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Professor Mike Wald serves as a Professorial Fellow in Education at the University of Southampton's Faculty of Engineering and Physical Sciences, Department of Electronics and Computer Science. His work bridges cutting-edge technology with real-world accessibility challenges through the Web and Internet Science research group. Wald's research spans Accessibility Technology , Artificial Intelligence , and Human-Computer Interaction , with particular focus on assistive technologies for disabled communities. His work integrates natural language processing for text simplification, recommender systems for outdoor tourism, and AI-driven adaptations for augmentative communication. Current projects examine autonomous vehicle trust perception and context-aware climbing recommendation systems, demonstrating his interdisciplinary approach spanning computer science, health informatics, and social inclusion. His recent publications reveal strong trends in applied AI for social good , particularly in disability inclusion and outdoor accessibility. The research consistently combines technical innovation with user-centered evaluation , often focusing on visually impaired and communication-disabled populations. Key methodological themes include practical metric development, contextual integration in recommender systems, and real-world deployment challenges. Professor Wald actively supervises postgraduate researchers including PhD candidate Yue Zheng in Computer Science. His research is supported by major funders including EPSRC, European Union (H2020/FP7), Qatar National Research Fund, and the Alan Turing Institute through projects like SemanticNews, Net4Voice, SlideWiki, and Michelangelo. As a recognized expert, Wald has delivered keynote addresses including "Building Rating System for Indoor Navigation by Blind People" (2018) and "How AI can overcome barriers to inclusion" (2021), demonstrating his leadership in translating research into practical accessibility solutions.

• Accessibility Technology
• Artificial Intelligence
• Human-Computer Interaction
• +4 more

Liam McComish is a Senior Lecturer in Graphic & Advertising Design at Belfast School of Art, Ulster University. With a professional background spanning the 1980s-1990s across organizations like BBC, British Coal, and Scottish Curriculum Council, he transitioned to academia in 2000. Research Focus: Typographic communication, exploring meaning construction through visual/graphical means in physical/digital spaces Key Themes: Identity in design, cultural/commercial discourse, semiotic intentions, and urban design pedagogy His research spans interdisciplinary collaborations through projects like the Machine for Experiential Urban Learning and LesEnfonts , with publications addressing typographic ethics, urban semiotics, and design pedagogy. Notable conference presentations include Nordic Design Conference (Copenhagen) and Design&Emotion Conference (Gothenburg). Professional Affiliations Member and International Assessor, International Society of Typographic Designers Fellow, Higher Education Academy Academic Contributions External Examiner for Bolton University Co-Supervisor for two PhD students Peer-reviewer for international design publications

• Typographic Communication
• Graphic Design
• Urban Design
• +5 more

Mauro Vallati is a Full Professor of Artificial Intelligence at the Department of Computer Science, School of Computing and Engineering, University of Huddersfield. He is also the Director of the Centre for Autonomous and Intelligent Systems and holds leadership roles in several AI-focused research centres, including the Centre for Planning, Autonomy and Representation of Knowledge and the Centre of Artificial Intelligence for Mental Health. Additionally, he is a member of the Sustainable Living Research Centre. Vallati is currently accepting PhD students and is an active researcher with over 200 publications. His research expertise lies in Artificial Intelligence, with a strong focus on Automated Planning and Argumentation. He applies these techniques to real-world problems, particularly in Urban Traffic and Mobility, which is the central theme of his UKRI Future Leaders Fellowship. He also explores innovative applications of AI in Medicine and Computational Creativity. His work aligns with UN Sustainable Development Goals, especially in sustainable cities and communities. The recent articles highlight a consistent trend in applying AI and planning techniques to urban mobility challenges, including traffic signal optimization, autonomous vehicle routing, and passenger demand prediction. There is also a strong theoretical foundation in argumentation, plan robustness, and macro-actions. The research spans both practical implementations and algorithmic advancements, with increasing emphasis on sustainability and real-world impact. Scientific Awards and Recognitions: UKRI Future Leaders Fellow ACM Senior Member ACM Distinguished Speaker on AI for the UK Vallati has secured significant research funding through projects such as AI4ME, AI for Autonomic Urban Traffic Control, MIREL, and SimplifAI. He supervises PhD students and early-career researchers, contributing to the development of the next generation of AI scientists. His leadership in organizing key academic events, such as the UK Planning and Scheduling Special Interest Group workshop, underscores his active role in the international AI community. He leads and contributes to multiple research centres, including the Centre for Autonomous and Intelligent Systems, where he drives innovation in AI applications for traffic, mental health, and sustainability. His interdisciplinary collaborations span engineering, computer science, and environmental research, particularly evident in projects involving textile waste recycling and legal text mining.

