Answer Set Programming Professors and Supervisors in United Kingdom

Alessandra RussoView profile

Professor

Imperial College LondonUnited Kingdom

Professor Alessandra Russo leads the Structured and Probabilistic Knowledge Engineering (SPIKE) research group at Imperial College London's Department of Computing. With expertise spanning computational logic, symbolic machine learning, and neuro-symbolic AI, she develops foundational AI techniques applied to security, network management, healthcare, and adaptive systems. Professor Russo holds a PhD in Computing from Imperial College London and an MSc in Computer Science from Ionian University. Her research pioneers logic-based learning systems for intelligent adaptive technologies, with projects including declarative networking for security management, privacy-preserving federated learning, and hybrid neuro-symbolic approaches for robust reasoning. Her current work focuses on developing interpretable AI systems through neuro-symbolic integration, creating frameworks that combine neural networks with symbolic reasoning for explainable decision-making. Recent publications explore rule learning from knowledge graphs, transformer-based world models, and formal methods for representation learning. Professor Russo teaches courses on Logic-Based Learning and AI Applications, and has received the Google PhD Fellowship for her research contributions. She mentors numerous PhD students in areas spanning theoretical foundations and practical applications of computational logic and machine learning.

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Marina De VosView profile

Senior Lecturer

University of BathUnited Kingdom

Dr. Marina De Vos is a Senior Lecturer in the Department of Computer Science at the University of Bath. She leads research in knowledge representation and normative systems, with affiliations to multiple research centers including the Centre for Mathematical Biology and UKRI CDT in Accountable AI. Education: Postgraduate Certificate in Learning and Teaching in Higher Education, University of Bath (2010) Doctor of Science in Computer Science, Vrije Universiteit Brussel (2002) Master of Computing, Vrije Universiteit Brussel (1998) Research focuses on declarative programming paradigms, particularly Answer Set Programming (ASP) and normative multi-agent systems. Her work enables intuitive problem description in knowledge representation languages, with applications spanning structural engineering, music composition, legal reasoning, and policy modeling. Additional research interests include inductive machine learning, explainable AI, hybrid AI systems, and game theory. Recent publications demonstrate strong focus on norm synthesis in multi-agent systems, contextual reasoning, and governance frameworks for autonomous systems. This reflects a consistent pattern of applying formal computational methods to social and ethical dimensions of AI. Professional activities include keynote presentations at international conferences, workshop chair roles for major AI events, and external PhD examination responsibilities across European institutions.

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Ilias TachmazidisView profile

Senior Lecturer

University of HuddersfieldUnited Kingdom

Ilias Tachmazidis is a Senior Lecturer in the Department of Computer Science at the University of Huddersfield's School of Computing and Engineering. He actively contributes to research within the Centre for Autonomous and Intelligent Systems and the Centre of Artificial Intelligence for Mental Health. His academic journey includes a PhD from the University of Huddersfield (2015) and previous research roles in the PARK group. Education: University of Crete (B.Sc. 2010, M.Sc. 2012) Current academic rank: Senior Lecturer Active research centers: Centre for Autonomous and Intelligent Systems, Centre of Artificial Intelligence for Mental Health His research spans foundational AI concepts and applied implementations, with specific emphasis on: Knowledge Representation and Reasoning Semantic Web Technologies High Performance and Distributed Computing Artificial Intelligence Applications in Medicine and Law Recent publications highlight his work at the intersection of large language models and formal verification, alongside practical implementations for ADHD diagnosis using AI. The research demonstrates consistent engagement with UN Sustainable Development Goals through technology solutions that balance scientific rigor with societal impact. While not explicitly listing scientific awards, his activities show substantial contributions through peer-review work at IEEE conferences (2016-2020) and collaborative projects like MIREL (MIning and REasoning with Legal texts) that produced notable outputs in regulatory compliance systems.

