Feng Yu is a Senior Lecturer in Statistics at the School of Mathematics, University of Bristol, focusing on probability and statistical methods. Research interests include Bayesian inference, statistical hypothesis testing, machine learning applications in medical research, and interdisciplinary work with biomedical engineering. Key contributions involve developing methods for multiple comparison procedures, dose-finding trials, and 3D printing parameter optimization using AI. Recent publications highlight trends in integrating statistical rigor with biomedical innovation, spanning clinical trials, dose-finding studies, and additive manufacturing for bone scaffolds and implants. Collaborations include work on hybrid systems for tissue regeneration. Projects include "Girsanov Transformation and the Rate of Adaptation" (2011-2013), exploring probabilistic models in evolutionary biology. Affiliations are centered on the University of Bristol, with no scientific awards mentioned.
Dr Daniel Herring is a Research Fellow in Industrial Mathematics at the School of Mathematics, University of Birmingham, working within Professor Fabian Spill's research group. His position focuses on applied mathematical research bridging theoretical computation and real-world industrial applications. His academic qualifications include: MSci in Natural Sciences from the University of Exeter (2017), with thesis work on optimisation methods for Alzheimer's EEG data modeling PhD in Computer Science from the University of Birmingham (2023), completed under the Priestley Scholarship Award with extended research at the University of Melbourne Dr Herring specializes in dynamic multi-objective optimisation for both continuous and combinatorial problems, advancing evolutionary computation frameworks. His work integrates evolutionary machine learning with novel classification algorithms, targeting applications in healthcare diagnostics, energy infrastructure optimization, and social network dynamics. Current projects address building occupancy optimization and dissociation analysis in adolescent populations, while upcoming research explores clean-air turbulence prediction and information diffusion in graph-based social networks. Scientific recognition includes: Priestley Scholarship Award for doctoral research excellence He actively contributes to the international computer science community through presentations at premier conferences including GECCO and CEC. Dr Herring's research group collaborates on cross-disciplinary industrial mathematics challenges with strong emphasis on translational impact in public health and infrastructure systems.
Dr. Baoru Huang is an academic staff member primarily involved in teaching. He serves as the Module Co-ordinator for Computational Intelligence (Module Code: COMP575) in the 2024-25 academic year. His research interests align with computational intelligence, encompassing areas such as artificial intelligence, machine learning, neural networks, evolutionary algorithms, and optimization techniques.
Simone Severini is a Professor of Physics of Information at the University College London , affiliated with the Department of Computer Science . He is a Royal Society University Research Fellow and contributes to multidisciplinary groups including Intelligent Systems , UCL CS Quantum , UCL Quantum Science and Technology Institute , and CoMPLEX . Research Interests: His work bridges Quantum computing Machine learning Graph theory Quantum information theory Computational biology with a focus on quantum algorithms, classical simulation of quantum systems, and mathematical frameworks for physical correlations. Scientific Contributions: Recent publications span quantum state learning, non-Markovian dynamics, adversarial quantum learning, and graph isomorphism. His projects include Quantum Computing, Information, and Algebras of Operators and the Distributed Information initiative . Awards: Royal Society University Research Fellowship Best Paper Award at FCT2017
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.
Professor John McCall is a distinguished academic and researcher at Robert Gordon University's School of Computing, Engineering & Technology, where he previously served as Head of School. He currently serves as Director of the National Subsea Centre, leading initiatives to accelerate energy transition through smart technologies applied to industrial and environmental challenges in subsea and related marine sectors. With over 25 years of research experience in nature-inspired computing and artificial intelligence, Professor McCall has established himself as a leading expert in optimization algorithms and explainable AI. Professor McCall's research interests span data science, artificial intelligence, nature-inspired computing, and optimization, with significant applications in energy transition and subsea technologies. His work bridges theoretical foundations with practical implementations, having founded two spinout companies that deliver real-world optimization solutions to industry. He leads both the Complex Optimisation Research Group and the Computational Intelligence Research Group, where his team explores cutting-edge approaches to solving complex computational problems. Analysis of Professor McCall's recent publication record (2023-2025) reveals a strong focus on explainable AI, particularly in the context of evolutionary computation and metaheuristics. His research demonstrates an increasing emphasis on practical applications in energy systems, transportation, and subsea technologies, reflecting his commitment to addressing real-world challenges related to climate change and industrial transformation. The interdisciplinary nature of his work is evident in publications spanning computer science, operations research, renewable energy, and transportation planning. Lead of the Computational Intelligence Research Group ResearcherID: G-1423-2011 Scopus Author ID: 36797474900 ORCID: https://orcid.org/0000-0003-1738-7056 Professor McCall is actively involved in mentoring the next generation of researchers, currently supervising multiple PhD students across diverse topics including explainability of non-deterministic solvers, optimization of electrical machines, and computational intelligence applications in hydrocarbon systems. His research is supported by numerous grants from industry and government sources, with projects totaling millions of pounds focused on solving challenges in energy transition and smart technologies. At the National Subsea Centre, Professor McCall leads a multidisciplinary team working on digital twin technologies, subsea AI applications, and data-driven solutions for the energy sector. His work emphasizes collaboration between academia and industry to develop transformative solutions that address both current challenges and future opportunities in the subsea domain.
