What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- The Internet
David S. Ebert mostly deals with Computer graphics, Rendering, Data visualization, Artificial intelligence and Computer graphics.
His work on Volume rendering as part of his general Computer graphics study is frequently connected to Halo, thereby bridging the divide between different branches of science.
His work investigates the relationship between Rendering and topics such as Animation that intersect with problems in Interactive design.
His study with Data visualization involves better knowledge in Visualization.
His Artificial intelligence research incorporates elements of Opacity and Computer vision.
David S. Ebert interconnects Procedural modeling and Procedural texture in the investigation of issues within Computer graphics.
His most cited work include:
- Texturing and Modeling: A Procedural Approach (558 citations)
- Visualization and computer graphics (355 citations)
- The Use of Mobile Devices in Aiding Dietary Assessment and Evaluation (263 citations)
What are the main themes of his work throughout his whole career to date?
David S. Ebert spends much of his time researching Visual analytics, Visualization, Data science, Data visualization and Computer graphics.
The Visual analytics study combines topics in areas such as Analytics, Geospatial analysis and Human–computer interaction.
His work carried out in the field of Visualization brings together such families of science as Multimedia and Computer vision.
His Data science research is multidisciplinary, incorporating elements of Business intelligence and Big data.
His work deals with themes such as Interactive visualization, Information retrieval and Graphics, which intersect with Data visualization.
His work on Rendering, Volume rendering, Computer graphics and Animation as part of general Computer graphics study is frequently linked to Flow visualization, bridging the gap between disciplines.
He most often published in these fields:
- Visual analytics (36.26%)
- Visualization (35.99%)
- Data science (22.80%)
What were the highlights of his more recent work (between 2016-2021)?
- Visual analytics (36.26%)
- Data science (22.80%)
- Visualization (35.99%)
In recent papers he was focusing on the following fields of study:
David S. Ebert focuses on Visual analytics, Data science, Visualization, Social media and Data mining.
His Visual analytics research entails a greater understanding of Artificial intelligence.
His Data science study combines topics in areas such as Domain, Quality and Big data.
His specific area of interest is Visualization, where David S. Ebert studies Data visualization.
His study in Social media is interdisciplinary in nature, drawing from both Sentiment analysis, Natural disaster, Internet privacy and Identification.
His Work study integrates concerns from other disciplines, such as Scientific visualization, Computer graphics and Center of excellence.
Between 2016 and 2021, his most popular works were:
- Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models (72 citations)
- Data Flow Analysis and Visualization for Spatiotemporal Statistical Data without Trajectory Information (21 citations)
- An Ontological Framework for Supporting the Design and Evaluation of Visual Analytics Systems (16 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- The Internet
David S. Ebert focuses on Visual analytics, Data visualization, Social media, Data science and Visualization.
David S. Ebert has researched Visual analytics in several fields, including Knowledge management and Human–computer interaction.
His Data visualization research is classified as research in Artificial intelligence.
His Social media study incorporates themes from Surveillance camera, Natural disaster and Internet privacy.
His research integrates issues of Field, Computer graphics and Big data in his study of Data science.
His biological study spans a wide range of topics, including Data flow diagram and Data-flow analysis.
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