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. He is currently the Head of the Human Centric AI research group. His recent research has addressed a range of issues in human centric AI to support knowledge discovery, visual data analytics, image analysis, pattern recognition and parallel computing (GPU). In particular, he is interested in causal learning from data to support decision making in healthcare.
In brief, Feng Dong's profile can be summarised as follows:
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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.
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Network with leading research organisations and researchers across the UK and Europe through jointwork in research grants.
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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.
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Close working relationships with the industry through joint work in research grants.
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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.
Qualifications
- PhD in Computer Science, Zhejiang University, China
- PGCERT Higher Education
- The Higher Education Academy Fellow -
Publications
- Adopting AI in architectural design : a systematic bibliometric analysis (1975-2025)
- Chen Liangyu, Chen Zhen, Dong Feng
- Journal of Asian Architecture and Building Engineering, pp. 1-23 (2026)
- https://doi.org/10.1080/13467581.2026.2696089
- Machine learning for autism spectrum disorder prediction : a review of data augmentation and feature selection techniques
- Alkhaibari Sahar Ahmed M, Dong Feng
- Health Care Science (2026)
- Exploring the use of AI-generated counterfactual chest X-rays to enhance diagnostic learning in medical education
- Mohr Greta, Zhu Yifei, Ye Xujiong, Lennon Marilyn, MacLellan Calum, Maclay John, Lowe David J, Sainsbury Christopher, Dong Feng, Lagnado David
- BMC Medical Education (2026)
- https://doi.org/10.1186/s12909-026-09597-7
- Causal counterfactual simulation for treatment decisions in multimodal lung disease data
- Zhu Yifei, Zhang Lei, Sainsbury Chris, Dong Feng, MacLay John, Lowe David J, Ye Xujiong
- Computers in Biology and Medicine Vol 213 (2026)
- https://doi.org/10.1016/j.compbiomed.2026.111807
- Latent prediction-based generative semantic communication for video transmission in wireless networks
- Lokumarambage Maheshi, Sivalingam Thushan, Dong Feng, Rajatheva Nandana, Fernando Anil
- IEEE Open Journal of the Communications Society Vol 7, pp. 3974-3986 (2026)
- https://doi.org/10.1109/OJCOMS.2026.3684230
- Adopting artificial intelligence in architectural conceptual design : a systematic bibliometric analysis
- Chen Liangyu, Chen Zhen, Dong Feng
- Architecture Vol 6 (2026)
- https://doi.org/10.3390/architecture6020060
Research Interests
Human centric AI, intelligent data analytics and visualization to addressed a range of issues in:
- Causal discovery and inference
- Explainable AI and causal counterfactual emulation to support human decision making
- Clinical trial emualtion based on causal inferences from real-world data
- Visual data analytics
- Computer vision and image analysis
- Medical visualization and computer graphics
- Health data interoperability
Professional Activities
- Harnessing Machine Learning for Constitutive Modelling of Viscoelastic Fluids in CFD
- Contributor
- 16/12/2025
- Machine Learning for Viscoelastic Fluid Modelling: Training and Microfluidic Simulations
- Contributor
- 3/9/2025
- Frontiers in Artificial Intelligence (Journal)
- Guest editor
- 24/7/2025
- Exploring the Use of Feed-Forward Multilayer Perceptron Networks for Modeling the Non-Linear Behaviour of Viscoelastic Fluids
- Speaker
- 30/5/2025
- Assessing Neural Network Performance in the RUDE Framework for Complex Models
- Contributor
- 17/4/2025
- What do I Not Know? AI, Risk and Probation
- Participant
- 12/3/2025
Projects
- Who knows best? Working with uncertainty
- Weaver, Beth (Principal Investigator) Belton, Ian (Co-investigator) Dong, Feng (Co-investigator) Gillon, Fern Rebecca Louise (Researcher) Heron, Gavin (Co-investigator) Lagnado, David (Co-investigator) Sanna, Greta (Researcher)
- Through interactive workshops, interdisciplinary collaboration, and structured practitioner engagement, we can influence how JSW’s interpret, trust, and act upon information when conducting risk assessments; strengthen AI literacy among practitioners; augment their critical thinking using causal modalities; support responsible and ethical use of AI. This addresses an urgent unmet need for improved professional critical thinking, AI literacy, practical/policy guidance in responsible use of AI, and critical awareness and trust in adoption of AI technologies.
Project partners are South Lanarkshire HSCP (SLHSCP), a Strathclyde Strategic partner, and Social Work Scotland (SWS), a National Professional body. While SLHSCP will directly participate in the workshops they will be key to local impact. SWS are instrumental to achieving national impact across social work and social care. - 15-Jan-2025 - 15-Jan-2026
- An AI Integrated Metaheuristic Framework for Architectural Design Justification
- Chen, Zhen (Principal Investigator) Dong, Feng (CoPI)
- 28-Jan-2024 - 27-Jan-2027
- 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 - 30-Jan-2026
- 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
Contact
Professor
Feng
Dong
Computer and Information Sciences
Email: feng.dong@strath.ac.uk
Tel: 548 3409