Cheng Ouyang BEng MS DIC PhD

Dr. Cheng Ouyang is a Departmental Lecturer at the Institute of Biomedical Engineering, Department of Engineering Science. His research centres on data-efficient, robust, and user-friendly machine learning approaches for medical image/signal computing. His research topics include but are not limited to domain generalization, few-/zero-shot learning, uncertainty modeling, and multimodal machine learning for the interpretation and analysis of medical data, primarily images such as ultrasound, MRI, and CT. Prior to joining Oxford, he was a postdoctoral researcher on cardiac imaging at the Institute of Clinical Sciences, Imperial College London. He obtained his PhD from the Department of Computing at Imperial College London in 2023. Please find his personal webpage for more details.

• Multimodal machine learning for medical data (e.g., image, signal, and language).
• Domain generalization and uncertainty modeling for trustworthy machine learning in medical image computing (e.g., image reconstruction, classification, and segmentation).
• Few-/zero-shot learning for data-efficient machine learning in medical image computing.
• Machine learning for medical sciences (e.g., for the understanding of cardiovascular diseases).

Applications of the above learning approaches in addressing clinical and medical research challenges. We seek for real-world benefits of machine learning techniques that may go beyond one-off publications.

Please find the Google Scholar profile for more details.

I am looking for self-motivated DPhil (Oxford abbreviation of PhD) students in medical image computing. Topics include but are not limited to uncertainty modeling, few-shot/zero-shot learning, multi-modal learning, etc. — all in the broad context of improving the efficiency, trustworthiness, and user-friendliness for human-machine collaboration in medical imaging. Please find more information about DPhil in Engineering Science program here.

Please feel free to drop me an email if you are interested.

Challenge winner:

• Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge @MICCAI 2022
• Multi-sequence Cardiac MR Segmentation Challenge @MICCAI 2019