Dr. Lee Christie is a Research Fellow at Robert Gordon University's School of Computing, Engineering & Technology, where he conducts research in optimization and artificial intelligence. He is affiliated with the Complex Optimisation Research Group and maintains connections with the National Subsea Centre through his research on net-zero operations. His educational background includes: BSc (Hons) in Computer Science from Robert Gordon University (2003-2007) MSc in Information Engineering (Distinction) from Robert Gordon University (2009-2011, part-time) PhD in Computational Intelligence from Robert Gordon University (2011-2016) Dr. Christie's research primarily focuses on combinatorial optimization, structure learning, and blockchain technologies. He investigates how to make non-deterministic solvers more transparent through trajectory mining and feature extraction. His work bridges theoretical optimization techniques with practical applications in transportation systems (particularly connected autonomous vehicles), renewable energy (wind farm optimization), and supply chain management. He has published extensively on explainable metaheuristics, with a growing emphasis on making optimization algorithms interpretable for end-users while maintaining effectiveness. His recent publications (2021-2025) demonstrate a clear research trajectory toward explainable AI for optimization algorithms, with applications spanning transportation systems, renewable energy infrastructure, and complex supply chains. These works showcase his ability to translate theoretical advances into practical solutions for real-world problems with societal impact. Dr. Christie has secured research funding for projects including the A.R.T. Forum NSR (2019-2022), which developed implementation roadmaps for automated road transport in the North Sea Region. He teaches programming for business analytics courses and actively contributes to the Aberdeen Python User Group as a steering committee member. His academic service includes supervision of PhD students, with Dr. Martin Fyvie recently completing a dissertation on 'Explainability of Non-Deterministic Solvers' under his guidance as second supervisor. Dr. Christie maintains active collaborations with researchers including John McCall, A.-C. Zăvoianu, and A.E.I. Brownlee, resulting in consistent publication output across reputable venues in evolutionary computation and artificial intelligence.
Dr. Ciprian Zavoianu is an academic researcher at Robert Gordon University (RGU) in the School of Computing, Engineering & Technology. He leads the Net Zero Operations research programme at the National Subsea Centre and is affiliated with the Complex Optimisation Research Group. His work focuses on applying artificial intelligence, particularly evolutionary computation algorithms, to solve complex real-world optimization problems with practical engineering applications. Dr. Zavoianu earned his academic qualifications from West University of Timisoara, Romania (BSc and MSc in Computer Science) and Johannes Kepler University Linz, Austria (PhD in Computer Science, 2015). His doctoral research focused on enhancing multi-objective evolutionary algorithms for computationally-intensive optimization problems. His primary research interests include: Evolutionary Computation Multi-Objective Optimization Data Mining & Machine Learning (particularly for surrogate modeling) Timetabling and Rostering Parallel/Distributed Computing Dr. Zavoianu's recent publications (2021-2025) demonstrate a strong focus on applying optimization techniques to transportation systems, electrical machine design, and sustainable energy solutions. His work consistently addresses the challenge of computationally expensive optimization through innovative surrogate modeling approaches, enabling practical applications of evolutionary algorithms to real-world engineering problems. He has secured multiple research grants including 'Data For Net Zero' and 'Ferry Passenger and Freight Modelling for Shetland,' demonstrating the practical relevance and industry applicability of his research. Dr. Zavoianu actively supervises five PhD/EngD students across diverse topics including predictive analytics for subsea installations, optimization of electrical machines, and operations optimization for harbor operations. His supervision approach emphasizes bridging theoretical algorithm development with practical implementation in engineering contexts. His laboratory work is centered around the National Subsea Centre, where he leads the Net Zero Operations research programme, focusing on sustainable solutions for the energy transition through advanced computational methods.
