Find Academic Supervisors

• Formal Methods
• Program Extraction
• Interactive Theorem Proving
• +6 more

Monika Seisenberger is an Associate Professor at the Department of Computer Science , Swansea University, within the Faculty of Science and Engineering . Her academic role is centered on Formal Methods , Interactive Theorem Proving , and Specification & Verification , with significant contributions to logic, proof theory, and well-quasiorders. Research Interests : Her work bridges Computer Science and Mathematics , focusing on Program extraction from proofs Formal verification of safety-critical systems Applications of AI in medical and railway domains Computational content of choice principles Development of concurrent algorithms and toolchains Article Trends : Her publications over the past decade highlight a consistent focus on formal methods applied to railway logistics , AI explainability in healthcare, and constructive mathematics . Notable themes include Counterfactual explanation generation Multi-agent optimization in transportation Temporal model analysis via gradients Railway system safety verification Verification of geographic data Computational logic foundations Supervision & Collaboration : She actively supervises postgraduate research in areas like formal software verification , AI-driven railway technologies , and SHAP refinement , often collaborating with experts in Markus Roggenbach , Anton Setzer , and Fabio Caraffini . Labs & Teams : Based at the Computational Foundry (Bay Campus), she contributes to Swansea University's Formal Methods research group, advancing tools for proof theory and program synthesis .

Manuel Lopez-IbanezView profile

Professor

• Optimisation
• Multi-criteria Decision Making
• Evolutionary Computation
• +6 more

Manuel Lopez-Ibanez is a Professor of Optimisation in the Department of Management Sciences at the University of Manchester. His research contributes to the UN Sustainable Development Goals through advancements in optimization and decision analytics. He actively collaborates on international projects in multiobjective optimization and evolutionary computation. Research areas: Optimisation, Evolutionary Computation, Multi-criteria Decision Making, Machine Learning, Operations Research External affiliations: Institute of Electrical and Electronics Engineers (IEEE), ACM SIGEVO, editorial boards of Evolutionary Computation and Operations Research Perspectives

• Neural Computation
• Artificial Intelligence
• Machine Learning
• +10 more

Christopher L. Buckley is Professor of Neural Computation (Informatics) at the School of Engineering and Informatics, University of Sussex, where he has been a faculty member since 2014. He was promoted from Lecturer to Senior Lecturer in 2018 and to Professor in 2022. His research bridges theoretical neuroscience and artificial intelligence, with a focus on active inference, predictive coding, and embodied cognition. Master of Physics (First Class), University of Edinburgh, 2000 MSc in Evolutionary and Adaptive Systems, University of Sussex, 2001 PhD in Cognitive Robotics, University of Southampton, 2008 His research interests center on understanding how neural systems give rise to robust behavior and how such principles can inform AI. He investigates machine learning models grounded in neuroscience, particularly through the lens of the free energy principle and active inference. His work spans artificial intelligence, machine learning, theoretical neuroscience, cognitive robotics, and artificial life , with applications in planning, perception, and adaptive systems. His recent publications reveal a strong trend in scaling predictive coding, hybrid active inference models, and rate-distortion theory for action representation. He frequently collaborates with leading researchers like Karl Friston and explores topics such as tool use, synthetic awareness, and ecosystem-level intelligence. His work appears in high-impact journals and preprint servers including Neural Computation , Frontiers in Network Physiology , and arXiv. He has received multiple research grants from prestigious funders including Innovate UK, the John Templeton Foundation, BBSRC, and the European Union, supporting projects on synthetic awareness, neural abstraction, and brain-wide dynamics in vertebrates. A metapredictive model of synthetic awareness for enabling tool invention (Innovate UK) Modelling Abstractions in Deep Reinforcement Learning and Rate-Distortion Theory (VERSES INC) The Scaling-up of Purpose in Evolution (John Templeton Foundation) DIMENSIVE: Data-driven Inference of Models from Embodied Neural Systems (EU) Distributed neural processing of self-generated visual input (BBSRC) Christopher Buckley leads an active research group at Sussex, contributing to the development of next-generation AI systems grounded in biological principles. He has no listed students in the provided data, but his collaborative network is extensive. He is not part-time, not retired, and not a former staff member, indicating ongoing active engagement in research and teaching.

