![Shou-Han Zhou BA (Hons), MPhil, PhD](https://profiles-cardiff.imgix.net/attachments/6818?w=200&h=200&auto=format&crop=faces&fit=crop&q=90")
Dr Shou-Han Zhou
(he/him)
BA (Hons), MPhil, PhD
- Available for postgraduate supervision
Teams and roles for Shou-Han Zhou
Lecturer in Robotics and Autonomous Systems
Overview
I am a Lecturer in the Department of Mechanical Engineering at Cardiff University and a member of the Robotics and Intelligent Machines Group. My research lies at the intersection of control engineering, robotics, and human health, with a particular focus on developing intelligent control systems that enhance motor and cognitive outcomes.
I lead the Autonomous Health Monitoring and Control Group. My work integrates control theory, computational modelling, and human–machine interaction to design advanced robotic and brain–computer interface systems that promote patient independence and adaptive recovery. I am especially interested in how control design can be used to optimise human–robot interaction, enable learning in autonomous systems, and deliver measurable benefits in healthcare settings.
My background spans both engineering and neuroscience, allowing me to bridge the gap between theoretical control design and its practical application in health-focused technologies. I collaborate closely with healthcare academics, NHS and international partners to ensure that the systems developed in my laboratory deliver both scientific and clinical impact.
Publication
2025
- Moarrefi, S. et al. 2025. Adsorption thermodynamics of methane reforming over solid oxide fuel cell anodes. Journal of Power Sources 655, article number: 237905. (10.1016/j.jpowsour.2025.237905)
- Pearce, D. J. et al. 2025. Target selection signals causally influence human perceptual decision-making. The Journal of Neuroscience 45(24), article number: e2048242025. (10.1523/JNEUROSCI.2048-24.2025)
- Biabani, M. et al. 2025. Neurophysiology of perceptual decision-making and its alterations in attention-deficit hyperactivity disorder (ADHD). The Journal of Neuroscience 45(14), article number: e0469242025. (10.1523/JNEUROSCI.0469-24.2025)
2024
- Wongtrakun, J., Zhou, S., Bellgrove, M. A., Chong, T. T. and Coxon, J. P. 2024. The effect of congruent vs. incongruent distractor positioning on electrophysiological signals during perceptual decision-making. The Journal of Neuroscience 44(45), article number: e2079232024. (10.1523/JNEUROSCI.2079-23.2024)
2023
- Brosnan, M. et al. 2023. Evidence accumulation rate moderates the relationship between enriched environment exposure and age-related response speed declines. The Journal of Neuroscience 43(37), pp. 6401-6414. (10.1523/JNEUROSCI.2260-21.2023)
- Cadwallader, C. J. et al. 2023. Acute exercise as a modifier of neocortical plasticity and aperiodic activity in the visual cortex. Scientific Reports 13(1), article number: 7491. (10.1038/s41598-023-34749-w)
2021
- Stefanac, N. R., Zhou, S., Spencer-Smith, M. M., O'Connell, R. and Bellgrove, M. A. 2021. A neural index of inefficient evidence accumulation in dyslexia underlying slow perceptual decision making. Cortex: A Journal devoted to the Study of the Nervous System and Behavior 142, pp. 122-137. (10.1016/j.cortex.2021.05.021)
- Zhou, S., Loughnane, G., O'Connell, R., Bellgrove, M. A. and Chong, T. T. 2021. Distractors selectively modulate electrophysiological markers of perceptual decisions. The Journal of Cognitive Neuroscience 33(6), pp. 1020–1031. (10.1162/jocn_a_01703)
2019
- McGuigan, S., Zhou, S., Brosnan, M. B., Thyagarajan, D., Bellgrove, M. A. and Chong, T. T. 2019. Dopamine restores cognitive motivation in Parkinson’s disease. Brain 142(3), pp. 719–732. (10.1093/brain/awy341)
2017
- Zhou, S., Tan, Y., Oetomo, D., Freeman, C., Burdet, E. and Mareels, I. 2017. Modeling of endpoint feedback learning implemented through point-to-point learning control. IEEE Transactions on Control Systems Technology 25(5), pp. 1576-1585. (10.1109/TCST.2016.2615083)
2016
- Zhou, S., Fong, J., Crocher, V., Tan, Y., Oetomo, D. and Mareels, I. 2016. Learning control in robot-assisted rehabilitation of motor skills--a review. Journal of Control and Decision 3(1), pp. 19-43. (10.1080/23307706.2015.1129295)
