What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Operating system
- Statistics
Data visualization, Visualization, Algorithm, Volume rendering and Artificial intelligence are his primary areas of study.
His Data visualization study deals with the bigger picture of Data mining.
His Visualization research is multidisciplinary, relying on both Image processing and Streamlines, streaklines, and pathlines.
His work in the fields of Algorithm, such as Computational geometry, overlaps with other areas such as Line integral convolution.
His Volume rendering study contributes to a more complete understanding of Rendering.
In his research on the topic of Artificial intelligence, Hyperplane, Slicing and Geometric modeling is strongly related with Computer vision.
His most cited work include:
- A near optimal isosurface extraction algorithm using the span space (260 citations)
- A fast volume rendering algorithm for time-varying fields using a time-space partitioning (TSP) tree (157 citations)
- View selection for volume rendering (144 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Visualization, Data visualization, Artificial intelligence, Data mining and Rendering.
The concepts of his Visualization study are interwoven with issues in Streamlines, streaklines, and pathlines, Theoretical computer science, Computer graphics and Data science.
His research integrates issues of User interface, Scalability, Computer graphics and Computational geometry in his study of Data visualization.
His work investigates the relationship between Artificial intelligence and topics such as Computer vision that intersect with problems in Level of detail.
In Data mining, he works on issues like Information theory, which are connected to Entropy.
His Rendering study combines topics from a wide range of disciplines, such as Algorithm, Computer hardware and Texture memory.
He most often published in these fields:
- Visualization (49.56%)
- Data visualization (32.02%)
- Artificial intelligence (25.88%)
What were the highlights of his more recent work (between 2018-2021)?
- Visualization (49.56%)
- Artificial intelligence (25.88%)
- Data visualization (32.02%)
In recent papers he was focusing on the following fields of study:
Han-Wei Shen focuses on Visualization, Artificial intelligence, Data visualization, Visual analytics and Scalability.
His Visualization research incorporates elements of Image and Data science.
His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Pattern recognition.
Han-Wei Shen has researched Data visualization in several fields, including Multivariate normal distribution, Multivariate statistics and Computer vision.
His biological study spans a wide range of topics, including Semantics and Reinforcement learning.
His studies deal with areas such as Supercomputer, Distributed computing, Computational science, Asynchronous communication and Feature extraction as well as Scalability.
Between 2018 and 2021, his most popular works were:
- DQNViz: A Visual Analytics Approach to Understand Deep Q-Networks (47 citations)
- Visualization and Visual Analysis of Ensemble Data: A Survey (35 citations)
- DeepVID : Deep Visual Interpretation and Diagnosis for Image Classifiers via Knowledge Distillation (28 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- Statistics
His primary areas of investigation include Visualization, Data modeling, Data visualization, Visual analytics and Data mining.
Han-Wei Shen conducts interdisciplinary study in the fields of Visualization and Particle tracking velocimetry through his research.
His study in Data visualization is interdisciplinary in nature, drawing from both Creative visualization, Perspective, Cluster analysis, Variety and Multivariate normal distribution.
His Visual analytics study is concerned with the larger field of Artificial intelligence.
His work in the fields of Feature overlaps with other areas such as Generative model, Domain and Process.
His Data mining research incorporates themes from Parameter space, Image synthesis, Space exploration and Flexibility.
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.
