What is he best known for?
The fields of study he is best known for:
- Operating system
- Artificial intelligence
- Software
His primary scientific interests are in Distributed computing, Scheduling, Scalability, Data-intensive computing and Dynamic priority scheduling.
Dan Chen combines subjects such as Network architecture, Software deployment, Network packet, Cloud computing and High-level architecture with his study of Distributed computing.
His Cloud computing study incorporates themes from Petabyte, Reliability, Workflow and Distributed design patterns.
The various areas that Dan Chen examines in his Scheduling study include Supercomputer, HPC Challenge Benchmark and Resource allocation.
Dan Chen has researched Scalability in several fields, including Real-time computing, Data transmission and Warning system.
His Data-intensive computing research incorporates elements of Data access, Distributed database, Database, Parallel I/O and Big data.
His most cited work include:
- G-Hadoop: MapReduce across distributed data centers for data-intensive computing (273 citations)
- Energy-aware parallel task scheduling in a cluster (151 citations)
- Crowd modeling and simulation technologies (127 citations)
What are the main themes of his work throughout his whole career to date?
Dan Chen spends much of his time researching Distributed computing, Artificial intelligence, Pattern recognition, General-purpose computing on graphics processing units and Scalability.
His studies deal with areas such as Grid computing, Cloud computing, Workflow and High-level architecture as well as Distributed computing.
His work in Workflow covers topics such as Data-intensive computing which are related to areas like File system.
His study in the fields of Entropy under the domain of Artificial intelligence overlaps with other disciplines such as Noise.
His Pattern recognition research incorporates themes from Artificial neural network, Hyperparameter, Synchronization and Sensitivity.
His Graphics processing unit research focuses on Signal processing and how it connects with Hilbert–Huang transform.
He most often published in these fields:
- Distributed computing (35.88%)
- Artificial intelligence (20.61%)
- Pattern recognition (14.50%)
What were the highlights of his more recent work (between 2017-2021)?
- Pattern recognition (14.50%)
- Artificial intelligence (20.61%)
- Reinforcement learning (4.58%)
In recent papers he was focusing on the following fields of study:
His main research concerns Pattern recognition, Artificial intelligence, Reinforcement learning, Synchronization and Cloud computing.
His Pattern recognition study integrates concerns from other disciplines, such as Artificial neural network and Sensitivity.
Dan Chen interconnects Stability and Discrete system in the investigation of issues within Artificial neural network.
His Bayesian probability, Hyperparameter and Deep learning study in the realm of Artificial intelligence interacts with subjects such as Noise and Sleep disorder.
His Reinforcement learning study combines topics in areas such as Vaccination, Bounded rationality and Risk of infection.
Many of his research projects under Cloud computing are closely connected to Service level with Service level, tying the diverse disciplines of science together.
Between 2017 and 2021, his most popular works were:
- Incremental Factorization of Big Time Series Data with Blind Factor Approximation (24 citations)
- Towards Brain Big Data Classification: Epileptic EEG Identification With a Lightweight VGGNet on Global MIC (23 citations)
- Cloud‐aided online EEG classification system for brain healthcare: A case study of depression evaluation with a lightweight CNN (20 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- Computer network
Dan Chen focuses on Pattern recognition, Artificial intelligence, Eeg classification, Sensitivity and Bayesian inference.
His Pattern recognition study combines topics from a wide range of disciplines, such as Artificial neural network, Bayesian optimization and Hyperparameter.
Depression, Thesaurus, Cloud computing, Information retrieval and Performance improvement are fields of study that intersect with his Eeg classification study.
Dan Chen has included themes like Synchronization, Big data and Identification in his Sensitivity study.
His Bayesian inference research overlaps with Factorization, Tensor and Rank.
Exponential family, GPU cluster, Algorithm, Massively parallel and Time complexity are fields of study that overlap with his Factorization research.
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.
