My work is centred on sensorimotor control and sits at the intersection of control, learning, robotics, autonomous systems, and theoretical neuroscience. I am interested in how biological and artificial systems estimate state, learn, adapt, and control skilled action under real-world constraints, including uncertainty, delay, noise, partial observability, and the physical limits of embodiment. My research combines human sensorimotor experiments, computational modelling, haptic and robotic interfaces, and real-time embedded control systems.

My research spans different stages of development and different motor systems, from speech acquisition in infants to the learning of novel arm movements in adults. I have also designed and built robotic interfaces for studying human movement, haptic interaction, motor learning, and movement assessment. These systems allow theoretical ideas about feedback, learning, state estimation, and control to be tested in controlled but physically meaningful tasks. They have supported research in our laboratory and in collaborations in the UK and internationally.

I have a background in electronic and electrical engineering, speech science, motor neuroscience, and robotics. This allows me to take a multidisciplinary approach to understanding intelligence, movement, learning, and autonomy. A central theme across my work is that human intelligence and artificial autonomy are embodied, closed-loop control problems that require theoretical analysis, experimental investigation, and robust engineering implementation.

Research interests:

My research investigates the control, learning, and representation of skilled action in biological and artificial systems. I use human sensorimotor experiments, computational modelling, robotic interface design, and real-time embedded control to study motor learning, haptic interaction, speech motor control, movement assessment, and autonomous systems.

A central theme of my work is that intelligence and autonomy should be understood as embodied, closed-loop control problems. This links my experimental work on human movement with broader theoretical questions about latent state, stabilising variables, abstraction, hierarchy, reward, and biologically plausible learning.

I also design and build robotic systems that allow theories of motor control and learning to be tested in real physical tasks, including haptic robotic interfaces for movement assessment, motor-learning experiments, and human interaction with autonomous or semi-autonomous systems.

Links:

ResearchGate

Google Scholar

Motor Learning and Robotics Lab

My teaching covers robotics, autonomous systems, machine learning, sensors and actuators, embedded systems, real-time programming, mobile and humanoid robots, motor control, and control engineering. Across these areas, I am particularly interested in helping students understand autonomy as an embodied control problem, where sensing, estimation, actuation, feedback, learning, and decision-making must be integrated in real physical systems.

Module leader for:

• ROCO352 Introduction to Machine Learning
• ELEC352 Real-Time Embedded Programming for Autonomous and AI Systems
• ROCO322 Autonomous Mobile and Humanoid Robots

Previously module leader for:

• ROCO219 Control Engineering
• ROCO224 Introduction to Robotics
• AINT516Z Topics in Advanced Intelligent Robotics
• ROCO222 Introduction to Sensors and Actuators
• SOFT561 Robot Software Engineering
• SOFT141 Network Programming

• Society for the Neural Control of Movement
• Society for Neuroscience
• International Speech Communication Association (ISCA) 
• Member of the Institution of Engineering and Technology, MIET