Professor Feng Dong

Computer and Information Sciences


Personal statement

Feng Dong joined the University of Strathclyde from 2nd Sept 2019. He is currently a professor at the Department of Computer and Information Sciences. He was awarded a PhD from Zhejiang University, China.  His recent work has also developed new areas in visual analytics, pattern recognition, AI, parallel computing and GPU, image-based rendering and figure animation.

In brief, Feng Dong's profile can be summarised as follows:

  • Leading and managing collaborative research projects and teams across Europe to conduct externally funded cross-disciplinary research projects in health technology and computational creativity, with a substantial track record in attracting external research funding by gaining around £7 million external research fund (as PI) from the EC and EPSRC since Sept 2007. These include 5 European grants and 3 EPSRC grants (as PI) and project coordinator & leading investigator for 4 collaborative research projects.

  • Network with leading research organisations and researchers across the UK and Europe through jointwork in research grants.

  • Collaboration with medical professionals through collaborative research projects and joint clinical pilots, and active engagement with the end users to empower the society at large in healthcare, targeting significant impact beyond academia.

  • Close working relationships with the industry through joint work in research grants.

  • Over 15 years of teaching practice in the UK with substantial experience in the design and delivery of a wide range of research-informed teaching activities at both post-graduate and under-graduate levels.

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Area of Expertise

Main knowledge contributions towards intelligent data analytics fall into a range of areas including:

-   Knowledge discovery in AI for healthcare to support patient self-management of general health and chronic conditions, involving smart monitoring, data validation from heterogeneous sensors,  personal activity and event recognition,  health information recommendation, personal health status estimation and serious gaming.

-    Intelligent data analytics for computational creativity in AI by coordinating the EC-funded Dr Inventor research project and leading the development of the Dr Inventor platform. The Dr Inventor surrogate acts as a personal research assistant, utilising machine-empowered search and computation to bring researchers extended perspectives for scientific innovation by informing them of a broad spectrum of relevant research concepts and approaches, by assessing the novelty of research ideas, and by offering suggestions of new concepts and workflows with unexpected features for new scientific discovery.

-   Visualization and parallel computing (GPU) for large-scale medical data, , including transfer function for feature enhancement in volume rendering of medical data; viewpoint selection and lighting design for volume rendering of medical data; Non-photorealistic volume rendering for feature enhancement from medical data; GPU-based iso-surface extraction from volume data and automated GPU-based parallelisation for images operations and image feature extractions.

-   Visual analytics for health data to support the navigation, query and understanding of health records, clinical driven research in predictive models for cancer growth in response to treatment options, and the discovery of data patterns within patient cohort in both clinical and lifestyle domains

-   Computer vision and machine learning for computer graphics research, including sparse modelling and representation for human motions,  blind motion deblur for natural images,  adaptive texture synthesis for high fidelity images and image based rendering based on inferences in machine learning

-    Health data interoperability to support long-term collection of personal health information by aggregating  electronic and personal health records, lifestyle data and drug information in a decentralised approach to offer easy access to personal medical history, empower the patients, improve self-management, and facilitate clinical research with significant advantages in privacy, security, safety, transparency and data integrity.

The recent active research projects include: 

REAMIT- The project proposes to adapt and apply existing innovative technology to food supply chains in NWE to reduce food waste and hence improve resource efficiency (Project Information: European Commission Interreg North-West Europe, €608,118 for the local institution, from 2019 to 2022.) - Role: Co– Investigator

Aquaculture 4.0 -- The project will bring together several cutting-edge digital technologies including sensor networks for online monitoring, diagnosis, control and optimisation of aquaculture production, 5G communication for low-latency, high data rate, real-time transmission of big data, internet-of- things (IoT) system for big data storage, analytics, modelling and model-based decision making. By integration of these digital technologies, the project will deliver a prototype system of precision Aquaculture 4.0, and demonstrate the economic, environmental and social benefits through pilot applications in China (Project Information: Innovate UK, over £223,209 for the local institution, Feb 2019 – Dec 2021) - Role: Co-Investigator


