Dr Xinhuan Shu

Role: Lecturer in Data Science
Research Group: Scalable Computing
Email: xinhuan.shu@newcastle.ac.uk

Xinhuan is a Lecturer in Computer Science. Her research aims to engage humans in interacting with the world of data through visualisation.

Bio

Xinhuan Shu is a lecturer at the School of Computing, Newcastle University. Prior to that, she was a PostDoc Fellow, advised by Prof. Benjamin Bach in VisHub at University of Edinburgh. Xinhuan obtained my Ph.D. from the Hong Kong University of Science and Technology (HKUST), supervised by Prof. Huamin Qu in VisLab.

Xinhuan’s Personal Website

Research

Xinhuan’s research lies at the intersection of data visualisation, human-computer interaction, and machine learning, where her work aims to engage humans in interacting with the world of data through visualisation. I work on developing expressive visualisation techniques and human-AI interfaces that facilitate human-data interaction at various data activities, including data transformation, analysis, communication, and decision-making.

Publications

  • Shu, X., Pister, A., Tang, J., Chevalier, F., & Bach, B. (2024).. Does This Have a Particular Meaning? Interactive Pattern Explanation for Network Visualizations. IEEE Transactions on Visualization and Computer Graphics (VIS’24). https://arxiv.org/pdf/2408.01272.

  • Wang, J., Shu, X., Bach, B., & Hinrichs, U. (2024). Visualization atlases: Explaining and exploring complex topics through data, visualization, and narration. IEEE Transactions on Visualization and Computer Graphics (VIS’24). https://arxiv.org/pdf/2408.07483.

  • Zhongsu Luo, Kai Xiong, Jiajun Zhu, Ran Chen, Xinhuan Shu, Di Weng, Yingcai Wu (2024). Ferry: Toward Better Understanding of Input/Output Space for Data Wrangling Scripts. IEEE Transactions on Visualization and Computer Graphics (VIS’24). https://ieeexplore.ieee.org/document/10670464.

  • Yanwei Huang, Yurun Yang, Xinhuan Shu, Ran Chen, Di Weng, and Yingcai Wu. 2024. Table Illustrator: Puzzle-based interactive authoring of plain tables. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’24). https://doi.org/10.1145/3613904.3642415