• Artificial Intelligence
• Machine Learning
• Human-Computer Interaction
• +16 more

Professor Damien Coyle is the Director of the Bath Institute for the Augmented Human at the University of Bath, where he leads cutting-edge research in artificial intelligence, machine learning, and human-computer interaction. His work is centered on brain-computer interfaces (BCI), neurotechnology, and cognitive computing, with applications in healthcare, rehabilitation, and intelligent systems. His research interests include Brain-Computer Interfaces , Spiking Neural Networks , Deep Learning , Emotion Recognition , Virtual and Extended Reality , and Neurofeedback Systems . He applies advanced AI techniques to decode neural signals, enabling real-time interaction between the brain and external devices. His interdisciplinary approach integrates neuroscience, computer science, and engineering. His recent publications from 2023–2024 reveal a strong focus on anomaly detection in industrial systems , EEG-fMRI data fusion for inner speech decoding , multi-level IT incident prediction using AI , and enhancing neurofeedback with immersive VR . These works demonstrate a trend toward multimodal data integration, real-time BCI applications, and AI-driven decision support in both clinical and technological domains. He has supervised numerous students and collaborators, many of whom are co-authors on his publications. His research is frequently published in top-tier IEEE and Springer conferences. He actively contributes to the advancement of neuroadaptive technologies and is involved in high-performance computing for BCI calibration. His leadership in organizing major conferences further underscores his influence in the field.

Simon LovellView profile

Professor

• Protein Structure
• Sequence-structure Relationships
• Evolutionary Biology
• +2 more

Simon Lovell is a Professor at the Division of Evolution, Infection and Genomics, University of Manchester. He obtained his BSc from the University of Bath (1988–1992) and PhD from the University of Bristol (1992–1995). His career includes academic and research roles at the University of Bath, Duke University, University of Cambridge, and University of Manchester. Dr. Lovell’s research focuses on computational analysis of protein structure, evolutionary constraints, and functional prediction. His work bridges structural biology, bioinformatics, and molecular evolution to understand how amino acid sequences determine protein function and how evolutionary pressures shape these relationships. He has contributed to methods for pathogenic variant interpretation and protein interaction modeling. Key Research Themes: Sequence-structure relationships in proteins Evolutionary restraints on protein structure Functional site prediction via structural analysis Applications in genetic disorder diagnostics Recent publications highlight trends in machine learning for cancer gene prediction, structural modeling of pathogenic variants, and evolutionary analysis of protein interactions. His collaborations span cardiovascular research, genomics, and rare disease studies.

• Computer Vision
• Image Analysis
• Ontology-based Information Modeling
• +4 more

Dr. Chris Town is an Affiliated Lecturer and Research Fellow in the Department of Computer Science & Technology at the University of Cambridge. He serves as a Fellow and Director of Studies in Computer Science at Wolfson College, where he also acts as a Tutor and coordinates the postgraduate mentoring scheme. Additionally, he holds a position as Bye Fellow and Director of Studies in Computer Science at Jesus College. His academic journey includes completing a PhD at Cambridge Computer Laboratory under Professor John Daugman OBE, with prior undergraduate studies at Trinity College, Cambridge. PhD in Computer Science, University of Cambridge (Computer Laboratory) Undergraduate degree with first class honours in Computer Science from the University of Cambridge (Trinity College) Dr. Town's research spans the intersection of computer vision, information retrieval, machine learning, and language processing. He pioneered the ontology-based approach to automated visual information processing alongside Dr. David Sinclair. His work demonstrates how ontologies representing task-specific attributes can narrow the semantic gap between human and computer interpretations of visual content. His research extends to pattern matching algorithms for biological sciences, including developing tools used in ecology and zoology such as Manta Matcher and Nature Pattern Match. His work has significant applications in image retrieval, automated visual surveillance, and visually mediated human-computer interaction. His publications reveal a strong focus on applying computer vision techniques to biological pattern recognition, particularly in marine wildlife identification, egg pattern analysis, and medical imaging. The articles show consistent development of methods for species identification, pattern analysis, and image recognition across diverse domains from deep-sea corals to medical diagnostics. BCS Distinguished Dissertation Award (2005) Trinity College Rouse Ball Scholarship Industrial Fellowship from the Royal Commission for the Exhibition of 1851 JRF at Wolfson College Runner-up prize for best cognitive vision paper (2003) Best Student Paper Award at ICPRS 2022 Dr. Town has supervised over 50 final-year undergraduate, Masters, and Diploma students for their projects and dissertations, as well as co-supervising PhD students. His supervision spans diverse topics including deep learning features, shell recognition, marine wildlife identification, and fossil recognition. He has developed relationships with organizations such as Imense Ltd (where he serves as Chief Technology Officer), the Foundation for the Protection of Marine Megafauna, and ECOCEAN Whale Shark Photo-identification Library. His research has been supported by fellowships from AT&T Labs Research, the Royal Commission for the Exhibition of 1851, and various scholarships from Trinity College. Dr. Town leads the PhD Mentoring Scheme at Wolfson College and has developed tools like Manta Matcher that are used by the academic community for marine wildlife identification. His work bridges academic research with practical applications, particularly in biological pattern recognition and medical imaging.

