麻豆传媒

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 麻豆传媒, 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

Lecturer in Artificial Intelligence, 麻豆传媒, UK (2023 - Now)

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

Roles and Responsibilities

Degree Programme Director: MSc Embedded Systems and Internet of Things (ES-IoT)

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 (), 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:

Trustworthy AI Systems for Pervasive Healthcare, 4 years studentship funded by EPSRC Doctoral Landscape Awards (DLA), start from January or April 2026

This project explores the design of scalable and privacy-preserving AI systems for pervasive healthcare environments, where embedded devices and dynamic sensor networks continuously generate sensitive data. You will investigate adaptive learning frameworks – such as multi-agent or multi-task approaches – that ensure robustness, explainability, and trust across volatile edge deployments. Technical challenges include decentralised optimisation under resource constraints, agent coordination across dynamic topologies, and robust to data drift, device failure, and adversarial conditions. You will investigate how trustworthy AI models can be deployed across embedded healthcare devices, ensuring that decision-making remains interpretable, context-aware, and respectful of data boundaries. Applications may include remote monitoring, ambient diagnostics, or wearable intelligence, but the focus remains on technique innovation. Benchmarking will be conducted across synthetic and real-world datasets, with scope to prototype on embedded platforms and assess performance under realistic deployment scenarios with emphasis on methodological innovation and deployment realism. The project offers flexibility to explore algorithmic design, system-level integration, or evaluation strategies depending on your interests and strengths.

• Self-funded Projects:

EEE8161 Machine Learning for Engineering

EEE8097 Individual Project

EEE8165 Research Skills and Development for Engineers

Digital Skills Lab

• Articles

  • Tarasyuk O, Gorbenko A, Eberl M, Topley N, Zhang J, Shafik R, Yakovlev A. . Bioinformatics Advances 2025, 5(1), vbaf140.
  • Sauchelli S, Pickles T, Voinescu A, Choi H, Sherlock B, Zhang J, Colyer S, Grant S, Sundari S, Lasseter G. . 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. . 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. . Molecular Cancer 2022, 21, 183.
  • Daniel R, Zhang J, Farewell D. . 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.

• Conference Proceedings (inc. Abstract)

  • Gorbenko A, Tarasyuk O, Zhang J, Shafik R, Yakovlev A, Eberl M, Topley N. . In: International Symposium on the Tsetlin Machine (ISTM 2025). 2025, Rome: IEEE.