جست‌وجوی استاد و پژوهشگر

Vanessa Popeمشاهده پروفایل

پژوهشگر ارشد

• Cardiovascular Research
• Music and Physiology
• Autonomic Nervous System
• +۳ مورد دیگر

Vanessa Pope serves as a Postdoctoral Research Associate specializing in Cardiovascular Imaging, conducting interdisciplinary research at the intersection of cardiovascular physiology and music science. Her work investigates how musical structures influence autonomic responses, with emphasis on blood pressure regulation and heart rate variability. Her research interests center on physiological entrainment to music, examining synchronization mechanisms between expressive musical elements (tempo, loudness, phrase structures) and cardiovascular dynamics. Key focus areas include hypertension's impact on music responsiveness and computational modeling of autonomic variables, leveraging machine learning for diagnostic applications in cardiovascular conditions. Recent publications reveal a strong trend toward integrating biomedical engineering with music psychology, utilizing graph convolutional networks and signal processing to analyze ECG/respiration data during music exposure. This work spans cardiovascular medicine, computational physiology, and therapeutic music applications, particularly for hypertension management. She actively disseminates findings through conference presentations, including the Society for Psychophysiological Research Annual Meeting (2024), and contributes to Sustainable Development Goals through cardiovascular health innovation.

Poulomi Palمشاهده پروفایل

پژوهشگر ارشد

• Music Optimization
• Cardiovascular Signal Processing
• Deep Learning
• +۳ مورد دیگر

Poulomi Pal is a Research Fellow in Cardiovascular Imaging with a focus on leveraging music-based technologies and deep learning for precision cardiovascular diagnostics. Her work integrates physiological responses to music with cardiovascular signal processing to develop novel diagnostic models. She holds a PhD in Medical Instrumentation and Bio-signal processing from the Indian Institute of Technology, Kharagpur (2023), with prior degrees in Biomedical Engineering and Electronics and Instrumentation Engineering. Research Interests: Her research spans music optimization for cardiovascular diagnostics, signal processing for CVD detection, and deep learning applications in biomedical engineering. She explores how music-induced physiological changes can enhance diagnostic accuracy for conditions like hypertension and ischemic heart disease. Her work also contributes to UN Sustainable Development Goals related to health and innovation. Collaborations & Trends: Pal collaborates on interdisciplinary projects involving music therapy, cardiovascular signal fusion (ECG/PPG/respiration), and AI-driven medical imaging. Recent work includes a music-based graph convolutional network for hypertension detection and meta-learning approaches for cardiopathy diagnosis using PPG signals. Labs & Teams: Engaged with cardiovascular imaging research groups focused on non-invasive diagnostic tools and biomedical sensor development. Her lab integrates machine learning with clinical data for predictive cardiovascular modeling.

• Recommender Systems
• Digital Health
• Responsible AI
• +۷ مورد دیگر

Dr. Suncica Hadzidedic is an Assistant Professor in the Department of Computer Science at Durham University and a Fellow of the Wolfson Research Institute for Health and Wellbeing. She holds a PhD from the University of Warwick, MSc from Binary University (Malaysia), and BSc from SSST (Bosnia and Herzegovina) and Buckingham University (UK). Her research bridges computer science with healthcare, psychology, and design, focusing on recommender systems, affective computing, and responsible AI applications in health and wellbeing. She leads projects like MoreLife UK KTP (Innovate UK-funded) and is part of research networks such as the EU COST Action VascAgeNet and the Alan Turing Institute’s Knowledge Graphs Interest Group. Her research interests include digital health interventions, mental health support systems, and ethical AI frameworks. Notable projects include developing PORT.org.ba, a cancer website with affect-aware recommendations, and studying authentication methods in youth populations. She teaches courses on recommender systems and business analytics. Dr. Hadzidedic’s work spans interdisciplinary collaborations, including the Erasmus+ e-VIVA project enhancing educational competencies in Balkan universities. She supervises postgraduate students in AI-driven health technologies and actively contributes to the AIHS (Artificial Intelligence and Human Systems) research group at Durham.

