Ioannis Stefanakos is a Research Associate at the Department of Computer Science, University of York, affiliated with the High Integrity Systems research group. His work focuses on formal verification of autonomous systems, safety-critical robotics, and software performance analysis. Current projects include assurance frameworks for drones, adaptive reinforcement learning in healthcare robotics, and probabilistic modeling of software performance. Recent research emphasizes interdisciplinary applications in UAV systems, medical decision support systems, and collaborative manufacturing robots. No academic awards are listed, though his work has been published in top-tier conferences. Advising and grants details are not explicitly stated, but his involvement in doctoral forum papers suggests potential supervision roles in software systems research.
Oana Lang is a Lecturer in Mathematics at Babeş-Bolyai University and a former STUOD Research Associate at Imperial College London. Her research focuses on stochastic analysis, particularly nonlinear stochastic partial differential equations (SPDEs) and their applications in fluid dynamics and data assimilation. She holds a PhD from Imperial College London (2020), with a thesis on stochastic transport equations and data assimilation. Affiliations: Academic Women in Mathematics, Mathematics of Planet Earth, Stochastic Analysis Research Group at Imperial Education: PhD in Mathematics, Imperial College London (2016–2020) MRes in Mathematics of Planet Earth, Imperial College London (2015–2016) MSc in Applied Mathematics, University of Bucharest (2013–2015) BSc in Mathematics, University of Bucharest (2010–2013) Her research interests emphasize SPDEs driven by transport noise, particularly in ocean and climate modeling. She has developed analytical frameworks for stochastic Euler equations, rotating shallow water models, and their applications in data assimilation. Key contributions include proving well-posedness for transport SPDEs and advancing calibration methods for stochastic fluid models. She organizes the Stochastic Analysis Seminar at Imperial College and the STUOD SPDEs Seminar. Recent work includes invited sessions on stochastic models in fluid dynamics at international conferences and editorial roles for Emergent Scientist . Awards: MRes degree with distinction (Imperial College London, 2016), multiple conference organization roles, and active grant-funded research in stochastic fluid dynamics. Labs/Teams: STUOD research group, Mathematics of Planet Earth Centre for Doctoral Training (MPE CDT), and collaborations with institutions like Reading University and the University of Aachen.
Clare Dixon is a Professor of Computer Science in the Department of Computer Science at the University of Manchester, where she leads the Autonomy and Verification research group. Previously, she held a Professorship at the University of Liverpool (2001–2020) and was a Senior Research Fellow at Manchester Metropolitan University (1995–2000). She also holds an honorary professorship at the University of Liverpool (2020–2023). Her research focuses on verification for robotics and autonomous systems, formal methods, temporal and modal logics, and theorem proving. Key applications include ensuring systems meet safety and reliability requirements through tools like model checkers and theorem provers. She collaborates with robotics engineers to apply formal verification techniques in dynamic environments and swarm systems. Research Interests: Verification of autonomous systems, formal specification, temporal logics, automated reasoning, and reliable robotics. Her work aligns with UN Sustainable Development Goals, particularly in advancing reliable technology and improving quality of life. Awards: Best Paper Award (2018) Invitation to Sister Conference Best Paper Track (2017) Springer Best Paper Award (2014) Projects: She is a Co-Investigator in major initiatives such as the Centre for Robotics and Artificial Intelligence , FAIR-SPACE Hub , and S4: Science of Sensor System Software , focusing on autonomous systems, space robotics, and secure sensor networks. Labs/Teams: Leads the Autonomy and Verification group at Manchester and contributes to interdisciplinary teams in robotics, formal methods, and AI ethics.
