Staff profile

I am a PhD candidate who has recently passed my thesis viva successfully with minor corrections, supervised by Prof. Hubert P. H. Shum and Dr Noura Al Moubayed in Computer Science at Durham University. Currently, I am also a research assciorste in the University of Edingburgh, focusing on bronshcopic robotics vision.

My research focuses on integrating Geometric Information into computer vision applications, particularly in the analysis of Biomedical Engineering videos. Before this, I received my MRes degree from King's College London, my MSc degree from the University of Southampton, and my Bachelor's degree from Beijing University of Chinese Medicine.

I am incredibly thankful to my supervisors and collaborators and enjoy exploring new domains through collaborations. If you have questions or would like to work together, please feel free to reach out through email.

If you are interested in more detailed information about my experience, please visit my personal website: https://francisxzhang.github.io.

• Computer Vision
• Egocentric Vision
• Geometric Deep Learning

Conference Paper

• Depth-Aware Endoscopic Video Inpainting
  
  Xiatian Zhang, F., Chen, S., Xie, X., & Shum, H. P. (in press). Depth-Aware Endoscopic Video Inpainting. Presented at 27th International Conference on Medical Image Computing and Computer Assisted Intervention, Marrakesh, Morocco.
• Using Fixed and Mobile Eye Tracking to Understand How Visitors View Art in a Museum: A Study at the Bowes Museum, County Durham, UK
  
  Warwick, C., Beresford, A., Casteau, S., Shum, H. P. H., Smith, D., & Zhang, F. X. (2025, July 14 – 2025, July 18). Using Fixed and Mobile Eye Tracking to Understand How Visitors View Art in a Museum: A Study at the Bowes Museum, County Durham, UK [Conference paper]. Presented at 2025 ADHO Digital Humanities Conference, Lisbon, Portugal.
• Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRI
  
  Zhang, X., Zheng, S., Shum, H. P., Zhang, H., Song, N., Song, M., & Jia, H. (2023). Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRI. In Neural Information Processing 30th International Conference, ICONIP 2023, Changsha, China, November 20–23, 2023, Proceedings, Part IX (pp. 298-312). Springer. https://doi.org/10.1007/978-981-99-8138-0_24
• Towards Graph Representation Learning Based Surgical Workflow Anticipation
  
  Zhang, X., Al Moubayed, N., & Shum, H. P. (2022). Towards Graph Representation Learning Based Surgical Workflow Anticipation. Presented at 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Ioannina, Greece. https://doi.org/10.1109/bhi56158.2022.9926801
• Pose-based Tremor Classification for Parkinson’s Disease Diagnosis from Video
  
  Zhang, X., Zhang, H., & Shum, H. P. (2022). Pose-based Tremor Classification for Parkinson’s Disease Diagnosis from Video. In Lecture Notes in Computer Science (pp. 489-499). The MICCAI Society. https://doi.org/10.1007/978-3-031-16440-8_47

Journal Article

• Advancing healthcare practice and education via data sharing: demonstrating the utility of open data by training an artificial intelligence model to assess cardiopulmonary resuscitation skills
  
  Constable, M. D., Zhang, F. X., Conner, T., Monk, D., Rajsic, J., Ford, C., Park, L. J., Platt, A., Porteous, D., Grierson, L., & Shum, H. P. H. (2025). Advancing healthcare practice and education via data sharing: demonstrating the utility of open data by training an artificial intelligence model to assess cardiopulmonary resuscitation skills. Advances in Health Sciences Education, 30, 15-35. https://doi.org/10.1007/s10459-024-10369-5
• Adaptive Graph Learning from Spatial Information for Surgical Workflow Anticipation
  
  Zhang, F. X., Deng, J., Lieck, R., & Shum, H. P. (2025). Adaptive Graph Learning from Spatial Information for Surgical Workflow Anticipation. IEEE Transactions on Medical Robotics and Bionics, 7(1), 266-280. https://doi.org/10.1109/TMRB.2024.3517137
• Unraveling the brain dynamics of Depersonalization-Derealization Disorder: a dynamic functional network connectivity analysis
  
  Zheng, S., Zhang, F. X., Shum, H. P. H., Zhang, H., Song, N., Song, M., & Jia, H. (2024). Unraveling the brain dynamics of Depersonalization-Derealization Disorder: a dynamic functional network connectivity analysis. BMC Psychiatry, 24, Article 685. https://doi.org/10.1186/s12888-024-06096-1
• Pose-based tremor type and level analysis for Parkinson’s disease from video
  
  Zhang, H., Ho, E. S. L., Zhang, X., Del Din, S., & Shum, H. P. H. (2024). Pose-based tremor type and level analysis for Parkinson’s disease from video. International Journal of Computer Assisted Radiology and Surgery, 19(5), 831-840. https://doi.org/10.1007/s11548-023-03052-4