Find Academic Supervisors

• Natural Language Processing
• Machine Learning
• Cognitive Science
• +2 more

Dr. Peter Lane is a Lecturer in the Department of Computer Science at the University of Hertfordshire, affiliated with the Centre for AI and Robotics Research. His academic background includes a PhD in Computer Science with expertise spanning machine learning and cognitive modeling. Research Focus: Dr. Lane's work integrates cognitive science with computational methods, emphasizing Natural Language Processing (especially parsing and sentiment analysis) and Cognitive Modeling (using architectures like CHREST). He develops genetic programming techniques to evolve cognitive models and explores applications in cloud computing optimization. Publication Trends: Recent articles focus on synthesizing cognitive models through evolutionary algorithms, optimizing cloud resource allocation, and advancing sentiment analysis methodologies. His interdisciplinary approach bridges computer science, psychology, and artificial intelligence. Projects and Collaboration: Key projects include: GEMS (Genetically Evolving Models in Science) Dynamic scheduling architectures for cloud computing CHREST-based cognitive models for board game interpretation Methodology Development: Dr. Lane pioneers rigorous testing frameworks for cognitive models and promotes best practices in computational scientific software development.

• Artificial Intelligence
• Information Theory
• Sensor Evolution
• +2 more

Daniel Polani is Professor of Artificial Intelligence at the University of Hertfordshire, leading research in adaptive systems and cognitive architectures. His work integrates information theory with robotics to model intelligent decision-making processes in biological and artificial systems. Core research themes: Information-theoretic foundations of cognition Embodied intelligence in robotics Intrinsic motivation systems Perception-action loop modeling Recent publications (2023-2025) explore robot learning with Lyapunov stability, decentralized traffic optimization, and information-theoretic analysis of cognitive systems. Research advances principled approaches to AI that mimic biological information processing.

Patricia A. VargasView profile

Associate Professor

• Robotics
• Evolutionary Robotics
• Swarm Robotics
• +7 more

Patricia A. Vargas is an Associate Professor/Reader in Computer Science and Robotics at the School of Mathematical & Computer Sciences, Heriot-Watt University, UK. She is the Founder Director of the Robotics Laboratory and Director of Ethics. Her research focuses on interdisciplinary robotics, including Evolutionary Robotics, Swarm Robotics, Biologically-Inspired Algorithms, Computational Neuroscience, and Neurorobotics. She holds IEEE Senior Member and Fellow of the Higher Education Academy titles. Education: PhD in Robotics, postdoc at the Centre for Computational Neuroscience and Robotics (University of Sussex, UK). She has contributed to over 66 research outputs since 2009, with recent work emphasizing neurorobotics, SLAM systems, and precision robotics. Her research aligns with UN Sustainable Development Goals, particularly in healthcare and industrial innovation. Research interests span from bio-inspired algorithms to human-robot interaction, with notable contributions in Parkinson’s disease modeling and swarm robotics. She co-founded the Edinburgh Centre for Robotics and is an executive member of the IEEE Ro-Man Standing Steering Committee. Awards include the 2017 Spirit of Heriot-Watt Award (Outward Looking). Her work bridges robotics with neuroscience, aiming to advance assistive technologies and ethical robotic systems.

Qinghua ZhouView profile

Researcher

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +11 more

Qinghua Zhou is a Researcher in the Department of Mathematics at King's College London , within the Faculty of Natural, Mathematical & Engineering Sciences . His work focuses on robust, stable, and trustworthy AI systems through theoretical and computational analysis of computer vision and large language models. Education: BSc in Applied Mathematics and Physics from the University of Sydney PhD in AI from the University of Leicester Research interests include: Adversarial and stealth attacks on AI models Model watermarking and locking without retraining High-dimensional low-sample-size data analysis Neuromorphic and reservoir computing Weight manipulation with theoretical guarantees His recent publications highlight trends in: 2024–2025 : AI safety via deterministic weight edits, vulnerabilities of LLMs, and theoretical frameworks for adversarial robustness. 2023 : Ensemble methods for medical data, intrinsic dimensionality, and neuromorphic feature space optimization. Collaborations include researchers such as Dr. Oliver Sutton and Prof. Ivan Tyukin. Projects involve developing high-performance software and interactive demos for AI verification.