James Hopgood is a Professor of Statistical Signal Processing at the University of Edinburgh's School of Engineering, leading the Institute for Data, Imaging, and Communications. He holds roles as Dean of Quality and Enhancement for the College of Science and Engineering and Director of the EPSRC/MoD CDT in Sensing, Processing & AI for Defence and Security (SPADS). His research focuses on statistical signal processing applications in medical imaging, acoustic sensing, and multi-target tracking. Hopgood earned his M.A., M.Eng., and Ph.D. from the University of Cambridge, joining Edinburgh in 2004. He has developed algorithms for Bayesian signal processing, acoustic source localization, and medical imaging, with over 100 publications. As Editor-in-Chief of the IET Journal of Signal Processing since 2011, he contributes to academic leadership. Teaching includes courses on signal processing, probability, and sensor networks, emphasizing visualization and real-world applications. Current projects span adaptive optical imaging for medical diagnostics and probabilistic graphical models for multi-target tracking. His work bridges academia and industry, collaborating with companies like Leonardo and Agilent Technologies. He supervises numerous PhD students and leads multidisciplinary initiatives in defense, healthcare, and autonomous systems. **Education**: PhD in Statistical Signal Processing, University of Cambridge (2001) MEng in Electrical and Information Sciences, University of Cambridge (1997) MA, University of Cambridge (2000) **Research Interests**: His work spans model-based Bayesian methods, acoustic source localization, medical imaging (e.g., fluorescence lifetime imaging), and multi-target tracking. Recent projects include optimizing UAV trajectories in dynamic environments and developing algorithms for distributed sensor networks. **Grants & Collaborations**: Funded by EPSRC/MoD, GSK, and Leonardo, his projects address defense sensing, drug development, and clinical imaging. Collaborations include Firefinch (software/data science) and the UDRC Summer School. **Labs/Teams**: Leads the Acoustics and Audio Group and the SPADS CDT, fostering innovation in sensing and AI for defense/security applications.
Nelson Trujillo-Barreto is a Lecturer in Computational Neuroscience at the School of Health Sciences, University of Manchester since January 2023. With over 25 years of experience in brain dynamics analysis, he specializes in developing probabilistic and biophysical generative models of neuroimaging data. His academic journey includes a PhD in Physical Sciences from Havana University (2006) and prior work as Head of the Department for Brain Dynamics at the Cuban Neuroscience Centre (1995-2014). His research focuses on bridging the gap between recorded neuroimaging data and underlying neuronal activity through solving forward and inverse problems. Key areas include Brain Electromagnetic Tomography (BET), Bayesian models for estimating time-varying dynamical connectivity, and multimodal integration of EEG/MEG and fMRI data. His work has significant clinical applications in neurofeedback, brain-computer interfaces, and non-invasive brain stimulation for conditions like Neurofibromatosis Type 1 and Multiple Sclerosis. His recent publications demonstrate strong trends in applying computational neuroscience to neurological disorders, particularly focusing on working memory, brain connectivity, and therapeutic interventions. His research shows increasing emphasis on multimodal integration and clinical translation of advanced neuroimaging techniques. UoM PCHN Best Poster Award (2025) Dr. Trujillo-Barreto maintains active research collaborations with leading institutions including University College London, Cambridge University, McGill University, and various European neuroscience centers. His editorial roles include Associate Editor for Frontiers in Brain Imaging Methods and Frontiers in Computational Neuroimaging. He serves on prestigious review panels including the EPSRC Peer Review College and European Commission College of Experts.
Marco De Angelis is a Lecturer at the Centre for Intelligent Infrastructure within the Department of Civil and Environmental Engineering at the University of Strathclyde's Faculty of Engineering. His work focuses on computational methods for handling uncertainty in engineering systems, with applications in structural reliability and health monitoring. Education: PhD in Risk and Uncertainty (2015) from University of Liverpool's Institute for Risk and Uncertainty Master of Engineering (cum laude) in Civil and Environmental Engineering from University of Rome, Roma Tre Bachelor of Engineering (cum laude) in Civil and Environmental Engineering from University of Rome, Roma Tre Dr. De Angelis specializes in computing with imprecision, developing methods to propagate uncertainty through models using interval analysis, probability bounds, and other mathematical frameworks. His research enables rigorous inference with scarce empirical data and builds trust in simulation for structural reliability assessment. His work intersects civil engineering, computer science, and statistics, with particular emphasis on practical applications in infrastructure monitoring and risk assessment. His recent publications demonstrate a strong focus on high-dimensional uncertainty analysis, optimization under uncertainty, and verified computational methods for reliability engineering. The research shows increasing sophistication in handling complex uncertainty representations while maintaining computational tractability for real-world engineering problems. Scientific Awards: Best student paper (June 18, 2025) The NASA and DNV Challenge on Optimization under Uncertainty (June 17, 2025) Bronze poster award (July 27, 2021) Teaching and Learning Award (May 17, 2017) ISIPTA-IJAR Young Researcher Award (August 2015) Dr. De Angelis teaches structural engineering theory, computer programming, interval computation, probability theory, and machine learning to undergraduate students. He has developed teaching materials from scratch for advanced dynamics courses. He serves as Co-investigator on the REUN project (Reduction of Uncertainties in risk assessment of structures and infrastructures against Natural hazards) funded by the Royal Society of Edinburgh, running from April 2025 to March 2027. His professional activities include conference participation, journal peer review, and invited talks in his specialty areas. He is actively involved with the Centre for Intelligent Infrastructure, where he contributes to research on digital twins and computational methods for infrastructure monitoring and assessment.
