4.96%)
- Speedup (6.01%)
In recent papers he was focusing on the following fields of study:
Wentong Cai mostly deals with Distributed computing, Traffic simulation, Speedup, Artificial intelligence and Real-time computing.
His study in Distributed computing is interdisciplinary in nature, drawing from both Overhead, Road traffic, Resource and Server, Cloud gaming.
His study looks at the relationship between Speedup and topics such as Data structure, which overlap with Parallel computing and Graph.
Wentong Cai interconnects Crowd simulation and Machine learning in the investigation of issues within Artificial intelligence.
His Crowd simulation study integrates concerns from other disciplines, such as Data-driven and Process.
His Real-time computing research integrates issues from Mathematical optimization, Bin packing problem and Global Positioning System.
Between 2015 and 2021, his most popular works were:
- Self-Learning Gene Expression Programming (63 citations)
- Dynamic Bin Packing for On-Demand Cloud Resource Allocation (45 citations)
- Multifactorial Genetic Programming for Symbolic Regression Problems (30 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- Programming language
His primary scientific interests are in Distributed computing, Artificial intelligence, Cloud computing, Machine learning and Mathematical optimization.
His Distributed computing research incorporates elements of Overhead, Quality of service, Profiling, Cloud gaming and Speedup.
His Artificial intelligence research includes themes of Crowd simulation and Computer vision.
Wentong Cai usually deals with Cloud computing and limits it to topics linked to Server and Software distribution, Provisioning, Cost efficiency, Service and Software.
His work on Symbolic regression as part of general Machine learning study is frequently linked to Geospatial analysis, bridging the gap between disciplines.
The various areas that he examines in his Mathematical optimization study include Upper and lower bounds, Competitive analysis and Bin packing problem.
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