جست‌وجوی استاد و پژوهشگر

Mohamad Reza Shahabian Alashtiمشاهده پروفایل

پژوهشگر ارشد

• Robotics
• Deep Learning
• Human Activity Recognition
• +۵ مورد دیگر

Mohamad Reza Shahabian Alashti is a Postdoctoral Research Fellow in the Robotics Research Group within the Department of Computer Science at the University of Hertfordshire's School of Physics, Engineering & Computer Science. He holds a PhD in Computer Science from the same institution and maintains an active research profile in robotics and deep learning applications for healthcare. His educational background includes a PhD in Computer Science from the University of Hertfordshire focusing on human activity recognition in assisted living, a Master's degree in Mechatronics Engineering specializing in medical image segmentation for spinal cord injury detection, and a Bachelor's degree in Electronic Engineering. His interdisciplinary expertise bridges robotics, deep learning, and industrial automation. Dr. Shahabian's research interests focus on advancing healthcare technologies through robotics and artificial intelligence, with particular emphasis on human activity recognition, ambient assisted living systems, and assistive technologies. His work integrates computer vision, multi-view analysis, and machine learning to develop practical solutions for rehabilitation and home-based care scenarios. His research fingerprint shows strong activity in Human Activity Recognition (100%), Ambient Assisted Living (55%), Robotics (53%), and Activity Recognition (38%). His recent publications demonstrate a clear trend toward developing efficient, lightweight AI systems for real-world healthcare applications, with increasing sophistication in multi-view analysis and skeleton-based recognition techniques. His work consistently addresses the practical challenges of implementing activity recognition systems in ambient assisted living environments. Dr. Shahabian is actively involved in two major research projects: the SWAG (Soft Wearable Assistive Garments for Human Empowerment) project funded by the European Horizon program, and the 'Hospital at Home' project supported by the NHS. These initiatives reflect his commitment to translating academic research into practical healthcare solutions. He has teaching experience at both undergraduate and postgraduate levels, having taught courses such as Research Methods and Advanced Research Topics at the University of Hertfordshire, and delivered modules on Cloud Computing Systems with AWS at CU London, including Problem Solving and Programming, Principles of Computer Systems, and Serverless Web Applications. His industrial experience includes roles as a Mechatronics Designer at SYNTECH, where he led the design of a 7-DOF robotic arm and participated in international robotics competitions, and as a Senior Software and Hardware Design Engineer at ARTA Vision Ava Engineering.

Colin Forsterمشاهده پروفایل

مدرس

• Primary Science Education
• Action Research
• Behavior Management
• +۴ مورد دیگر

Colin Forster is a Lecturer at the University of Gloucestershire, where he has taught since 2005 following a career as a primary school deputy head teacher. His expertise spans primary science education, behavior management, and action research methodologies. He emphasizes developing critical thinking in children and supporting teacher trainees through reflective practice. Education: BEd (Hons) in Primary Education (Science & Maths) - Kingston University (1997) MA in Education - Kingston University PGCert in Further and Higher Education - University of Gloucestershire (2003) EdD - University of Gloucestershire (2011) Research focuses on transformative approaches in primary education, particularly: Innovative questioning techniques to foster scientific dialogue Action research as professional development for teachers Pencil-free homework models grounded in constructivist theory Mentorship dynamics in teacher training programs Gender-specific retention strategies in education degrees His work consistently bridges theoretical frameworks with classroom practicality. Awards & Recognition: Fellow of the Higher Education Academy He maintains professional membership in the Association for Science Education and contributes to the university's Society and Learning Research Priority Area, focusing on collaborative knowledge-sharing initiatives.

