What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Operating system
- The Internet
Data mining, Trajectory, Set, Artificial intelligence and Information retrieval are his primary areas of study.
In the field of Data mining, his study on Query optimization overlaps with subjects such as Sampling.
His Query optimization study incorporates themes from Query expansion and Theoretical computer science.
His Trajectory study combines topics from a wide range of disciplines, such as Variety, Data management, Mobile computing and Similarity.
Kai Zheng has researched Artificial intelligence in several fields, including Machine learning and Written language.
His Information retrieval research is multidisciplinary, relying on both Context, Natural language processing, Vocabulary and Text segmentation.
His most cited work include:
- Discovering Urban Functional ZonesUsing Latent Activity Trajectories (223 citations)
- Public Awareness, Perception, and Use of Online Physician Rating Sites (204 citations)
- Supporting information retrieval from electronic health records (189 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Data mining, Artificial intelligence, Information retrieval, Computer network and Set.
His Data mining research integrates issues from Query expansion, Trajectory and Search algorithm.
The Artificial intelligence study combines topics in areas such as Machine learning and Natural language processing.
His Information retrieval study typically links adjacent topics like Context.
Particularly relevant to Network packet is his body of work in Computer network.
He most often published in these fields:
- Data mining (11.33%)
- Artificial intelligence (10.44%)
- Information retrieval (7.61%)
What were the highlights of his more recent work (between 2019-2021)?
- Bioactive glass (5.84%)
- Artificial intelligence (10.44%)
- Machine learning (6.19%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Bioactive glass, Artificial intelligence, Machine learning, Bone regeneration and Task.
Kai Zheng interconnects Nanoparticle, Mesenchymal stem cell, Mesoporous material and Nuclear chemistry in the investigation of issues within Bioactive glass.
The study incorporates disciplines such as Graph, Greedy algorithm and Social network in addition to Artificial intelligence.
The various areas that Kai Zheng examines in his Bone regeneration study include Simulated body fluid and Gelatin.
His Task research focuses on Crowdsourcing and how it connects with Task analysis.
His Information retrieval study frequently involves adjacent topics like Context.
Between 2019 and 2021, his most popular works were:
- Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition) (38 citations)
- SRA: Secure Reverse Auction for Task Assignment in Spatial Crowdsourcing (25 citations)
- A survey of trajectory distance measures and performance evaluation (18 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- The Internet
Kai Zheng mainly focuses on Bioactive glass, Crowdsourcing, Bone regeneration, Artificial intelligence and Machine learning.
His Bioactive glass study contributes to a more complete understanding of Chemical engineering.
His Crowdsourcing research includes themes of Assignment problem, Schedule and Task, Task analysis.
His Bone regeneration research is multidisciplinary, incorporating perspectives in Simulated body fluid, Surface modification, Biocompatibility, Bioceramic and Polyetherketoneketone.
His work on Domain knowledge as part of general Artificial intelligence study is frequently connected to Coherence, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His studies in Machine learning integrate themes in fields like Data-driven, Subsequence, Graph embedding and Graph partition.
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.
