What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Operating system
- Machine learning
Lei Chen mainly focuses on Data mining, Crowdsourcing, Artificial intelligence, Uncertain data and Pruning.
His Data mining research incorporates elements of Query expansion, Graph, Web query classification and Graph.
In his study, which falls under the umbrella issue of Crowdsourcing, Competitive analysis is strongly linked to Greedy algorithm.
In Artificial intelligence, Lei Chen works on issues like Machine learning, which are connected to Online algorithm.
His study in Uncertain data is interdisciplinary in nature, drawing from both Probabilistic logic, Tuple and Database.
His work in Pruning covers topics such as Metric which are related to areas like Euclidean distance, Edit distance and Algorithm.
His most cited work include:
- Robust and fast similarity search for moving object trajectories (905 citations)
- On the marriage of Lp-norms and edit distance (573 citations)
- Light converting inorganic phosphors for white light-emitting diodes (374 citations)
What are the main themes of his work throughout his whole career to date?
Lei Chen spends much of his time researching Data mining, Artificial intelligence, Theoretical computer science, Crowdsourcing and Graph.
His research in Data mining intersects with topics in Probabilistic logic, Database and Pruning.
Lei Chen has researched Artificial intelligence in several fields, including Machine learning and Pattern recognition.
Many of his studies on Crowdsourcing involve topics that are commonly interrelated, such as Data science.
Graph and Graph are frequently intertwined in his study.
His research integrates issues of Query expansion, Web query classification and Sargable in his study of Query optimization.
He most often published in these fields:
- Data mining (17.77%)
- Artificial intelligence (12.13%)
- Theoretical computer science (11.28%)
What were the highlights of his more recent work (between 2019-2021)?
- Theoretical computer science (11.28%)
- Artificial intelligence (12.13%)
- Crowdsourcing (8.94%)
In recent papers he was focusing on the following fields of study:
Theoretical computer science, Artificial intelligence, Crowdsourcing, Machine learning and Algorithm are his primary areas of study.
He combines subjects such as Scalability, Graph, Power graph analysis, Core and Node with his study of Theoretical computer science.
He is interested in SimRank, which is a field of Graph.
His work in Artificial intelligence is not limited to one particular discipline; it also encompasses Pattern recognition.
He interconnects Matching, Task analysis and Competitive analysis in the investigation of issues within Crowdsourcing.
Within one scientific family, Lei Chen focuses on topics pertaining to Embedding under Algorithm, and may sometimes address concerns connected to Knowledge graph.
Between 2019 and 2021, his most popular works were:
- Perinatal depressive and anxiety symptoms of pregnant women during the coronavirus disease 2019 outbreak in China. (115 citations)
- Spatial crowdsourcing: a survey (33 citations)
- Dynamic Connection-Based Social Group Recommendation (20 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- Machine learning
The scientist’s investigation covers issues in Theoretical computer science, Artificial intelligence, Crowdsourcing, Machine learning and Algorithm.
The concepts of his Theoretical computer science study are interwoven with issues in Permutation, Image, Encryption, Heterogeneous network and Node.
His Entropy study in the realm of Artificial intelligence connects with subjects such as Gestational diabetes.
His Crowdsourcing research incorporates elements of Task analysis, Greedy algorithm, Competitive analysis and Task.
His work carried out in the field of Machine learning brings together such families of science as Distillation and Graph.
Lei Chen studied Algorithm and Autoregressive integrated moving average that intersect with Embedding.
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