جست‌وجوی استاد و پژوهشگر

• Computer Science
• Construction Management
• Data Science
• +۵ مورد دیگر

Kay Rogage is an Assistant Professor in the Computer and Information Sciences Department at Northumbria University. Their research integrates computer science, construction management, and sustainability, focusing on BIM automation, digital twins, cybersecurity for smart buildings, and sustainable infrastructure. Rogage holds a PhD in Computing Science (2013), MA in Media Studies (2007), and BSc in Computing Science (2001). Education: PhD in Computing Science, Awarded: 27 Dec 2013 MA in Media Studies, Awarded: 1 Sept 2007 BSc in Computing Science, Awarded: 20 Jun 2001 Research Interests: Explores AI-driven solutions for construction challenges, including automated BIM data generation, carbon classification in materials, digital twin applications, and smart building security. Recent work emphasizes IoT cybersecurity and heritage-led immersive experiences. Key Projects: Developed a digital design management platform for construction collaboration (2019-2021) Co-led research on decarbonizing construction equipment and net-zero strategies Advanced malware prevention in smart building IoT using blockchain-federated learning Awards: Implementing Information and Communication Technology Award (2018) Advising & Grants: Supervised 3 thesis projects, including A.A. Abdulkadir's work on machine learning in BIM. Co-Investigator on Innovate UK-funded DDM project (2019-2021), developing decision support platforms for construction productivity. Labs/Teams: Active in cross-disciplinary teams applying AI to construction forensics and sustainability, with collaborations in Europe and the UK.

Rene Bretonمشاهده پروفایل

استاد

• Astrophysics
• Neutron Stars
• Pulsars
• +۵ مورد دیگر

Rene Breton is a Professor of Astrophysics at The University of Manchester, leading research in neutron stars, pulsars, transients, and binary systems. He also applies remote sensing techniques to agricultural challenges, such as tracking invasive species like Parthenium in Pakistan using satellite data. His academic roles include Deputy Head of the Department of Physics and Astronomy and former Head of Research. Breton holds significant grants, including the ERC Starting Grant and Marie Curie Fellowship, and has supervised 19 students. Education: PhD in Astrophysics from McGill University (2009), BSc in Physics from Université Laval (2003). Key Positions: Professor at The University of Manchester (2020–present), Reader (2017–2020), Lecturer (2014–2017). Research Group: Jodrell Bank Centre for Astrophysics Pulsars and Time Domain Astrophysics Group. His research explores fundamental physics via extreme astrophysical environments, including probing neutron star matter and gravity theories. Notable projects include the GOTO telescope array for transient detection and the TRAPUM pulsar survey with MeerKAT. He actively engages in public outreach, including collaborations with citizen science platforms like Kilonova Seekers. Recent Work: Focus on fast radio bursts, supernova progenitors, and gravitational wave follow-up. Recent publications include discoveries of hyperactive FRBs and insights into helium-interaction-driven supernovae. Awards: FP7 Marie Curie Fellowship (2014), H2020 ERC Starting Grant (2017). Teaching: Co-leads the Introduction to Data Science course for physics undergraduates. Labs/Teams: GOTO collaboration, BlackGEM telescope array, and MeerKAT TRAPUM project.

Chengzhi Pengمشاهده پروفایل

مدرس ارشد

• Architectural Computing
• Urban Climate Science
• Building Energy Modelling
• +۵ مورد دیگر

Dr Chengzhi Peng serves as Senior Lecturer and Director of Postgraduate Research at the University of Sheffield's School of Architecture and Landscape. With qualifications including BArch, DipLA, MSc, and PhD, he maintains dual research foci in Design Studies/Architectural Computing (DADA) and Architectural-Urban Science/Climate (AUSC). His leadership spans a research team comprising one postdoctoral associate and six PhD students. Research interests concentrate on building stock energy modelling, climate change impacts on built environments, ageing societies' challenges, and air quality simulation. His work integrates environmental simulation with sustainable design for climate adaptation, particularly through the MSc Digital Architecture and Design program where he serves as Director. Teaching emphasizes dissertation supervision across undergraduate, MArch, and postgraduate levels while embedding computational methods into architectural pedagogy. Recent publications reveal strong trends in urban climate resilience, with 2023-2025 works focusing on heat-health warning systems for vulnerable populations, PM2.5 infiltration analysis in educational buildings, and machine learning applications for air quality improvement. The research consistently bridges architectural design with environmental performance metrics, emphasizing real-world urban contexts and climate adaptation strategies. Significant grant funding includes AXA Research Fund (£124,739) for care home heat stress mitigation, ESRC fellowships (£91,953) on thermal vulnerability, and WUN collaborations (£14,932) on transcultural climate-responsive design. His team's work spans from city-scale housing stock modelling to micro-level indoor environmental quality assessments. Professional engagement includes editorial roles at ArchNet-IJAR (2012-2023) and Buildings journal, AHRC Peer Review College membership (2009-2012), and conference committee participation in eCAADe and CAADRIA. The Architectural and Urban Science (AUS) research group drives projects on solar micro-grid optimization, institutional building stock analysis, and vegetation-air quality interactions.

