【Scientific Visualization】

• Scientific Visualization combines visual representation techniques with domain-specific knowledge to support the exploration, analysis, and communication of complex scientific data. Our focus is on developing visualization methods for vector fields and tensor fields, enabling the detailed examination of flow behavior, stress distributions, and other spatially varying phenomena.

【Scientific Machine Learning】

• Scientific Machine Learning (SciML) integrates data-driven modeling with domain-specific knowledge to enhance the accuracy, interpretability, and efficiency of scientific simulations. Our focus is on developing Physics-Informed Neural Networks (PINNs), which embed partial differential equations (PDEs) directly into the learning process, addressing challenging problems such as materials discovery and uncertainty quantification in climate models.

【Visual Analytics】

• Visual Analytics combines data analysis, visualization, and human-computer interaction to support analytical reasoning and decision-making. Our aim is to create interactive toolkits that enable users to explore and evaluate their work across diverse domains, such as 3D Gaussian splatting reconstruction, physics-based simulations, and robotics .

【Shapes, Sounds, and Digital Twins】

• Digital Twins are virtual replicas of physical systems, continuously updated with real-time data and computational models. Our focus includes shape modeling, layout generation, and sound synthesis, supporting applications in manufacturing, design, and interactive environments.