• Bioinformatics
• Health Informatics
• Affective Computing
• +۵ مورد دیگر

Dr. Stamos Katsigiannis is an Associate Professor in the Department of Computer Science at Durham University, specializing in bioinformatics, health informatics, affective computing, and GPU computing. His research bridges machine learning with medical applications, including EEG-based biometrics, emotion recognition, and medical imaging analysis. He holds a PhD in Computer Science from the National and Kapodistrian University of Athens (Greece) and previously held roles at the University of the West of Scotland and the University of Athens. Education: BSc (Hons.) in Informatics and Telecommunications (National and Kapodistrian University of Athens), MSc in Computer Science (Athens University of Economics and Business), PhD in Computer Science (National and Kapodistrian University of Athens). Research focuses on applying AI to healthcare, including biometric identification via EEG signals, remote physiological monitoring, and automated tutoring systems. Recent work involves deep learning for medical image analysis (e.g., chest X-rays, masonry façade cracks) and AI ethics governance (e.g., contributions to UK parliamentary inquiries on AI regulation). Publications span IEEE journals and conferences, emphasizing interdisciplinary applications of machine learning in health, education, and security. Active supervision of postgraduate students in AI-driven health technologies and computer vision.

• Computer Graphics
• Machine Learning and Deep Learning
• Geometric Modelling and Processing
• +۵ مورد دیگر

Dr. Frederick Li is an Associate Professor in the Department of Computer Science at the University of Durham. He holds a Ph.D. in Computer Graphics from City University of Hong Kong and has held academic roles at The Hong Kong Polytechnic University prior to his current position. His research focuses on Computer Graphics, Machine Learning, Visual Aesthetics, and Educational Technologies. He serves as an Associate Editor for Frontiers in Education and Virtual Reality & Intelligent Hardware, and has organized major conferences such as ICWL 2022 and ISVC 2021. Education: B.A. (Hons.) in Computing Studies from The Hong Kong Polytechnic University, M.Phil. in Computer Science, and Ph.D. in Computer Graphics. Research interests include mesh saliency, human-object interaction recognition, cloud modeling, and student performance analytics. He has published extensively in top venues like IEEE Transactions, ACM SIGGRAPH, and ECCV. Notable awards include Best Paper (ITiCSE 2014) and Outstanding Paper (ICALT 2013). He currently chairs the Undergraduate Board of Examiners and serves as an external examiner for Northumbria University's MSc program. His work spans educational technologies, collaborative virtual environments, and visual aesthetics, with applications in VR therapy, face forgery detection, and artistic style transfer. He supervises numerous Ph.D. students and contributes to Durham’s academic governance, including curriculum development and postgraduate management.

Adele Marshallمشاهده پروفایل

استاد

• Data Mining
• Bayesian Modelling
• Survival Analysis
• +۸ مورد دیگر

Adele Marshall is a Professor at Queen's University Belfast (QUB), affiliated with the School of Mathematics and Physics, Mathematical Sciences Research Centre, and Intelligent Autonomous Manufacturing Systems. She holds roles such as Chair of the Statistics and Operational Research Examination Board and Programme Director of the MSc Data Analytics course, launched in 2017. Her research focuses on Survival Analysis, Bayesian Modelling, and healthcare applications, with notable contributions to Coxian phase-type distributions and infrastructure modelling. She has secured grants from national/international bodies and co-leads the Cumberland Initiative Group for healthcare policy impact. Education Pathways: Developed Mathematics, Statistics & Operational Research pathways (2005) and the MSc Data Analytics program. Research Interests: Combines statistical methodologies with real-world applications in healthcare, infrastructure, and data science. Recent work includes real-time patient flow simulations, knowledge graph expansions, and disease modeling for bovine tuberculosis. Awards: Elected member of the International Statistics Institute (2011). Grants & Projects: Leads projects like the Nudge-based intervention for ventilator wean (2019–present) and KTP collaborations with industry partners (e.g., SciLeads Limited, Terex GB Ltd). Outreach: Organized MathsQUBe, a public engagement initiative, and participated in events like the IEEE Computer-Based Medical Systems Symposium. Labs/Teams: Active in CenSSOR (Centre for Statistical Sciences and Operational Research) and collaborates with regional health groups on consultancy work.

