Hongbo Du

Reader in Computing, Programme Director


Hongbo Du, Lecturer at the University of Buckingham, top UK university for student satisfactionBSc (Beijing), MSc, MPhil (Essex)
Hongbo Du holds a BSc from Beijing University of Science and Technology, China, and an MSc and MPhil in Computing both from the University of Essex, UK. At Buckingham, Hongbo is a Reader in Computer Science, and the programme director for the taught MSc programmes. He has long experience in teaching various modules on the Computing programmes, and has played a significant role in the curriculum development over the years. Currently he is responsible for teaching Principles of Database Systems and Technologies for Business Intelligence modules for the BSc Computing programme, and Applied Techniques of Data Mining for the MSc programmes. Hongbo also taught data mining modules at City University London and Sarajevo School of Science and Technology in the past.

Hongbo’s primary area of research is big data, data mining and machine learning. He is broadly interested in data mining techniques and their applications in solving various practical problems. He is currently interested in the application of classification and clustering techniques in biomedical image analysis, time series/data stream analysis, and anomaly detections. He has been involved with collaborative projects with external institutions and is supervising research students in these areas. He jointly led a Knowledge Transfer Partnership (KTP) project in biometrics-based security systems (KTP009709) between 2015 and 2017. He is currently the Coordinator for the Joint Research and Development Centre between Ten-D Innovations and University of Buckingham. His other research interests also include human-computer interaction and visual languages.

Hongbo is currently a professional member of the British Computer Society.
Tel: +44 (0)1280 828298 / 828322

Select publications

  • M Ahmed, H. Du, and A. Al Zoubi, “An ENAS Based Approach for Constructing Deep Learning Models for Breast Cancer Recognition from Ultrasound Images”, International Conference on Medical Image with Deep Learning (MIDL2020), Montréal, 6-8 July 2020
  • F. Mohammad, A. Al Zoubi, H. Du, and S. Jassim, “Automatic Glass Crack Recognition for High Building Façade Inspection”, Proc. SPIE 11399, Mobile Multimedia/Image Processing, Security, and Applications 2020, 113990W (19 May 2020), doi: 10.1117/12.2567409
  • J. Ghafuri, H. Du, and S. Jassim, “Topological aspects of CNN convolution layers for medical image analysis”, Proc. SPIE 11399, Mobile Multimedia/Image Processing, Security, and Applications 2020, 113990X (19 May 2020), doi: 10.1117/12.2567476
  • José Martínez-Más,  Andrés Bueno-Crespo, Shan Khazendar , Manuel Remezal-Solano , Juan-Pedro Martínez-Cendán , Sabah Jassim , Hongbo Du , Hisham Al Assam Tom Bourne , Dirk Timmerman,  “Evaluation of machine learning methods with Fourier Transform features for classifying ovarian tumors based on ultrasound images”, PLOS ONE, 26 July 2019, DOI: 10.1371/journal.pone.0219388
  • Al-Karawi, D., Landolfo, C., Du, H., Al-Assam, H., Sayaneh, A., Timmerman, D., Bourne, T. and Jassim, S., “Prospective clinical evaluation of texture‐based features analysis of ultrasound ovarian scans for distinguishing benign and malignant adnexal tumors”, Australian Journal of Ultrasound in Medicine, Vol.22, No.2, May 2019, p144
  • A. A. A. Alazeez, S. Jassim and H. Du, “SLDPC: Towards Second Order Learning for Detecting Persistent Clusters in Data Streams,” 2018 10th Computer Science and Electronic Engineering (CEEC), Colchester, United Kingdom, 2018, pp. 248-253.
  • A.Al Abd Alazeez, S. Jassim and H. Du, “TPICDS: A Two-Phase Parallel Approach for Incremental Clustering of Data Streams”, Euro-Par 2018 International Workshops, Turin, Italy, August 27-28, 2018, Revised Selected Papers, Lecture Notes in Computer Science by Springer International Publishing, Vol. 11339, No.1, January 2019, pp5-16, DOI: 10.1007/978-3-030-10549-5
  • D. Han, H. Du and S. Jassim, “Controlling Sensitivity of Gaussian Bayes Predictions based on Eigenvalue Thresholding”, EAI Transactions on Industrial Networks and Intelligent Systems, 5(16), November 2018, DOI: 10.4108/eai.29-11-2018.155885
  • A. Al Abd Alazeez, S. Jassim and H. Du, “EDDS: An Enhanced Density-based Method for Clustering Data Streams”, Proceedings of 46th International Conference on Parallel Processing Workshops, University of Bristol, August 2017, DOI 10.1109/ICPPW.2017.27, pp103-112
  • D. Ibrahim, H. Al-Assam, S. Jassim & H. Du, “Multi-level Trainable Segmentation for Measuring Gestational and Yolk Sacs from Ultrasound Images“, MIUA 2017: Medical Image Understanding and Analysis (July 2017, CCIS Series vol. 723), 86-97
  • O. Al-Okashi, H. Al-Assam & H. Du, “Automatic pelvis segmentation from x-ray images of a mouse model”, Proc. SPIE, Mobile Multimedia/Image Processing, Security, and Applications (May 2017), pp.1022108-1022108-5
  • D. Ibrahim, H. Al-Assam, H. Du & S. Jassim, “Trainable segmentation of multilocular cysts based on local basic pixel features”, Proc. SPIE, Mobile Multimedia/Image Processing, Security, and Applications (May 2017), pp.102210B-102210B-8
  • D. Al-Karawi, A. Sayasneh, H. Al-Assam, S. Jassim, N. Page, D. Timmerman, T. Bourne & H. Du, “Automated differentiation of ovarian mature teratomas from other benign tumours using neural networks classification of 2D ultrasound static images”, Proc. SPIE, Mobile Multimedia/Image Processing, Security, and Applications (May 2017), pp.102210F-102210F-10
  • O. Al Okashi, H. Du & H. Al-Assam, “Automatic spine curvature estimation from X-ray images of a mouse model”, Journal of Computer Methods and Programs in Biomedicine 140 (March 2017), 175–184
  • A. Alazeez, S. Jassim & H. Du, “EINCKM: An Enhanced Prototype-based Method for Clustering Evolving Data Streams in Big Data”, Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2017) (Porto, February 2017), 173-183
  • D. Han, H. Du & S. Jassim, “Towards a Confidence-Centric Classification Based on Gaussian Models and Bayesian Principles”, Proceedings of 9th York Doctoral Symposium on Computer Science and Electronics (University of York, November 2016), 46-56
  • D. Ahmed Ibrahim, H. Al-Assam, H. Du, D. Al-karawi, S. Jassim et al., “Automatic segmentation and measurements of gestational sac using static B-mode ultrasound images”, Proc. SPIE 9869, Mobile Multimedia/Image Processing, Security, and Applications 2016
  • D. Han, N. Al Jawad & H. Du, “Facial expression identification using 3D geometric features from Microsoft Kinect device”, Proc. SPIE 9869, Mobile Multimedia/Image Processing, Security, and Applications 2016
  • S. Khazendar, A. Sayasneh, H. Al-Assam, H. Du, J. Kaijser, L. Ferrara, D. Timmerman, S. Jassim & T. Bourne, “Automated characterisation of ultrasound images of ovarian tumours: the diagnostic accuracy of a support vector machine and image processing with a local binary pattern operator”, Facts, Views and Visions in ObGyn 7.1 (March 2015), 7-15

Selected Publications

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