What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Programming language
- Machine learning
Heidrun Schumann spends much of her time researching Visualization, Data visualization, Information visualization, Visual analytics and Human–computer interaction.
Her Visualization study incorporates themes from Variety, Theoretical computer science, Data science and Taxonomy.
The Data visualization study combines topics in areas such as Graphical user interface, Computer vision and Geographic information system.
Her research in Information visualization intersects with topics in Creative visualization and Event.
Her Interactive visual analysis study in the realm of Visual analytics interacts with subjects such as Lens.
Her work in Human–computer interaction tackles topics such as Multimedia which are related to areas like User interface and Interaction Styles.
Her most cited work include:
- Visualization of Time-Oriented Data (408 citations)
- Space, time and visual analytics (297 citations)
- Visualizing time-oriented data-A systematic view (252 citations)
What are the main themes of her work throughout her whole career to date?
Her primary areas of investigation include Visualization, Visual analytics, Artificial intelligence, Human–computer interaction and Information visualization.
Her research investigates the connection between Visualization and topics such as Theoretical computer science that intersect with issues in Graph drawing.
Her work investigates the relationship between Visual analytics and topics such as Data science that intersect with problems in Field.
Her Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning, Optical coherence tomography, Computer vision and Pattern recognition.
As a part of the same scientific family, Heidrun Schumann mostly works in the field of Human–computer interaction, focusing on Multimedia and, on occasion, Mobile device and Presentation.
The various areas that Heidrun Schumann examines in her Information visualization study include Interactive visualization and Scientific visualization.
She most often published in these fields:
- Visualization (48.64%)
- Visual analytics (25.91%)
- Artificial intelligence (23.18%)
What were the highlights of her more recent work (between 2016-2021)?
- Visual analytics (25.91%)
- Visualization (48.64%)
- Artificial intelligence (23.18%)
In recent papers she was focusing on the following fields of study:
Heidrun Schumann mostly deals with Visual analytics, Visualization, Artificial intelligence, Human–computer interaction and Optical coherence tomography.
Her research integrates issues of Interactive visualization, Information visualization, Software engineering and Data science in her study of Visual analytics.
As part of one scientific family, Heidrun Schumann deals mainly with the area of Data science, narrowing it down to issues related to the Field, and often Principal.
Her 3d terrain and Data visualization study in the realm of Visualization connects with subjects such as Workflow.
She works mostly in the field of Data visualization, limiting it down to topics relating to Data exchange and, in certain cases, Data modeling.
Heidrun Schumann interconnects Computer vision and Pattern recognition in the investigation of issues within Artificial intelligence.
Between 2016 and 2021, her most popular works were:
- Interactive Lenses for Visualization: An Extended Survey (57 citations)
- A Review and Characterization of Progressive Visual Analytics (17 citations)
- Visual analysis of retinal changes with optical coherence tomography (10 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Machine learning
Her scientific interests lie mostly in Visual analytics, Visualization, Human–computer interaction, Workflow and Pattern recognition.
Visual analytics and Data science are commonly linked in her work.
Heidrun Schumann has included themes like Field and Iterative and incremental development in her Data science study.
Her Visualization study frequently links to other fields, such as Information needs.
Her Human–computer interaction research is multidisciplinary, incorporating elements of Interactive visualization, Pipeline, Information visualization and Conceptual model.
Her Pattern recognition research integrates issues from Interface and Artificial intelligence.
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