What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Operating system
- Statistics
His main research concerns Visualization, Artificial intelligence, Computer graphics, Data visualization and Visual analytics.
Daniel Weiskopf is interested in Information visualization, which is a branch of Visualization.
His study brings together the fields of Computer vision and Artificial intelligence.
His Computer graphics course of study focuses on Frame rate and Viewport, GPU cluster, Framebuffer and Volume visualization.
His studies in Data visualization integrate themes in fields like Flow and User interface.
In the subject of general Visual analytics, his work in Interactive visual analysis is often linked to Movement, thereby combining diverse domains of study.
His most cited work include:
- Real-Time Volume Graphics (418 citations)
- The State of the Art in Flow Visualization : Dense and Texture-Based Techniques (297 citations)
- Smart hardware-accelerated volume rendering (206 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Visualization, Artificial intelligence, Computer graphics, Computer vision and Data visualization.
His Visualization research includes elements of Eye tracking and Human–computer interaction.
Daniel Weiskopf has included themes like Machine learning and Pattern recognition in his Artificial intelligence study.
His studies deal with areas such as Theoretical computer science and Information retrieval as well as Data visualization.
The study incorporates disciplines such as Analytics and Data science in addition to Visual analytics.
His Rendering study combines topics from a wide range of disciplines, such as Algorithm and Software rendering.
He most often published in these fields:
- Visualization (56.25%)
- Artificial intelligence (29.40%)
- Computer graphics (23.84%)
What were the highlights of his more recent work (between 2018-2021)?
- Visualization (56.25%)
- Artificial intelligence (29.40%)
- Human–computer interaction (10.65%)
In recent papers he was focusing on the following fields of study:
Daniel Weiskopf mainly focuses on Visualization, Artificial intelligence, Human–computer interaction, Eye tracking and Visual analytics.
He has included themes like Algorithm, Software and Data science in his Visualization study.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning, Scalar, Computer vision and Pattern recognition.
His Eye tracking study incorporates themes from Gaze, Eye movement, Key, User studies and Component.
His study explores the link between Visual analytics and topics such as Analytics that cross with problems in Structure.
His Data visualization study integrates concerns from other disciplines, such as Representation, Information visualization, Graph drawing and Information retrieval.
Between 2018 and 2021, his most popular works were:
- Eye tracking support for visual analytics systems: foundations, current applications, and research challenges (9 citations)
- PerfVis: Pervasive Visualization in Immersive Augmented Reality for Performance Awareness (6 citations)
- Exploranative Code Quality Documents (6 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- Statistics
His primary scientific interests are in Visualization, Human–computer interaction, Artificial intelligence, Usability and Augmented reality.
His Visualization research is multidisciplinary, incorporating perspectives in Software, Eye tracking and Software visualization.
His Eye tracking research integrates issues from Point cloud, Gaze, Visual analytics, Structure and Eye movement.
The Human–computer interaction study which covers Computer graphics that intersects with Trajectory, Point, Position and Gradient descent.
His research integrates issues of Scalar and Pattern recognition in his study of Artificial intelligence.
His research in Usability intersects with topics in Software system and Rendering.
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.