Eric SilvermanView profile

Research Fellow

• Complex Systems Modeling
• Public Health
• Agent-Based Simulation
• +3 more

Dr. Eric Silverman is a Research Fellow at the MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, specializing in agent-based simulation approaches to complex public health challenges. Previously, he held roles at Teesside University and the University of Southampton, focusing on complexity science applications in social science. He earned a BA in Psychology from Pennsylvania State University and a PhD in Computing from the University of Leeds, followed by postdoctoral research at the University of Tokyo on robotics and simulation. His research interests center on agent-based modeling (ABM), including pandemic dynamics, social care provision, environmental policy, and complex systems in public health. Notable projects include modeling the Glasgow Low Emission Zone's impact and simulating socio-political processes behind events like the 2016 US election. Dr. Silverman has contributed to over 40 publications, including works in Nature Communications , Artificial Life , and PLoS ONE . He leads the PHASE network for population health simulation and has secured grants from the Medical Research Council and Economic and Social Research Council. His work bridges computational methods with real-world policy evaluation, emphasizing robust model development and interdisciplinary collaboration.

• Machine Learning
• Graph Neural Networks
• Optimization
• +3 more

Yingfang Yuan is a Research Associate at the School of Mathematical & Computer Sciences, Heriot-Watt University. Her work contributes to UN Sustainable Development Goals through computational research in AI fairness and socio-technical systems. Research Focus: She specializes in machine learning applications addressing societal challenges, including graph neural networks for molecular modeling, optimization techniques for topic modeling in circular economy, and fairness quantification in digital services. Her interdisciplinary approach bridges computer science with social equity and sustainability. Awards: Best Paper Presentation Award, ICOSP 2025 (for work on user modeling and GNNs) Excellent Oral Presentation Award at the 5th International AI Conference (2025) PRIME Award for Sustainable Futures (2024) for impact in sustainability research Datasets & Collaboration: Co-developed public datasets on circular economy attention metrics and ethnic minority engagement with digital services. Regularly collaborates with cross-disciplinary teams including Dr. Wei Pang and Dr. Lynne Baillie on projects involving socio-technical systems analysis.

Claudio ZitoView profile

Assistant Professor

• Cognitive Robotics
• Robot Manipulation
• Robot Learning
• +6 more

Dr. Claudio Zito is an Assistant Professor at Heriot-Watt University Dubai (HWUD) in the School of Mathematical and Computer Sciences. He serves as Director of the Lab of AI and Robotics Research (LAIRR) and leads the UAE Centre for Doctoral Training (CDT) in AI and Robotics. His research focuses on cognitive robotics, including robot manipulation, physical human-robot interaction (pHRI), and autonomous systems in hazardous environments. He has secured grants from the EU's FP7, H2020, and local bodies like Dubai RDI. Notable projects include the CogWaters initiative and the GBOT autonomous debris collection robot. Dr. Zito also chairs Heriot-Watt's Robotics and AI Global Research Institute (GRI) in Dubai and collaborates with Expo City Dubai on national robotics initiatives. Education and Career: PhD in AI and Robotics. Previously, Lead Scientist at Technology Innovation Institute (Abu Dhabi) and roles at the University of Birmingham, including Assistant Professor and Senior Research Fellow. Member of IEEE RAS, with over 20 peer-reviewed publications. Research Interests: Cognitive robotics, probabilistic robotics, reinforcement learning, and wearable robotics. Key projects include developing prostheses with super-human performance and teleoperation systems for hazardous tasks. Grants and Awards: Principal Investigator for Dubai RDI's CogWaters and Mubadala's GBOT project. Supported by Airbus, EPSRC, and the UK's National Centre of Nuclear Robotics (NCNR). Labs and Teams: Directs LAIRR, which integrates with the Global Research Institute (GRI) to develop solutions for hazardous environments and autonomous systems.

• Human Evolution
• Vertebrate Functional Morphology
• Imaging Techniques
• +3 more

Dr. Christopher Dunmore is a Lecturer in Animal Biology and Director of the Imaging Centre for Life Sciences at the University of Kent’s School of Anthropology and Conservation. His research focuses on human evolution, particularly reconstructing fossil hominin hand use during locomotion and tool-use emergence. He completed his PhD at the University of Kent in 2019, studying the functional morphology of Australopithecus sediba’s hand. His work integrates micro-CT scans, machine learning algorithms, and comparative analyses of extant and extinct vertebrates to interpret bone architecture and behavior. Education : PhD, University of Kent, 2019 (Paleobiology) Research Interests : Functional morphology of post-cranial skeletons Imaging techniques (micro-CT, 3D analysis) Trabecular bone architecture and its relation to mechanical loads Evolution of hominin locomotion and tool use Publications Trends : Recent work emphasizes computational methods for trabecular bone analysis, comparisons of hominid locomotor adaptations, and the application of machine learning to paleontological data. Key topics include metacarpal morphology, femur structure, and Neanderthal manipulation abilities. Professional Affiliations : Member of the American Association of Biological Anthropologists (AABA) and the European Society for the Study of Human Evolution (ESHE). Research Infrastructure : Leads the Imaging Centre for Life Sciences, advancing methodologies in bone analysis and 3D imaging. Collaborates extensively with global researchers on hominin fossils and experimental studies.