2015
- Zhou, S., Oetomo, D., Tan, Y., Mareels, I. and Burdet, E. 2015. Effect of sensory experience on motor learning strategy. Journal of Neurophysiology 113(4), pp. 1077-1084. (10.1152/jn.00470.2014)
2013
- Zhou, S., Tan, Y., Oetomo, D., Freeman, C. and Mareels, I. 2013. On on-line sampled-data optimal learning for dynamic systems with uncertainties. Presented at: 2013 9th Asian Control Conference (ASCC), Istanbul, Turkey, 23-26 June 20132013 9th Asian Control Conference (ASCC). IEEE pp. 1-7., (10.1109/ASCC.2013.6606377)
- Zhou, S., Tan, Y., Oetomo, D., Freeman, C., Burdet, E. and Mareels, I. 2013. Point-to-point learning in human motor systems. Presented at: 2013 American Control Conference, Washington, DC, USA, 17-19 June 20132013 American Control Conference. IEEE pp. 5923-5928., (10.1109/ACC.2013.6580767)
2012
- Zhou, S., Oetomo, D., Tan, Y., Burdet, E. and Mareels, I. 2012. Modeling individual human motor behavior through model reference iterative learning control. IEEE Transactions on Biomedical Engineering 59(7), pp. 1892-1901. (10.1109/tbme.2012.2192437)
2011
- Zhou, S., Oetomo, D., Mareels, I. and Burdet, E. 2011. Modelling of human motor control in an unstable task through operational space formulation. Presented at: 2010 11th International Conference on Control Automation Robotics & Vision, Singapore, 07-10 December 20102010 11th International Conference on Control Automation Robotics & Vision. IEEE pp. 2030-2035., (10.1109/ICARCV.2010.5707869)
- Zhou, S., Oetomo, D., Tan, Y., Burdet, E. and Mareels, I. 2011. Human motor learning through iterative model reference adaptive control. IFAC Proceedings Volumes 44(1), pp. 2883-2888. (10.3182/20110828-6-IT-1002.02688)
Articles
- Moarrefi, S. et al. 2025. Adsorption thermodynamics of methane reforming over solid oxide fuel cell anodes. Journal of Power Sources 655, article number: 237905. (10.1016/j.jpowsour.2025.237905)
- Pearce, D. J. et al. 2025. Target selection signals causally influence human perceptual decision-making. The Journal of Neuroscience 45(24), article number: e2048242025. (10.1523/JNEUROSCI.2048-24.2025)
- Biabani, M. et al. 2025. Neurophysiology of perceptual decision-making and its alterations in attention-deficit hyperactivity disorder (ADHD). The Journal of Neuroscience 45(14), article number: e0469242025. (10.1523/JNEUROSCI.0469-24.2025)
- Wongtrakun, J., Zhou, S., Bellgrove, M. A., Chong, T. T. and Coxon, J. P. 2024. The effect of congruent vs. incongruent distractor positioning on electrophysiological signals during perceptual decision-making. The Journal of Neuroscience 44(45), article number: e2079232024. (10.1523/JNEUROSCI.2079-23.2024)
- Brosnan, M. et al. 2023. Evidence accumulation rate moderates the relationship between enriched environment exposure and age-related response speed declines. The Journal of Neuroscience 43(37), pp. 6401-6414. (10.1523/JNEUROSCI.2260-21.2023)
- Cadwallader, C. J. et al. 2023. Acute exercise as a modifier of neocortical plasticity and aperiodic activity in the visual cortex. Scientific Reports 13(1), article number: 7491. (10.1038/s41598-023-34749-w)
- Stefanac, N. R., Zhou, S., Spencer-Smith, M. M., O'Connell, R. and Bellgrove, M. A. 2021. A neural index of inefficient evidence accumulation in dyslexia underlying slow perceptual decision making. Cortex: A Journal devoted to the Study of the Nervous System and Behavior 142, pp. 122-137. (10.1016/j.cortex.2021.05.021)
- Zhou, S., Loughnane, G., O'Connell, R., Bellgrove, M. A. and Chong, T. T. 2021. Distractors selectively modulate electrophysiological markers of perceptual decisions. The Journal of Cognitive Neuroscience 33(6), pp. 1020–1031. (10.1162/jocn_a_01703)
- McGuigan, S., Zhou, S., Brosnan, M. B., Thyagarajan, D., Bellgrove, M. A. and Chong, T. T. 2019. Dopamine restores cognitive motivation in Parkinson’s disease. Brain 142(3), pp. 719–732. (10.1093/brain/awy341)
