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Yanda Meng

Assistant Professor, Bioengineering

Biological and Environmental Science and Engineering Division
Email icon yanda.meng@kaust.edu.sa

By uniting data and intelligent systems, we can empower earlier diagnoses, fairer medical decisions, and healthier lives for people everywhere.

Program Affiliations

Center of Excellence

Biography

Yanda Meng is an assistant professor of bioengineering at KAUST, where his research focuses on developing advanced artificial intelligence methods for healthcare and biomedical sciences. Prior to joining KAUST, he held a lectureship in computer science at the University of Exeter and completed postdoctoral and doctoral training in eye and vision science at the University of Liverpool. He has published extensively in leading venues across medical imaging and AI, securing multiple external grants as a principal investigator and contributed to impactful clinical collaborations. Meng also serves in several leadership and editorial roles, including as an associate editor of Frontiers in Medicine (Ophthalmology), Guest editor for multiple journal special issues, and area chair for MICCAI 2025. 

Research Interests

Meng’s research focuses on  AI methods for healthcare, with an emphasis on medical imaging, multimodal learning, and trustworthy machine learning. His work integrates visual, clinical, and physiological data to improve disease detection, diagnosis, and risk prediction. He specializes in creating robust and reliable AI systems that can work effectively with the complexity of real biomedical data. Ultimately, his research aims to build interpretable and impactful technologies that support clinicians and enhance patient care across diverse healthcare settings.  
Keyword tag icon
artificial intelligence medical image analysis

Education Profile

Ph.D., Eye and Vision Science, University of Liverpool, (U.K.), 2022 
M.S., Computer Science, University of Leeds, (U.K.), 2018 
B.Eng., Computer Science, Capital Normal University (China), 2017

Awards and Recognitions

  • Best Paper and Young Scientist Award Shortlist MICCAI, 2025 

  • IEEE TMI Distinguished Reviewer Bronze Level, 2024 

  • LaScar@MICCAI’22 Best Paper Runner-up Award, 2022

Publications

  • Meng, Y., Zhang, Y., Xie, J., Duan, J., Joddrell, M., Madhusudhan, S., Peto, T., Zhao, Y. and Zheng,Y., 2024. Multi-granularity learning of explicit geometric constraint and contrast for label-efficient 
    medical image segmentation and differentiable clinical function assessment. Medical ImageAnalysis, 95, p. 103183. 

  • Meng, Y., Bridge, J., Addison, C., Wang, M., Merritt, C., Franks, S., Mackey, M., Messenger, S., Sun,R., Fitzmaurice, T. and McCann, C., 2023. Bilateral adaptive graph convolutional network on CT 
    based Covid-19 diagnosis with uncertainty-aware consensus-assisted multiple instancelearning. Medical Image Analysis, 84, p. 102722. 

  • Meng, Y., Zhang, H., Zhao, Y., Gao, D., Hamill, B., Patri, G., Peto, T., Madhusudhan, S. and Zheng,Y., 2022. Dual consistency enabled weakly and semi-supervised optic disc and cup segmentationwith dual adaptive graph convolutional networks. IEEE Transactions on Medical Imaging, 42(2),p. 416-429. 

  • Meng, Y., Zhang, H., Zhao, Y., Yang, X., Qiao, Y., MacCormick, I.J., Huang, X. and Zheng, Y., 2021.Graph-based region and boundary aggregation for biomedical image segmentation. IEEETransactions on Medical Imaging, 41(3), p. 690-701. 

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