Dr Erfu Yang
Reader
Design, Manufacturing and Engineering Management
Personal statement
Dr Erfu Yang is a Reader in Robotics and Autonomous Systems (RAS) Group within the Department of Design, Manufacturing and Engineering Management (DMEM) at the University of Strathclyde. He is the Co-lead of the RAS Group, program lead of MSc in Mechatronics and Automation, Vertically Integrated Projects (VIP) champion, Mechatronics theme champion of both UG and MSc. In 2008, he received his Ph.D. degree in robotics from the University of Essex, Colchester, UK, within the School of Computer Science and Electronic Engineering. Before joining the DMEM in May 2014, he was a research fellow in the Cognitive Signal Image and Control Processing Research (COSIPRA) Laboratory at the University of Stirling, UK. Previously, as a research fellow he also worked in the Department of Mechanical and Control Systems Engineering (Tokyo Institute of Technology, Japan) and School of Engineering (University of Edinburgh, UK). His main research interests include robotics, autonomous systems, human-robot collaborations, computer vision, image/signal processing, mechatronics, manufacturing automation, and applications of machine learning and artificial intelligence. He has over 200 publications in these areas, including more than 100 journal papers and 10 book chapters. He is Stanford’s Top 2% Scientist for 2024 & 2025. Dr Yang has been awarded over 15 research grants as PI (principal investigator) or CI (co-investigator). He is the Fellow of the UK Higher Education Academy, Member of the UK Engineering Professors’ Council, Senior Member of the IEEE Society of Robotics and Automation, IEEE Control Systems Society, IEEE Systems, Man, and Cybernetics Society, Publicity Co-Chair (pre-April 2024) and now Engagement Chair of the IEEE UK and Ireland Industry Applications Chapter. Dr Yang serves as an associate editor, and editorial board member for many international journals, including Cognitive Computation (Springer), Sensors, Manufacturing Review, Frontiers in Robotics and AI, etc.
Prize And Awards
- Top 2% Scientists 2025
- Recipient
- 19/9/2025
- Best Conference Paper Award
- Recipient
- 2/7/2025
- TOP CITED ARTICLE 2023-2024
- Recipient
- 8/4/2025
- Top 2% Scientists
- Recipient
- 16/9/2024
- IEEE Outstanding Service Award
- Recipient
- 30/8/2023
- Best Poster for RoVER VIP Project at the ESD@Strath Conference 2023
- Recipient
- 30/3/2023
Publications
- Cobot-assisted capacitive sensing for in-process, micrometre-scale thickness estimation of liquid films
- Othman Uqba, Ameur Mohamed Adlan Ait, Walker Charlie, Khedr Mohamed, Yang Erfu, Torres Ophelie
- Next Research (2026)
- https://doi.org/10.1016/j.nexres.2026.102570
- Advances in sensing, control and path planning for robotic systems
- Szczepański Rafał, Yang Erfu
- Sensors Vol 26 (2026)
- https://doi.org/10.3390/s26196253
- Robotic teleoperation in hazardous environments : A review of feedback architectures, stability, and learning-based adaptation
- Khedr Mohamed, Le Quang Dan, Cameron-Robson Tye, Morsi Nour, Yang Erfu, Luo Chaomin
- Complex Engineering Systems Vol 6 (2026)
- https://doi.org/10.20517/ces.2026.07
- SMART: Semantic Merging Adaptive Regional Transformer for Image Captioning
- Jiang Fengling, Cao Yujin, Zou Le, Yang Erfu, Li Chenglong, Luo Chaomin
- IEEE Transactions on Multimedia (2026)
- https://doi.org/10.1109/TMM.2026.3717485
- Engineering design through dialogue : a method for analysing speech-based human-AI conversation
- Gunn Kieran, Brisco Ross, Holliman Freddie, Yang Erfu, MacFie Rebecca
- Proceedings of the Design Society Vol 6, pp. 2313-2322 (2026)
- https://doi.org/10.1017/pds.2026.10589
- KMPS : a reinforcement learning scheduler for Kubernetes edge-cloud systems
- Huang Congyue, Tan Wei, Ou Miaohua, Yang Erfu, Li Yun
- IEEE Internet of Things Journal Vol 13, pp. 16021-16034 (2026)
- https://doi.org/10.1109/jiot.2026.3658608
Professional Activities
- The 30th International Conference on Automation and Computing
- Chair
- 29/8/2026
- Examiner
- Examiner
- 16/6/2026
- DMEM Research Seminar on Connecting Researchers, Systems, and Communities: IEEE SMC and AI in One Health
- Organiser
- 5/6/2026
- Examiner
- Examiner
- 5/6/2026
- International Journal on Interactive Design and Manufacturing (Journal)
- Guest editor
- 1/5/2026
- MDPI UK Editors' Forum (Event)
- Editor
- 29/4/2026
Projects
- EPSRC Centre for Doctoral Training in Materials 4.0 | Irvine, Anthony
- Huang, Jianglin (Principal Investigator) Yang, Erfu (Co-investigator) Irvine, Anthony (Research Co-investigator)
- 01-Jan-2026 - 01-Jan-2030
- Robust Perception for Improving Robot Navigation in Highly Dynamic Manufacturing Environments
- Yang, Erfu (Principal Investigator) Mehnen, Jorn (Co-investigator) Weston, Sam (Post Grad Student)
- 01-Jan-2026 - 30-Jan-2029
- AI Surrogate Modelling to Enhance the Digital Twin of Titanium Cogging
- Huang, Jianglin (Principal Investigator) Yang, Erfu (Co-investigator) Rahimi, Salaheddin (Co-investigator)
- The "AI Surrogate Modelling to Enhance the Digital Twin of Titanium Cogging" is a fully funded PhD project at the University of Strathclyde, linked with the EPSRC Centre for Doctoral Training (CDT) in Materials 4.0 and the Henry Royce Institute.