• Machine Learning
• Artificial Intelligence
• Sustainable Development Goals
• +5 more

Chris Watkins is a Professor in Computer Science at Royal Holloway, University of London. He is affiliated with the Department of Computer Science and actively contributes to the Bioinformatics Centre for Machine Learning, Digital Organisation and Society Research Centre, Centre for Intelligent Systems, and the Centre for Reliable Machine Learning. His research focuses on machine learning applications in sustainable development, audio classification, energy systems, and human-computer interaction. He has led/co-investigated major projects including 'Intelligence in Context' (2020-2021) and 'Impact of Smart Meter Roll-out' (2018-2021). His work spans interdisciplinary areas such as evolutionary algorithms, neural network architectures, and smart grid technologies. Recent research explores inclusive online discussion systems and hybrid load forecasting methods. Watkins has contributed to 57 peer-reviewed publications, with notable work in journals like Neurocomputing and Theoretical Population Biology. He has held continuous EPSRC peer review membership since 2010, demonstrating ongoing engagement in academic governance. Key projects include investigating data science in museums (AHRC funded) and studying female experiences in male-dominated learning environments. His research aligns with UN Sustainable Development Goals, particularly those addressing climate action and sustainable communities.

Anqi LiuView profile

Senior Lecturer

• Financial Mathematics
• Quantitative Finance
• Computational Finance
• +10 more

Dr Anqi Liu is a Senior Lecturer in Financial Mathematics at the School of Mathematics, Cardiff University. She specializes in market microstructure, trading behavior, time series models, Hawkes processes, and fractal activity time geometric Brownian motion (FATGBM) in finance, with a focus on FinTech applications including cryptocurrencies and blockchain technology. BSc in Mathematics and Applied Mathematics, Northwest University, China MSc and PhD in Financial Engineering, Stevens Institute of Technology, USA Her research integrates quantitative finance and computational finance methodologies to improve pricing and risk modeling frameworks for financial markets, emphasizing systemic perspectives. Recent work explores trading behavioral patterns in crypto markets and blockchain-based financial network reactions to systemic events. Key research trends include: Applications of Hawkes processes and entropy approaches to market dynamics Agent-based modeling of financial networks and behavioral patterns Machine learning integration for sentiment analysis and trading strategies Systemic risk evaluation in traditional and crypto markets Novel pricing models like FATGBM extensions Scientific contributions include: Stevens Innovation and Entrepreneurship Scholarship (4 years during PhD) She has published extensively in journals like Quantitative Finance , European Journal of Finance , and Entropy , with recent works analyzing SVB collapse contagion effects and cryptocurrency price bubbles using wavelet analysis. Her supervision areas include cryptocurrency market microstructure, Hawkes processes, and agent-based modeling in behavioral finance.

Jonathan ThompsonView profile

Professor

• Graph Theory
• Meta-heuristics
• Scheduling
• +6 more

Professor Jonathan Thompson serves as Head of School in the School of Mathematics at Cardiff University. He holds multiple administrative roles including Year Three Director of Studies, Chair of School Board, and has significant teaching responsibilities for both undergraduate and postgraduate students. His academic career spans over two decades with previous positions at Edinburgh University (Lecturer in Statistics and Operational Research, 1996-97) and Swansea University (Research Assistant, 1994-96). Dr. Thompson's research focuses on operational research with particular expertise in graph theoretic modelling, meta-heuristics (especially ant systems, genetic algorithms and simulated annealing), and various scheduling problems including examination scheduling, sports fixture scheduling, and manpower planning. His work bridges theoretical computer science with practical applications in healthcare, transportation, and logistics. He has established strong industry connections, having completed projects with WH Smiths, John Menzies, and the International Rugby Board. His research demonstrates consistent evolution from foundational work in graph coloring and ant colony optimization toward increasingly complex real-world applications in dynamic environments. Operational Research group member External funding from Office of National Statistics (2005-2006) Editorial Board member of International Journal of Operational Research Programme Committee member for major conferences (GECCO, PPSN, PATAT) Professor Thompson has successfully supervised numerous PhD students since 2000, with completed theses covering examination timetabling, nurse scheduling, vehicle routing, and other operational research problems. His supervision portfolio reflects the breadth of his research interests, from theoretical graph theory to practical healthcare and transportation applications. He has secured external funding for research projects and maintains active collaborations with both academic and industry partners.