• Polymer Composites
• Nanocomposites
• 3D Printing of Composites
• +۴ مورد دیگر

Dr. Feras Korkees is a Senior Lecturer in Materials Science and Engineering at Swansea University's College of Engineering. He specializes in polymers and composites research with strong industrial links across the UK. His academic credentials include a PhD, PGDip, and BSc, along with professional qualifications (IEng, AIMMM, FHEA). PhD in Engineering Postgraduate Diploma (PGDip) Bachelor of Science (BSc) IEng (Incorporated Engineer) AIMMM (Associate of the Institute of Materials, Minerals and Mining) FHEA (Fellow of the Higher Education Academy) Dr. Korkees' research focuses on the long-term behavior of polymer composites, nanocomposites, and 3D printing of advanced composite materials. His work spans from fundamental material science to applied industrial problems, with particular emphasis on graphene-reinforced composites and moisture absorption phenomena. His research group collaborates with multiple UK-based companies to address real-world engineering challenges in composite material performance and durability. Analysis of his recent publications (2018-2023) reveals a strong focus on graphene-modified composites, moisture diffusion in polymer systems, and additive manufacturing of advanced materials. His work bridges fundamental material science with practical engineering applications, particularly in aerospace, automotive, and biomedical sectors. The publications demonstrate consistent output in high-impact journals across polymer science, composite materials, and additive manufacturing fields. Dr. Korkees actively supervises postgraduate research, with multiple successful PhD and MSc completions in 2023-2024. His supervision portfolio includes projects on next-generation sandwich composites, armor development, bio-based polymers, and graphene-reinforced nanocomposites, reflecting the breadth of his research interests and industrial relevance. Next generation sandwich composite structures with enhanced impact properties (PhD, 2024) Protection Optimisation via Novel Simulation (PhD, 2024) Characterisation of Bio-Based Polymers (MSc, 2023) Development of Graphene Reinforced Silica Aerogel Nanocomposites (MSc, 2023) His research group maintains strong connections with industry partners across the UK, facilitating technology transfer and addressing practical engineering challenges in composite material development and application. The group utilizes advanced characterization facilities at Swansea University's Bay Campus for mechanical testing, material analysis, and composite manufacturing.

Atm Alamمشاهده پروفایل

مدرس

• Wireless Communications
• 5G and Beyond Networks
• Internet of Things (IoT)
• +۱۰ مورد دیگر

Dr. Atm Alam is a Lecturer in the School of Electronic Engineering and Computer Science at Queen Mary University of London, where he serves as Programme Director for B.Eng. Electronic Information Engineering (JEI). He is also a Fellow of the Higher Education Academy (FHEA). Previously, he worked at the 5G Innovation Centre (5GIC), University of Surrey as a Research Fellow and contributed to H2020 and Innovate UK projects at the University of Bradford. Dr. Alam's research spans several key areas: Wireless Communications and 5G/Beyond Networks Internet of Things (IoT) and Edge Computing UAV Networks and Cognitive Radio Systems AI Applications for Communication Systems Smart Grids, Intelligent Transport Systems, and Smart Cities Educational Technology and Gamification His recent publications demonstrate a strong focus on UAV-assisted communication networks, active inference approaches for resource allocation, and educational technology applications. The research shows a consistent pattern of applying advanced computational methods to solve practical problems in wireless communications and education technology. Professional Recognition: Fellow of the Higher Education Academy (FHEA) Dr. Alam actively supervises research students and welcomes PhD candidates interested in wireless communications and its applications in vertical domains such as intelligent transportations, smart grids, smart manufacturing, healthcare, and water management. He supports potential PhD candidates through various scholarship programs including IsDB Merit Scholarship, HEC scholarship for Pakistani Students, and CSC scholarship for Chinese students. His teaching portfolio includes Cloud Computing, Digital Circuit Design, Image and Video Processing, and Information Systems Management, all delivered through the BUPT joint programme.