• Artificial Intelligence
• Automated Planning
• Argumentation
• +13 more

Minghao Liu is a Postdoctoral Research Associate in the Department of Computer Science at the University of Oxford, working under the supervision of Prof. Marta Kwiatkowska and previously with Dr. Andrew Cropper. He is affiliated with the Artificial Intelligence and Machine Learning theme and the FAIR project at Oxford. His research integrates symbolic reasoning with machine learning, focusing on automated reasoning, constraint programming, and combinatorial optimization. PhD in Computer Science and Technology, University of Chinese Academy of Sciences (UCAS), 2023 BSc in Computer Science and Technology, Northeast Normal University (NENU), 2017 His research interests span automated reasoning, constraint programming, combinatorial optimization, and the integration of symbolic reasoning with machine learning. He develops novel algorithms for SMT solving, optimization modulo theories, and neural-symbolic systems, often leveraging machine learning to enhance classical reasoning systems. The recent publications show a strong trend in hybrid AI systems, particularly using graph neural networks to solve combinatorial problems like MaxSAT and Pseudo-Boolean Satisfiability. There is also a significant focus on improving solvers for nonlinear arithmetic and modal logics, often guided by reinforcement learning or probabilistic methods. His work bridges formal methods with deep learning, aiming to create more robust and scalable reasoning systems. Notable scientific awards include: ACM SIGSOFT Distinguished Paper Award at ISSTA 2023 Best Student Abstract Honorable Mention Award at AAAI 2023 2nd Place in SMT Competition (Nonlinear Real Arithmetic Track, 2022) Gold Medal in ACM-ICPC Asia Regional (2016) National Scholarship of China (2014) Minghao Liu has been actively involved in academic service and teaching. He has served as a Class Tutor for Logic and Proof and Knowledge Representation and Reasoning, a Practical Demonstrator for Design and Analysis of Algorithms, and a Student Project Supervisor for Group Design Practical at Oxford. He was also a Teaching Assistant for Theoretical Computer Science at UCAS. He has received multiple scholarships and honors, reflecting his academic excellence. His service includes being a PC member for AAAI (2023–2025), ECAI 2024, and ICTAI 2023, and a reviewer for IEEE TNNLS, IEEE TKDE, and CSSE. He is actively involved in research projects such as FAIR and maintains open-source implementations of his work on GitHub, including solvers for MaxSAT, SMT(NRA), and Holey Latin Squares, demonstrating strong software engineering and reproducibility practices.

• Automated Reasoning
• Constraint Programming
• Combinatorial Optimization
• +7 more

Jack Black is Associate Professor of Culture, Media, and Sport at the Academy of Sport and Physical Activity in Sheffield Hallam University . He holds a PhD in Sociology of Sport from Loughborough University (2015) and has taught at multiple institutions including University of Leicester and Nottingham Trent University. Research Focus: Psychoanalytic theory applied to cultural studies, examining intersections with race, gender, and digital media in sports contexts Key Projects: AHRC-IRC funded Tackling Online Hate in Football investigator Editorial Roles: Associate Editor for International Journal of the Sociology of Leisure and Psychoanalysis, Culture & Society His publications include: 2023 : The Psychosis of Race: A Lacanian Approach to Racism and Racialization 2021 : Race, Racism and Political Correctness in Comedy 2024 : Co-editor of Sport and Psychoanalysis: What Sport Reveals about Our Unconscious Desires, Fantasies, and Fears 2022 : Co-editor of Sport and Physical Activity in Catastrophic Environments Recent articles analyze: AI chatbots and the concept of lying Disavowal mechanisms in sportswashing Online hate speech detection in sports Cultural Marxist conspiracies in football protests Psychoanalytic approaches to race and racism

• Media and Communications
• Sociology of Sport
• Psychoanalytic Theory
• +7 more