Claudia Wascher is Professor of Behavioural Biology and Deputy Head of School for Research, Innovation and Income Generation at Anglia Ruskin University's School of Life Sciences. She joined the institution in September 2015 and is an active member of the Behavioural Ecology Research Group. Her work spans both fundamental research in animal behavior and practical applications in conservation and welfare. Dr. Wascher completed her PhD in Zoology at the University of Vienna, focusing on social modulation of heart rate in greylag geese at the Konrad-Lorenz research station in Austria. She holds an MSc in Biology and Environmental Science from the University of Graz, Austria. Her international research experience includes postdoctoral positions at CNRS Strasbourg, NTNU Trondheim, Max Planck Institute for Ornithology Seewiesen, and the University of Valladolid. Her research centers on the evolution of social behavior in animals, particularly examining the physiological and cognitive mechanisms that underpin group living. She specializes in studying corvids (crows, ravens) and greylag geese, with particular interest in social cognition, vocal communication, and the physiological costs and benefits of social interactions. Her innovative work combines field observations with physiological measurements to understand how animals navigate complex social environments. She has made significant contributions to understanding how social factors affect physiological stress responses and parasite burden, with findings relevant to both evolutionary biology and conservation. Her publication record shows a consistent focus on social cognition in corvids, vocal communication patterns, and the physiological correlates of social behavior. Recent work has expanded into anthropogenic impacts on wildlife, equity and diversity research in academia, and methodological approaches in bioacoustics. Her interdisciplinary approach bridges behavioral ecology, physiology, and conservation science. L'Oréal Austria Women in Science Fellowship for Young Female Scientists Editorial board member for multiple journals including Animal Cognition (2023-present), Animal Behaviour (2018-2020), and others Dr. Wascher has successfully supervised multiple PhD students working on topics ranging from vocal communication in corvids to social bonds in horses and the 'leaky pipeline' in STEM fields. She has secured substantial research funding from diverse sources including Wild Animals Initiative, Anglia Ruskin University, ASAB, and international funding bodies. Her commitment to equity, diversity, and inclusion is evident through her leadership of the Athena SWAN self-assessment team (2019-2024), which successfully secured a Silver award for the Faculty of Science and Engineering in February 2024. She maintains active research collaborations internationally and contributes to scientific outreach through media appearances, The Conversation articles, and conference presentations. Her work on animal cognition has received significant media attention, including features in BBC, Scientific American, and The Guardian.
Paul Udoh serves as a Lecturer in Project Management at Aberdeen Business School, Robert Gordon University (RGU). With industry experience spanning healthcare and law sectors, he applies pragmatic project management expertise to translate business requirements into actionable plans through cross-functional team leadership and strategic risk mitigation. His academic credentials include: LLM MSc MAPM (Member of the Association for Project Management) Research centers on Risk Management and Change Management within project frameworks, with emerging focus on Artificial Intelligence integration in construction. His work investigates how AI-driven solutions enhance efficiency, reduce costs, and optimize decision-making in complex project environments through advanced predictive modeling and adaptive systems. His 2025 literature review analyzes AI's evolutionary trajectory in construction project management, identifying critical trends including machine learning for risk forecasting, computer vision for site monitoring, and natural language processing for document analysis. The research highlights growing industry adoption of digital twins and blockchain for transparency, while forecasting increased AI-human collaboration in future project ecosystems. No scientific awards or honors were documented in available materials. Professional activities include teaching Project Management Fundamentals (BSM084) and Personal/Professional Skills development (BSM260), though specific student supervision or grant funding details remain unreported in current sources. He contributes to RGU's Project Management Research Group, collaborating on industry-focused initiatives that bridge academic theory with practical construction management challenges through interdisciplinary partnerships.
