What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Operating system
- The Internet
The scientist’s investigation covers issues in Artificial intelligence, Computer network, Graph, Computer vision and Information retrieval.
The concepts of his Artificial intelligence study are interwoven with issues in Natural language processing, Key and Pattern recognition.
His Computer network study integrates concerns from other disciplines, such as Computer security and Cryptography.
His Graph study combines topics from a wide range of disciplines, such as Graph theory and Graph.
The study incorporates disciplines such as Probabilistic logic and Robustness in addition to Computer vision.
His work on Recommender system as part of general Information retrieval research is frequently linked to Popularity, bridging the gap between disciplines.
His most cited work include:
- Opinion word expansion and target extraction through double propagation (710 citations)
- Graph Regularized Sparse Coding for Image Representation (470 citations)
- Expanding domain sentiment lexicon through double propagation (353 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Computer vision, Pattern recognition, Wireless sensor network and Computer network.
His studies in Artificial intelligence integrate themes in fields like Machine learning and Natural language processing.
His research in Computer vision intersects with topics in Frame and Facial expression.
His research on Pattern recognition often connects related topics like Speech recognition.
His studies deal with areas such as Wireless, Distributed computing, Key distribution in wireless sensor networks, Real-time computing and Dissemination as well as Wireless sensor network.
His Computer network study combines topics in areas such as Computer security and Wireless network.
He most often published in these fields:
- Artificial intelligence (42.19%)
- Computer vision (19.01%)
- Pattern recognition (15.36%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (42.19%)
- Web accessibility (2.86%)
- Graph (3.91%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Web accessibility, Graph, Information retrieval and Machine learning.
His research integrates issues of Computer vision and Natural language processing in his study of Artificial intelligence.
His Computer vision research is multidisciplinary, incorporating perspectives in Range and Set.
His Graph research includes themes of Computer network, Embedding and Theoretical computer science.
His Identifier study, which is part of a larger body of work in Computer network, is frequently linked to Efficient energy use, bridging the gap between disciplines.
Jiajun Bu combines subjects such as Active learning, Web page and Group with his study of Information retrieval.
Between 2016 and 2021, his most popular works were:
- ANRL: Attributed Network Representation Learning via Deep Neural Networks (79 citations)
- Learning Personalized Preference of Strong and Weak Ties for Social Recommendation (51 citations)
- Embracing Corruption Burstiness: Fast Error Recovery for ZigBee under Wi-Fi Interference (30 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- The Internet
Jiajun Bu spends much of his time researching Artificial intelligence, Computer network, Theoretical computer science, Web accessibility and Network packet.
Much of his study explores Artificial intelligence relationship to Natural language processing.
His Computer network research integrates issues from Decoding methods, Throughput and Code.
Jiajun Bu has included themes like Structure learning, Embedding, Feature learning and Pooling in his Theoretical computer science study.
Within one scientific family, Jiajun Bu focuses on topics pertaining to Wireless sensor network under Network packet, and may sometimes address concerns connected to Communication source, Hop, Wireless and Wireless ad hoc network.
His Deep learning research incorporates elements of Range and Feature extraction, Computer vision.
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.