• Artificial Intelligence
• Machine Learning
• Evolutionary Computation

Richard Preen is a Senior Research Fellow affiliated with the Faculty of Environment and Technology at the University of the West of England, Bristol. He specializes in Artificial Intelligence, Machine Learning, and Evolutionary Computation within the Department of Computer Science and Creative Technologies. Qualifications: Ph.D. in Artificial Intelligence His research focuses on AI-driven solutions and evolutionary algorithms. He is based at the Computer Science Research Centre and can be contacted via Richard2.Preen@uwe.ac.uk.

Carola-Bibiane SchönliebView profile

Professor

• Image Analysis
• Image Processing
• Inverse Problems
• +3 more

Carola-Bibiane Schönlieb is a Professor of Applied Mathematics and head of the Cambridge Image Analysis (CIA) group at the Department of Applied Mathematics and Theoretical Physics, University of Cambridge. She concurrently serves as co-director of the Cambridge Mathematics of Information in Healthcare (CMIH) Hub, leading interdisciplinary initiatives at the intersection of mathematics, healthcare, and data science. Her research centers on variational methods, partial differential equations, and machine learning for image analysis, processing, and inverse problems. She maintains active collaborations with clinicians, biologists, physicists, chemical engineers, plant scientists, artists, and art conservators, driving innovations in biomedical imaging, image sensing, and digital art restoration. This interdisciplinary approach bridges theoretical mathematics with real-world applications across healthcare and cultural heritage domains. Analysis of her recent publications reveals a dominant focus on deep learning applications for medical imaging challenges, particularly in cardiology, oncology, and neuroimaging. Her work consistently addresses inverse problems in reconstruction and segmentation while emphasizing robustness against artifacts, model efficiency, and integration of physical constraints. A clear trend emerges toward foundation models and transfer learning techniques specifically adapted for medical image analysis with limited annotated data. Prof. Schönlieb leads the Cambridge Image Analysis research group and co-directs the CMIH Hub, which unites mathematicians, computer scientists, and clinicians to translate advanced data science into clinical practice through collaborative healthcare innovation.

Jun LiuView profile

Professor

• Artificial Intelligence
• Trustworthy AI
• Explainable AI
• +4 more

Professor Jun Liu is a Professor of Artificial Intelligence and Director of the Artificial Intelligence Research Centre (AIRC) at the School of Computing, Ulster University. With over 270 publications and more than £18 million in research funding, he is a leading figure in artificial intelligence, particularly in trust and explainable AI systems and logic-based reasoning methods. Dr. Liu received his BSc and MSc degrees in Applied Mathematics, and PhD degree in Information Engineering from Southwest Jiaotong University, Chengdu, China, in 1993, 1996, and 1999, respectively. Prior to joining Ulster University, he held postdoctoral positions at The University of Manchester, UK (Feb. 2002 - Dec. 2004) and the Belgian Nuclear Research Centre (SCK*CEN) (Mar. 2000 - Feb. 2002). Professor Liu's research focuses on trust and explainable data-knowledge integrated AI decision models with applications in safety and risk analysis, policy decision making, security/disaster management, and healthcare; and logic and automated reasoning methods for intelligent systems, including resolution-based automated reasoning and lattice-valued logics for handling incomparability, inconsistency, and imprecision. His work spans theoretical foundations to practical applications in smart homes, healthcare, and industrial settings. His recent publications demonstrate a strong trend toward developing more trustworthy and explainable AI systems, with particular emphasis on belief rule-based approaches for handling uncertainty in decision-making. The research spans multiple domains including smart home activity recognition, medical imaging, food quality analysis, and environmental monitoring, showing the versatility and applicability of his methodologies. Ulster University best computer science paper award for 2016 IEEE Senior Member including IEEESMC and IEEECI Fellow of the UK Higher Education Academy Associate Editor of IEEE Transaction on Fuzzy Systems Current Chair of IEEE CIS Emergent Technologies Technical Committee As Director of the Artificial Intelligence Research Centre, Professor Liu has secured significant research funding as principal investigator and co-investigator. His current projects include "The use of Agentic AI in judicial decision-making" funded by EPSRC and "Adaptive Modeling Method for Deep Belief Rule Base" for smart home applications. He serves on editorial boards of multiple high-impact journals and organizes international conferences including the 23rd UK Workshop on Computational Intelligence. The Artificial Intelligence Research Centre under Professor Liu's leadership focuses on developing cutting-edge AI methodologies with practical applications. The center collaborates extensively with industry partners including BT through the BTIIC Phase 2 initiative and PwC through their Advanced Engineering and Research Centre, ensuring research has real-world impact across multiple sectors.