• Computer Vision
• Machine Learning
• Healthcare
• +۵ مورد دیگر

Dr. Jianbo Jiao is an Associate Professor in the School of Computer Science at the University of Birmingham , leading the Machine Intelligence + x (MIx) group . He holds a PhD in Computer Science from City University of Hong Kong (Hong Kong PhD Fellowship Scheme) and an MSc from Peking University. He was a visiting scholar at the University of Illinois at Urbana-Champaign and a postdoctoral researcher in the Biomedical Image Analysis (BioMedIA) group and Visual Geometry Group (VGG) at the University of Oxford. Research Focus: Computer Vision, Machine Learning, Medical Imaging, AI for Science Key Themes: Learning from limited supervision (self-supervised, weakly-supervised), multi-modal data analysis (vision-audio-text-chemical spectra), open-world problems (incremental learning, open-vocabulary segmentation), 3D scene understanding Awards & Recognition: Amazon Research Award (2024) Royal Society Short Industry Fellowship (2023) Best Paper Awards at MICAD 2024 and MICCAI 2024 ASMUS Workshop Outstanding Reviewer Awards from CVPR, ECCV, ICCV, NeurIPS, and MICCAI Fellow of the Higher Education Academy (FHEA) Community Contributions: Area Chair for NeurIPS 2025, ACM MM 2025, and ICLR 2025; Senior Program Committee member for IJCAI 2021; Editorial roles at IEEE T-CSVT, Springer The Visual Computer, and TMLR. Collaborations & Labs: Works with Oxford's BioMedIA and VGG groups, Meta Project Aria, and leads the MIx group at Birmingham, which focuses on open-world AI, 3D modeling, and cross-modal healthcare applications.

Adnan Syedمشاهده پروفایل

مدرس

• Materials Degradation Processes
• High-temperature corrosion
• Fireside corrosion
• +۷ مورد دیگر

Dr. Adnan Syed serves as a Lecturer at Cranfield University's Materials department, where he has been appointed since December 2018. His research focuses on high-temperature degradation processes, particularly in power generation applications, with significant contributions to understanding fireside corrosion mechanisms in waste-to-energy plants and advanced power systems. He collaborates with major industry partners including Rolls Royce, ITP Aero, MTU Aero Engines, Welding Alloys Ltd, and UKAEA. University of Karachi: BSc in Chemistry University of Reading: MSc in Electro Chemistry by research Cranfield University: PhD in Energy Materials (research focused on effects of co-fired coal and biomass in air and oxy-firing combustion on fireside corrosion of superheater materials) Dr. Syed's research interests center on materials degradation processes, particularly high-temperature corrosion in challenging environments. His work encompasses fireside corrosion of superheater materials in coal/biomass co-fired power plants, waste-to-energy systems, and oxy-fuel combustion environments. He utilizes advanced thermodynamic database software and microscopy techniques to understand corrosion reaction mechanisms. His current research direction focuses on heat exchanger materials in boilers for waste-to-energy plants and thermal energy storage systems for renewable energy. His expertise spans carbon capture and storage, conventional and advanced fuels, structures and materials, and surface engineering, with particular emphasis on how environmental factors affect material performance. Analysis of Dr. Syed's publication record reveals a strong focus on corrosion mechanisms in energy applications, particularly examining how chlorine, sulfur compounds, and high temperatures affect material performance. His research spans multiple energy sectors including waste-to-energy plants, conventional power generation, and renewable energy storage systems. The publications demonstrate increasing sophistication in experimental methods, moving from basic corrosion studies to advanced characterization techniques including SEM-EDX, XRD, and dimensional metrology. A consistent theme throughout his work is the application of materials science to solve practical engineering problems in power generation, with particular attention to how alloy composition affects corrosion resistance. Dr. Syed has established strong industry connections, working with major players in aerospace and power generation including Rolls Royce, ITP Aero, MTU Aero Engines, Welding Alloys Ltd, and UKAEA. His research directly addresses industrial challenges in material performance under extreme conditions. While specific grant information isn't detailed in the available text, his extensive publication record and industry collaborations suggest successful funding for his research programs. His work bridges fundamental materials science with practical engineering applications, particularly in developing improved materials for gas turbines and heat exchangers. Dr. Syed's laboratory work involves advanced microscopy techniques for alloy microstructure analysis, thermodynamic modeling of corrosion reactions, and experimental validation of corrosion mechanisms. His research group appears to focus on dimensional metrology for corrosion measurement, high-temperature testing rigs for simulating waste-burning environments, and analysis of material degradation processes. The team collaborates with industry partners to ensure research relevance to real-world power generation challenges, particularly regarding material lifing in demanding environments.

