What is he best known for?
The fields of study he is best known for:
- Computer network
- Artificial intelligence
- Operating system
Kai Chen focuses on Computer network, Distributed computing, Testbed, Data center and Artificial intelligence.
His Computer network research incorporates elements of Cloud computing and Visual sensor network.
The Distributed computing study combines topics in areas such as Autonomous system, Throughput, Byte and Backward compatibility.
His Testbed research integrates issues from Scheduling, Dynamic priority scheduling, Two-level scheduling, Priority queue and Load balancing.
His work carried out in the field of Data center brings together such families of science as Computer architecture, Topology, Optical switch, Queue and Flexibility.
His Artificial intelligence research includes themes of Machine learning and Pattern recognition.
His most cited work include:
- Collaborative filtering and deep learning based recommendation system for cold start items (271 citations)
- OSA: an optical switching architecture for data center networks with unprecedented flexibility (256 citations)
- A LSTM-based method for stock returns prediction: A case study of China stock market (181 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Computer network, Artificial intelligence, Distributed computing, Testbed and Pattern recognition.
Computer network is closely attributed to Cloud computing in his study.
Kai Chen has included themes like Machine learning and Computer vision in his Artificial intelligence study.
His Distributed computing research is multidisciplinary, incorporating elements of Scheduling and Scalability.
His Testbed research incorporates elements of Queue, Latency and Real-time computing.
His research in Pattern recognition intersects with topics in Artificial neural network and Feature.
He most often published in these fields:
- Computer network (35.05%)
- Artificial intelligence (27.57%)
- Distributed computing (19.63%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (27.57%)
- Computer network (35.05%)
- Machine learning (7.94%)
In recent papers he was focusing on the following fields of study:
Kai Chen spends much of his time researching Artificial intelligence, Computer network, Machine learning, Deep learning and Testbed.
His Artificial intelligence study combines topics in areas such as Speedup, Computer vision and Pattern recognition.
His Machine learning research incorporates themes from Divergence, Robustness and Federated learning.
His Deep learning research is multidisciplinary, relying on both Artificial neural network, Field and Rotation.
His Testbed research includes themes of Flow network, Sketch, Network monitoring, Reduction and Load balancing.
His Network packet research integrates issues from Exploit, Data center and Application layer.
Between 2019 and 2021, his most popular works were:
- Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance (24 citations)
- Secure Federated Matrix Factorization (12 citations)
- Modulating Energy Level on an A‐D‐A′‐D‐A‐Type Unfused Acceptor by a Benzothiadiazole Core Enables Organic Solar Cells with Simple Procedure and High Performance (11 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- Operating system
- Artificial intelligence
His primary areas of investigation include Computer network, Acceptor, Core, Optoelectronics and Simple.
His Packet loss and Network packet study are his primary interests in Computer network.
The study incorporates disciplines such as Testbed, Block, Latency and Throughput in addition to Packet loss.
His work carried out in the field of Network packet brings together such families of science as Exploit, Handshaking, Latency and Data transmission.
In his work, Kai Chen performs multidisciplinary research in Transport layer and Data center.
Kai Chen combines topics linked to Application layer with his work on Data center.
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