What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Huamin Qu mainly focuses on Data visualization, Visualization, Visual analytics, Data science and Artificial intelligence.
His Data visualization study combines topics in areas such as Creative visualization, Computer graphics, Text mining and World Wide Web.
The various areas that Huamin Qu examines in his Visualization study include Computer graphics and Usability.
His Visual analytics study combines topics from a wide range of disciplines, such as Timeline, Analytics, Human–computer interaction and Advanced Traffic Management System.
His Data science research is multidisciplinary, incorporating perspectives in Data modeling, Feature extraction and The Internet.
His Artificial intelligence research includes themes of Machine learning, Simulation, Computer vision and Pattern recognition.
His most cited work include:
- TextFlow: Towards Better Understanding of Evolving Topics in Text (266 citations)
- Geometry-Based Edge Clustering for Graph Visualization (232 citations)
- Context preserving dynamic word cloud visualization (187 citations)
What are the main themes of his work throughout his whole career to date?
Huamin Qu spends much of his time researching Visualization, Visual analytics, Artificial intelligence, Data visualization and Data science.
His Visualization research incorporates elements of Multimedia and Human–computer interaction.
His research in Visual analytics intersects with topics in Analytics, Information retrieval, Usability and Set.
Huamin Qu works mostly in the field of Artificial intelligence, limiting it down to concerns involving Computer vision and, occasionally, Computer graphics.
His studies in Data visualization integrate themes in fields like Text mining, Creative visualization, Computer graphics and Data modeling.
The concepts of his Data science study are interwoven with issues in Social media and World Wide Web.
He most often published in these fields:
- Visualization (50.73%)
- Visual analytics (33.94%)
- Artificial intelligence (33.21%)
What were the highlights of his more recent work (between 2019-2021)?
- Visual analytics (33.94%)
- Visualization (50.73%)
- Artificial intelligence (33.21%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Visual analytics, Visualization, Artificial intelligence, Data visualization and Human–computer interaction.
His studies deal with areas such as Interactive visualization, Usability and Data science as well as Visual analytics.
His Visualization research entails a greater understanding of Data mining.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Chart.
His work carried out in the field of Data visualization brings together such families of science as User experience design, Scalable Vector Graphics and Information retrieval.
His research in Human–computer interaction intersects with topics in Sentence, Mobile device, Workflow and Reinforcement learning.
Between 2019 and 2021, his most popular works were:
- ViSeq: Visual Analytics of Learning Sequence in Massive Open Online Courses (17 citations)
- Towards Automated Infographic Design: Deep Learning-based Auto-Extraction of Extensible Timeline (15 citations)
- DeepDrawing : A Deep Learning Approach to Graph Drawing (14 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of study are Data visualization, Visual analytics, Visualization, Human–computer interaction and Artificial intelligence.
His Data visualization research is multidisciplinary, incorporating perspectives in Sequence, Information retrieval, Multimedia and Infographic.
His Visual analytics study combines topics in areas such as Analytics, Scale, Information cascade and Presentation.
Borrowing concepts from Glyph, he weaves in ideas under Visualization.
His Human–computer interaction study integrates concerns from other disciplines, such as Workflow and Leverage.
His work deals with themes such as Point and Machine learning, which intersect with Artificial intelligence.
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