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Yanda Meng

Assistant Professor, Bioengineering

Biomedical Sciences
Email icon yanda.meng@kaust.edu.sa

By uniting clinical data and AI, we can empower earlier diagnoses, reliable medical decisions, and healthier lives for people everywhere. 

Program Affiliations

Center of Excellence

Biography

Prof. 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 was a lecturer in the Computer Science department at the University of Exeter and completed postdoctoral and doctoral training in medical school (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 principal investigator and contributing to impactful clinical collaborations with the NHS Foundation Trust in the U.K. and the clinical sectors in Saudi Arabia. Prof. Meng also serves in several leadership and editorial roles, including Section Editor of Thrombosis and Haemostasis, Guest Editor for multiple journal special issues such as IEEE-Journal of Biomedical and Health Informatics, and Area Chair for MICCAI 2025 & 2026 and MIDL 2026.

Research Interests

Prof Yanda Meng’s research focuses on developing artificial intelligence methods for healthcare, with an emphasis on medical imaging, vision-language models, 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 Machine Learning Large Language Models

Education Profile

  • Ph.D. Eye and Vision Science, University of Liverpool, Liverpool, UK, 2022 

  • M.S. Computer Science, University of Leeds, Leeds, UK 2018 

  • B.Eng. Computer Science, Capital Normal University, Beijing, China, 2017 

Awards and Recognitions

  • Best Paper and Young Scientist Award Shortlist MICCAI, 2025 

  • IEEE TMI Distinguished Reviewer 2024 & 2025

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

Publications

  • Yu, Q., Zhang, C., Jin, G., Huang, T., Zhou, W., Li, W., Jin, X., Huang, B., Zhao, Y., Yang, G. and Lip, 
    G.Y., Zheng, Y., Villavicencio, Meng, Y., 2026. StealthMark: Harmless and Stealthy Ownership 
    Verification for Medical Segmentation via Uncertainty-Guided Backdoors. IEEE Transactions on 
    Image Processing. vol. 35, pp. 1290-1304. 

  • 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 Image 
    Analysis, 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 instance 
    learning. 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 segmentation 
    with dual adaptive graph convolutional networks. IEEE Transactions on Medical Imaging, 42(2), 
    pp.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. IEEE 
    Transactions on Medical Imaging, 41(3), pp.690-701.  

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