جست‌وجوی استاد و پژوهشگر

Nguyen Dangمشاهده پروفایل

مدرس

• combinatorial optimisation
• algorithms
• machine learning
• +۵ مورد دیگر

Nguyen Dang is a Lecturer at the School of Computer Science, University of St Andrews, actively supervising PhD students and teaching AI-related modules including Artificial Intelligence (CS3105), Artificial Intelligence Practice (CS5011), Machine Learning (CS5014), and Uncertainty in Artificial Intelligence (CS5016). He leads the Centre for Interdisciplinary Research in Computational Algebra and maintains an active research profile with numerous publications in top conferences. University of St Andrews, School of Computer Science Lecturer (equivalent to assistant professor) Supervising PhD students including Tai Nguyen Teaching multiple AI and Machine Learning courses Dr. Dang's research focuses on the intersection of machine learning and optimization, particularly automated algorithm configuration and design. His work centers on leveraging machine learning techniques to automate the development of optimization algorithms, with special emphasis on deep reinforcement learning for Dynamic Algorithm Configuration and integrating machine learning into constraint programming. His research has significant applications across various domains, especially in automated constraint modeling. The publications reflect strong activity in combinatorial optimization, algorithm selection, and benchmark instance generation. His recent publications demonstrate consistent output in top venues including Artificial Intelligence Journal, GECCO, FOGA, and CP conferences, with notable achievements including Best Paper Awards at GECCO'2025 and GECCO'2022. The research spans theoretical foundations of parameter control, practical applications in constraint programming, and innovative approaches to algorithm configuration. Best paper award at GECCO'2025 Best paper award at GECCO'2022 Nomination for best paper award at FOGA'2023 Best paper award at GECCO'2017 Dr. Dang holds a Leverhulme Early Career Fellowship (2020-2023) worth £90,000 for his project on constraint-based automated generation of synthetic benchmark instances. He has secured additional funding including EPSRC High Performance Computing grants totaling over 2.2 million CPU hours and a COST Action grant. His research group actively develops tools and frameworks for automated algorithm configuration and benchmark instance generation, with several open-source datasets available on GitHub. He is involved with multiple research groups including the Centre for Interdisciplinary Research in Computational Algebra and collaborates extensively with researchers at University of St Andrews and internationally, including at Université de Paris I Panthéon-Sorbonne where he conducted visiting research.

• Robotics
• Machine Learning
• Reinforcement Learning
• +۳ مورد دیگر

Pawel Ladosz is a Lecturer in Engineering Systems for Robotics at the Department of Mechanical and Aerospace Engineering, The University of Manchester. His research focuses on applying machine learning and computer vision to mobile robots, particularly in extreme environments such as total darkness or cluttered spaces. He is actively involved in developing autonomous navigation systems, wireless signal mapping, and high-level decision-making for robotic swarms. He teaches courses including Robotic Systems Design Project and Autonomous Mobile Robots. Education: PhD in Establishing and Optimising Unmanned Airborne Relay Networks (Loughborough University, 2014–2019) MEng in Aerospace Engineering (The University of Manchester, 2010–2014) Research Interests: Ladosz’s work emphasizes reinforcement learning for robotics, vision-based autonomous systems, and exploration in challenging environments. His projects often intersect with UN Sustainable Development Goals, contributing to innovations in robotic autonomy and sensor networks. Awards: He received the 2nd Autonomous Flying Technology Competition award in 2021, recognizing his contributions to autonomous flight systems. His research has also led to the establishment of the Centre for Robotic Autonomy in Demanding and Long-Lasting Environments (CRADLE), fostering cross-disciplinary collaborations. Grants & Projects: As Principal Investigator in the Aerospace Engineering initiative (2010–2035), he explores UAV communication networks and trajectory planning. His work addresses urban environment challenges, including relay positioning and signal prediction. Labs/Teams: Ladosz contributes to CRADLE, advancing robotic autonomy in extreme scenarios. His lab focuses on integrating AI and robotics for real-world applications.