Keith SmithView profile

Lecturer

• Biomedical Network Analysis
• Neuroimaging Data Analysis
• Machine Learning in Neuroscience
• +4 more

Keith Smith is a Lecturer in the Department of Computer and Information Sciences at the University of Strathclyde, where he leads the Digital Health & Wellness Research Group (DHaWG). Previously, he held positions as a lecturer at Nottingham Trent University and as a UKRI Innovations Fellow in Medical Informatics at the University of Edinburgh. He earned his PhD in biomedical signal processing from the University of Edinburgh's IDCOM lab. His research focuses on computational tools for analyzing biomedical networks, including neuroimaging data from structural and functional brain networks, protein interaction networks, and metabolic networks. Key areas include hierarchical complexity of brain networks, machine learning applications for neuroimaging, and theoretical foundations of network geometry and complexity. He collaborates widely with clinical and academic partners to address challenges in neurological and mental health conditions like Alzheimer’s, depression, and schizophrenia. His work contributes to UN Sustainable Development Goals, particularly improving health and well-being through advanced data-driven methodologies. He currently oversees PhD projects exploring brain networks, deep learning for disease prediction, and evolutionary protein biology. Notable achievements include the UKRI fellowship and impactful interdisciplinary collaborations. His talks and presentations, such as on statistical complexity of brain networks, highlight his commitment to advancing theoretical and applied network science.

• Bioinformatics
• Genetics
• Molecular Biology
• +3 more

Dr Joe Parker is a Senior Research Fellow at the University of Southampton. He is affiliated with the National Biofilms Innovation Centre (NBIC) and the Centre for Democratic Futures. His research focuses on bioinformatics, evolutionary biology, and molecular diagnostics, with notable contributions to SARS-CoV-2 detection methods and the genetic analysis of bats and other organisms. Key research interests include: bioinformatics workflows, rapid species identification using nanopore sequencing, evolutionary adaptation to environmental stressors, and diagnostic technologies for infectious diseases. His work integrates computational methods with fieldwork applications, such as portable bioinformatics and precision agriculture frameworks. Dr Parker has supervised PhD student Callum Andrew Findlay in the field of Infection, Inflammation, and Immunity. His publications span disciplines from virology to ecology, emphasizing interdisciplinary approaches to biological challenges. He maintains an ORCID profile and is reachable via Joe.Parker@soton.ac.uk.

Cait NewportView profile

Researcher

• Animal Behavior
• Visual Ecology
• Cognitive Science
• +3 more

Dr. Cait Newport is a Researcher in the Department of Zoology at the University of Oxford, focusing on the visual ecology and cognitive behavior of fish. Her work explores how fish perceive and interact with their environments, particularly in navigating and recognizing objects under varying conditions. She is a Schmidt AI in Science Fellow, emphasizing computational approaches in her research. Her research investigates challenges fish face in object recognition during motion and navigation, including adaptations to vertical movement and environmental factors like turbidity. She has developed tools such as the Detect+ Track software for analyzing fish behavior. Her studies span coral reef fish, archerfish, and goldfish, addressing topics like decision-making under uncertainty, abstract concept learning, and the impact of visual pollution. Dr. Newport's work bridges zoology, computational biology, and cognitive science, contributing to understanding animal cognition and sensory adaptations. Her findings have implications for both basic science and applied contexts, such as conservation and technology.

Martin TrefzerView profile

Professor

• Bio-Inspired Hardware
• Neuromorphic Systems
• Evolutionary Computation
• +1 more

Martin Trefzer is Professor of Bio-inspired Systems and Technologies at University of York's School of Physics, Engineering and Technology. His research develops novel fault-tolerant architectures using bio-inspired approaches, evolutionary computation, and reconfigurable systems. He leads EPSRC projects including Edgy Organism and Nervous Systems, focusing on self-optimizing hardware and neuromorphic computing. Current work explores nanomagnetic reservoir computing, developmental graph automata, and evolutionary circuit optimization.