The research unites physics-based modelling, machine learning, and experimental validation. It is designed to tackle the computationally intensive nature of physical simulations in heavy manufacturing.
Key aspects of this research include:
1) Digital Twin Advancement: The project develops rapid predictive models to simulate the hot forging and cogging of titanium.
2) The FutureForge Facility: The research applies these surrogate models directly to real-world data generated by the FutureForge platform, which is a pioneering hot forging research facility located at Strathclyde's Advanced Forming Research Centre (AFRC).
3) Optimization and Speed: Machine learning (surrogate) models act as fast, mathematical stand-ins for heavy Finite Element Analysis (FEA), enabling real-time decision support, microstructural prediction, and process optimization without standard computational delays. - 01-Jan-2026 - 30-Jan-2030
- ML-Driven Extra Estimation and Tool Life Extension for Resource-Efficient CNC Manufacturing (MEXTOOL)
- Yang, Erfu (Principal Investigator) Mehnen, Jorn (Co-investigator) Xie, Wenkun (Co-investigator)
- 01-Jan-2026 - 31-Jan-2027
- Inclusive Network Pathways for Human-Robot-AI Healthcare Research and Knowledge Exchange Awarded £3450.00
- Yang, Erfu (Principal Investigator) McGeown, William (Co-investigator) Buchanan, Sarah (Fellow) Ait Ameur, Mohamed Adlan (Post Grad Student)
- This project will build CONNECT-HRC-AI Healthcare as an inclusive, visible and sustainable RKEI culture network for human-robot-AI healthcare research and knowledge exchange at Strathclyde. The purpose is to use conference engagement as an evidence-gathering and external-learning stage that informs a wider programme to connect existing expertise, make contributions visible, and create practical collaboration pathways. The project responds directly to the RKEI culture challenge identified at Strathclyde: relevant activity already exists, but people, capabilities, opportunities and everyday contributions are often fragmented, unevenly visible and difficult to connect.
The network will build on initial collaborative work between DMEM and the Department of Psychology, using existing MICA/social robotics and AI-in-the-loop healthcare research as a concrete anchor (Ait Ameur et al., 2025). However, the purpose of the network is to broaden this collaboration beyond a single doctoral project, laboratory, or discipline. Human-robot-AI healthcare brings together engineering and robotics, computer science and AI, psychology and cognitive science, health and wellbeing research, ethics, design, data governance, technical support, professional services and knowledge exchange, clinicians, formal care partners, and lived-experience or community voices. It therefore provides a strong test case for the kind of inclusive, visible, and sustainable collaborative research culture that the RKEI Network aims to enable.
The project will build on existing Strathclyde activity rather than create a disconnected new group. It will follow a structured mapping approach inspired by existing institutional network analysis: identify relevant people, themes, projects, facilities, datasets, methods and gaps; validate this map through expert-led workshops; and convert it into collaboration pathways, pitch ideas and future grant routes. Participants will be selected through a combination of targeted invitations across faculties and roles, open expressions of interest, doctoral/researcher networks, KE contacts and existing health, cognitive science, digital, design and robotics links. - 01-Jan-2026 - 14-Jan-2026
- Cobot-enabled Nondestructive Quality Control in Production Lines with Advanced Vision and Machine Learning for Smart Manufacturing
- Yang, Erfu (Principal Investigator) Millar, Richard (Co-investigator)
- Student: Tye Cameron-Robson
- 01-Jan-2025 - 30-Jan-2028
Contact
Dr
Erfu
Yang
Reader
Design, Manufacturing and Engineering Management
Email: erfu.yang@strath.ac.uk
Tel: 574 5279