Arkadiusz Wisniowski is a Professor of Social Statistics & Demography at the The University of Manchester , affiliated with the Cathie Marsh Institute for Social Research and the Institute for Data Science and AI . He serves on the Advisory Board of the Cathie Marsh Institute and is the Associate Editor of Demographic Research . His academic career spans leadership roles as Head of Department (2022-2025), Postgraduate Director, and Undergraduate Teaching Director. Education: PhD in Economics (Warsaw School of Economics) MSc in Economics (Warsaw School of Economics) MSc in Quantitative Methods (Warsaw School of Economics) Erasmus Programme in Econometrics (University of Groningen) Research Interests focus on statistical methods for migration forecasting , integrating traditional and novel data sources. He specializes in Bayesian hierarchical models , time series analysis, survey methodology, and tackling demographic challenges like ageing populations . His work addresses Global Inequalities and aligns with UN Sustainable Development Goals (SDGs) related to migration and population change. Recent Research Trends include: Bayesian techniques for bilateral migration flows (South America-Europe) Probabilistic multiregional population projections Data integration strategies for migration estimation Machine learning applications in social statistics Statistical foundations for demographic forecasting Advisory and Collaboration : Supervises 6 current PhD students and co-advises 4 graduates, including projects on India's migration patterns , meritocracy in China , and mortality forecasting in South America . Collaborates with institutions like the Max Planck Institute , Australian National University , and University of Shanghai . Labs and Teams : Led the Statistical Modelling Research Group (2019-2023) and contributes to the ESRC Centre for Population Change . His projects involve split-site Manchester-MPIDR funding and interdisciplinary teams across demography, data science, and social policy.
Dr. Stamatios Sotiropoulos is an Associate Professor at the Sir Peter Mansfield Imaging Centre , University of Nottingham , with prior affiliations as Principal Investigator and Postdoctoral Research Associate at the FMRIB Centre , University of Oxford . He leads the Computational Neuroimaging (CoNI) Laboratory , focusing on advanced MRI methodology for brain connectome mapping. PhD in Brain Image Analysis (University of Nottingham, 2010) MSc in Biomedical Engineering (University of Minnesota, 2005) BSc in Electronics & Computer Engineering (Technical University of Crete, 2003) His research spans diffusion MRI , tractography , and connectome modeling , with applications in brain aging , neurodegenerative diseases , and cross-species neuroanatomy . Recent work includes AI-driven QC , probabilistic inference , and cross-modal integration . Grants include major awards from the European Research Council (2020), Wellcome Trust , and EPSRC . The lab has developed widely-used tools like XTRACT , CUDIMOT , and NFacT within the FSL framework. 2021 Clarivate Highly Cited Researcher ISMRM 2022 Merit Awards Notable students include Shaun Warrington (PhD, 2021) and José-Pedro Manzano-Patrón (PhD, 2022). Collaborations span the Human Connectome Project , Developing Human Connectome Project , and Centre for Mesoscale Connectomics .
Dr Carl Scarth is a Researcher in the Department of Mechanical Engineering at the University of Bath. His work focuses on data-driven methods for composite aircraft structures, addressing uncertainties, manufacturing defects, and process features. Key research areas include uncertainty quantification, machine learning, composite design, and aeroelasticity. Education: MEng in Engineering Design with study in industry from the University of Bristol (2010), specialising in structural mechanics and nonlinear dynamics; PhD in Advanced Composites from ACCIS CDT (2017), sponsored by Embraer. Carl's research applies Bayesian methods and finite element analysis to optimize composite aerospace components. Notable projects include the EPSRC ADAPT and CerTest programme grants, targeting high-rate manufacturing defect reduction and experimental-numerical data fusion. His collaborations span Embraer, Swansea University, and EPFL.
Professor Adam Prugel-Bennett is a faculty member at the School of Electronics and Computer Science , University of Southampton. His research spans artificial intelligence, machine learning, and robotics, with a focus on 3D perception, explainable AI, and marine data analysis. He contributes to interdisciplinary projects including coastal morphological analysis and underwater robotics. Key research areas include: LiDAR-based 3D object detection Multilevel explainable artificial intelligence Georeferenced seafloor imaging Variational autoencoder applications Bathymetric mapping His recent work explores cross-domain adaptation, sensor fusion, and algorithmic stability. He supervises PhD students in computer science and engineering while collaborating with research groups like Vision, Learning and Control. Current projects involve EPSRC-funded initiatives on complex computational systems and AI-based knowledge exchange for coastal analysis.
