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

Projects

• Patient-centric blood sampling for improved healthcare (COMFORT), Horizon Europe, €350K, 2024-2028, CO-I

• GW4-PATH: Perception and Attitudes of Technologies for Healthcare, GW4 Crucible, £5413.4, 2019-2020, CO-I

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 proposal and CV.

• Funded Projects:

Federated Multi-task Learning under Dynamic Sensor Networks for Edge-enabled Sleep Management, 4 years studentship funded by EPSRC Doctoral Landscape Awards (DLA), start from 1st October 2025

Edge-based sleep management offers an accessible solution for monitoring sleep patterns, gaining insights into sleep-related issues, and personalised sleep health management. However, the dynamic nature of sensor networks caused by frequently adding and removing nodes has become the bottleneck in achieving optimal performance and trustworthiness. This project will focus on how a federated multi-task learning framework can be effectively designed and optimised to address the challenges of dynamic sensor networks for sleep management. Through the joint supervision between multiple disciplines, the student will be offered a unique opportunity to develop a robust personal portfolio in edge intelligence for healthcare while gaining comprehensive training in both professional and ethical aspects of research. 

• Self-funded Projects:

Explainable AI for Digital Healthcare

Causal AI for Proactive Self-healthcare

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.
  • Buhigas C, CRUK-ICGC Prostate Cancer Group, Anne Y Warren, Wing-Kit, Hayley C Whitaker, Hayley J Luxton, Steve Hawkins, Jonathan Kay, Adam Butler, Yaobo Xu, Dan J Woodcock, Sue Merson, Fiona M Frame, Atef Sahli, Federico Abascal, Inigo Marticorena, G Steven Bova, Christopher S Foster, Peter Campbell, Norman J Maitland, David E Neal, Charlie E Massie, Andy G Lynch, Rosalind A Eeles, Colin S Cooper, David C Wedge, Daniel S Brewer. The architecture of clonal expansions in morphologically normal tissue from cancerous and non-cancerous prostates. Molecular Cancer 2022, 21, 183.
  • 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.