An Nguyen An Nguyen is a Researcher in the Department of Computer Science at the University of Oxford. Their work focuses on advancing AI systems through metacognition principles, reinforcement learning, and robotics applications. Key research themes include human-AI collaboration in robotic teleoperation, decision-making frameworks, and spatiotemporal task coordination. Nguyen's recent studies explore confidence calibration mechanisms in joint human-AI systems and their implications for autonomous decision support. Research interests span: Metacognition AI architectures for self-aware systems Reinforcement learning in dynamic environments Robotic teleoperation interfaces Human-robot trust dynamics Publications from 2020-2025 reflect a progression from transportation modeling to cutting-edge AI-robotics integration. Work combines unsupervised learning techniques with real-world robotics applications, demonstrating expertise in both theoretical and applied computer science domains.
Dr. Annabel Latham is a Senior Lecturer in Computer Science at Manchester Metropolitan University within the Faculty of Science and Engineering’s Computing and Mathematics department. She holds a PhD in Artificial Intelligence, an MSc in Computing, a Postgraduate Certificate in Academic Practice, a CIM Diploma in Marketing, and a BSc(Hons) in Computation. As a Fellow of the Higher Education Academy (FHEA) and Senior Member of IEEE (SMIEEE), she contributes extensively to AI research and education. PhD in Artificial Intelligence MSc in Computing PGC Academic Practice CIM Diploma in Marketing BSc(Hons) Computation FHEA (2015) SMIEEE (2018) Research Interests include Artificial Intelligence in Education , Ethics of AI , and Computational Intelligence . She specializes in conversational agents, intelligent tutoring systems, and public trust in AI. Her work addresses fairness, accountability, and accessibility in AI-driven education, leveraging technologies like large language models and fuzzy logic. Research Outputs span 15 years, with a focus on explainable AI, educational applications of conversational agents, and ethical frameworks. Recent work (2024-2025) explores postdigital citizen science, trustworthy AI implementation, and XAI usability for non-specialists. 2023 IEEE Region 8 Outstanding Women in Engineering Volunteer Award 2019 IEEE Region 8 Outstanding Women in Engineering Affinity Group of the Year 2018 Outstanding Peer Reviewer, Elsevier: Computers & Education Senior Member IEEE (2018) FHEA (2015) Teaching and Supervision includes undergraduate Databases, postgraduate units in Information Systems and AI Ethics. She supervises MSc and PhD students in areas like explainability-aware machine learning, data responsibility, and AI trustworthiness. Her grants involve collaborations with international funding bodies such as NAFOSTED (Vietnam) and Croatia’s National Council for Science. Labs and Groups include the Computational Intelligence Lab, Machine Intelligence research group, and the Data and AI Ethics research group, where she co-leads initiatives on ethical AI in education.
James Hardy is a Senior Lecturer in Computer Networks at the College of Science and Engineering. His research focuses on optimizing network systems, with particular emphasis on IoT healthcare applications, edge computing, social media data analysis, and energy efficiency in next-generation networks. He has contributed to frameworks like Mobilouds and algorithms for service matching in IoT environments. His work spans diverse areas including traffic congestion reduction through queueing theory applications, URI encoding schemes for IPv6, and cloud computing infrastructure optimization. Recent publications highlight advancements in real-time streaming scheduling and user-interest-driven content discovery in peer-to-peer networks. Hardy's research demonstrates a strong interdisciplinary approach, blending computer science fundamentals with practical applications in healthcare, transportation, and urban systems. His output includes over 700 views and 290 downloads across his publications, reflecting significant academic impact.
Daune West is a Senior Lecturer in the School of Computing, Engineering and Physical Sciences. Her research focuses on systems inquiry, requirements engineering, and action research, with emphasis on human-technology interaction. She leads the Educational Research Group, exploring teaching methodologies in computing and requirements analysis. Current projects involve analyzing human-horse interactions using physiological signals to study equine-assisted therapy's psychological benefits. She has collaborated with signal processing colleagues to explore emotion detection through brain signals in equine contexts. Her work contributes to UN Sustainable Development Goals related to mental health and well-being. Major awards include the Sir Geoffrey Vickers Memorial Award (1990). She has organized CPD courses on systems thinking and agile methodologies, and reviewed for journals like the International Journal of Systems and Society. Research activities span over three decades with publications in action research, systems theory, and interdisciplinary applications.