• Internet Security
• Operating Systems
• Programming
• +۱۲ مورد دیگر

Dr. Xiaochun Cheng is a Senior Lecturer in the Department of Computer Science at Middlesex University, School of Science and Technology. Their research spans Internet Security, Operating Systems, and Programming, with a strong focus on cybersecurity, artificial intelligence, and IoT applications. They are actively contributing to both academic and applied research in secure systems and intelligent computing. Research Interests: Dr. Cheng's work is centered on cybersecurity, deep learning, and intelligent systems. Key areas include intrusion detection in IoT, secure communication in autonomous vehicles, medical image analysis using neural networks, and privacy-preserving data sharing in healthcare systems. Their interdisciplinary approach bridges computer science with biomedical and social applications. The recent publications reflect a strong trend toward applying deep learning and hybrid AI models to critical domains such as healthcare (e.g., diabetic retinopathy, melanoma detection), cybersecurity (e.g., malware classification, threat detection), and smart infrastructure (e.g., smart buildings, UAVs). These works often combine advanced neural architectures with real-world constraints like resource limitations and data privacy. Scientific Contributions: Dr. Cheng has contributed significantly to journals such as IEEE Transactions on Intelligent Transportation Systems, Computers & Security, PLoS ONE, and Applied Sciences. Their collaborative research spans multiple continents and disciplines, including computer science, public health, and bioinformatics. Grants and Advising: While specific grants and students are not mentioned in the provided text, Dr. Cheng's extensive publication record suggests active research supervision and project leadership. They likely mentor students in AI, cybersecurity, and software engineering, and may be involved in funded projects related to secure IoT and intelligent transportation systems. Laboratories and Teams: Dr. Cheng appears to be part of a multidisciplinary research group at Middlesex University focused on intelligent systems and cybersecurity. Their collaborations with researchers in blockchain, cloud computing, and medical AI suggest involvement in a dynamic, cross-functional research environment.

Annabel Lathamمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence in Education
• Ethics and Trust of AI
• Computational Intelligence
• +۸ مورد دیگر

Dr. Annabel Latham is a Senior Lecturer in Computer Science at Manchester Metropolitan University within the Faculty of Science and Engineering’s Computing and Mathematics department. She holds a PhD in Artificial Intelligence, an MSc in Computing, a Postgraduate Certificate in Academic Practice, a CIM Diploma in Marketing, and a BSc(Hons) in Computation. As a Fellow of the Higher Education Academy (FHEA) and Senior Member of IEEE (SMIEEE), she contributes extensively to AI research and education. PhD in Artificial Intelligence MSc in Computing PGC Academic Practice CIM Diploma in Marketing BSc(Hons) Computation FHEA (2015) SMIEEE (2018) Research Interests include Artificial Intelligence in Education , Ethics of AI , and Computational Intelligence . She specializes in conversational agents, intelligent tutoring systems, and public trust in AI. Her work addresses fairness, accountability, and accessibility in AI-driven education, leveraging technologies like large language models and fuzzy logic. Research Outputs span 15 years, with a focus on explainable AI, educational applications of conversational agents, and ethical frameworks. Recent work (2024-2025) explores postdigital citizen science, trustworthy AI implementation, and XAI usability for non-specialists. 2023 IEEE Region 8 Outstanding Women in Engineering Volunteer Award 2019 IEEE Region 8 Outstanding Women in Engineering Affinity Group of the Year 2018 Outstanding Peer Reviewer, Elsevier: Computers & Education Senior Member IEEE (2018) FHEA (2015) Teaching and Supervision includes undergraduate Databases, postgraduate units in Information Systems and AI Ethics. She supervises MSc and PhD students in areas like explainability-aware machine learning, data responsibility, and AI trustworthiness. Her grants involve collaborations with international funding bodies such as NAFOSTED (Vietnam) and Croatia’s National Council for Science. Labs and Groups include the Computational Intelligence Lab, Machine Intelligence research group, and the Data and AI Ethics research group, where she co-leads initiatives on ethical AI in education.