  • PhD in Computer Science, Zhejiang University, China
  • PGCERT Higher Education 
  • The Higher Education Academy Fellow -
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A multidisciplinary hyper-modeling scheme in personalized in silico oncology : coupling cell kinetics with metabolism, signaling networks, and biomechanics as plug-in component models of a cancer digital twin
Kolokotroni Eleni, Abler Daniel, Ghosh Alokendra, Tzamali Eleftheria, Grogan James, Georgiadi Eleni, Büchler Philippe, Radhakrishnan Ravi, Byrne Helen, Sakkalis Vangelis, Nikiforaki Katerina, Karatzanis Ioannis, McFarlane Nigel J B, Kaba Djibril, Dong Feng, Bohle Rainer M, Meese Eckart, Graf Norbert, Stamatakos Georgios
Journal of Personalized Medicine Vol 14 (2024)
Exploration and assessment of critical covariates of breast cancer outcomes via between-group test of survival rates at Sir Run Run Shaw Hospital
Zhao Youbing, Zhang Lingli, Wu Jiajun, Hu Wenxian, Dong Feng, Qin Aihong, Zeng Hao, Xie Hao, Ma Tongqing, Liu Enjie, Lin Shengyou, Jin Zhefan
2023 13th International Conference on Information Technology in Medicine and Education (ITME) 13th International Conference on Information Technology in Medicine and Education (ITME) IEEE International Conference on Information Technology in Medicine and Education (ITME) Vol 2023, pp. 462-467 (2024)
Assessing the needs of clinicians in adult critical care in Scotland for a sepsis fluid management Artificial Intelligence tool using a human factors approach
Preston Kate, Dunlop Emma, Ferguson Aimee Margaret Denver, MacLellan Calum Robert, Dong Feng
2023 Digital Heath & Care Fest (2023)
Breast cancer survival analysis with molecular subtypes : an initial step
Zhang Lingli, Wu Jiaiun, Zhao Youbing, Hu Wenxian, Qin Aihong, Dong Feng, Liu Enjie, Zeng Hao, Xie Hao, Du Hui
2022 IEEE 22nd International Conference on Bioinformatics and Bioengineering (BIBE) IEEE 22nd International Conference on Bioinformatics and Bioengineering (BIBE) 2022 IEEE 22nd International Conference on Bioinformatics and Bioengineering (BIBE), pp. 363-366 (2022)
Picture fuzzy large-scale group decision-making in a trust- relationship-based social network environment
Peng Juan Juan, Chen Xin Ge, Tian Chao, Zhang Zhi Qiang, Song Hai Yu, Dong Feng
Information Sciences Vol 608 (2022)
Applying human factors models to healthcare technologies : an overview of two PhD projects
Ferguson Aimee Margaret Denver, Preston Kate, Dunlop Emma, Bennie Marion, Newham Rosemary, Dong Feng
Health and Care Futures at Strathclyde Showcase Event (2022)

More publications

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Research Interests

Intelligent data analytics and visualization to addressed a range of issues in:

  • AI to support knowledge discovery
  • Visual data analytics
  • Computer vision and image analysis
  • Health data interoperability
  • Medical visualization and computer graphics

Professional Activities

Artificial Intelligence Workshop
AI-Powered Clinical Trials: Emulating Real-World GLP-1 Efficacy with Synthetic Patient Populations using Causal Effect Learning
Assessing the needs of clinicians working in adult critical care in Scotland for a sepsis fluid management Artificial Intelligence tool.

More professional activities


Causal Counterfactual visualisation for human causal decision making – A case study in healthcare
Dong, Feng (Principal Investigator)
This EPSRC funded research will investigate novel causal counterfactual visualisation, which will, in contrast to the direct visualisation of real data, have a new functionality to render causal counterfactuals that did not occur in reality. The counterfactuals will be generated by a counterfactual simulation model that is trained with real data. This extends standard data visualisation by visualising hypothetical exemplars beyond real data. It will support "explanation-with-examples" by enabling decision makers to interactively create synthetic data and examine "close possible worlds" (e.g. different outcomes from a small causal change). Visualising concrete exemplars will allow people to view key evidence and contest their decisions against the counterfactuals to gain actionable insights.
03-Jan-2023 - 31-Jan-2025
Causal Counterfactual visualisation for human causal decision making – A case study in healthcare
Dong, Feng (Principal Investigator) Lennon, Marilyn (Co-investigator) Maguire, Roma (Co-investigator)
This project aims at a robust, fast paced proof-of-concept to unlock the potential of AI in biomedical and health research. It will apply the newly emerging generative AI technology to transform biomedical and health research by enabling virtual clinical trial emulation with synthetic data. The research outcome will address key limitations in both Randomised Controlled Trials (RCTs) and observational studies.
01-Jan-2023 - 31-Jan-2025
DTP 2224 University of Strathclyde | Cummings, Joshua
Oliveira, Monica (Principal Investigator) Dong, Feng (Co-investigator) Cummings, Joshua (Research Co-investigator)
01-Jan-2022 - 01-Jan-2026
Virtual Clinical Trial Emulation with Generative AI Models
Dong, Feng (Principal Investigator) Maguire, Roma (Co-investigator)
31-Jan-2022 - 27-Jan-2023
Clinical Imaging Innovation and Partnership award (SYNAPSE)
Banger, Matthew (Principal Investigator) Riches, Phil (Principal Investigator) Banger, Matthew (Co-investigator) Dong, Feng (Co-investigator) Riches, Phil (Co-investigator)
01-Jan-2021 - 30-Jan-2021
EPSRC Centre for Doctoral Training in Future Power Networks and Smart Grids | MacLellan, Calum Robert
Dong, Feng (Principal Investigator) McConnell, Gail (Co-investigator) MacLellan, Calum Robert (Research Co-investigator)
01-Jan-2018 - 01-Jan-2023

More projects

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Professor Feng Dong
Computer and Information Sciences

Tel: 548 3409