Dr. Matthieu Poyade is a Programme Leader and Lecturer in Medical Visualisation, Human Computer Interaction, and Extended Reality at the School of Innovation and Technology, The Glasgow School of Art. He holds a PhD in Human Computer Interaction from the University of Malaga, Spain, focusing on haptic feedback devices for industrial maintenance tasks. His academic roles include leading the MSc Medical Visualisation and Human Anatomy programme, a joint initiative with the University of Glasgow. **Research Focus**: Poyade's work spans medical visualization, VR/AR applications in healthcare education, and human-computer interaction. He has led EU-funded projects like ManuVAR (AR/VR in industrial maintenance) and contributed to initiatives in veterinary medicine, surgical training, and public health education. His research has been showcased globally, including at the Consumer Electronic Show (CES 2015) and TopCoder Open 2014. **Funding & Partnerships**: Projects are supported by the European Social Fund, Scottish Government, AHRC, MRC, Advanced Forming Research Centre, and European Space Agency. Collaborations include institutions like the University of Glasgow, NHS Education Scotland, and EuroXR (European Association for Extended Reality). **Key Projects**: Development of VR surgical simulators, AR tools for anatomy education (e.g., holographic brain visualization), 3D-printed medical models, and interactive apps for patient education (e.g., pancreatic cancer, neuroblastoma). Recent work includes AR applications for veterinary practices and public understanding of SARS-CoV-2 variants. **Awards & Recognition**: While no explicit awards are listed, his contributions to medical visualization and XR education have been recognized through publications in Springer’s Biomedical Visualization series and international conference presentations. **Labs & Teams**: Member of the Institute of Medical Illustrators and actively involved in EuroXR. Leads the MSc programme and collaborates with interdisciplinary teams in healthcare, technology, and art.
Daisy Abbott is a Research Developer at the School of Innovation and Technology, The Glasgow School of Art. Her work focuses on interdisciplinary research in game-based learning, serious games, and digital heritage visualization. She holds a PhD in Game-based approaches in Higher Education (2025) and is a Senior Fellow of the Higher Education Academy. Her research explores the application of games for teaching research skills in academia, digital heritage preservation, and decolonizing narratives through augmented reality. Key projects include the AHRC-funded 'Decolonising the British Empire Exhibition of 1938 through Augmented Reality Narratives' (2024-25) and the EPSRC-supported SECRIOUS project (2020-23), which developed cybersecurity education through game design. Abbott has published widely on serious game design methodologies and co-designed tools like the Empire 3D semantic annotation system. She serves as a peer reviewer for serious game journals and the AHRC/EPSRC funding bodies. Grants: £1m EPSRC grant (SECRIOUS), AHRC grants for digital heritage projects, Creative Founders Fund for game-based research skills training. Teaching: Leads courses on academic research skills and serious game design at postgraduate levels. Labs/Teams: Part of the Digital Design Studio and collaborates with interdisciplinary teams on projects like the Agents of Change Toolkit.
Dr. Maxwell Farrell is a Lecturer in Artificial Intelligence at the University of Glasgow's School of Biodiversity, One Health and Veterinary Medicine and an Affiliate Researcher at the MRC-University of Glasgow Centre for Virus Research . He earned a PhD in Biology from McGill University and conducted postdoctoral work at the University of Georgia , University of Toronto , and University of Glasgow . Since 2024, he has led a research group focusing on the ecology and evolution of infectious diseases through a macroecological lens. Research Affiliations: MRC-University of Glasgow Centre for Virus Research Leverhulme Programme for Doctoral Training in Ecological Data Science Crucible-funded Data Sonification Working Group Research Interests include: Host-pathogen interaction networks Text mining and AI for biodiversity science Phylogenetic comparative methods in disease ecology Macroecological modeling of multi-species pathogens Computational statistics for ecological synthesis Biodiversity genomics for disease surveillance Publication Trends show sustained contributions to Proceedings of the Royal Society B , Nature Microbiology , Lancet Planetary Health , and Philosophical Transactions of the Royal Society B , with recent articles emphasizing large language models for pest control synthesis, text mining frameworks in ecology, and global threat interconnections between climate change and zoonotic diseases. Supervision: Avery Holmes (Wellcome Trust IIB PhD) Erwin John Sioson (NorthWest Bio DTP) Claire Walden (VetFund PhD Scholarship) Collaborative Networks include the Viral Informatics, Biostatistics, and Evolution (VIBE) Lab , the SBOHVM Stats Support Group , and the Leverhulme Ecological Data Science DTP .