Irwanda Laoryمشاهده پروفایل

دانشیار

• Structural Health Monitoring
• Damage Detection
• Intelligent Infrastructure
• +۳ مورد دیگر

Dr. Irwanda Laory is an Associate Professor in the School of Engineering at the University of Warwick, specializing in Structural Health Monitoring (SHM), damage detection, and intelligent infrastructure. He holds a BSc from Bandung Institute of Technology (Indonesia), an MSc from Bauhaus-Universität Weimar (Germany), and a PhD from EPFL (Switzerland). His research focuses on data interpretation methods, measurement system design, and computer-aided engineering for infrastructure resilience. Education: BSc Civil Engineering, Bandung Institute of Technology MSc Natural Hazard and Mitigation in Structural Engineering, Bauhaus-Universität Weimar PhD Swiss Federal Institute of Technology Lausanne (EPFL) Research emphasizes SHM techniques, Bayesian structural identification, and the integration of machine learning in civil infrastructure. Key contributions include thermal strain extraction for bridge assessment and anomaly detection using neural networks. His work spans smart materials, sensor networks, and sustainable construction practices. Publications highlight advancements in model-free data interpretation, damage detection algorithms, and infrastructure monitoring. Notable awards include the SHMII-8 Best Paper Award (2017). Grants and Projects: ENHANCE Erasmus+ Project (€1M, 2018) Warwick Impact Fund (£28.3k, 2015) Newton Fund Institutional Links Grant (£67.7k, 2016) Advises PhD students including Mr. Pamuncak (2017–2021) and oversees the Warwick SHM Group. Collaborates with institutions like the Warwick Institute for Science of Cities and the Center for Intelligent Infrastructure in Indonesia (IntelStruct).

• Sustainable Energy
• Climate Change Mitigation
• Renewable Energy Systems
• +۳ مورد دیگر

Andrew Peacock is an Associate Professor at the School of Energy, Geoscience, Infrastructure and Society at Heriot-Watt University, and a member of the Institute for Sustainable Building Design. His research focuses on energy demand modelling, climate change mitigation, and sustainable development, particularly in the context of renewable energy systems and community-scale energy solutions. He contributes to UN Sustainable Development Goals, emphasizing clean energy and climate action. Key research interests include heat pump efficiency, demand forecasting, solar-powered irrigation in Nigeria, and the impact of climate change on urban environments. He has published extensively on energy policy, smart grids, and data-driven approaches to energy systems. Notable collaborations include work on India’s cooling demand and South Asian megacity resilience. Peacock has received awards for his research, including the Best Presentation Award (2019) and Rushlight Awards for Resource Innovation (2018) and Water Management (2018). His work integrates technical, behavioral, and policy dimensions to address energy challenges in communities globally.

• Computational analysis of urban morphology
• Data science pertaining to urban systems
• Spatial data visualization
• +۲ مورد دیگر

Gareth Simons is a Senior Research Fellow at the Bartlett School of Environment, Energy & Resources, University College London, specializing in computational methods for urban analysis. His work bridges theoretical urbanism with data-driven workflows to generate scalable insights for sustainable cities. PhD, University College London (2016-2021) His research focuses on computational analysis of urban morphology, data science for urban systems, and spatial data visualization. Recent work applies machine learning and network-based analytics to pedestrian-scale urban metrics, including mixed-use measures and centrality analysis. Selected trends from his publications include predictive modeling of urban archetypes, development of the cityseer Python package, and energy efficiency strategies for urban infrastructure. These works integrate domain expertise with advanced computational tools.

• Soundscapes
• Environmental Acoustics
• Machine Learning in Acoustics
• +۳ مورد دیگر

Dr. Volkan Acun is a Research Fellow at the School of Science, Engineering & Environment at the University of Salford, UK. His work focuses on soundscapes, environmental acoustics, and the application of machine learning to auditory perception analysis. He is affiliated with the Acoustics Innovation Institute , contributing to research on noise barriers for sustainable technologies like air source heat pumps. Research Fellow, University of Salford Affiliated with Acoustics Innovation Institute Focus on environmental acoustics and soundscape modeling Dr. Acun’s research explores how humans perceive sound in diverse environments, including historical spaces , open-plan offices , and public study areas . He employs methodologies like grounded theory , structural equation modeling , and machine learning to decode auditory experiences and emotional responses. Recent work highlights include high-fidelity analysis of air source heat pump noise , which intersects with the UK’s net-zero carbon goals, and studies on predictive soundscape modeling using AI. His publications span journals like Applied Acoustics and Building Acoustics .