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.
Ferdian Jovan is an Assistant Professor (Lecturer) at the University of Aberdeen, affiliated with the Aberdeen - South China Normal University Joint Institute within the School of Natural and Computing Sciences. He is also a visiting research fellow at the Faculty of Engineering, University of Bristol. PhD in Computer Science, University of Birmingham (2019) MSc in Computational Logic, Technische Universitaet Dresden (2014) BSc in Computer Science, Universitas Indonesia (2010) His research focuses on mobile robotics, multimodal learning, and time series analysis , with applications in service robotics, energy, healthcare, and extreme environments. He is particularly interested in AI systems that function efficiently in dynamic, resource-constrained settings. His expertise bridges statistical machine learning and real-world robotic deployment. His recent publications show a strong trend in applied AI for robotics and health , including work on digital biomarkers for Parkinson’s disease, battery health prediction, multirobot windfarm maintenance, and adaptive planning. These works have been presented at top venues like KDD, IAAI, and published in journals such as Autonomous Robots and Journal of Field Robotics. DAAD AInet Fellow for Human Centered AI (2023) Ferdian advises PhD students and is actively involved in research collaborations across the UK and internationally. He has held research positions at the University of Bristol, Royal Holloway University of London, and the University of Oxford. His work often involves interdisciplinary teams and real-world data, emphasizing robust and deployable AI solutions. He is associated with the Department of Computer Science at the Meston Building, University of Aberdeen, and leads research in intelligent systems for extreme and dynamic environments.
Dr. Iraklis Giannakis is a Lecturer at the University of Aberdeen's School of Geosciences. He holds a PhD from the University of Edinburgh (2015), with earlier degrees from Aristotle University of Thessaloniki. His research focuses on geophysics, machine learning, and GPR applications in landmine detection, planetary science, and forestry. Dr. Giannakis is a core contributor to the open-source gprMax software, used globally for FDTD simulations. He has published over 55 articles, with recent work emphasizing lunar and Martian radar data analysis, deep learning for subsurface imaging, and planetary surface characterization. Education: Bachelor's and Master's in Geophysics, Aristotle University of Thessaloniki (2009, 2011) PhD in Geophysics from the University of Edinburgh (2015), funded by DSTL and EPSRC Research Interests: Machine learning in geophysics, GPR for landmine detection, non-destructive testing, planetary subsurface exploration, and forestry applications. His work integrates computational geophysics with advanced signal processing and inversion techniques. Key Projects: gprMax: Open-source FDTD solver for GPR Lunar/Mars radar analysis (Chang’E-4, Yutu-2 missions) COST Action TU1208 collaborative research Industry partnerships (e.g., D-Box demining tools) Awards: Best Paper Award at the 15th International Conference on GPR (2015). Grants & Collaborations: Funded by Google Fiber, EPSRC, and international partnerships. Active in journals like Icarus and Geophysics . Labs/Teams: Part of the Planetary Geophysics group at Aberdeen, contributing to lunar and Martian surface studies using radar data.
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.
Xiao Li is a Lecturer (equivalent to Assistant Professor) in the Department of Computer Science within the School of Natural and Computing Sciences at the University of Aberdeen. Holding a PhD in Computer Science and an MSc in Artificial Intelligence, Dr. Li is actively engaged in research and teaching, with a focus on AI and machine learning applications. Research Interests: Artificial Intelligence and Machine Learning Natural Language Processing and Generation Time series analysis in domains like language, music, and finance Computational metaphor processing and text modeling Ultrasound image classification and health claim analysis Dr. Li's recent publications (2017–2025) demonstrate a consistent focus on enhancing AI models, particularly variational autoencoders and attention mechanisms, for natural language tasks and multimodal data. The research spans theoretical improvements in latent space manipulation and practical applications in health, dialogue systems, and metaphor cognition in AI vs. humans. Scientific Awards: No awards listed in the provided text. Advising and Grants: Dr. Li is currently accepting PhD students in Computing Science and encourages prospective candidates to reach out with research ideas. While no specific grants or funded projects are mentioned, the consistent publication record suggests active research engagement. There is no explicit mention of students supervised, but advising is implied through PhD recruitment. Labs and Teams: No specific lab or research team name is mentioned in the text. Collaborations are evident with researchers such as Mao, R., Cambria, E., Lin, C., and Van Deemter, K.