John See Su Yangمشاهده پروفایل

دانشیار

• Computer vision
• Artificial intelligence
• Affective computing
• +۷ مورد دیگر

Dr. John See Su Yang is an Associate Professor at Heriot-Watt University's School of Mathematical and Computer Sciences, Malaysia Campus. He serves as Programme Director for BSc Computing Science and leads the Multimedia Data Analysis (MuDA) Lab. Previously, he was a Senior Lecturer at Multimedia University and founded the Visual Processing (ViPr) Lab, and served as a Visiting Research Fellow at Shanghai Jiao Tong University from 2017-2019. Received Bachelor, Masters, and PhD degrees from Multimedia University Published over 140 articles in top journals and conferences including IEEE T-PAMI, CVPR, and NeurIPS Secured over MYR 3 million in research funding as PI/Co-PI Dr. See's research spans multimedia signal processing and computer vision with particular focus on affect and emotion understanding from images and videos. His work includes facial micro-expression analysis, image aesthetics, visual surveillance tasks, activity recognition, and classical image/video processing algorithms. His research has practical applications in micro-expression detection, human-robot interaction, and multimodal content generation. His recent publications (2023-2025) demonstrate strong activity in wind turbine blade inspection using UAVs, micro-expression analysis networks, drowsiness detection, and human-robot interaction. His work bridges theoretical computer vision advances with practical applications in renewable energy, transportation safety, and human-computer interaction. Best Reviewer Award, IEEE ICIP 2020 Outstanding Reviewer Award, CVPR 2021 Dr. See actively mentors PhD students and serves on editorial boards for Signal Processing (as Subject Editor), IEEE Transactions on Multimedia, and several other prestigious journals. He is a Senior Member of IEEE and has served on technical committees for IEEE Multimedia Systems and Applications and Multimedia Signal Processing. His MuDA Lab collaborates with the Visual Processing Lab at Multimedia University and Shanghai Jiao Tong University, focusing on emotional analysis and multimodal systems for solving real-world problems. Dr. See accepts PhD students for projects on aesthetics and emotion processing in media and new paradigms for understanding human emotions in real-world scenarios.

• Tribology of polymer materials
• Forensic identification techniques
• Medical glove performance
• +۴ مورد دیگر

Dr Daniel Preece is a Lecturer in Forensic Science and Programme Director at Hull York Medical School, University of Hull. Holding a PhD in Tribology of contaminated gloves from the University of Sheffield and designated as a Senior Fellow of the Higher Education Academy (SFHEA), his academic career spans forensic education, clinical equipment analysis, and pedagogical innovation. BSc (Hons) Forensic and Analytical Science, Sheffield Hallam University PhD in Tribological Properties of Contaminated Gloves, University of Sheffield His research focuses on: Tribological properties of polymer materials Glove membrane interactions affecting forensic mark identification Innovative teaching methodologies in forensic science Ergonomic evaluation of medical gloves Recent publications demonstrate expertise in: Forensic science communication strategies Academic performance metrics in forensic education Medical glove efficiency under environmental stressors CSI effect's influence on forensic program enrollment Scientific recognitions include: Best Paper Award 2020 (International Journal of Ergonomics) Foster and Freeman Award for Undergraduate Excellence NPIF Innovation Placement Funding (2019) As an educational researcher, he supervises: 40-credit undergraduate forensic research projects 60-credit postgraduate analytical forensic science projects Pharmaceutical education initiatives