Seyed Ali Ghorashiمشاهده پروفایل

دانشیار

• Optimization
• Signal processing
• Digital health
• +۱۲ مورد دیگر

Dr. Seyed Ali Ghorashi is a Reader (equivalent to Associate Professor) in the Department of Engineering & Computing at the University of East London's School of Architecture Computing and Engineering. With over 20 years of experience in both industry and academia, he holds significant professional recognition including Fellow of the Higher Education Academy, Senior Member of IEEE, and professional membership in the British Computer Society. His educational background includes BSc and MSc in Electrical and Computer Engineering from the University of Tehran, and a PhD in Telecommunications and Networks from King's College London where he also worked as a postdoctoral researcher. He has held academic positions in the UK and Middle East and worked as a Senior Researcher at Samsung Electronics (UK) Ltd. Dr. Ghorashi's research spans multiple cutting-edge domains with particular emphasis on practical AI applications. His work integrates telecommunications, signal processing, and machine learning to address real-world challenges in digital health, 5G/6G networks, IoT, smart cities, and robotics. Recent research directions include Digital Health, AI for Sustainability, Digital Twin technologies, Internet of Behavior, and applications of Generative Adversarial Networks. His publication record shows consistent output in high-impact venues with a focus on human activity recognition using wireless signals, indoor positioning systems, radar technology, and structural damage assessment. The research demonstrates strong interdisciplinary connections between telecommunications engineering, computer vision, and practical AI applications across multiple sectors. Fellow of the Higher Education Academy (FHEA) Senior Member of IEEE Professional member of the British Computer Society (BCS) Four US patents Google Scholar: H-index 32, i10-index 84 Dr. Ghorashi has supervised over 200 MSc students, 16 PhD candidates, and 2 post-doctoral researchers. His research has been supported by significant projects including UK Mobile VCE (Core 2 & 4), the European Dreams4Cars project, 3GPP technical meetings, and an EPSRC project at King's College London. He serves as a technical reviewer for prestigious journals including IEEE Transactions on Cybernetics, IEEE IoT Journal, and IEEE Transactions on Wireless Communications, and has guest-edited special issues on Machine Learning in IoT. He leads research within the AI Centre for Public Sector and Smart Cities Research Group, focusing on applying advanced computational techniques to urban infrastructure and public service challenges. His current work bridges theoretical advances in machine learning with practical implementations in telecommunications infrastructure and healthcare applications.

• Remote Sensing
• Geographic Information Systems
• Digital Agriculture
• +۶ مورد دیگر

Dr. Daniel Simms is a Senior Lecturer in Remote Sensing at Cranfield University's Environment Centre, specializing in applied remote sensing and GIS for land and agricultural information. With over two decades of experience, his career spans from GIS technician roles to leading significant international projects focused on environmental monitoring and agricultural applications. His educational background includes a degree from the University of Plymouth (2000), followed by an MSc in Geographical Information Management at Cranfield University. Dr. Simms has held various professional positions including Spatial Data Manager for Kent County Council and has been with Cranfield University since 2004. University: Cranfield University Department: Cranfield Environment Centre Academic Rank: Senior Lecturer Professional Recognition: Fellow of the Higher Education Academy (FHEA) Dr. Simms' research focuses on the integration of satellite and aerial imagery with ancillary spatial datasets for crop detection and cultivation estimation from field to regional scale. His work emphasizes developing methodologies for deriving accurate and timely information from remotely sensed data with minimal ground-based sampling requirements. Key research areas include Digital Agriculture, Monitoring and Environmental Informatics, and Natural Capital assessment. His publication record shows a clear progression from traditional remote sensing applications toward more advanced machine learning and deep learning techniques, particularly evident in his recent work (2022-2024) which heavily features convolutional neural networks, semantic segmentation, and hyperspectral analysis for agricultural monitoring and environmental assessment. Fellow of the Higher Education Academy (FHEA) Dr. Simms has extensive experience delivering training in remote sensing and GIS techniques, including to Afghan nationals under UN-sponsored capacity building projects and for the UK component of the 2013 EU LUCAS survey. His research has been supported by diverse clients including the UK Government, United Nations Office on Drugs and Crime, EPSRC, EU Framework Programme 7, and Energy Technologies Institute. His current research projects include Enhancing Potato Dormancy, RestREco (Restoring resilient ecosystems), OMENZ (Optimising Miscanthus Establishment), and opium cultivation monitoring in Afghanistan using Earth observation data.

