Key details
Dr Jingqiong Zhang
Lecturer in Digital Engineering (Robotics and Applied AI)
Dr Jingqiong (Jane) Zhang is a Lecturer in Digital Engineering (Robotics and Applied AI) at the University of Greenwich. Prior to joining the University of Greenwich in 2025, she worked as a Postdoctoral Research Associate with the University of Sheffield from 2021 t0 2025, contributing to interdisciplinary research projects spanning visual data analytics, intelligent sensing, human–robot collaboration, AI for advanced manufacturing.
She received her PhD degree in Electronic Engineering with the University of Kent in 2020, and MSc degree in Automation and Measurement from North China Electric Power University, China, in 2016.
Her research expertise spans intelligent systems and robotics, sensing and measurement, applied AI, visual computing, and digital engineering. So far she has published over 20 research papers in peer-reviewed journals and international conferences. She works across diverse application domains, including robotics, smart manufacturing, Industry 5.0, healthcare, energy, chemistry, and materials science, with a strong focus on addressing real-world industrial challenges.
Responsibilities within the university
- Module leader for Intelligent Distributed Systems ELEE1162 (Level 7), and Robotics
- Module instructor for Introduction to Data Science COMP1857 (Level 4)
- Module instructor for Information Analysis and Visualisation COMP1844 (Level 5)
- Postgraduate and Undergraduate Project Supervision
Awards
- Grant/Award for Doctoral and Young Researchers, 17th European Microscopy Congress, 2024.
- Vice Chancellor’s Research Scholarship, PhD, University of Kent, 2017.
- Best MSc dissertation Prize titled “Predicting the amount of coke deposition on catalyst through image analysis and soft computing”, North China Electric Power University, China, 2016.
Recognition
- Member of IEEE
- Member of BMVA
Research / Scholarly interests
- Autonomous and intelligent systems, signal and image processing, sensor and data fusion
- Applied AI, statistical machine learning and deep learning
- Data-centric engineering, instrumentation and measurement, digital twin, condition monitoring and sensing (e.g., for human-robot collaboration, manufacturing, healthcare, chemistry and materials)
Key funded projects
- The University of Sheffield’s Internal Knowledge Exchange Scheme, “Towards Improved Safety and Reliability of Cobots”, £4000, 2022
Recent publications
Article
Zhang, Jingqiong , Farr, Nicholas T. H., Nohl, James, Lai, Yufeng , Abrams, Kerry J. , Black, Kate , Willmott, Jon , Rodenburg, Cornelia , Mihaylova, Lyudmila (2025), Toward automated chemical analysis of materials using secondary electron hyperspectral imaging and unsupervised learning. Institute of Electrical and Electronics Engineers (IEEE). In: , , , . Institute of Electrical and Electronics Engineers (IEEE), IEEE Access, 13 . pp. 173976-174000 2169-3536 (Online) (doi: https://doi.org/10.1109/ACCESS.2025.3615908).
Conference proceedings
Alsari, Ali , Zhang, Jingqiong, Farr, Nicholas T.H, Rodenburg, Cornelia , Mihaylova, Lyudmila (2026), A spectral-spatial deep learning for secondary electron hyperspectral image super-resolution. Institute of Electrical and Electronics Engineers (IEEE). In: , , In: FUSION2026 Conference Committee (ed.), 29th International Conference on Information Fusion (FUSION). Institute of Electrical and Electronics Engineers (IEEE), Piscataway, New Jersey (doi: ).