What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- The Internet
The scientist’s investigation covers issues in Global Positioning System, Data mining, Beijing, World Wide Web and Graph.
His Global Positioning System research is multidisciplinary, relying on both Service, Ubiquitous computing, Simulation, Variety and Geographic information system.
In his work, Cluster analysis, Taxis, Scalability and Mobile computing is strongly intertwined with Real-time computing, which is a subfield of Simulation.
His study looks at the relationship between Data mining and topics such as Machine learning, which overlap with Web application.
Yu Zheng specializes in World Wide Web, namely Collaborative filtering.
His biological study spans a wide range of topics, including Geospatial analysis, Location-based service and Social network.
His most cited work include:
- Mining interesting locations and travel sequences from GPS trajectories (1426 citations)
- Trajectory Data Mining: An Overview (891 citations)
- Urban Computing: Concepts, Methodologies, and Applications (833 citations)
What are the main themes of his work throughout his whole career to date?
Yu Zheng focuses on Data mining, Global Positioning System, Urban computing, World Wide Web and Information retrieval.
His work on Big data as part of general Data mining research is often related to Beijing, thus linking different fields of science.
The Global Positioning System study combines topics in areas such as Collaborative filtering, Focus, Simulation and Service.
His Simulation research integrates issues from Taxis, Transport engineering and Real-time computing.
His studies in Urban computing integrate themes in fields like Data management and Data science.
In his study, Topic model is strongly linked to Point of interest, which falls under the umbrella field of Information retrieval.
He most often published in these fields:
- Data mining (47.34%)
- Global Positioning System (27.22%)
- Urban computing (13.61%)
What were the highlights of his more recent work (between 2015-2020)?
- Data mining (47.34%)
- Urban computing (13.61%)
- Artificial intelligence (10.06%)
In recent papers he was focusing on the following fields of study:
Yu Zheng spends much of his time researching Data mining, Urban computing, Artificial intelligence, Quality and Machine learning.
His study in the field of Big data is also linked to topics like Beijing.
He has included themes like Ubiquitous computing, Real-time computing, Data management and Data science in his Urban computing study.
His research in Data management tackles topics such as Profiling which are related to areas like Global Positioning System.
His work on Deep learning as part of his general Artificial intelligence study is frequently connected to Urbanization and Series, thereby bridging the divide between different branches of science.
His study in the fields of Time series and Collaborative filtering under the domain of Machine learning overlaps with other disciplines such as Inverse distance weighting and Statistic.
Between 2015 and 2020, his most popular works were:
- Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction (418 citations)
- DNN-based prediction model for spatio-temporal data (224 citations)
- Urban water quality prediction based on multi-task multi-view learning (173 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- The Internet
His primary areas of study are Data mining, Simulation, Task, Crowds and Residual.
Borrowing concepts from Closeness, Yu Zheng weaves in ideas under Data mining.
His Simulation study combines topics from a wide range of disciplines, such as Key and Transport engineering, Traffic congestion.
His studies deal with areas such as Scalability, Aggregate and Missing data as well as Crowds.
His Aggregate study combines topics in areas such as Artificial neural network, Outflow and Task.
The various areas that he examines in his Residual study include Exploit, Partition and Big data.
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.
