What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
The scientist’s investigation covers issues in Routing, Data mining, Simulation, Operations research and Trajectory.
His Routing research focuses on Enhanced Data Rates for GSM Evolution and how it connects with PageRank, Adjacency list and Vehicle routing problem.
Bin Yang has included themes like Series, Search engine indexing, Location-based service and k-nearest neighbors algorithm in his Data mining study.
The study incorporates disciplines such as Boosting, Theoretical computer science and Storage model in addition to Search engine indexing.
In his papers, Bin Yang integrates diverse fields, such as Simulation and Context.
His Trajectory research includes themes of Range query, Representation, Object, Node and Line segment.
His most cited work include:
- Graph Model Based Indoor Tracking (138 citations)
- Personalized route recommendation using big trajectory data (104 citations)
- Travel cost inference from sparse, spatio temporally correlated time series using Markov models (102 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Routing, Artificial intelligence, Data mining, Mathematical optimization and Trajectory.
The various areas that Bin Yang examines in his Routing study include Stochastic process, Enhanced Data Rates for GSM Evolution and Operations research.
In his research, Global Positioning System, Reduction and Fuel efficiency is intimately related to Simulation, which falls under the overarching field of Operations research.
Bin Yang combines subjects such as Machine learning, Time series and Pattern recognition with his study of Artificial intelligence.
His biological study spans a wide range of topics, including Scalability, Theoretical computer science and Search engine indexing.
In Trajectory, Bin Yang works on issues like Convolution, which are connected to Independence.
He most often published in these fields:
- Routing (16.67%)
- Artificial intelligence (16.67%)
- Data mining (15.69%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (16.67%)
- Mathematical optimization (14.71%)
- Routing (16.67%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Mathematical optimization, Routing, Recurrent neural network and Convolutional neural network are his primary areas of study.
His Artificial intelligence study combines topics in areas such as Machine learning and Pattern recognition.
His Mathematical optimization research is multidisciplinary, incorporating elements of Probabilistic logic, Partition, Search engine indexing and Random variable.
The concepts of his Routing study are interwoven with issues in Stochastic process, Enhanced Data Rates for GSM Evolution, Preference and Trajectory.
As a member of one scientific family, Bin Yang mostly works in the field of Trajectory, focusing on Algorithm and, on occasion, Cluster analysis.
His study on Recurrent neural network also encompasses disciplines like
- Time series which connect with End user,
- Series and related Data mining, Timestamp, Deep neural networks, Traffic planning and Real-time computing,
- Learning to rank most often made with reference to Embedding.
Between 2017 and 2021, his most popular works were:
- Outlier Detection for Multidimensional Time Series Using Deep Neural Networks (56 citations)
- Outlier Detection for Time Series with Recurrent Autoencoder Ensembles (46 citations)
- Finding Top-k Shortest Paths with Diversity (46 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Bin Yang focuses on Artificial intelligence, Mathematical optimization, Routing, Pattern recognition and Algorithm.
His Artificial intelligence research incorporates themes from Series and Time series.
His Mathematical optimization study incorporates themes from Time series approach, Partition, Random variable and Search engine indexing.
His work in Routing tackles topics such as Trajectory which are related to areas like Cluster analysis.
His research in Pattern recognition tackles topics such as Autoencoder which are related to areas like Feature extraction, Dimensionality reduction, Outlier and Feature vector.
His studies in Algorithm integrate themes in fields like Enhanced Data Rates for GSM Evolution and Representation.
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.
