Dr Mostafa Sarker
Contact information
Research groups
Mostafa Sarker
Senior AI Scientist
Dr Mostafa Sarker is a Senior AI Scientist in the MORU Epidemiology Department, Centre for Tropical Medicine and Global Health, University of Oxford. His research focuses on the development and application of Artificial Intelligence (AI) to address challenges in Global Health Settings, with expertise in Foundation Models, Multimodal AI, Generative AI, Clinical AI, Large Language Models (LLMs), Federated Learning, and Agentic AI.
Over 14 years of experience in AI research and development across academia and industry, Dr Sarker has built a sustained track record of leading the design and deployment of AI systems across diverse healthcare data modalities — from clinical records and imaging to epidemiological surveillance data. He has a strong history of securing and leading large-scale collaborative research, with contributions to successful EU-funded projects and leadership of multidisciplinary AI teams across EU- and UKRI-funded initiatives.
Dr Sarker leads the MORU Epidemiology's AI in Global Health (AIGH) group at the Centre for Tropical Medicine and Global Health. His current research programme focuses on the development of domain-specific foundation models and Agentic AI systems for healthcare and scientific discovery in global health contexts.
He serves as AI task force lead for the Horizon Europe PANDAI project, where he directs the development of the foundation model for the European Pandemic AI Observatory. The project is delivered by a consortium of leading Euro-Asia institutions, with the World Health Organization (WHO) Global Hub for Pandemic and Epidemic Intelligence serving as a key partner and end user, supporting the translation of AI technologies into operational tools for global pandemic preparedness and response.
Recent publications
HarmonicEchoNet: Leveraging harmonic convolutions for automated standard plane detection in fetal heart ultrasound videos
Journal article
Sarker MMK. et al, (2025), Medical Image Analysis, 106, 103758 - 103758
Review of Federated Learning and Machine Learning-Based Methods for Medical Image Analysis
Journal article
Hernandez-Cruz N. et al, (2024), Big Data and Cognitive Computing, 8, 99 - 99
Segmentation Framework for Heat Loss Identification in Thermal Images: Empowering Scottish Retrofitting and Thermographic Survey Companies
Conference paper
Hasan MJ. et al, (2024), ADVANCES IN BRAIN INSPIRED COGNITIVE SYSTEMS, BICS 2023, 14374, 220 - 228
COMFormer: Classification of Maternal–Fetal and Brain Anatomy Using a Residual Cross-Covariance Attention Guided Transformer in Ultrasound
Journal article
Sarker MMK. et al, (2023), IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 70, 1417 - 1427
Automated Description and Workflow Analysis of Fetal Echocardiography in First-Trimester Ultrasound Video Scans
Conference paper
Yasrab R. et al, (2023), 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), 1 - 5