Visual Abstraction and Composition for Complex Data Understanding
About the Project
Real-world data are often complex, heterogeneous, and high-dimensional, making them difficult to interpret through conventional visualization methods. Existing approaches can reveal rich details, but they often rely on complex representations or multiple coordinated views that increase users’ cognitive load and make it difficult to form a coherent understanding.
This PhD project will investigate AI-assisted visual abstraction and visualization composition for complex data. It will explore how AI techniques, including deep learning and large language models, can automatically distill complex data into expressive, task-relevant visual forms and combine multiple visualizations into coherent representations. The project will also examine how visualization pipelines can adapt to user intent, analytical context, and data characteristics.
The research will involve developing AI-assisted visualization methods, building interactive prototypes, and conducting user studies. The expected outcome is a set of frameworks, algorithms, and design principles that bridge machine reasoning and human cognition, enabling users to understand complex data with greater clarity, efficiency, and insight.
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