Interactive Visualization

Interactive visualization aims to enhance understanding and analysis of complex data through dynamic, user-controlled visual representations. Current research emphasizes developing tools tailored to specific data types (e.g., medical images, genomic data, textual narratives) and incorporating advanced techniques like machine learning models (e.g., variational autoencoders, transformers) for data processing and feature extraction to improve visualization clarity and interpretability. This field is crucial for making complex scientific findings accessible, facilitating model explainability in machine learning, and enabling more effective data-driven decision-making across diverse domains.