2020 - IEEE Fellow For contributions to time series management and spatial crowdsourcing
2018 - ACM Distinguished Member
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.
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.
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.
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.
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.
Robust and fast similarity search for moving object trajectories
Lei Chen;M. Tamer Özsu;Vincent Oria.
international conference on management of data (2005)
On the marriage of Lp-norms and edit distance
Lei Chen;Raymond Ng.
very large data bases (2004)
k-automorphism: a general framework for privacy preserving network publication
Lei Zou;Lei Chen;M. Tamer Özsu.
very large data bases (2009)
Light converting inorganic phosphors for white light-emitting diodes
Lei Chen;Lei Chen;Chun Che Lin;Chiao Wen Yeh;Ru Shi Liu.
Exosomal transfer of tumor-associated macrophage-derived miR-21 confers cisplatin resistance in gastric cancer cells
Peiming Zheng;Lei Chen;Xiangliang Yuan;Qin Luo.
Journal of Experimental & Clinical Cancer Research (2017)
Traffic prediction in a bike-sharing system
Yexin Li;Yu Zheng;Huichu Zhang;Lei Chen.
advances in geographic information systems (2015)
Monochromatic and bichromatic reverse skyline search over uncertain databases
Xiang Lian;Lei Chen.
international conference on management of data (2008)
gStore: answering SPARQL queries via subgraph matching
Lei Zou;Jinghui Mo;Lei Chen;M. Tamer Özsu.
very large data bases (2011)
Mining Frequent Trajectory Patterns for Activity Monitoring Using Radio Frequency Tag Arrays
Yunhao Liu;Yiyang Zhao;Lei Chen;Jian Pei.
IEEE Transactions on Parallel and Distributed Systems (2012)
Event detection over twitter social media streams
Xiangmin Zhou;Lei Chen.
very large data bases (2014)
Profile was last updated on December 6th, 2021.
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