Professor Sven Schewe is affiliated with the University of Liverpool, focusing on finite games of infinite duration and automata over infinite structures. He emphasizes collaborative research and teaching, though admits to challenges in maintaining web pages. EPSRC grant (2017-2021) for Parity Games EPSRC grant (2015-2019) for Energy Efficient Control DST (UK) grant (2018-2020) for AI Test Metrics EU grant (2017-2019) for Parametrised Verification Leverhulme Trust grant (2011-2012) for Probabilistic Systems EPSRC grant (2010-2013) for Markov Decision Processes His research spans formal verification, game theory, probabilistic systems, and secure computation. Key publications include work on parity games, adversarial training, and multi-party querying. He serves as a reviewer for major conferences and journals, including ACM Transactions on Computational Logic and IEEE Symposiums. Scientific awards include the Dr. Eduard Martin Preis (2009) and the GI Dissertation Award (2009). He supervises theses on topics like semantic testing for neural networks, symbolic discrete control, and power grid frameworks. As PhD Admission Tutor for Computer Science, he contributes to academic governance. His recent articles highlight collaborations in AI security, chemical space exploration, and automata theory.

• Theoretical Computer Science
• Formal Verification
• Game Theory
• +7 more

Jun Wang is a Professor of Information and Data Science in the Department of Computer Science at University College London. He serves as the Founding Director of the MSc Web Science and Big Data Analytics program and is the Co-founder and Chief Scientist of MediaGamma Ltd (acquired), a UCL startup focused on AI for intelligent audience decision-making. His educational background includes: Doctor of Philosophy from Technische Universiteit Delft (2007) Master of Science from National University of Singapore (2003) Bachelor's degree from Southeast University Nanjing (1997) Professor Wang's primary research focuses on AI and intelligent systems, with particular expertise in multiagent reinforcement learning, deep generative models, and their applications across several domains. His work spans information retrieval, recommender systems and personalization, data mining, smart cities, bot planning, and computational advertising. His research has practical implications in healthcare, robotics, and intelligent systems for decision-making, aligning with Sustainable Development Goal 3 (Good Health and Well-Being). His recent publications demonstrate a strong trend toward integrating large language models with reinforcement learning techniques to create more efficient and capable systems. The research spans healthcare applications, robotics, information systems, and strategic decision-making, with an emphasis on privacy preservation, efficient computation, and multi-agent coordination. Professor Wang has received several prestigious awards: Beyond Search – Semantic Computing and Internet Economics award from Microsoft Research Yahoo! FREP Faculty award UCLB One-to-Watch award 2016 for MediaGamma First place in global real-time bidding algorithm contest (80+ participants worldwide) Multiple Best Paper awards With over 15 years of experience, Professor Wang has advised numerous startups including Last.Fm, Passiv Systems, Massive Analytic, Context Scout, and Polecat. He has also collaborated with major industry players such as BT, Microsoft, Yahoo!, Alibaba, and Didi. His knowledge transfer activities bridge academic research with practical industry applications. Professor Wang has developed the Information Retrieval and Data Mining MSc module and the MSc/MRes programme on Web Science and Big Data Analytics. He also teaches a multiagent AI MSc module, sharing his expertise in cutting-edge AI techniques with the next generation of computer scientists.

• Artificial Intelligence
• Reinforcement Learning
• Multi-agent Systems
• +7 more

Professor Nick Chater serves as Professor of Behavioural Science within the Behavioural Sciences Group at Warwick Business School (WBS), University of Warwick. His academic profile spans cognitive science, behavioral economics, and decision-making psychology, with significant contributions to understanding the fundamental mechanisms of human thought and behavior. Chater's research interests encompass cognitive science, behavioral economics, and the psychology of decision-making. He has pioneered work challenging traditional views of deep, unconscious mental processes, most notably through his influential 2018 book "The Mind is Flat: The Remarkable Shallowness of the Improvising Brain." His research program investigates how people process information, make decisions under uncertainty, and develop language and social coordination mechanisms. Key themes include the role of noise in cognition, Bayesian approaches to modeling human thought, and the application of behavioral insights to public policy design. Chater's extensive publication record reveals a strong trend toward interdisciplinary integration of computational modeling with empirical research. His recent work demonstrates consistent engagement with cutting-edge developments across cognitive science, particularly in Bayesian cognitive modeling and behavioral public policy. The S-Frame agenda for behavioral public policy research represents a systematic approach to translating behavioral insights into policy design. His collaborative network spans psychology, economics, linguistics, and artificial intelligence, reflecting the interdisciplinary nature of modern cognitive science. As an educator, Chater teaches "Behavioural Sciences for the Manager" for Executive MBA programs and "Judgement and Decision Making" for various MSc programs at Warwick Business School. His teaching focuses on applying behavioral science principles to business contexts, helping students understand how cognitive biases and heuristics influence managerial decision-making. His work on coordination in dynamic interactions and moral cognition provides valuable insights for understanding organizational behavior and team dynamics.