• Eye Tracking Technology
• TV Customer Experience
• Process Mining
• +5 more

Michael Milliken serves as a Researcher at Ulster University's School of Computing, Belfast campus (Room BB-02-010, 2-24 York Street). His primary affiliation is with the Faculty of Computing, Engineering and the Built Environment. His research spans eye tracking technology, TV customer experience optimization, and intrusion detection systems. Key methodologies include discrete-time Markov chains for modeling user behavior, process mining for IPTV analytics, and ensemble learning for cybersecurity. His work intersects computer science, human-computer interaction, and multimedia systems with practical applications in personalized television interfaces and network security. His publication trends reveal consistent focus on user experience analytics (2018-2023), with recent expansion into DDoS intrusion detection (2023). The research demonstrates progression from foundational eye-tracking studies toward AI-driven content personalization and cybersecurity applications. Milliken holds a PhD awarded in July 2023 and a Bachelor's degree from July 2013. His h-index is 7 with 2 citations according to Pure/Scopus metrics. Current research activity shows sustained output with publications in 2018, 2022, and 2023.

Haiying WangView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Complex Network Analysis
• +5 more

Dr. Haiying Wang is a Reader in computer science at Ulster University's School of Computing , and a full member of the Computer Science Research Institute . He holds a PhD in artificial intelligence and biomedicine (2004) and a Postgraduate Certificate in Teaching in Higher Education (2008). Active in AI, machine learning, and complex network analysis Applications in bioinformatics, healthcare informatics, and systems biology Grant holder for Innovate UK and EU-Horizon2020 projects Publisher of 120+ peer-reviewed works Research Focus : Large-scale data integration, activity recognition, gait analysis in smart environments, metagenomics data analysis, and network-based systems biology approaches. His work explores microbiome-host interactions, methane prediction in agriculture, and AI-assisted healthcare technologies. Scientific Awards : Ulster University Research Excellence Award - Business Category (2019) Collaborations : Organized European Research Conferences (2020-2021) and collaborated on projects analyzing rumen microbiomes, Alzheimer's disease, and wearable sensor systems.

Arthur G RichardsView profile

Professor

• Trajectory Optimization
• Unmanned Aerial Vehicles (UAVs)
• Model Predictive Control
• +4 more

Arthur G Richards serves as Professor of Robotics and Control within the Dynamics and Control department at the University of Bristol's School of Engineering Mathematics and Technology. His research specializes in trajectory optimization for aerospace applications, focusing on UAV autonomy, spacecraft rendezvous, and air traffic management through advanced optimization techniques. His educational foundation includes an M.Eng. from the University of Cambridge and S.M./Ph.D. degrees from MIT. Research interests center on solving complex aerospace challenges through: Non-convex optimization for obstacle avoidance in cluttered environments Robust model predictive control for real-time disturbance compensation Distributed optimization enabling large-scale vehicle cooperation Scalable algorithms for high-traffic scenarios with minimal fuel consumption Analysis of his 132 research outputs reveals evolving emphasis on reliability-aware UAV path planning, interpretable reinforcement learning for aircraft control, and swarm robotics with real-world validation. Recent work increasingly integrates machine learning with traditional control theory while addressing practical constraints like sensor noise and system failures. Professor Richards has supervised 22 research students and secured funding for 11 projects, including the active Aerial Robotics for Search and Rescue (2022-2026) and PORTAL (2022-2024) initiatives. His industry collaborations with Thales and focus on technology transfer demonstrate strong academic-industrial integration. He actively contributes to the Smart Networks for Sustainable Futures and Robotics research groups, developing frameworks for multi-robot systems that balance theoretical rigor with practical deployment requirements in conservation, inspection, and exploration scenarios.