• Cybersecurity
• Machine Learning
• Data Analytics
• +۵ مورد دیگر

Dr Zhiyuan (Thomas) Tan is an Associate Professor at Edinburgh Napier University’s School of Computing, Engineering and the Built Environment . He is internationally recognised for his cybersecurity research and has been listed among Stanford University’s Top 2% Scientists for 2021–2023. Education BEng (2005) with high distinction – North-eastern University, China MEng (2008) – Beijing University of Technology, China PhD in Computer Systems (2014) – University of Technology Sydney, Australia Research Interests Dr Tan’s research integrates cybersecurity with machine learning and data analytics. His core areas include: Intrusion detection and defence of critical service systems Adversarial machine learning for malware and anomaly detection Virtualisation security through non-parametric behaviour modelling IoT and vehicular network security—cloud/edge/cloudlet frameworks Privacy-preserving AI and federated machine unlearning Smart-city digital forensics and cyber-physical system resilience Research Output Trends His recent articles (2020–2025) reveal a strong focus on federated learning, edge & mobile computing, UAV coordination, and AI-driven security. Topics span advanced steganography, metamorphic malware, graph injection attacks, and trustworthiness in vehicular platoons, all anchored in real-world IoT and transportation applications. Awards & Distinctions Stanford University Top 2% Scientists List (2021, 2022, 2023) National Research Award 2017 – Research Council of the Sultanate of Oman Best Paper Awards (three instances) Kaspersky Lab Student Cyber Security Conference – Finalist Award SICSA Supervisor of the Year 2019 – Honourable Mention Grants & Leadership Dr Tan has attracted over £200k in external funding including: Carnegie Trust (£73,564) – Federated Machine Unlearning ENU Development Trust (£29,998) – Machine Unlearning Royal Society (£12,000) – VANET Security & Privacy SICSA & other awards for MemoryCrypt, AI Secrets, behaviour biometrics, and visiting-fellow schemes. Supervision & Mentoring Since 2013 he has supervised or co-supervised 16 doctoral candidates and several master’s students; six PhDs have successfully graduated. Roles range from Director of Studies to additional supervisor across diverse topics from malware evolution to VR olfactory interfaces. Research Groups & Collaboration He is affiliated with the Centre for Artificial Intelligence and Robotics , the Centre for Distributed Computing, Networking and Security , and the Centre for Cybersecurity, IoT and Cyber-physical Systems at ENU, fostering interdisciplinary collaboration with national and international partners.

Cormac Lucasمشاهده پروفایل

مدرس ارشد

• Mathematical Optimisation
• Stochastic Optimisation
• Asset and Liability Management (ALM)
• +۵ مورد دیگر

Dr. Cormac Lucas is a Senior Lecturer in the Department of Mathematics at Brunel University London, affiliated with the College of Engineering, Design and Physical Sciences. His work bridges mathematical optimization with practical applications in finance and operations management. Lucas specializes in Mathematical Optimisation Stochastic Optimisation Asset and Liability Management (ALM) Risk Analytics Portfolio Optimization Supply Chain Planning Under Uncertainty His research combines theoretical advancements with industrial projects, such as US Coast Guard Cutter Scheduling, Insight Investment's ALM, and Unilever's Natural Oil Buying Policy. Recent publications (2013–2024) highlight his focus on Portfolio Rebalancing with Transaction Costs Scenario Generation for Stochastic Programming Heuristic Algorithms for Cardinality Constraints Queuing Systems with Standby Servers Robust Supply Chain Planning Financial Derivative Modeling These works utilize methods like Variable Neighbourhood Search, Differential Evolution, and Lagrangian Relaxation. Email: cormac.lucas@brunel.ac.uk

• Multi-objective optimisation
• Evolutionary computation
• Explainable artificial intelligence
• +۴ مورد دیگر

Dr David Walker is a Senior Lecturer in Computer Science at the University of Exeter and a member of the Institute for Data Science and Artificial Intelligence . He also contributes to the Environmental Intelligence @Exeter research network. Education: PhD in Computer Science, University of Exeter (2008–2013) BSc (Hons) in Computer Science, University of Exeter (2004–2007) Research Interests Dr Walker’s work sits at the intersection of multi-objective optimisation , evolutionary computation , explainable AI and hyper-heuristics . He develops algorithms and visual analytics that help engineers and scientists understand complex optimisation landscapes, with recent emphasis on renewable-energy planning (especially offshore wind farms) and trustworthy AI systems. Publication Trends Between 2022 and 2025 he produced a prolific stream of articles on explainable optimisation , many-objective wind-farm design and visual analytics for evolutionary algorithms . These works combine rigorous algorithmic innovation with real-world case studies, demonstrating a clear trajectory toward transparent, human-centred AI for engineering decision-making. Scientific Awards No specific awards or fellowships are mentioned in the provided material. Advising & Funding No explicit list of PhD students, post-docs or grant awards is supplied. Laboratory & Teams Dr Walker is affiliated with the Institute for Data Science and Artificial Intelligence and the Environmental Intelligence @Exeter network, indicating collaborative, interdisciplinary research environments.

