Biomedical Sciences Division (BioMed)

Decoding Life, Advancing Health

Overview

The Biomedical Sciences (BioMed) division at KAUST brings together world-class science, technology, and education to address today’s most pressing health challenges. Through interdisciplinary research, advanced computational and digital tools, and strong partnerships across the healthcare ecosystem, BioMed connects discovery with real-world impact - supporting national priorities while contributing to global advances in health and disease.

Vision

BioMed will improve health in Saudi Arabia and beyond by harnessing cutting edge science and technology to investigate, educate on, and translate discoveries that make people’s lives better.

Mission

BioMed applies the scientific and technical expertise of KAUST by interdisciplinary collaboration and intersectional partnerships both within and beyond the university, leveraging and boosting the efforts of the health care ecosystem to understand, prevent, and treat disease.

We will encourage, educate, and promote expertise in the study and taming of disease to help engineer a healthier life for all, using knowledge generation with widespread deployment of health-translated advances underpinned by our cutting edge, world class technical expertise.

A Message from the Dean of Biomedical Sciences (BioMed)

Professor Peter J Goadsby FMedSci FRS

BioMed is KAUST's newest division, harnessing the University's world-class science and technology to advance healthcare outcomes in Saudi Arabia and beyond. We apply cutting-edge computational and digital tools to understanding, combating and preventing disease.

BioMed is at the start of an exciting journey. The division offers a collaborative environment where scientific excellence meets a passion for teaching and mentoring a new generation of graduates who will advance health in the 21st century.

Our priorities are those of the Kingdom, our mission aligned with Vision 2030's aspirations for better health. Our graduate programs uniquely integrate biology with engineering and bioinformatics and, our faculty and students strive to be future-ready for the rapidly emerging challenges and opportunities in biomedical science.

BioMed draws on the KAUST ecosystem of outstanding research and technical capabilities, all set against the backdrop of the Red Sea. We are beginning an ambitious journey, and invite those who share our vision of a healthier life for all.

View the Dean’s Biography

Our Programs

Bioengineering

Bioscience

Yanda Meng

Assistant Professor, Bioengineering

Biomedical Sciences

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

Bioengineering

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. 

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.

Multimedia

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