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Sotirios BatsakisView profile

Research Fellow

University of HuddersfieldUnited Kingdom

Dr. Sotirios Batsakis is a Senior Research Fellow at the University of Huddersfield, School of Computing and Engineering. He holds a diploma in Computer Engineering and Informatics from the University of Patras (Greece) with highest distinction, and a Master’s/Ph.D. in Electronic & Computer Engineering from the Technical University of Crete. His work contributes to UN Sustainable Development Goals through research in knowledge representation, semantic web, and spatio-temporal reasoning. Centre for Planning, Autonomy and Representation of Knowledge Centre of Artificial Intelligence for Mental Health Centre for Autonomous and Intelligent Systems His research spans knowledge representation, semantic technologies, spatio-temporal reasoning, and AI applications in mental health. Recent work focuses on large language models for formal verification, defeasible reasoning, and compliance checking in legal contexts. He has contributed to qualitative spatial reasoning frameworks, temporal representation in OWL, and semantic IoT systems. Active in academic service as a programme committee member for IEEE conferences, he has published extensively on semantic web technologies, ontologies, and AI applications. His research fingerprint includes ontology engineering (100%), semantic web (66%), and temporal reasoning (15%) across 63 publications since 2009.

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Krysia BrodaView profile

Senior Lecturer

Imperial College LondonUnited Kingdom

Krysia Broda is an Honorary Senior Lecturer in the Department of Computing at Imperial College London's Faculty of Engineering. She holds roles as HiPEDS CDT Programme Director and PhD Teaching Scholar Programme Coordinator. Her research focuses on computational logic, artificial intelligence, neural-symbolic integration, inductive logic programming, and multi-agent systems. She is affiliated with the Artificial Intelligence Network, Human-Like Computing group, and the SPIKE (Structured and Probabilistic Intelligent Knowledge Engineering) team. Her academic contributions span theorem proving systems (e.g., DARE for distributed abductive reasoning), logic-based machine learning, and neural-symbolic systems. She has advised over 15 PhD students and collaborates internationally, including with the National Institute of Informatics (NII) in Tokyo. Broda has developed teaching tools like Pandora and Raptor for logic education and contributed to EPSRC-funded initiatives. Her work bridges theoretical foundations (e.g., Labelled Deductive Systems) with applied domains like bioinformatics and policy design for reactive agents. She actively participates in conference PCs (e.g., SGAI, ICMLA) and chairs Imperial's PhD Teaching Scholarship Programme.

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Patrick ProsserView profile

Lecturer

University of GlasgowUnited Kingdom

Dr. Patrick Prosser is an Honorary Lecturer in the School of Computing Science at the University of Glasgow. His research focuses on constraint programming, graph algorithms, and combinatorial optimization, particularly addressing challenging problems like subgraph isomorphism and maximum clique detection. Recent work develops specialized solvers for graph problems and investigates empirical hardness in optimization. He has created novel approaches for parallel constraint solving and solution-biased search techniques. Dr. Prosser's Glasgow Subgraph Solver represents a significant contribution to handling difficult variants of subgraph isomorphism problems. Publications explore parallel branch-and-bound methods, the impact of search tree structure on algorithm efficiency, and symmetry breaking techniques in graph representation. His work has applications in graph databases and combinatorial problem solving.

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Antonios BikakisView profile

Professor

University College LondonUnited Kingdom

Professor Antonios Bikakis is a Professor of Artificial Intelligence in the Department of Information Studies at University College London (UCL). He serves as the Director of Research in the Department of Information Studies and co-director of the Knowledge, Information and Data Science research group. His educational background includes: PhD in Computer Science from the University of Crete (2009) M.Sc. in Computer Science from the University of Crete (2004) Bachelor of Engineering (Honours) in Electrical and Computer Engineering from Aristotle University of Thessaloniki (2002) Professional Certificate in Teaching and Learning in Higher and Professional Education from University of London Professor Bikakis's research focuses on foundational aspects of Artificial Intelligence, with particular emphasis on Knowledge Representation, Nonmonotonic Reasoning, Computational Argumentation, Semantic Technologies, and Multiagent Systems. His work extends to developing knowledge-based systems for Digital Cultural Heritage, Digital Libraries, Ambient Intelligence, and E-commerce applications. He has made significant contributions to contextual reasoning in Ambient Intelligence environments, argumentation frameworks, and knowledge graphs. His research bridges theoretical AI with practical implementations in cultural heritage and everyday problem-solving scenarios. His recent publications (2023-2025) reveal a strong trend toward integrating large language models with structured reasoning for commonsense applications, developing knowledge representation for crisis management and cooking domains, and advancing semantic technologies for cultural heritage. His work consistently combines theoretical rigor with practical applications, particularly in the cultural heritage sector where he has made significant contributions to digital museum technologies and knowledge representation. His notable scientific achievements include: Best Paper Award at the International Conference on Logic and Argumentation (CLAR 2021) Extensive editorial work including Special Issues on Semantic Web for Cultural Heritage Significant citations for his work on Crypto collectibles and museum funding (194 total citations) Professor Bikakis actively supervises PhD students and MSc dissertations in Knowledge, Information and Data Science and Digital Humanities programs. He has secured major research funding through projects including "Repurposing of Resources" (Leverhulme Trust), "CORDIAL-AI" (ESRC), and "CrossCult" (European Commission Horizon 2020). His collaborative approach is evident in his extensive network of co-authors across European institutions and his service on program committees of major AI conferences (IJCAI, AAAI, ECAI, KR). As co-director of the Knowledge, Information and Data Science research group, he leads interdisciplinary research at the intersection of AI theory and cultural heritage applications. The group develops technology that combines the latest advancements in semantic technology, mobile computing, and context-aware systems to create engaging experiences with cultural heritage materials, reflecting his commitment to making AI research impactful in real-world settings.