• Sustainable Energy
• Energy Efficiency Technologies
• Renewable Energy Systems
• +۳ مورد دیگر

Dr. Xiaoli Ma is a Senior Research Fellow at the University of Hull, affiliated with the Energy and Environment Institute and the Centre for Sustainable Energy Technologies under the Faculty of Science and Engineering. Her research focuses on sustainable building services, renewable/sustainable energy systems, energy efficiency technologies, and instrumentation technologies. She has secured over £2.7 million in research funding from bodies such as the EU, EPSRC, Innovate UK, and the National Science and Technology Committee of China, leading or co-leading 26 projects. Her research interests include innovative cooling systems for data centers (e.g., super-performance dew point cooling achieving 90% energy savings), solar-driven energy systems, waste heat recovery, and thermoelectric technologies. Notable projects include the development of a novel Loop-Heat-Pipe-based data center cooling system funded by the EU FP7 and a solar façade hot water heating system supported by the EU FP7 Programme. Dr. Ma has published over 90 journal articles, two books, and three book chapters, with recent works focusing on machine learning-driven energy optimization, solar cooking systems with energy storage, and advanced heat pump technologies. She holds two patents and actively supervises PhD students in renewable energy, energy efficiency, and built environment applications. Her lab and team are part of the Energy and Environment Institute, collaborating on projects like the pioneering near-zero-carbon air conditioning system using atmospheric latent heat and natural light energy (EPSRC-funded). Key grants include a £814k EPSRC project and a £692k IEEA initiative for data center cooling advancements.

Dirk Sudholtمشاهده پروفایل

استاد

• Randomised Algorithms
• Algorithmic Analysis
• Combinatorial Optimisation
• +۵ مورد دیگر

Dr. Dirk Sudholt is a Full Professor at the University of Passau and a Visiting Professor at the University of Sheffield. He holds a Ph.D. from Technische Universität Dortmund and has held postdoctoral positions at the International Computer Science Institute (ICSI) in Berkeley and the University of Birmingham. His research focuses on randomized algorithms, algorithmic analysis, and combinatorial optimization, with expertise in the theoretical analysis of bio-inspired search heuristics like evolutionary algorithms and ant colony optimization. His work emphasizes rigorous runtime analysis to understand algorithmic performance and design principles. Education: PhD in Computer Science, Technische Universität Dortmund (2008) Diploma in Computer Science, Technische Universität Dortmund (2004) Research Interests: Runtime analysis of evolutionary algorithms Algorithmic design for multimodal optimization Noise robustness in metaheuristics Parallel and distributed evolutionary computation Grants: SAGE: Speed of Adaptation in Population Genetics and Evolutionary Computation (EU FP7, 2014–2016) Teaching: University of Passau: Courses on algorithms, evolutionary computation, and randomized algorithms

• Evolutionary Swarm Robotics
• Optimisation and Learning
• Artificial Intelligence
• +۱ مورد دیگر

Peter Ross is a Researcher at the Edinburgh Napier University , affiliated with the School of Computing Engineering and the Built Environment and the Centre for Algorithms, Visualisation and Evolving Systems . He collaborates with academics like Emma Hart, Alistair Lawson, and Andrew Webb on projects involving evolutionary swarm robotics , artificial immune systems , and optimisation algorithms . His research focuses on applying bio-inspired computing to robotics, including work on perceptual aliasing , adaptive scheduling , and swarm intelligence . He has supervised postgraduate research, notably serving as Director of Studies for Dr Neil Urquhart’s thesis on evolutionary machine learning in metamorphic malware analysis (1999-2003). His publications span 1998-2003 and explore intersections between immunology , sparse distributed memory , and robotic systems . Recent projects like VanFill Innovation Voucher (2025) indicate ongoing involvement in AI-driven solutions and human-robot interaction research.

