What is he best known for?
The fields of study he is best known for:
- Computer network
- Statistics
- Telecommunications
His main research concerns Computer network, Queue, Queueing theory, Scheduling and Von Neumann architecture.
Cheng-Shang Chang has included themes like Throughput and Topology in his Computer network study.
His Topology research is multidisciplinary, relying on both Markov process, Admission control, Traffic shaping, Network calculus and Markov chain.
His Queue study integrates concerns from other disciplines, such as Discrete mathematics, Distributed computing and Importance sampling.
His Distributed computing research is multidisciplinary, incorporating perspectives in M/M/1 queue, M/D/c queue, M/G/k queue, M/G/1 queue and G/G/1 queue.
His study in Queueing theory focuses on Multilevel queue in particular.
His most cited work include:
- Performance Guarantees in Communication Networks (773 citations)
- Stability, queue length, and delay of deterministic and stochastic queueing networks (760 citations)
- Effective bandwidths for multiclass Markov fluids and other ATM sources (510 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Computer network, Queueing theory, Network packet, Queue and Topology.
Cheng-Shang Chang combines subjects such as FIFO, Throughput and Jitter with his study of Computer network.
His study in Queueing theory is interdisciplinary in nature, drawing from both Distributed computing, Stochastic process, Buffer overflow, Priority queue and Real-time computing.
As a part of the same scientific study, Cheng-Shang Chang usually deals with the Network packet, concentrating on Scheduling and frequently concerns with Mathematical optimization and Multistage interconnection networks.
His Queue research is multidisciplinary, incorporating elements of Discrete mathematics and Importance sampling.
His work deals with themes such as Cognitive radio, Communication channel and Optical switch, which intersect with Topology.
He most often published in these fields:
- Computer network (25.00%)
- Queueing theory (22.28%)
- Network packet (21.20%)
What were the highlights of his more recent work (between 2017-2021)?
- Rendezvous problem (5.98%)
- Cognitive radio (6.52%)
- Graph (5.98%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Rendezvous problem, Cognitive radio, Graph, Topology and Algorithm.
His Cognitive radio research includes themes of Function, Markov chain, Channel and Reinforcement learning.
His Graph research includes elements of Probabilistic logic, Theoretical computer science, Euclidean space and Centrality.
His Topology research includes elements of Asymptotically optimal algorithm and Partition.
His research integrates issues of Network packet, Aloha, Throughput, Random access and Interval in his study of Algorithm.
Network packet is a subfield of Computer network that Cheng-Shang Chang tackles.
Between 2017 and 2021, his most popular works were:
- A Time-Dependent SIR Model for COVID-19 With Undetectable Infected Persons (52 citations)
- A Time-dependent SIR model for COVID-19 with Undetectable Infected Persons (49 citations)
- A Time-dependent SIR model for COVID-19. (26 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- Statistics
- Algorithm
Cheng-Shang Chang focuses on Statistics, Random graph, Epidemic model, Spectral radius and Data mining.
His biological study spans a wide range of topics, including Group testing and Matrix.
His Matrix study combines topics from a wide range of disciplines, such as Sample, Decoding methods, Pooling and Range.
Cheng-Shang Chang has included themes like Fraction and Basic reproduction number in his Random graph study.
His work deals with themes such as Reduction, Transmission and Herd immunity, which intersect with Basic reproduction number.
He combines subjects such as Matrix decomposition, Recommender system, Data set and Synthetic data with his study of Data mining.
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