Dr Dongxu Han is a Lecturer in the School of Computing at The University of Buckingham. He teaches across undergraduate and postgraduate programmes, with a particular focus on artificial intelligence, machine learning, and applied data science. His teaching interests span databases, programming, intelligent systems, and the application of emerging digital technologies in education.
Dr Han’s research lies at the intersection of artificial intelligence and healthcare, with a specialism in medical image analysis and clinical decision support systems. His doctoral research, completed at Buckingham, explored confidence measures in classification decisions to enhance trust and interpretability in AI-assisted diagnosis. His wider work addresses ultrasound image analysis, computer vision, and explainable AI, with publications in Ultrasonic Imaging and other international venues.
Prior to his current appointment, Dr Han led algorithm development at Ten-D Innovations & Medical Technologies Ltd., where he directed a team in creating AI-driven diagnostic software for real-time ultrasound screening, which was successfully certified for clinical use. He has collaborated with industry in several projects to develop machine learning techniques for automated tumour evaluation.
He is a member of the British Computer Society (MBCS) and maintains active collaborations with international researchers. His work aims to bridge the gap between machine intelligence and human decision-making, ensuring AI is interpretable, reliable, and clinically useful.
Selected Publications
- D. Han, N. Ibrahim, F. Lu, Y. Zhu, H. Du and A. AlZoubi, Automatic Detection of Thyroid Nodule Characteristics From 2D Ultrasound Images, Ultrasonic Imaging, 2023.
- D. Han, H. Du and S. Jassim, Controlling Sensitivity of Gaussian Bayes Predictions based on Eigenvalue Thresholding, Industrial Networks and Intelligent Systems, 2018.
- D. Han, H. Du and S. Jassim, Towards a Confidence-Centric Classification Based on Gaussian Models and Bayesian Principles, York Doctoral Symposium on Computer Science and Electronics, 2016.
- D. Han, N. Al-Jawad and H. Du, Facial Expression Identification Using 3D Geometric Features from Microsoft Kinect Device, SPIE Mobile Multimedia/Image Processing, Security, and Applications, 2016.