Asefe Forghaniمشاهده پروفایل

پژوهشگر ارشد

• Data-driven optimization
• Simulation modeling
• Maritime logistics
• +۵ مورد دیگر

Dr. Asefe Forghani is a Postdoctoral Research Fellow at Cranfield University , specializing in data-driven optimization , simulation modeling , and maritime logistics . Her work contributes to the UK National Clean Maritime Research Hub , a collaboration involving 13 universities and 70+ industry partners. Research Focus includes: Developing energy-aware decision support systems using machine learning and mathematical modeling for emission reduction in port/vessel operations Integrating digital twin technology with ferry scheduling optimization Exploring decarbonization strategies for Roll-on/Roll-off terminals under uncertainty Publications demonstrate expertise in: Game-theoretical frameworks for supply chain competition TOU electricity tariff-driven production scheduling Bi-level modeling for facility interdiction problems Academic Background : BSc, MSc, and PhD in Industrial Engineering with specialization in data-driven optimization and maritime logistics. She has supervised 30+ industry-driven undergraduate dissertations and 2 MSc theses on topics like Big Data Analytics for Trade Facilitation and Micro-Fulfilment Centres and Industry 5.0 .

Glenn Haweمشاهده پروفایل

مدرس

• Machine Learning
• Multi-objective Optimization
• Bayesian Optimization
• +۴ مورد دیگر

Dr. Glenn Hawe is a Lecturer at Ulster University's School of Computing within the Faculty of Computing, Engineering and the Built Environment. He has been with the university since 2013 and is a member of the Artificial Intelligence Research Group. His work focuses on the intersection of machine learning, optimization, and emergency response systems. Education: MPhys in Mathematics and Physics (first class) from University of Warwick (2004) PhD in Electronics and Electrical Engineering from University of Southampton (2008) Dr. Hawe's research spans machine learning, multi-objective optimization, and agent-based simulation, with applications in emergency response systems, robotics, and autonomic computing. His work often explores how computational methods can address complex real-world problems, particularly in the context of resource allocation during major incidents. He has developed innovative approaches in Bayesian optimization and has made significant contributions to the field of autonomic pulse communications for robot swarms. Analysis of his recent publications shows a clear trajectory toward increasingly sophisticated applications of machine learning in robotics and emergency response systems. His work demonstrates growing integration of autonomic computing principles with swarm robotics, particularly for space applications and disaster response scenarios. The research shows increasing focus on explainability in AI systems alongside practical implementation in constrained environments. Scientific Recognition: Won two best paper awards at conferences during his PhD Work featured in 2009 'From recession to recovery' report by Universities UK as an example of successful academia-industry collaboration Dr. Hawe has been actively involved in multiple Knowledge Transfer Partnership (KTP) projects with industry partners and has served as a reviewer for prestigious journals including Journal of Simulation, International Journal of Machine Learning and Cybernetics, and IEEE Transactions on Magnetics. He is a member of the EPSRC peer review college and serves on the technical committee for the Science and Information Conference. His current projects include decarbonization of maritime transportation and autonomy for CubeSats. As part of the Artificial Intelligence Research Group, Dr. Hawe collaborates on projects involving agent-based simulation for emergency response and autonomic computing applications. His work on the REScUE project at Durham University laid the foundation for his current research in computational approaches to major incident management.

• Environmental Health
• Public Health
• Hand Hygiene
• +۳ مورد دیگر

Dr Marie Vaganay Miller is a Senior Lecturer at the Belfast School of Architecture & the Built Environment, Ulster University. Her work bridges environmental health, public health, and urban planning, with a focus on hand hygiene, informal settlements, and problem-based learning. MSc in Human Nutrition (2014) PgC in Higher Educational Practice (2010) PhD in Environmental Health and the Built Environment (2009) BSc in Environmental Health (2002) Diplôme Universitaire in Hygiene, Safety & Environment (2001) Her research explores environmental health challenges in informal settlements, particularly during the COVID-19 pandemic, and innovative problem-based learning strategies in public health education. Recent publications highlight global south contexts, hand hygiene compliance, and cross-European behavioral studies. Notable awards include Fellow of the Higher Education Academy . She has contributed to projects like the 2018 investigation of handwashing facilities in Ireland and the 2025 Tender on Environmental Health's value. Her academic roles include external examiner positions and editorial leadership.