- Zhou, S., Tan, Y., Oetomo, D., Freeman, C., Burdet, E. and Mareels, I. 2017. Modeling of endpoint feedback learning implemented through point-to-point learning control. IEEE Transactions on Control Systems Technology 25(5), pp. 1576-1585. (10.1109/TCST.2016.2615083)
- Zhou, S., Fong, J., Crocher, V., Tan, Y., Oetomo, D. and Mareels, I. 2016. Learning control in robot-assisted rehabilitation of motor skills--a review. Journal of Control and Decision 3(1), pp. 19-43. (10.1080/23307706.2015.1129295)
- Zhou, S., Oetomo, D., Tan, Y., Mareels, I. and Burdet, E. 2015. Effect of sensory experience on motor learning strategy. Journal of Neurophysiology 113(4), pp. 1077-1084. (10.1152/jn.00470.2014)
- Zhou, S., Oetomo, D., Tan, Y., Burdet, E. and Mareels, I. 2012. Modeling individual human motor behavior through model reference iterative learning control. IEEE Transactions on Biomedical Engineering 59(7), pp. 1892-1901. (10.1109/tbme.2012.2192437)
- Zhou, S., Oetomo, D., Tan, Y., Burdet, E. and Mareels, I. 2011. Human motor learning through iterative model reference adaptive control. IFAC Proceedings Volumes 44(1), pp. 2883-2888. (10.3182/20110828-6-IT-1002.02688)
Conferences
- Zhou, S., Tan, Y., Oetomo, D., Freeman, C. and Mareels, I. 2013. On on-line sampled-data optimal learning for dynamic systems with uncertainties. Presented at: 2013 9th Asian Control Conference (ASCC), Istanbul, Turkey, 23-26 June 20132013 9th Asian Control Conference (ASCC). IEEE pp. 1-7., (10.1109/ASCC.2013.6606377)
- Zhou, S., Tan, Y., Oetomo, D., Freeman, C., Burdet, E. and Mareels, I. 2013. Point-to-point learning in human motor systems. Presented at: 2013 American Control Conference, Washington, DC, USA, 17-19 June 20132013 American Control Conference. IEEE pp. 5923-5928., (10.1109/ACC.2013.6580767)
- Zhou, S., Oetomo, D., Mareels, I. and Burdet, E. 2011. Modelling of human motor control in an unstable task through operational space formulation. Presented at: 2010 11th International Conference on Control Automation Robotics & Vision, Singapore, 07-10 December 20102010 11th International Conference on Control Automation Robotics & Vision. IEEE pp. 2030-2035., (10.1109/ICARCV.2010.5707869)
Research
Dr Shou-Han Zhou is a robotics and control engineer specialising in health-focused autonomous systems. His research integrates mechanical engineering, control theory, and neuroscience to design intelligent systems for diagnosis, monitoring, and intervention in medical and rehabilitation contexts. By modelling the mechanisms of human physiology and behaviour, his work aims to develop autonomous technologies that adapt seamlessly to individual users, supporting longterm motor and cognitive recovery.
He leads the Autonomous Health Monitoring and Control Group at Cardiff University, which focuses on advancing control and robotic solutions for rehabilitation, healthcare monitoring, and assistive technologies. The group combines theoretical control design with data-driven computational modelling to create systems capable of real-time adaptation, functional autonomy, and personalised human–machine interaction.
Through collaborations with the healthcare academcis, NHS and international research partners, Dr Zhou’s work bridges engineering and healthcare to deliver clinically relevant technologies that promote patient independence and improve quality of life.
Active Projects
- Agile Cymru: A feasibility study enhancing a wearable navigation device for visually impaired users
Past Projects
- Academic-Industry Partnership with Frontier Therapeutics Ltd on Medtech Development
Teaching
Dr Zhou teaches a range of topics in engineering, focusing on dynamics, control, and robotics. He is the module leader for EN2055: Systems and Control, where students learn how to model, analyse, and design control systems used across modern engineering applications. He also teaches the robotics component of EN3462: Robotics and Image Processing, introducing students to robotic motion, sensing, and autonomy. In addition, he supervises third- and fourth-year projects in EN4110: Mechatronics and EN3100: Projects, supporting students as they design and build their own robotic and control systems.