Amanda Clare is a Senior Lecturer in the Department of Computer Science at Aberystwyth University. She holds a PhD from Aberystwyth University and has expertise in data analysis, bioinformatics, and natural language processing. Her research focuses on genomic data interpretation, sequence analysis, and developing computational methods for addressing biological questions. Education: BA (Oxon) MSc (Edinburgh) PhD (Aberystwyth) Research Interests: Bioinformatics, sequence analysis, genomics, machine learning, time series analysis, and anomaly detection. She specializes in using algorithms and AI to analyze DNA/RNA sequences, microbial communities, and text data. Grants/Projects: 2023-2025: Intelligent Tools for Risk Monitoring (Dŵr Cymru Welsh Water) 2021-2022: Advanced Statistical Process Control for Water Treatment (Royal Academy of Engineering) 2018-2021: Intelligent Decision Support Systems for Water Quality Monitoring (Welsh European Funding Office) Key Collaborations: Collaborations with researchers in biology, computer science, and engineering, including projects on phenomics, genome-wide association studies, and automated laboratories. Awards/Recognition: Recognition for contributions to bioinformatics and AI applications, including media coverage for breakthroughs in animal infection detection and water quality monitoring.
Gareth Tucker is a Professor of Railway Systems Engineering at the University of Huddersfield's School of Computing and Engineering, affiliated with the Institute of Railway Research (IRR). He holds an ERDF-funded Smart Rolling Stock Maintenance Research Facility (SRSMRF) and focuses on solving near-term railway engineering challenges such as derailment prevention, suspension design optimization, and robotics-driven maintenance. His expertise spans vehicle-track interaction, RCM, and AI-driven operational planning. Education: PhD in Railway Engineering from Imperial College London (2009), with research on reducing railway track lifecycle costs through management of tangential wheel-rail loading. Academic exchange at Tokyo Institute of Technology (2006-2007). Research Interests: Robotics in train maintenance, predictive maintenance scheduling, condition monitoring, rail safety (e.g., squats analysis), and European Standards development (CEN TC256 WG10 member). Recent work emphasizes ontology-based virtual depots and machine learning applications in maintenance optimization. Grants: European Regional Development Fund (ERDF) grant supporting SRSMRF facility establishment. Collaborations include industry partners across Network Rail, RSSB, and BSI standardization efforts. Labs: Manages the SRSMRF laboratory focusing on automation, robotics, and AI integration in maintenance workflows. Active in developing standards for vehicle-track interface safety through CEN/BSI committees.
František Váša is a Lecturer in Machine Learning and Computational Neuroscience at King's College London, based in the Department of Neuroimaging within the School of Neuroscience and the Institute of Psychiatry, Psychology & Neuroscience. His research focuses on developing quantitative methods for analyzing structural and functional brain imaging data, particularly using network science, machine learning, and deep learning techniques. Key areas include pre-processing and enhancement of ultra-low-field neuroimaging, null models for statistical inference in network neuroscience, and clinical applications of neuroimaging. He co-leads third-year modules on Machine Learning in Neuroscience and Computational Neuroscience for the BSc Neuroscience and Psychology program. His work emphasizes methodological rigor and translational impact, with recent contributions to super-resolution techniques for paediatric MRI and global neuroimaging initiatives like UNITY for low-resource settings. Collaborators include prominent figures such as Prof Robert Leech (King's College London), Prof Edward Bullmore (University of Cambridge), and Dr Bratislav Mišić (Montréal Neurological Institute). His research bridges theoretical frameworks with practical clinical applications, aiming to improve diagnostic tools and understand neurodevelopmental processes.
Xiang Fei is an Assistant Professor in the Department of Computing at Coventry University, affiliated with the CEES School of Science. He holds a PhD in Computer Science and Engineering from Southeast University China (1999), following BSc (1992) and MSc (1995) degrees from the same institution. His research focuses on wireless internetworking, middleware for wireless sensor networks (WSN), and packet scheduling, with applications in data center optimization, machine learning, and cyber security. Professional Experience: Prior to his current role, Fei worked on European IST projects (WINE, Euro6IX) and the EPSRC-funded PROSEN project. He joined Cogent as a Research Fellow in 2008. He actively participates in academic events, organizing conferences like SENSORCOMM (2009-2011) and the International Conference on Automation and Computing (2010-2011). Research Interests: His work spans network analysis (community detection, graph embeddings), data stream processing, and cross-domain applications such as music information retrieval for stock market analysis. He has published extensively in IEEE journals and conferences, with notable contributions to nonnegative matrix factorization techniques and deep learning approaches for network problems. Teaching & Mentoring: Currently accepting PhD students in computing-related fields. His research has been supported by industry collaborations and grant-funded projects, reflecting his expertise in both theoretical and applied computing domains.
