Staff Profile

Dr Jingjing Zhang is a Lecturer in Artificial Intelligence at the School of Engineering. Her primary research interests cover machine learning, data analytics and causal inference with applications especially in digital health. Before joining Newcastle University, she held several posts in Universities of Dundee, Swansea, Cardiff, East Anglia and Essex and has been involved in several multi-disciplinary research projects funded by EU H2020/FP7, NIHR, WEllcome Trust and Royal Society. These were mainly on 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, statistics in population psychiatry, suicide and informatics, causal inference for high-dimensional data and prognosis for prostate cancer.

Qualifications

PhD in Intelligent System and Control, Queen's University Belfast, UK (2012)

Experience

2023 - Now Lecturer in Artificial Intelligence, Newcastle University, UK

2021 - 2023 Lecturer in Data Science and Statistics, University of Essex, UK

Memberships

Fellow of Higher Education Academy (HEA), UK

Jingjing is a member of the Microsystems Research Group.

Research Interests

• Artificial Intelligence
• Machine Learning
• Causal Inference
• Multi-modal AI

Awards & Honours

2019 GW4 Crucible Programme - Digital Innovation, UK

2014 British Association for Cancer Research Award for Best Cancer-related Paper, UK

Supervision

For those who are interested in pursuing a PhD in AI and machine learning for digital health, please feel free to email me with your proposed project and CV. Self-funded project can be found here.

EEE8161 Machine Learning for Engineering

EEE8097 Individual Project

EEE8165 Research Skills and Development for Engineers

Digital Skills Lab

• Articles

  • Sauchelli S, Pickles T, Voinescu A, Choi H, Sherlock B, Zhang J, Colyer S, Grant S, Sundari S, Lasseter G. Public attitudes towards the use of novel technologies in their future healthcare: a UK survey. BMC Medical Informatics and Decision Making 2023, 23, 38.
  • Williams C, Hather C, Conteh J, Zhang J, Popa R, Owen A, Jonas C, Choi H, Daniel R, Lloyd D, Porch A, George C. Non-thermal disruption of β-adrenergic receptor-activated Ca²⁺ signalling and apoptosis in human ES-derived cardiomyocytes by microwave electric fields at 2.4 GHz. Biochemical and Biophysical Research Communications 2023, 661, 89-98.
  • Daniel R, Zhang J, Farewell D. Making apples from oranges: Comparing noncollapsible effect estimators and their standard errors after adjustment for different covariate sets. Biometrical Journal 2021, 63(3), 528-557.
  • Gadalla, A, Friberg, A, Kift-Morgan, A, Zhang, J, Eberl, M, Topley, N, Weeks, I, Cuff, S, Wootton, M, Gal, M, Parekh, G, Davis, P, Gregory, C, Hood, K, Hughes, K, Butler, C, Francis, N. Identification of clinical and urine biomarkers for uncomplicated urinary tract infection using machine learning algorithms. Scientific reports 2019, 9. In Preparation.
  • Zhang, J, Friberg, IM, Kift-Morgan, A, Parekh, G, Morgan, MP, Liuzzi, AR, Lin, CY, Donovan, KL, Colmont, CS, Morgan, PH, Davis, P. Machine-learning algorithms define pathogen-specific local immune fingerprints in peritoneal dialysis patients with bacterial infections. Kidney International 2017, 92.