• Cognitive Science
• Behavioral Economics
• Decision Making
• +4 more

Dr. David Corsar is a Principal Lecturer in Informatics at Robert Gordon University's School of Computing, Engineering and Technology. He is an active researcher in Artificial Intelligence with a focus on knowledge representation, reasoning methods, and explainable AI systems. His academic journey began with a Bachelor's Degree in Computing Science (Artificial Intelligence) from the University of Aberdeen (2000-2004), followed by a PhD in Computing Science (2004-2009) where he researched knowledge reuse through ontology mapping and ontology based knowledge acquisition. After his doctoral studies, he worked as a Research Fellow at the University of Aberdeen in interdisciplinary teams before joining RGU. Dr. Corsar's research interests span across multiple AI domains: Knowledge Representation and Reasoning Semantic Web and Knowledge Graphs Provenance in AI systems Explainable Artificial Intelligence (XAI) Case-Based Reasoning Integration of machine learning with symbolic AI Transparency and trustworthiness of AI systems His recent publication record (2023-2025) shows a strong emphasis on developing practical XAI solutions, particularly through the iSee platform which addresses the challenge of personalized explanations for diverse AI users. His work bridges theoretical AI research with practical applications in healthcare, regulatory compliance, and industrial domains like Oil and Gas decommissioning. Dr. Corsar has received research funding for multiple projects: iSee: Intelligent Sharing of Explanation Experience (2021-2024) Prophecy Project: Intelligent Decommissioning Materials Management (2019-2020) Intelligent System for assisted medical diagnosis for CARDIOvascular diseases (2020-2021) Building resilience in Health and Social Care students during Covid-19 (2020) As an educator, Dr. Corsar coordinates several technical modules including Native Android App Development, Internet of Things, Data Structures and Algorithms, and Knowledge Modelling and Reasoning. He is actively involved in supervising PhD students working on cutting-edge AI research topics and welcomes Honours and Masters projects aligned with his research areas. Dr. Corsar is a member of the Artificial Intelligence & Reasoning Research Group at RGU, contributing to the university's research theme 'Living in a Digital World.' His work on the W3C Provenance Working Group has helped develop standards for provenance information interchange on the Web, demonstrating his commitment to advancing foundational aspects of AI systems.

• Knowledge Representation
• Knowledge Reasoning
• Semantic Web
• +11 more

Professor Eyad Elyan is a leading academic and researcher at Robert Gordon University's School of Computing, Engineering and Technology, where he serves as a Professor in Machine Learning and Computer Vision. He is the founder and head of the Machine Vision Research Group, driving innovative research in applied computer vision and deep learning with significant industry impact. Professor Elyan's research focuses on converting complex and unstructured data into knowledge and actionable insights, with particular emphasis on learning from images, videos, and other forms of unstructured data. His work spans engineering diagrams processing, remote inspection for oil and gas installations, intelligent condition monitoring of offshore assets, predictive maintenance, biometric applications, and medical datasets analysis. His expertise in ensemble-based learning and learning from unstructured and imbalanced datasets has been successfully implemented in various real-world applications. Professor Elyan was awarded the UK Knowledge Transfer Partnership Academic of the Year Award in 2023 for his transformative work in developing pioneering AI solutions for the oil and gas sector, and was a finalist for the Scottish Knowledge Exchange Award in 2024. These recognitions highlight his exceptional ability to bridge academic research with practical industry applications. His research has been supported by various public funding bodies including Innovate UK, the Data Lab Innovation Centre, Oil and Gas Innovation Centre (OGIC), NetZero Technology Centre (NTZ), and Historic Environment Scotland. Professor Elyan has supervised twelve PhD students to completion and examined more than fifteen others. He plays an active role in the academic community as a Fellow of the British Higher Education Academy and The International Neural Network Society, and serves as the Scotland Data Lab Innovation Centre Ambassador. Under Professor Elyan's leadership, the Machine Vision Research Group has developed innovative solutions including an end-to-end system for processing Piping and Instrumentation Diagrams (P&ID), AI-driven inspection systems for oil and gas assets, and defect recognition technologies. His work demonstrates a consistent commitment to translating cutting-edge research into practical tools that address real-world challenges, particularly in the energy sector.