Dr Zhou’s teaching approach is hands-on and interactive. He encourages students to connect mathematical theory with practical engineering challenges, developing both problem-solving ability and creativity. His goal is to help students gain the confidence and technical insight needed to apply control and robotics principles to real-world problems and emerging technologies.
Biography
Shou-Han Zhou obtained his PhD from the Department of Mechanical Engineering at the University of Melbourne, specialising in control, robotics and human behaviour modelling. He subsequently worked as a Research Associate in the Department of Bioengineering at Imperial College London, applying engineering principles to human-centred systems. He then spent over five years as a Postdoctoral Researcher at Monash University, advancing research in cognitive neuroscience, before holding a joint Lectureship in Engineering and Psychology at James Cook University.
He joined Cardiff University in 2024 as a Lecturer in the Department of Mechanical Engineering and a member of the Robotics and Intelligent Machines Group. His research integrates control theory, robotics, and neuroscience to design intelligent rehabilitation systems that enhance motor and cognitive recovery. He is particularly interested in control applications for human-in-the-loop applications, with the goal of improving patient independence and functional outcomes through intelligent robotic and brain–computer interface technologies.
Professional memberships
IEEE Membership
Academic positions
Lecturer Mechanical Engineering, Cardiff University (2024- present).
Supervisions
Announcement: CSC Scholarship Opportunity for October 2026
Application deadline: 12th November 2025
For informal inquiries, please contact Dr Shou-Han Zhou at [email protected].
https://www.cardiff.ac.uk/study/international/funding-and-fees/international-scholarships/china-scholarship-council
About the Group
The Autonomous Health Monitoring and Control Group at Cardiff University is dedicated to developing advanced control and robotics solutions for human rehabilitation. The group integrates control theory, robotics, neuroscience, and human–machine interaction to design intelligent systems that support motor and cognitive recovery, enabling patients to regain functional autonomy. Students benefit from world-class supervision, interdisciplinary collaboration, and opportunities to contribute to cutting-edge technologies in health-focused robotics.
Project Overview
Effective rehabilitation systems require advanced control strategies, accurate mathematical modelling, and real-time optimisation to support patient recovery in clinical and home environments. This studentship will focus on the application of control engineering principles to the design of intelligent robotic systems and brain–computer interfaces. The research will explore:
· Development of mathematical models for patient–robot interaction to optimise motor and cognitive rehabilitation outcomes.
- Design of learning control algorithms for home-based rehabilitation systems, enabling safe, adaptive, and patient-independent therapy.
- Learning control strategies for brain–computer interfaces, translating neural signals into meaningful commands for assistive devices.
- Computational data-driven approaches to extract biomarkers of motor and cognitive function, informing personalised rehabilitation strategies.
- Integration of control theory, robotics, and human–machine interaction to create systems that promote functional autonomy and real-time adaptation to user performance.
About you:
Essential Requirements:
- A graduate from Control Engineering, Electrical Engineering, Mechanical Engineering, Robotics, or a closely related field.
- Analytical and mathematical skills, with some knowledge of control theory and dynamical systems.
- Enthusiasm for applying control engineering to human health and rehabilitation technologies.
- Interest in interdisciplinary research and collaboration with neuroscience or healthcare teams.
Desirable Requirements
- Experience in programming and simulation tools (e.g., MATLAB, Python, or C++).
- Experience in robotics, adaptive or learning control, human–machine interaction, wearable robotics or brain-computer interface.
- Experience with signal processing, optimisation, or computational modelling.
What We Offer
- Access to state-of-the-art laboratory and computational facilities, with expert supervision.
- International collaborations in control engineering, computational modelling, and rehabilitation technology.
- Opportunities for industrial engagement with the NHS, providing real-world clinical impact.
How to Apply
Applications should include:
- A CV (max 2 pages)
- A cover letter outlining motivation and suitability for the project
- Academic transcripts
Past projects
Daniel Croul, Design and Control of autonomous cable-based UAV, James Cook University, 2022-2025
Jaeger Wongtrakun, Underlying Mechanism of Perceptual Decision Making in Humans, Monash University, 2019-2025(COVID affected)
Contact Details
Queen's Buildings - South Building, Room S/0.10, 5 The Parade, Newport Road, Cardiff, CF24 3AA
Research themes
Specialisms
- Control engineering, mechatronics and robotics
- Cognitive neuroscience
- Computational neuroscience
- Motor control
- Rehabilitation