Katie Wardleمشاهده پروفایل

مدرس

• Medical Education
• Curriculum Development
• E-Learning
• +۳ مورد دیگر

Dr. Katie Wardle serves as a Clinical Lecturer (Teaching) at University College London's Medical School, where she works full-time within the Curriculum Review team and as a Year 1 Personal Academic Tutor. With over six years of experience in medical education, she has held pivotal roles in UCL's MBBS curriculum development, assessments, and Clinical and Professional Practice tutoring. Her educational background includes an MBChB from the University of Liverpool (2012-2016) and a BSc (Hons) from the University of Manchester (2008-2011). She is currently pursuing a Master's in Clinical Education at Keele University with research focusing on professional identity formation. Her postgraduate training includes Postgraduate Certificates and Diplomas in Clinical Education from Keele University. Research interests center on curriculum review , curriculum mapping , e-learning , and professional identity development in medical education. Her recent publications demonstrate strong focus on curriculum mapping interventions, clinical apprenticeship models, and e-learning assessment validity. Analysis of her 15 most recent publications reveals consistent emphasis on practical curriculum implementation challenges, with significant attention to innovative clinical teaching models assessment validity and predictive value wellbeing impacts of teaching on clinicians digital resource development for pathology education Professional recognition includes: Senior Fellow (SFHEA) and Fellow (FHEA) from Advance HE Multiple Excellent Personal Tutoring Nominations (2021-2022) Faculty and Provost Education Team Awards for Curriculum Mapping Teacher of the Year award from Whittington Health NHS Trust (2018) As an active curriculum developer, she has led the e-learning case-based course "Case of the Month/Module" and co-organized the NIHR Academy Insight Day (2024). She established a Clinical Teaching Fellows network across UCL-affiliated trusts to support professional development in medical education. Her teaching spans undergraduate medicine fundamentals, Clinical and Professional Practice, and final-year assessment preparation.

• Statistical Science
• Data Science
• Machine Learning
• +۷ مورد دیگر

Nick Whiteley is a Professor in the School of Mathematics at the University of Bristol and serves as Director of the Institute for Statistical Science. He also held a Heilbronn Chair in Data Science and previously directed the EPSRC Centre for Doctoral Training in Computational Statistics and Data Science. His research spans statistical science and data science with interdisciplinary applications. Education : Ph.D. (University of Cambridge, 2009), M.Eng. (University of Oxford, 2003) Whiteley's research focuses on sequential Monte Carlo methods , Bayesian inference , hidden Markov models , and nonlinear filtering . His work connects statistical theory with practical applications in epidemic modeling, graph representation, and temporal data analysis. His recent publications emphasize scalable algorithms for high-dimensional problems and theoretical foundations of approximation methods. Key article trends include epidemic modeling , dynamic network analysis , stochastic processes , and machine learning theory . Sub-fields span from computational statistics to geometric methods in AI. Whiteley has received prestigious recognition through the Heilbronn Chair and Brunel Research Fellowship.