Thomas Jansenمشاهده پروفایل

دانشیار

• Evolutionary Algorithms
• Optimization
• Computational Intelligence
• +۳ مورد دیگر

Thomas Jansen is an Associate Professor and Head of the Department of Computer Science at Aberystwyth University. His research focuses on evolutionary algorithms, stochastic search heuristics, and theoretical computer science, with particular emphasis on optimization, dynamic environments, and algorithm performance analysis. He has led and contributed to several research projects, including the Wales Randomised Optimisation Algorithms Network (focused on advancing AI and optimization techniques) and the ImAppNIO project (bridging theory and practice in nature-inspired optimization). His academic contributions include foundational work on fixed budget analysis, multi-objective optimization, and the role of populations in dynamic optimization problems. Jansen has edited conference proceedings such as the 2015 ACM Conference on Foundations of Genetic Algorithms, further cementing his role in advancing the theoretical underpinnings of evolutionary computation. Key collaborations include work with researchers like Christine Zarges and Per Kristian Lehre, exploring topics like lexicase selection and immunological algorithms. His research has been presented at major conferences like PPSN and EvoCOP, and he actively participates in organizing workshops on randomized optimization algorithms.

• Biorobotics
• Computational Neuroscience
• Artificial Intelligence
• +۱۲ مورد دیگر

Professor Andy Philippides is a faculty member in the School of Engineering and Informatics at the University of Sussex, where he holds the title of Professor of Biorobotics (Informatics). He has been continuously affiliated with the university since 1995, progressing from an MSc and PhD student to a permanent academic, achieving full professorship in 2017. His work is deeply interdisciplinary, bridging robotics, neuroscience, and artificial intelligence. His research interests focus on the mechanisms of intelligent behavior through the interaction of body, brain, and environment. Key areas include visual navigation in insects and robots, neuromodulation in neural networks (e.g., GasNets), evolutionary robotics, and computer vision. He leads and contributes to high-impact projects such as Brains-on-Board, INSIGHT, and be.AI, funded by EPSRC, BBSRC, MRC, and the European Union. His recent publications (2022–2025) reflect a strong trend in bio-inspired algorithms, particularly in insect navigation, spiking neural networks, and adaptive robotics. These works span journals like PLoS Computational Biology , Frontiers in Physiology , and Biomimetics , emphasizing visual route learning, memory networks, and neuromorphic computing. He has supervised over 50 MSc theses and numerous PhD students, many of whom now hold academic or industry positions. His collaborations include prominent researchers such as Paul Graham, Phil Husbands, and Tom Collett. He is actively involved in grant-funded research, with current projects extending into 2028. Action-based bio-inspired autonomous navigation (Universities UK) Emergent embodied cognition in shallow neural networks (BBSRC) be.AI - biomimetic embodied Artificial Intelligence (Leverhulme Trust) ActiveAI - active learning and selective attention (EPSRC) He teaches courses such as Intelligence in Animals and Machines and Research Methods in Neuroscience, and has previously taught in computational neuroscience and robotics. His lab, part of the Centre for Computational Neuroscience and Robotics (CCNR), fosters interdisciplinary research in biorobotics and neural modelling.

Iryna Yevseyevaمشاهده پروفایل

دانشیار

• Cyber Security
• Operational Research
• Multi-Criteria Decision Analysis
• +۷ مورد دیگر

