Associate Professor in Medical Robotics and Industrial Automation
Dr Hajiyavand works on the design and development of automated systems, use of robotics and applications of Artificial Intelligence within robotics for inspections, and robotics for manipulation and micromanipulation.
Dr Amir Hajiyavand’s research advances intelligent robotics, automation, and Artificial Intelligence (AI) to address real-world challenges in manufacturing, healthcare, and the Circular Economy. His work integrates robotics, computer vision, smart sensing, and AI to develop autonomous systems that operate safely, efficiently, and reliably in complex environments.
His research covers robotic manipulation, industrial automation, inspection, precision robotics, human–robot collaboration, and AI-enabled autonomous systems. Applications include automated inspection and quality assurance, advanced manufacturing, micromanipulation, healthcare technologies, and sustainable manufacturing.
Robotics for Healthcare is an important area of his research, focusing on the development of intelligent and precision robotic technologies to support healthcare professionals and improve patient outcomes. His work explores AI-enabled robotic systems, medical robotics, precision manipulation, smart sensing, and human–robot interaction for healthcare applications. A particular emphasis is placed on developing safe, reliable, and clinically relevant technologies that can translate from research into practical healthcare settings.
A key focus is translating scientific advances into practical industrial solutions. Working closely with industry partners, he develops robotic technologies that are scalable, reliable, economically viable, and aligned with end-user needs. He has also been working with the West Midlands Combined Authority (WMCA) to support the adoption of robotics and automation across a range of industries. He is a Steering Group Member of the West Midlands Robotics & Autonomous Systems Cluster (WM-RAS), contributing to regional innovation, collaboration, and technology adoption.
Dr Hajiyavand leads and contributes to multidisciplinary research collaborations across the UK and internationally. His work spans the full range of Technology Readiness Levels (TRLs), from early-stage concepts to real-world deployment, with the aim of improving productivity, precision, sustainability, and quality of life.
Assistant Professor
Dr Jiaqi Ye'd research focuses on perception and intelligence for robotic and autonomous systems, integrating machine vision, multi-modal sensing, and deep learning for robust real-world engineering applications.
Dr Jiaqi is an Assistant Professor in Robotics and AI in the Department of Mechanical Engineering at the University of Birmingham, where he co-leads the PRIME Lab. His research interests include laser and camera systems, machine vision, deep learning algorithms, and robotic perception. His work focuses on developing universal perception frameworks that enable robust and accurate perception, as well as perception-driven planning, for robots and autonomous systems in complex engineering environments.
MTC Chair in Sustainable Manufacturing
Professor Butler is currently the MTC Chair in Sustainable Manufacturing and lead the Sustainable Manufacturing Group within the School of Engineering, University of Birmingham.
David Butler is currently the Manufacturing Technology Centre Professor in Sustainable Manufacturing. Prior to rejoining Birmingham (where he obtained his degrees in Metrology, Quality Systems, Manufacturing Engineering, and Economics), David was based in the University of Strathclyde where he held concurrent appointments with the National Physical Laboratory and the National Manufacturing Institute Scotland. David spent 18 years in Singapore where his interest in robotics grew through several industrial funded projects in areas including building cells for robotised propellor finishing and automated welding for oil & gas, and developing a remote operated dry ice cleaning solution using an in-house designed crawler robot.
David has recently led a West Midlands Combined Authority/Innovate UK funded project on promoting adoption of robots and automation in local SMEs (RAMP) and sits on the board of the West Midlands Robotics & Autonomous Systems Cluster (West Midlands RAS Cluster).
Interests: automation for recycling, decommissioning, and inspection.
Associate Professor in Computer Science
Dr Hyung Jin Chang is an Associate Professor in Computer Science at the University of Birmingham. His research focuses on human-centred AI and computer vision for intelligent and interactive robotic systems.
Dr Hyung Jin Chang is an Associate Professor in the School of Computer Science at the University of Birmingham and is affiliated with the Institute of Data and Artificial Intelligence (IDAI). He was also a University Turing Fellow of the Alan Turing Institute, the UK’s national institute for data science and artificial intelligence.
He received his BSc in Electrical and Computer Engineering and PhD in Electrical Engineering and Computer Science from Seoul National University in 2006 and 2013, respectively. Before joining the University of Birmingham, he worked as a Post-Doctoral Research Associate at Imperial College London, where he participated in several European research projects focusing on computer vision, machine learning, and human-robot interaction.
Dr Chang’s research lies at the intersection of artificial intelligence, computer vision, robotics, and human-robot interaction, with a particular emphasis on human-centred AI. His work aims to build intelligent robotic systems capable of understanding human perception, action, and intention through visual and multimodal learning.
He is especially interested in human-centred visual learning, including the estimation of human hand pose, body pose, and gaze, and the modelling of human actions and internal states. His broader goal is to advance robot vision and learning for intelligent and natural human–robot interaction.
