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Dr Jingjing Zhang

Dr Jingjing Zhang

Research Fellow in Medical Statistics

School of Medicine

Neuadd Meirionnydd, University Hospital of Wales, Heath Park, Cardiff, CF14 4YS

Dr Jingjing Zhang obtained the B.Eng., M.Eng. and PhD degrees from Beijing Institute of Technology, Tsinghua University and Queen’s University Belfast, respectively. After graduated from Queen’s, she held several research posts in University of Dundee, Swansea University and Cardiff University.

Dr Zhang’s primary research interests cover statistics, data analytics, machine learning, causal inference and high-dimensional mediation data analysis with applications especially in the area of medical and public medicine. She has therefore been involved in several multi-disciplinary research projects including automated analysis of 3D OPT images of colorectal cancer, EEG based early detection and treatment of traumatic brain injury, immune fingerprints determination in acute infection, and statistics in population psychiatry, suicide and informatics.

Currently, Dr Zhang is a Research Fellow in Medical Statistics (funded by the Wellcome Trust) with the School of Medicine at Cardiff University. This research is focused on the development of statistical methods for exploring the complex causal pathways from genetic variants to cardiovascular disease via high-dimensional blood biomarkers such as proteins and metabolites.








  • J. Zhang, I. M. Friberg et al., “Machine-learning Algorithms Define Pathogen-specific Local Immune Fingerprints in Peritoneal Dialysis Patients with Bacterial Infections," Kidney International, vol.92, no.1, pp.179-191, July 2017, DOI:http://10.1016/j.kint.2017.01.017.
  • Liuzzi, A. Kift-Morgan, M. Lopez-Anton, I. M. Friberg, J. Zhang, A. C. Brook, G. W. Roberts et al., “Unconventional Human T Cells Accumulate at the Site of Infection in Response to Microbial Ligands and Induce Local Tissue Remodeling," The Journal of Immunology, vol.197, no.6, pp.2195-2207, September 2016, DOI:10.4049/jimmunol.1600990.
  • W. Zhao, J. Zhang, K. Li, “A Novel Efficient LS-SVM Based Method for Fuzzy System Construction," IEEE Transactions on Fuzzy Systems, vol.23, no.3, pp.627-643, June 2015, DOI: 10.1109/TFUZZ.2014.2321594.
  • J. Zhang, J. G. Zhang, W. Li, M. Coat, F. A. Carey and S. J. McKenna, “Multi-scale Analysis of the Surface Morphology of Colorectal Polyps from Optical Tomography," Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, vol.5, no.5, pp.318-328, 2017, DOI: 10.1080/21681163.2015.1035404.


  • B. Albert, J.Zhang, A. Noyvirt, R. Setchi, H. Sjaaheim, S. Velikova and F. Strisland, “Automatic EEG Processing for the Early Diagnosis of Traumatic Brain Injury," In World Automation Congress (WAC), 2016 2016 Jul 31 (pp. 1-6). IEEE.
  • J. Zhang, S. J. McKenna, J. G. Zhang, M. Coats and F. A. Carey, “Analysis the Surface Morphology of Colorectal Polyps: Differential Geometry and Pit Pattern Prediction," Proceeding of 18th Conference on Medical Image Understanding and Analysis (MIUA), London, 9-11 July 2014, pp.67-72, ISBN: 1901725510. (Award: British Association for Cancer Research Award for Best Cancer-related Paper)
  • J. Zhang, K. Li, W. Q. Zhao, M. Fei and Y. Wang, “A Systematic Fire Detection Approach Based on Sparse Least-Squares SVMs," Communications in Computer and Information Science, vol. 462, pp. 351-362, Springer Berlin Heidelberg, 2014, DOI: 10.1007/978-3-662-45261-5 37.
  • J. Zhang and K. Li, “Heuristic based Model Selection for a New Least-squares SVM solution," 26th European Simulation and Modelling Conference-ESM'2012, Essen, Germany, October 22-24, 2012.
  • J. Zhang, K. Li, W. Q. Zhao and G. W. Irwin, “A Regression Approach to LS-SVM and Sparse Realization based on Fast Subset Selection," Proceeding of the 10th IEEE Word Congress on Intelligent Control and Automation (WCICA), Beijing, 6-8 July 2012, pp.612-617, DOI: 10.1109/WCICA.2012.6357952.
  • J. Zhang, Q. Niu, K. Li and G. W. Irwin, “Model Selection in SVMs using Differential Evolution," Proceeding of 18th International Federation of Automatic Control World Congress (IFAC), Milan,Italy, Aug 28-2 Sept 2011, pp.14717-14722, DOI: 10.3182/20110828-6-IT-1002.00584.


  • Talk in Infection & Immunity Annual Meeting of Cardiff University 2015 with title of “Machine Learning and supercomputing approaches to define and cross-validate pathogen-specific local immune fingerprints in peritoneal dialysis patients presenting with acute peritonitis”
  • Poster in Infection & Immunity Annual Meeting of Cardiff University 2016 with title of “Understanding immune response by applying machine learning methods”