Iryna Yevseyeva is an Associate Professor in Computer Science at De Montfort University, affiliated with the Faculty of Computing, Engineering and Media and the School of Computer Science and Informatics. She leads the Cyber Security subject group and serves as Deputy Director of the Cyber Technology Institute. Her academic journey includes research roles at Newcastle University, the University of Leiden, Polytechnic Institute of Leiria, INESC Porto, and the University of Algarve. PhD in Multicriteria Decision Aiding, University of Jyväskylä, Finland Postdoctoral experience in the Netherlands, Portugal, and the UK Fellow of the Higher Education Academy (FHEA) Her research focuses on the application of operational research methods—particularly multi-criteria decision analysis and multiobjective optimization—to critical challenges in cyber security, including risk assessment, investment decisions, threat intelligence, and human behavior. She integrates decision science with cybersecurity to develop practical, data-driven frameworks for security governance and incident response. The recent publications highlight a strong trajectory in applying advanced computational and optimization techniques to cybersecurity problems. Themes include evolutionary optimization for privacy metrics, gamified training for incident response, human error modeling (IS-CHEC), and portfolio optimization in both drug discovery and security controls. The interdisciplinary nature spans computer science, operational research, behavioral psychology, and healthcare informatics. DMU Commercialisation Award (2018) Academy of Finland Grant (2008) Erasmus Mundus Grant (2008) Iryna has supervised 4 PhD students to completion and currently co-supervises 5 others. She has led over 10 research projects and secured more than 10 small grants as Principal Investigator, along with industrial grants from Innovate UK and Airbus. Her academic service is extensive, including guest editing for Springer journals, organizing international workshops (LeGO 2018, EMO 2023 track), and peer reviewing for top-tier journals and funding councils like EPSRC, MRC, and Horizon2020. She is actively involved in the Cyber Technology Institute and leads research within the Multi-Criteria Decision Making and optimization domain. Her collaborations span Brazil (Unijuí), Finland, the Netherlands, and Portugal, reflecting a globally connected research profile.

• Combinatorial Optimisation
• Hyper-heuristics
• Evolutionary Computation
• +۱ مورد دیگر

Christopher Stone is a Lecturer at the School of Computing, Edinburgh Napier University. He completed his PhD (2015-2020) focusing on combinatorial optimisation via graph-based approaches under supervisors Prof Emma Hart and Prof Ben Paechter. His research explores automated methods for solving complex optimisation problems. Research interests include: Development of hyper-heuristic frameworks for cross-domain problem solving Application of grammatical evolution to generate constructive heuristics Graph-based approaches for combinatorial optimisation scenarios Computational methods for evolutionary algorithms Publications demonstrate interdisciplinary work spanning computational optimization, evolutionary algorithms, and child psychology. Recent research trends show strong focus on automated heuristic generation and computational problem-solving techniques. Affiliated with the Centre for Algorithms, Visualisation and Evolving Systems where he has presented research at seminars including: 'Hyper-Heuristics with Graph-Transformations' (2017) 'Connect the dots and spot the gaps' PhD workshop (2018)

• X-ray imaging and simulation
• Non-destructive testing by ionising radiation
• Inverse problems
• +۹ مورد دیگر

Dr. Franck Vidal is a Senior Lecturer and Acting Director of Research at the School of Computer Science & Electronic Engineering at Bangor University, where he is also a member of the Visualization and Medical Graphics (VMG) Group and the Research Institute of Visual Computing (RIVIC). His educational background includes: PhD in Computer Science from Bangor University (2008) Postgraduate Certificate in Higher Education (2016) Diplôme d'études approfondies Images et Systèmes from Institut National des Sciences Appliquées de Lyon (2003) Master of Science in Computer-Aided Graphical Technology Applications from Teesside University (2002) Dr. Vidal's research focuses on X-ray imaging and simulation, non-destructive testing by ionising radiation, inverse problems, optimisation and artificial evolution, high performance computing, computer vision, image analysis and machine learning, and image-based simulation. His work has significant applications in medical imaging and medical physics, including X-ray, CT, MRI, PET and radiotherapy. He applies evolutionary algorithms to solve complex inverse problems in medical imaging. His recent publications demonstrate a strong focus on GPU-accelerated X-ray simulation technologies, particularly through the gVirtualXRay library, which enables real-time simulation of X-ray images and CT volumes. His work bridges the gap between computational physics, medical imaging, and practical clinical applications, with significant contributions to PET reconstruction algorithms using the Fly algorithm. Among his notable achievements: 2nd place in the Eurographics 2009 Medical Prize for ImaGINe-S David Duce Prize for the Best Short Paper (2019) Best Poster Presentation award (2022) Development of the gVirtualXRay open source library Multiple publications on evolutionary algorithms for medical imaging Dr. Vidal has supervised multiple research students including Julien Lavauzelle and Adrien Dutertre from ENSTA ParisTech. He has secured funding for significant projects including Fly4PET (focused on PET reconstruction for radiotherapy) and RAMPVIS (visualization for pandemic response). He actively collaborates with institutions worldwide, including INRIA in France and the University of California, San Diego. He leads the development of several innovative projects including gVirtualXRay (a GPU-based X-ray imaging library), Fly4Arts (evolutionary art using the Fly algorithm), and RASimAs (a regional anaesthesia simulator and assistant), demonstrating his ability to translate theoretical research into practical applications across medical and non-medical domains.