Simon BaleView profile

Senior Lecturer

• Radio Frequency Engineering
• Electromagnetic Compatibility (EMC)
• Atomic Clocks and Oscillators
• +3 more

Dr. Simon Bale is a Senior Lecturer and 4th year engineering project coordinator at the University of York’s School of Physics, Engineering & Technology. He holds a PhD in radio frequency electronics and microwave engineering (2012) from the same institution. His research focuses on oscillators/atomic clocks, electromagnetic compatibility (EMC), shielding, and bio-inspired optimization methods. He is a co-investigator on major projects such as the EPSRC-funded “Compact Quantum Clocks For Precise And Autonomous Position Navigation And Timing” and the EU’s “European Doctoral Network for Safe and Sustainable Electromagnetic Shielding Solutions for Mobility.” Teaching responsibilities include courses on electrical circuits, AC machines, power electronics, computer architectures, and embedded systems for FPGAs. His academic contributions span over 30 publications in top-tier journals and conferences, emphasizing practical applications in EMC testing, oscillator design, and evolutionary computation. He actively collaborates with industry partners like QinetiQ and Keysight, translating research into real-world solutions. Awards include Fellow of the Higher Education Academy (FHEA). His work bridges theoretical innovation and industrial needs, with a focus on optimizing electronic systems through multi-objective approaches. Ongoing projects explore ultra-low phase noise oscillators, shielding effectiveness measurement methodologies, and AI-driven circuit design optimization.

• Bayesian Search and Optimization
• Evolutionary Algorithms
• Multi-objective Optimization
• +4 more

Dr Alma Rahat is an Associate Professor of Data Science at Swansea University, affiliated with the School of Mathematics and Computer Science. He specializes in Bayesian search and optimization, evolutionary algorithms, and multi-objective optimization. His work focuses on solving computationally expensive problems with applications in engineering, healthcare, and education. Education: BEng (Hons) in Electronic Engineering from the University of Southampton (UK), PhD in Computer Science from the University of Exeter (UK), and a Postgraduate Certificate in Teaching in Higher Education from Swansea University. He is a Fellow of the Higher Education Academy (FHEA). Research Interests: Dr Rahat’s expertise includes data-driven evolutionary optimization, surrogate-assisted methods, and active learning. He has contributed to pandemic response modeling for the Welsh Government and the UK Health Security Agency, leveraging machine learning and parameter optimization. His work on healthcare decision-making systems and educational assessment tools demonstrates a commitment to interdisciplinary applications of optimization. Key Contributions: He leads the Surrogate-Assisted Evolutionary Optimization (SAEOpt) workshop at GECCO and is a member of the IEEE Computational Intelligence Society Task Force on Data-Driven Optimization. His research bridges academic theory and industry needs, with patents and publications in top journals and conferences like IEEE Transactions and ACM. Grants: £750k from Welsh Government (Co-PI/Co-I), £230k EPSRC grant (EP/W01226X/1 as PI). Awards: Best Paper in Real-World Applications Track at GECCO, Patent for industrial fluid separation technology. Supervision: Currently guiding PhD students across AI, healthcare, education, and environmental modeling. His supervision emphasizes Bayesian methods and human-in-the-loop systems. Lab/Teams: Active in Swansea’s Computational Foundry, collaborating on projects like beach change forecasting and clinical decision support systems.

• Machine Learning
• Data Analysis
• Consumer Behavior
• +3 more

Yijing Li holds the position of Researcher in the Marketing Operations and Systems Department at Northumbria University. They earned a Doctor of Philosophy (PhD) from Northumbria University, focusing on interdisciplinary research at the intersection of computational methods and marketing analytics. Research interests include machine learning applications for consumer behavior analysis, data-driven decision-making in marketing, and optimization techniques in computational intelligence. Their work bridges artificial intelligence with practical business challenges, particularly leveraging advanced algorithms for feature selection and predictive modeling. Recent publications (2025) highlight contributions to feature selection via coevolutionary methods and consumer preference modeling using MulVAEK, demonstrating expertise in both algorithmic innovation and applied marketing analytics. These studies appear in journals like Swarm and Evolutionary Computation and IEEE Computational Intelligence Magazine . No scientific awards or grants are explicitly listed, though their research outputs indicate active participation in academic publishing. No advisees or lab affiliations are mentioned in the provided data.