What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Operating system
The scientist’s investigation covers issues in Visualization, Data science, Visual analytics, Data visualization and Information retrieval.
His Visualization research incorporates elements of Data modeling, Software and Node.
His work deals with themes such as Interactive visualization, Business intelligence, Variety, Information visualization and Big data, which intersect with Data science.
His study with Visual analytics involves better knowledge in Artificial intelligence.
His research integrates issues of Social media, Computer graphics, Usability and Opinion leadership in his study of Data visualization.
While the research belongs to areas of Information retrieval, Shixia Liu spends his time largely on the problem of Text mining, intersecting his research to questions surrounding Tag cloud.
His most cited work include:
- TextFlow: Towards Better Understanding of Evolving Topics in Text (266 citations)
- Towards Better Analysis of Deep Convolutional Neural Networks (247 citations)
- A survey on information visualization: recent advances and challenges (224 citations)
What are the main themes of his work throughout his whole career to date?
Shixia Liu mostly deals with Visualization, Artificial intelligence, Visual analytics, Information retrieval and Data visualization.
His Visualization study improves the overall literature in Data mining.
His studies in Artificial intelligence integrate themes in fields like Natural language processing, Machine learning, Computer vision and Pattern recognition.
His work carried out in the field of Visual analytics brings together such families of science as Interactive visualization, Graph drawing, Graph, Information visualization and Data science.
As a member of one scientific family, Shixia Liu mostly works in the field of Information retrieval, focusing on Text mining and, on occasion, Topic model.
His Data visualization research incorporates themes from Context, Tag cloud and Theoretical computer science.
He most often published in these fields:
- Visualization (36.36%)
- Artificial intelligence (28.48%)
- Visual analytics (25.45%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (28.48%)
- Visual analytics (25.45%)
- Visualization (36.36%)
In recent papers he was focusing on the following fields of study:
Shixia Liu mainly investigates Artificial intelligence, Visual analytics, Visualization, Machine learning and Data science.
His research in the fields of Interactive visualization, Deep learning and Artificial neural network overlaps with other disciplines such as Key.
His Visual analytics study incorporates themes from Variety and Graph drawing.
His Visualization research includes themes of Graph, Robustness and Datapath.
Shixia Liu combines subjects such as Computer graphics and Data management with his study of Data science.
His Data visualization study combines topics from a wide range of disciplines, such as Text mining and Glyph.
Between 2016 and 2021, his most popular works were:
- Towards Better Analysis of Deep Convolutional Neural Networks (247 citations)
- Towards better analysis of machine learning models: A visual analytics perspective (154 citations)
- Visual Analytics for Explainable Deep Learning (98 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Operating system
His main research concerns Visual analytics, Artificial intelligence, Data science, Machine learning and Data visualization.
As part of his studies on Visual analytics, Shixia Liu often connects relevant subjects like Data management.
His Data science research is multidisciplinary, relying on both Interactive visualization, Data type and Graph.
In the field of Machine learning, his study on Convolutional neural network and Feature overlaps with subjects such as Process.
His Data visualization study is associated with Visualization.
His work on Euler diagram as part of general Visualization research is frequently linked to Bridging, bridging the gap between disciplines.
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