Edwin Renمشاهده پروفایل

دانشیار

• Internet of Things
• 4G/5G Mobile Telecommunication Networks
• Cloud Computing
• +۸ مورد دیگر

Edwin Ren is an Associate Professor in the School of Computing Sciences at the University of East Anglia (UEA), UK. His research focuses on Internet of Things (IoT), 5G/6G networks, cloud computing, and cybersecurity. He holds a PhD in Information Communication Technology from the University of Agder, Norway, and has held postdoctoral positions at National Chiao Tung University, Taiwan. Ren is a member of the Cyber Intelligence and Networks and Data Science & AI groups at UEA. **Education**: - PhD in Information Communication Technology, University of Agder, Norway (2012) - Postdoctoral Fellow at National Chiao Tung University (2012–2017) **Research Interests**: - IoT and Smart Systems - Network Function Virtualization (NFV) and SDN - Blockchain-based Security Protocols - Mobile Edge Computing and 5G/6G Architectures - Privacy-Preserving AI and Federated Learning **Key Achievements**: - Best Paper Award at IEEE MDM 2012 - Active in interdisciplinary projects like the Training DIGIT initiative (EPSRC-funded) and Smart Environments Research Facility **Grants & Collaborations**: - EPSRC-funded projects on Digital Innovation and Cyber-Physical Systems - Royal Society-supported AIoT platform research **Labs/Teams**: - Leads the Cyber Intelligence and Networks research group at UEA - Involved in the Smart Environments Research Facility, a multidisciplinary EPSRC project

• Data Science
• Artificial Intelligence
• Healthcare Informatics
• +۲ مورد دیگر

Harry Yu is an Associate Professor in Data Science at the University of Derby's College of Science and Engineering. His research focuses on applying artificial intelligence and data science to healthcare challenges, including disease prediction models, medical image analysis, and mental health support systems. Dr. Yu's publications demonstrate strong interdisciplinary work spanning computer science, healthcare, and engineering. Recent research trends show increasing focus on explainable AI frameworks for clinical applications, with studies on breast lesion classification, mental health chatbots, and disease prediction systems. His technical approaches frequently incorporate deep learning, natural language processing, and knowledge graph technologies. Long-term research themes include semantic computing for biomedical research, IoT healthcare systems, and advanced neural network architectures. Dr. Yu maintains an active publication record with recent articles appearing in Bioengineering, Journal of Medical Artificial Intelligence, and Springer proceedings.

Nelson J Trujillo Barretoمشاهده پروفایل

دانشیار

• Computational Neuroscience
• Brain Dynamics
• Neuroimaging
• +۳ مورد دیگر

Dr. Nelson J Trujillo Barreto is a Reader (Associate Professor) in Computational Neuroscience at Manchester Metropolitan University's Faculty of Health and Education. He holds a BSc (Hons) in Nuclear Physics from the Higher Institute for Nuclear Science and Technology of Havana (1995) and a PhD in Physical Sciences from Havana University (2006). Previously, he served as Head of Brain Dynamics at the Cuban Neuroscience Centre (2007-2014) and held an EPSRC Research Fellowship at the University of Manchester. His research focuses on computational modeling of brain dynamics using multimodal approaches including fMRI, EEG, and brain stimulation techniques. Key interests include: Mechanisms of motor control and movement disorders Neurological conditions (Parkinson's, MS, epilepsy) Machine learning applications in neuroscience Optimization of brain stimulation therapies His recent publications (2023-2025) demonstrate strong methodological innovation in EEG analysis, brain connectivity modeling, and clinical translation of computational neuroscience. Research consistently addresses neurodevelopmental disorders, pain perception, and hallucination mechanisms through advanced signal processing. Dr. Trujillo Barreto actively advises PhD candidates including Yuping Yang and Jessica Haigh, and maintains collaborations with leading institutions such as UCL's Functional Imaging Lab and Max Planck Institute. He serves on peer review panels for EPSRC, European Commission, and multiple international research agencies. He directs the Brain Dynamics Lab where his team integrates wireless neuroimaging, motion capture, and computational modeling to study movement disorders and develop therapeutic interventions.