• Performance Art
• Live Art
• Video Installation
• +۷ مورد دیگر

Gary Stevens serves as Associate Professor in Fine Art at University College London's Slade School of Fine Art since 2016, following a part-time Lecturer position (2007-2016). His academic career spans decades across institutions including Goldsmith's, Ruskin School of Art, and Royal College of Art, where he developed performance pedagogy through workshops and master classes. As an artist, Stevens evolved from 1970s conceptual sculpture into internationally recognized live performances and video installations that challenge conventional boundaries between sculpture, theatre, and audience participation. Stevens' research centers on group dynamics as sculptural practice , exploring how ensembles generate collective intelligence through repetition and feedback systems. His work investigates extended mind theory through low-tech models of artificial consciousness, where performers operate without scripts using rules-based interactions. Key themes include temporal representation (contrasting painting's stillness with video's flow), psychological integrity in group settings, and the destabilization of narrative through durational structures. His methodology emphasizes studio-based development with diverse artist-performers who transcend disciplinary specialisms to solve common problems. Analysis of Stevens' recent works reveals consistent exploration of identity emergence through collective systems . From Outburst 's viral song propagation to A Story of Sorts ' text-based consciousness modeling, his practice examines how individuality dissolves and reforms within group structures. Performances frequently incorporate audiences into fictional frameworks while maintaining precarious physical relationships to stage spaces, creating intentional discomfort that questions theatrical conventions. Stevens directed the Performance Lab at Toynbee Studios (1999-2009), fostering experimental collaborations across artistic disciplines. His international residencies—often developed through open calls—provided unique opportunities for cross-disciplinary artists to engage with his ensemble-based methodology. While grant funding isn't specified in the text, his extensive exhibition history across European galleries, theatres, and public spaces demonstrates significant professional recognition and institutional support for his innovative practice.

Antonios Bikakisمشاهده پروفایل

استاد

• Artificial Intelligence
• Knowledge Representation and Reasoning
• Computational Argumentation
• +۵ مورد دیگر

Professor Antonios Bikakis is a Professor of Artificial Intelligence in the Department of Information Studies at University College London (UCL). He serves as the Director of Research in the Department of Information Studies and co-director of the Knowledge, Information and Data Science research group. His educational background includes: PhD in Computer Science from the University of Crete (2009) M.Sc. in Computer Science from the University of Crete (2004) Bachelor of Engineering (Honours) in Electrical and Computer Engineering from Aristotle University of Thessaloniki (2002) Professional Certificate in Teaching and Learning in Higher and Professional Education from University of London Professor Bikakis's research focuses on foundational aspects of Artificial Intelligence, with particular emphasis on Knowledge Representation, Nonmonotonic Reasoning, Computational Argumentation, Semantic Technologies, and Multiagent Systems. His work extends to developing knowledge-based systems for Digital Cultural Heritage, Digital Libraries, Ambient Intelligence, and E-commerce applications. He has made significant contributions to contextual reasoning in Ambient Intelligence environments, argumentation frameworks, and knowledge graphs. His research bridges theoretical AI with practical implementations in cultural heritage and everyday problem-solving scenarios. His recent publications (2023-2025) reveal a strong trend toward integrating large language models with structured reasoning for commonsense applications, developing knowledge representation for crisis management and cooking domains, and advancing semantic technologies for cultural heritage. His work consistently combines theoretical rigor with practical applications, particularly in the cultural heritage sector where he has made significant contributions to digital museum technologies and knowledge representation. His notable scientific achievements include: Best Paper Award at the International Conference on Logic and Argumentation (CLAR 2021) Extensive editorial work including Special Issues on Semantic Web for Cultural Heritage Significant citations for his work on Crypto collectibles and museum funding (194 total citations) Professor Bikakis actively supervises PhD students and MSc dissertations in Knowledge, Information and Data Science and Digital Humanities programs. He has secured major research funding through projects including "Repurposing of Resources" (Leverhulme Trust), "CORDIAL-AI" (ESRC), and "CrossCult" (European Commission Horizon 2020). His collaborative approach is evident in his extensive network of co-authors across European institutions and his service on program committees of major AI conferences (IJCAI, AAAI, ECAI, KR). As co-director of the Knowledge, Information and Data Science research group, he leads interdisciplinary research at the intersection of AI theory and cultural heritage applications. The group develops technology that combines the latest advancements in semantic technology, mobile computing, and context-aware systems to create engaging experiences with cultural heritage materials, reflecting his commitment to making AI research impactful in real-world settings.