• Machine Learning
• Computer Vision
• Deep Learning
• +7 more

Dr. Carlos Francisco Moreno-Garcia is an Associate Professor in Computing at Robert Gordon University (RGU) in Aberdeen, Scotland, UK, affiliated with the School of Computing, Engineering & Technology and the Machine Vision Research Group. His academic journey began with a Bachelor's in Electronic Engineering from Tecnologico de Monterrey, Mexico, followed by a Master's and PhD in Spain at Universitat Rovira i Virgili. Dr. Moreno-Garcia's research spans Pattern Recognition, Computer Vision, Medical Image Analysis, Document Image Analysis, and Systematic Review Automation. His work bridges theoretical AI development with practical applications, particularly in digitizing complex engineering drawings for the Oil & Gas sector, developing medical diagnostic tools for cardiovascular diseases and neonatal pain assessment, and automating systematic literature reviews in healthcare. His recent publications demonstrate strong expertise in attention mechanisms, symbol recognition in technical diagrams, and NLP applications for medical literature analysis. His publication record shows consistent output with 77 documented research outputs, including significant recent contributions in 2024-2025 across high-impact journals and conferences. His work often addresses imbalanced datasets, few-shot learning challenges, and the integration of domain knowledge into AI models. General Chair of BMVC 2023 (elevated to CORE A Conference) Associate Editor of IEEE Transactions on Neural Networks and Learning Systems Co-leader of the Cluster of Machine Learning, AI and Data Science Leader of the Science, Technology and Innovation Pillar and Red Global MX Dr. Moreno-Garcia actively supervises PhD students working on document image analysis, medical applications of AI, and systematic review automation. His research is supported by collaborations with institutions including Universidad Nacional Autonoma de México (UNAM), Jiva.ai, NHS Grampian, and the University of Aberdeen. His lab focuses on real-world applications of computer vision and machine learning, with particular emphasis on healthcare and engineering documentation.

• Pattern Recognition
• Computer Vision
• Medical Image Analysis
• +6 more

Professor Radhakrishna Prabhu is a distinguished academic at Robert Gordon University's School of Computing, Engineering & Technology, where he leads the Instrumentation & Sensors Research Group and is affiliated with the Robotics & Autonomous Systems Research Group. With over 20 years of applied research experience, his work spans optical technologies, environmental sensing, and clean energy systems. His research expertise encompasses Optical Materials & Waveguides , Optical Imaging , Optical Security , Subsea Sensing , Optoelectronics , Microscopy , Spectroscopy , Lasers , Fluorescence Instrumentation , Lab-on-a-chips , and Renewable Energy Systems . Recent publications (2024-2025) reveal a strong focus on underwater optical communication systems, microplastic environmental impact studies, and building-integrated photovoltaic technologies. Professor Prabhu's scientific contributions are recognized through his professional affiliations: Fellow of Higher Education Academy (FHEA) Chartered Engineer (EC-IET) Member of Institute of Physics (IOP) Member of The Institution of Engineering & Technology (IET) Member of Institute of Electrical and Electronics Engineers (IEEE) He currently supervises multiple PhD students across diverse projects including underwater wireless networks, microplastic soil studies, and building-integrated photovoltaics. His active research portfolio includes the 'Portable Luminescent Chemical Compass' project (2025-2027) and 'Reusing Subsea Natural Gas Pipeline Infrastructure for Bulk Hydrogen Transportation' (2022-2023), demonstrating his commitment to addressing contemporary environmental and technological challenges through innovative research. The Instrumentation & Sensors Research Group under his leadership develops cutting-edge sensing technologies with applications ranging from environmental monitoring to security systems, contributing significantly to Robert Gordon University's research themes in Environment, Energy & Sustainability.

• Optical Materials and Waveguides
• Optical Imaging
• Optical Security
• +6 more