Asiya Khanمشاهده پروفایل

مدرس ارشد

• Autonomous Systems
• Machine Learning
• Cybersecurity
• +۸ مورد دیگر

Dr Asiya Khan is an Associate Dean for Education and Student Experience at the Faculty of Science and Engineering , University of Plymouth. She has held significant roles including EDI Lead (2016-19) and Stage II Tutor (2017-2020), contributing to institutional diversity and academic mentorship. Research Focus : Autonomous systems, cybersecurity, and machine learning applications in renewable energy and healthcare Contributions : Led Athena SWAN Bronze award initiatives, supervised 6+ PhD students, and pioneered teaching innovations using Turning Point technology Her research spans autonomous marine systems, quality of experience (QoE) in cloud networks, and AI-driven diagnostics. Key trends include interdisciplinary collaborations in robotics, wind energy security, and multilingual education challenges. Scientific Awards : Athena SWAN Bronze Award (2019)

Akshi Kumarمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Natural Language Processing
• Explainable AI
• +۸ مورد دیگر

Dr. Akshi Kumar is a Senior Lecturer (Associate Professor) in Computing at Goldsmiths, University of London, serving as Director of Postgraduate Research and Chair of Research Ethics in Computing. Her expertise spans AI, NLP, and their applications in healthcare, finance, and education. With over 15 years of academic experience, she has authored influential books like Language Intelligence: Expanding Frontiers in NLP (IEEE-Wiley, 2025) and contributed to UK parliamentary inquiries on AI ethics and mental healthcare. Education: PhD (Web Mining), University of Delhi, India (2011) MTech (CSE), GGSIPU, Delhi (2005) BE (CSE), MDU, Rohtak (2003) Research Interests: Her work focuses on AI-driven decision-making in high-stakes domains, explainable AI, and cybersecurity. Notable areas include NLP for healthcare diagnostics, fraud detection, and ethical AI policy. Professional Activities: Steering Group Member, Mayor of London’s Violence Reduction Unit (VRU) on Online Harms Contributor to UK Parliament POSTnotes on AI in mental healthcare and media literacy Consultant for NLP-powered job matching systems and educational feedback tools Awards: Top 2% Highly Cited Scientist, 2021–2024 13 Research Excellence Awards Royal Academy of Engineering Exceptional Talent Recognition (2022) Labs & Projects: Leads initiatives like ExplainED (NLP for education feedback) and collaborates on AI ethics frameworks for digital safety. Her work integrates computational linguistics with societal impact, addressing challenges in misinformation, healthcare, and policy.

Li Guoمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Healthcare Informatics
• Machine Learning
• +۴ مورد دیگر

Dr. Li Guo is a Senior Lecturer at Manchester Metropolitan University, affiliated with the John Dalton Building. Their work focuses on interdisciplinary research spanning artificial intelligence, healthcare informatics, machine learning, and cloud computing. Dr. Guo has contributed to over 50 peer-reviewed publications, with recent emphasis on AI in healthcare, multimodal data integration, and social relation recognition. Their research addresses critical challenges in medical diagnostics, opioid overdose prevention, and urban environment analysis. Key research areas include: Healthcare AI applications in primary care and medical imaging Development of machine learning models for EHR integration and disease diagnosis Spatiotemporal reasoning for activity recognition and group interaction analysis Cloud resource optimization and elastic application provisioning Dr. Guo's work intersects technical innovation with societal impact, particularly in vulnerable populations. Their recent studies explore co-production approaches with prison and homeless populations to reduce opioid-related deaths. The research portfolio demonstrates strong methodological diversity, combining deep learning, graph neural networks, and weakly supervised techniques.