Aiping Xu serves as Assistant Professor Academic at Coventry University's Mathematics Support Centre (LIB), actively supervising PhD students while leading institutional mathematics education initiatives. Her research bridges advanced statistical methodology with practical educational applications across engineering, data science, and higher education contexts. Her academic foundation includes: Doctorate in Signal Processing and Telecommunication from Research Institute of Computer Science and Random Systems (awarded 2002) MSc in Operational Research and Control Theory from Shandong University (awarded 1998) Bachelor's degree in Mathematics from Shandong University (awarded 1995) Dr. Xu's research program demonstrates significant evolution from early work on nonlinear system fault diagnosis using adaptive observers (2001-2004) to contemporary expertise in functional data analysis, where she develops principal curve clustering methods and Gaussian process regression frameworks. Her fingerprint analysis confirms dominant specialization in Functional Data Mathematics (100%), Principal Curve Mathematics (100%), and Forecasting (100%), with strong contributions to mathematics education through support centre evaluations and mathematical modeling competitions. Recent publications (2017-2022) reveal a strategic pivot toward applying statistical learning in educational contexts, particularly through her leadership in Coventry University's Mathematics Support Centre. This work integrates machine learning techniques with pedagogical innovation to address student challenges in mathematical comprehension and application. As an active PhD supervisor, Dr. Xu contributes to academic development through the Mathematical Contest in Modelling and institutional support services. Her research outputs show consistent international collaboration, particularly with Newcastle University, spanning statistical methodology, educational practice, and interdisciplinary applications. The Mathematics Support Centre under her involvement operates as a critical academic infrastructure unit, providing cross-disciplinary mathematical assistance to students across Coventry University through structured tutoring, resource development, and pedagogical research initiatives.
Dingchang Zheng is a Professor and Director of the Research Centre for Intelligent Healthcare. He specializes in medical device development, particularly in physiological measurements and bio-signal processing, with a focus on cardiovascular technologies, wearable devices, and IoT monitoring systems. His work addresses unmet clinical needs such as novel blood pressure measurement techniques and pre-term labor prediction. Education: PhD in Medical Physics from Newcastle University, UK. Research Interests: Healthcare device innovation, wearable sensors, bio-signal processing, computer modeling, and multidisciplinary collaboration across engineering, medicine, and industry. His work contributes to UN Sustainable Development Goals related to health and well-being. Awards: IPEM Martin Black Prize (2012) IET JA Lodge Award (2009) Grants & Projects: Principal Investigator on projects including: Development of a hearing aid IoT platform (2018–2020) Wearable respiratory rate device for infants (2018–2021) Uterine electrohysterogram system for preterm labor prediction (2016–2021) funded by the Bill & Melinda Gates Foundation Labs/Teams: Leads the Research Centre for Intelligent Healthcare, fostering international collaborations in China, Jordan, and Nigeria to advance healthcare technology adoption.
Jim Briggs is a Professor of Informatics and Associate Dean (Research) at the University of Portsmouth's Faculty of Technology, School of Computing. He leads the Centre for Healthcare Modelling and Informatics (CHMI), focusing on clinical outcome modelling and healthcare analytics. His research spans health informatics, telehealth, and computers in sport, with a strong emphasis on improving patient outcomes through data-driven approaches. Education: BA in Computer Science and Mathematics, DPhil in Computer Science from the University of York. His academic career includes roles at the University of York before joining Portsmouth in 1995. Research Interests: Clinical outcome modelling, digital wellbeing, telehealth, healthcare analytics, and sport computing applications. He has contributed to advancements in early warning scores for patient deterioration and vital signs monitoring protocols. Key Projects: Developed scoring systems like the Aggregate National Early Warning Score (NEWS) and contributed to studies on robotic-assisted surgery outcomes. His work has been published in high-impact journals and cited widely in policy and clinical guidelines. Labs/Teams: Director of the Centre for Healthcare Modelling and Informatics (CHMI), part of the Institute of Life Sciences and Healthcare. Collaborates with multidisciplinary teams on translational healthcare research.
Andree Woodcock is a Professor in Education, Ergonomics & Design at Coventry University, affiliated with the Research Centre for Arts, Memory and Communities. She holds a BSc in Psychology and Social Biology, an MSc in Ergonomics from UCL, and a PhD in Automotive Concept Design from Loughborough University, earned during her Daphne Jackson Research Fellowship. Her research focuses on applying systems-thinking and user-centred design principles to address 'wicked problems' in health, education, mobility, sustainability, and inclusivity. With over £15 million in grants since 1999, her projects include H2020 TInnGO (gender and transport), WEMOBILE (gender transport poverty), CIVITAS SUITS (sustainable mobility), and Frank Jackson Foundation-funded initiatives on empathic transport design. Key research outputs highlight collaborations across Europe, with a focus on gender-responsive innovation, multimodal transport systems, and inclusive design methodologies. She actively supervises PhD students and participates in conferences, peer-review, and editorial activities. H2020 TInnGO (2019-2021): Pan European Transport innovation Gender Observatory (£3 million) WEMOBILE (2018-2019): Women's transport poverty in Malaysia/Pakistan (£45K) CIVITAS SUITS (2016-2020): Sustainable mobility capacity-building (£4 million)