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Steffen StaabView profile

Professor

University of SouthamptonUnited Kingdom

Professor Steffen Staab holds dual appointments as Professor of Web and Computer Science at the University of Southampton (since 2015) and Chair for Analytic Computing at the Institute for Parallel and Distributed Systems, Universität Stuttgart (since 2020). His research focuses on making data meaningful through ontologies, machine learning, and human-computer interaction, with a particular emphasis on Web Science. He leads the Institute for Web Science and Technologies (WeST) and is affiliated with organizations such as the Web Science Trust and ELLIS Unit Stuttgart. Education: PhD in Computer Science (1998) and Habilitation (2002) from Universität Karlsruhe (KIT). Prior roles include founding the WeST Institute at Universität Koblenz-Landau (2009–2020). Research interests span ontology development, data mining, and ethical AI applications. He is a Fellow of the European Association for Artificial Intelligence and has advised PhD students like Shatha Saad Algarni. His work bridges technical innovation with societal impact, particularly in accessibility and industrial automation.

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Dimitar KazakovView profile

Associate Professor

University of YorkUnited Kingdom

Dr Dimitar Kazakov is an Associate Professor (Reader) in the Department of Computer Science at the University of York. His research focuses on machine learning, natural language processing, AI in healthcare and finance, and multi-agent systems. He holds a PhD in AI and Biocybernetics from CTU Prague (2000) and has been with York since 1998, progressing through roles from Research Associate to Senior Lecturer before his current position since 2023. Education: PhD in AI and Biocybernetics, CTU Prague (2000) Eng Diploma in Control Engineering, CTU Prague (1993) York Certificate of Academic Practice (2001) Fellow of the Higher Education Academy (2019) Research interests include language evolution, AI ethics, and applications of machine learning in healthcare diagnostics and financial systems. His work bridges theoretical AI with practical systems, such as medical NLP tools and multi-agent protocols for banking. Notable contributions include: Development of hybrid NLP systems for radiology report analysis Ontology-based financial forecasting models Machine learning approaches for gait analysis and demographic prediction Awards: Recognized as a Fellow of the Higher Education Academy (2019) for teaching excellence. He participates in the University's Ethics Committee and contributes to the Artificial Intelligence research group. His recent work emphasizes explainable AI in clinical settings and causal modeling in epidemiology.

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Mauro VallatiView profile

Professor

University of HuddersfieldUnited Kingdom

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.

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George BargiannisView profile

Senior Lecturer

University of HuddersfieldUnited Kingdom

George Bargiannis is a Senior Lecturer at the Department of Computer Science, School of Computing and Engineering, University of Huddersfield. He serves as Deputy Director of the Centre for Autonomous and Intelligent Systems and an Associate Member of the Northern Productivity Hub. DiplEng, Electronic and Computer Engineering, Technical University of Crete (2006) MSc and PhD in Computer Science, University of Crete (2009, 2014) His research focuses on Knowledge Representation and Reasoning , Artificial Intelligence , and Explainable AI with applications in Supply Chains , Smart Homes , and Service-Oriented Computing . Recent work explores Blockchain and Semantic Web technologies. His 2025 publications include articles on UAV path planning , AI for supply chain sustainability , and causal machine learning in risk prediction . Collaborative projects span Answer Set Programming , Knowledge Graphs , and Climate Risk Modeling . He has secured over £2.5 million in external funding and supervises students in Artificial Intelligence and Computer Science projects. Current affiliations include editorial roles at the International Journal of AI in Scientific Disciplines and International Journal of Ambient Intelligence and Smart Environments .

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