Assistant Professor
Staff profile for Dr Mingchao Liu, University of Birmingham
Dr Mingchao Liu is an Assistant Professor in Mechanical Engineering at University of Birmingham. His research centres on the nonlinear mechanics of flexible slender structures, exploring how their large deformation, instability, and dynamics can be harnessed to achieve adaptive and programmable mechanical behaviours. He applies these principles to the design of morphing structures, mechanical metamaterials, and soft robotic systems that can reconfigure and interact intelligently with their environments.
Teaching Fellow in Law
Biographical and contact information for Angela Eggleton, Teaching Fellow in Law at the University of Birmingham
Angela’s research interests span a range of interdisciplinary and emerging areas, including emerging technologies, robotics, healthcare, environmental law, electric vehicles, the circular economy and intellectual property.
Angela has published widely across these areas. Most recently, she co-edited a book on patient safety law, ethics and the NHS, published by Routledge. Her previous publications include a chapter with Edward Elgar arising from her research into artificial intelligence and robotics, as well as an article for the University of Oxford’s Centre for Socio-Legal Studies blog exploring her robotics research.
As part of a NERC-funded project examining robotics and law, Angela organised and led an interdisciplinary conference at the University, bringing together participants from industry, academia and government from across the UK.
Angela has also contributed to the Faraday Institution’s ReLiB project, which focuses on the recycling and reuse of lithium-ion electric vehicle batteries. Through this work, she engaged with the European Commission in Brussels and collaborated with researchers across several universities and disciplines, including business, economics, metallurgy and materials science. Her involvement reflects the interdisciplinary nature of her research and her experience of working across academic, policy and industry contexts.
Shazad Ashraf is a Consultant Colorectal Surgeon and Clinical Director of Research and Development at University Hospitals Birmingham. He is also Honorary Professor of Surgical Engineering at the University of Birmingham.
His work focuses on the development, clinical evaluation and responsible implementation of artificial intelligence, surgical technologies and digital diagnostic systems to improve patient safety, healthcare efficiency and clinical outcomes.
He leads and contributes to several major collaborative programmes, including COBIx, a national NHS study evaluating AI-assisted digital pathology, and TEMSET-24K, a large-scale surgical video dataset developed to support the training and validation of surgical AI models. He also co-developed RAPTOR, an AI-enabled triage and pathway-optimisation platform presented at MICCAI, supporting the prioritisation and management of patients across colorectal cancer pathways.
Shazad completed his DPhil at the University of Oxford and subsequently held an Academic Clinical Lectureship within the Nuffield Department of Surgical Sciences, where his research focused on antibody engineering and its translation into clinical practice.
As a Birmingham Health Partners Fellow and NHS Responsible AI Champion, he works across the NHS, academia and industry to advance the ethical, secure, clinically effective and scalable adoption of artificial intelligence within healthcare.
Using a custom manually labelled dataset of ~2k images of strawberry farms to enable autonomous strawberry harvesting with a mobile robot. Focusing specifically on navigation, strawberry detection, ripeness estimation and localisation.
The surge in medical waste has highlighted the urgent need for cost-effective and advanced management solutions. In this project, a novel medical waste management approach, "MedBin," is proposed for automated sorting, reusing, and recycling. A comprehensive medical waste dataset, "MedBin-Dataset" is established, comprising 2,119 original images spanning 36 categories, with samples captured in various backgrounds. The lightweight "MedBin-Net" model is introduced to enable detection and instance segmentation of medical waste, enhancing waste recognition capabilities. Experimental results demonstrate the effectiveness of the proposed approach, achieving an average precision of 0.91, recall of 0.97, and F1-score of 0.94 across all categories with just 2.51 M parameters (where M stands for million, i.e., 2.51 million parameters), 5.20G FLOPs (where G stands for billion, i.e., 5.20 billion floating-point operations per second), and 0.60 ms inference time. Additionally, the proposed method includes a World Health Organization (WHO) Guideline-Based Classifier that categorizes detected waste into 5 types, each with a corresponding disposal method, following WHO medical waste classification standards. The proposed method, along with the dedicated dataset, offers a promising solution that supports sustainable medical waste management and other related applications. Access the MedBin-Dataset samples. See the source code for MedBin-Net.
Watch the SLICK robotic retrieval demonstration.
Hamid Mirshekarlou is a doctoral researcher specialising in medical robotics, with a focus on optimising robotic-assisted injection and penetration procedures. His research aims to enhance precision, reduce patient trauma, and minimise tissue deformation through advanced control strategies and biomechanical modelling. By integrating optimisation techniques with Software Simulation and experimental validation, he strives to develop safer, more efficient robotic systems that improve clinical outcomes in minimally invasive medical interventions.