Xiaohui Liuمشاهده پروفایل

استاد

• Intelligent Data Analysis
• Deep Learning
• Artificial Intelligence
• +۷ مورد دیگر

Professor Xiaohui Liu is a distinguished Professor of Computing at Brunel University London, serving within the Computer Science department of the College of Engineering, Design and Physical Sciences. He maintains his office in the Wilfred Brown Building (Room 218) and has established himself as a leading figure in intelligent data analysis and artificial intelligence research. With over 20 years of academic leadership, Professor Liu has held significant visiting appointments including Honorary Pascal Professor at Leiden University (2004), Visiting Scientist at Harvard Medical School (2005), and Visiting Professor at the Chinese Academy of Sciences (2010). Professor Liu's research spans intelligent data analysis, deep learning, dynamical systems, human factors, innovative AI applications, optimisation, statistical pattern recognition, and trustworthy decision making. His work bridges theoretical advances with practical implementations across various industries, demonstrating exceptional translational impact. He has pioneered approaches that integrate artificial intelligence with data science to enable effective data interpretation and trustworthy decision-making systems, with applications spanning healthcare, manufacturing, and business domains. Analysis of Professor Liu's recent publications reveals a strong focus on transformer architectures, transfer learning, and optimization techniques applied to real-world problems. His work demonstrates consistent innovation in neural network architectures, particularly for anomaly detection, fault diagnosis, and recommendation systems. The publications show interdisciplinary applications spanning manufacturing, healthcare, digital marketing, and network science, reflecting his commitment to solving practical challenges through advanced computational methods. Clarivate Highly Cited Researcher for 11 consecutive years (2014-2024) World's top 2% of scientists by Stanford University (2020-2024) ScholarGPS Highly Ranked Scholar – Lifetime: Neural Network (2022-2024) Daniel Berg Award (2023) Research.com United Kingdom Leader Award in Computer Science (2023-2025) IDA Founders Award (2025) Professor Liu has secured substantial research funding from diverse sources including the European Commission, Innovate UK, Royal Society, and EPSRC. His current projects include AI-assisted tax assessment, intelligent data-driven pipelines for manufacturing certified metal parts, and maintenance models for zero-unexpected-breakdowns. He leads collaborative efforts through knowledge transfer partnerships with industry partners like Veritas Advisory Limited and has directed multiple European Commission-funded initiatives focused on IoT platforms, water resource management, and predictive maintenance systems. His research group actively mentors PhD students and collaborates with international partners across multiple continents. Professor Liu leads research activities within the IEHS and CSSB research groups at Brunel University, fostering interdisciplinary collaboration between computer scientists, engineers, and domain experts. His teams integrate expertise in neural networks, optimization algorithms, and statistical pattern recognition to develop innovative solutions for complex real-world problems. The research environment emphasizes both theoretical rigor and practical application, with strong industry partnerships ensuring that research outputs deliver tangible societal and economic impact.

• Parallel Functional Programming
• Parallel Symbolic Computation
• Resource Analysis and Control
• +۴ مورد دیگر

Dr. Patrick Maier is a Lecturer in Computing Science at the University of Stirling's Division of Computing Science and Mathematics. His research focuses on parallel computing, including parallel functional programming, symbolic computation, and resource analysis. He has contributed to projects such as AJITPar (2013-2017), HPC-GAP (2009-2013), and MOBIUS (2006-2009). His work spans parallel algorithm frameworks (e.g., YewPar), high-performance computing systems, and formal methods in software verification. Research interests include parallel functional programming, symbolic computation optimization, temporal logic, and compositional verification. Notable publications cover neuromorphic vision systems, antibiotic dosing optimization, scalable combinatorial search, and fault-tolerant functional computation. Collaborations include work on distributed memory parallel Haskell (HdpH) and formal verification techniques for resource management. He is affiliated with the Data Science and Intelligent Systems, and Computational Mathematics and Optimisation research groups. His recent work emphasizes reproducible parallel search algorithms and interdisciplinary applications like computational biology and neuromorphic engineering.