Laura Davila Penaمشاهده پروفایل

پژوهشگر ارشد

• Operational Research
• Game Theory
• Logistics Optimization
• +۳ مورد دیگر

Dr Laura Davila Pena is a Research Fellow at the Kent Business School, University of Kent. She holds a PhD in Statistics and Operational Research (2023) from the University of Santiago de Compostela, alongside MSc (2019) and BSc (Mathematics) degrees from the same institution. Previously, she served as a lecturer at the Department of Statistics, Mathematical Analysis and Optimisation at the University of Santiago de Compostela. Her research focuses on vehicle routing problems, cooperative game theory, and their applications in logistics and healthcare. She has held visiting researcher positions at the University of Edinburgh, University of Porto, and Tilburg University, and was honored with the YoungWomen4OR distinction in 2022. Her education includes: BSc in Mathematics, University of Santiago de Compostela MSc in Statistical Techniques, University of Santiago de Compostela PhD in Statistics and Operational Research (joint program with University of A Coruña and University of Vigo) Laura's research interests emphasize optimization challenges in real-world scenarios, particularly addressing logistics distribution and healthcare systems through advanced analytical frameworks. Her work combines heuristic algorithms with cooperative game theory to solve complex routing and cost allocation problems. Recent publications highlight contributions to classification problems in healthcare and multi-compartment vehicle routing. Her awards include the 2022 YoungWomen4OR distinction recognizing her contributions to Operational Research. Laura has co-supervised eight undergraduate dissertations and actively participates in academic dissemination through conferences and society memberships (SEIO & SGAPEIO). Her research collaborations span multiple European institutions, reflecting her commitment to advancing interdisciplinary optimization solutions. Current work continues exploring applications of game theory in transportation and healthcare logistics.

Burak Cavdarogluمشاهده پروفایل

پژوهشگر

• Operations Research
• Sports Scheduling
• Network Optimization
• +۳ مورد دیگر

Burak Cavdaroglu is a researcher at Northumbria University with a focus on operations research and optimization. His work addresses complex problems in sports scheduling, network restoration, and infrastructure recovery, emphasizing fairness and efficiency. Key research areas: Sports analytics, Interdependent network systems, Scheduling algorithms His recent studies investigate rest difference minimization in sports leagues and integrated approaches for restoring critical infrastructure networks. While specific details about his academic background or departmental affiliation are not available in the scraped text, his publications demonstrate expertise in mathematical modeling and decision support systems. Notable scientific contributions include: 2025: Graph-based heuristics for rest difference minimisation 2023: Comparative analysis of referee appointment policies in European football 2012: Foundational work on interdependent network restoration

Yuchu Qinمشاهده پروفایل

مدرس ارشد

• Design for Additive Manufacturing
• Intelligent Manufacturing
• Knowledge Representation and Reasoning
• +۷ مورد دیگر

Dr. Yuchu Qin serves as a Senior Lecturer (Equivalent) in the School of Computing and Engineering and Senior Research Fellow with the EPSRC Future Advanced Metrology Hub for Sustainable Manufacturing at the University of Huddersfield. With dual PhD qualifications in Instrument Science and Technology and Mechanical Engineering, Dr. Qin bridges mechanical engineering and computer science disciplines to advance research in additive manufacturing and intelligent systems. Dr. Qin's research spans design for additive manufacturing, intelligent manufacturing systems, knowledge representation, and decision analysis, with particular expertise in fuzzy sets, optimization techniques, and category theory applications. Recent work demonstrates strong integration of AI methodologies with traditional manufacturing processes, creating innovative solutions for quality control and process optimization in additive manufacturing. Publications over the past three years reveal a clear trajectory toward AI-enhanced manufacturing solutions, with emphasis on surface characterization, process parameter optimization, and knowledge representation. The research combines deep theoretical foundations with practical manufacturing applications, addressing critical challenges in additive manufacturing quality and efficiency. Research Degree Scholarship in Engineering (2017) National Scholarship for Doctoral Students (2015) National Scholarship for Master's Students (2012) National Scholarship for Undergraduate Students (2008) As Principal Investigator for a Royal Society International Exchanges Grant and core researcher for major EPSRC funding totaling over £23 million, Dr. Qin actively supervises three PhD students while serving as reviewer for over 70 journals and holding editorial positions with multiple publications. Dr. Qin's teaching includes professional development courses and data mining instruction for both undergraduate and postgraduate students. Research activities center around the EPSRC Future Advanced Metrology Hub, where Dr. Qin contributes to developing next-generation metrology solutions for sustainable manufacturing through both theoretical advancements and practical implementations that address real-world manufacturing challenges.