• Computer Science
• Signal Processing
• Machine Learning
• +۳ مورد دیگر

Hussein Walugembe is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science. His work focuses on gesture recognition systems using low-cost sensors and cloud computing optimization. He has conducted research on improving accuracy of Leap Motion sensors through techniques like PCA and dynamic time warping, as well as load balancing in cloud environments. Research interests include: Human-Computer Interaction via gesture-based interfaces Machine learning applications in sensor data analysis Biomedical engineering applications in hand rehabilitation Cloud resource management algorithms Publications from 2015-2022 demonstrate progression from cloud computing fundamentals to advanced gesture recognition systems. His work bridges sensor technology improvements with practical applications in healthcare and computing infrastructure optimization. No formal awards or grants are listed in the provided materials. Advising activities are not documented here. Affiliated with the School's Electronic Engineering and Computer Science research groups. Office located at Engineering Building, Room 153.

Tessio Novackمشاهده پروفایل

استادیار

• GIScience
• Critical GIS
• Volunteered Geographic Information
• +۳ مورد دیگر

Tessio Novack is an Assistant Professor at the University of Warwick's Centre for Interdisciplinary Methodologies (CIM), where he convenes the Urban Analytics and Visualisation degree program. Previously, he was a postdoctoral researcher at Heidelberg University's GIScience Research Group and completed his PhD in urban remote sensing at the Technical University of Munich under a DAAD scholarship. His research focuses on theoretical and practical approaches to culture-bound spatial knowledges, leveraging digital data, computational methods, and GIS to represent alternative urban spatial perceptions for social change. Education includes a PhD in Urban Remote Sensing (Technical University of Munich, Germany, 2018) and a DAAD-funded scholarship. He also holds a postdoctoral background in GIScience from Heidelberg University (Germany). Research interests span GIScience, critical GIS, volunteered geographic information, citizen science, and urban geography. He teaches modules such as IM913 Spatial Methods and Practices in Urban Science, IM927 Digital Cities: Interdisciplinary Perspectives, and IM939 Data Science Across Disciplines. His publications emphasize urban spatial analysis, geoinformation systems, and interdisciplinary applications of GIS. He advises no listed students but contributes to interdisciplinary teams and research projects at CIM.

Emmanuel Emmanuelمشاهده پروفایل

مدرس

• Artificial Intelligence in Healthcare
• Machine Learning
• Medical Imaging Analysis
• +۳ مورد دیگر

Dr. Emmanuel Emmanuel is a Lecturer in the School of Engineering, Computing and Mathematics at the University of Plymouth, part of the Faculty of Science and Engineering. His research focuses on the intersection of artificial intelligence, medical imaging, and network engineering. He holds a PhD and has contributed significantly to advancing AI-driven solutions for neurodegenerative diseases (e.g., Alzheimer’s, Parkinson’s), autism diagnosis, and stroke risk prediction. His work also extends to optimizing multimedia service quality in 5G and SDN/NFV networks. Key research interests include machine learning applications in healthcare diagnostics, explainable AI for medical decision-making, and quality-of-experience (QoE) optimization in telecommunications. He has authored over 50 peer-reviewed articles, with recent studies emphasizing AI-based diagnosis tools, biomarker development, and adaptive network management systems. Dr. Emmanuel’s technical contributions span MRI analysis, EEG signal processing, and data-efficient deep learning models. His projects often bridge clinical needs with technical innovation, such as developing XAI frameworks for MRI interpretation and optimizing video streaming quality in heterogeneous networks. He is actively involved in interdisciplinary collaborations, including the LiveWell initiative promoting ICT-based interventions for Parkinson’s patients. Contact: Room 208D Smeaton Building, emmanuel.jammeh@plymouth.ac.uk.

• Bacterial Genomics
• Pathogen Evolutionary Biology
• Population Dynamics
• +۷ مورد دیگر

Professor Nicholas Robert Thomson is a globally recognized expert in bacterial genomics and evolution at the Wellcome Sanger Institute, where he serves as Head of the Parasites and Microbes Programme and Group Leader. He also holds a joint appointment as Professor of Bacterial Genomics and Evolution at the London School of Hygiene and Tropical Medicine and an honorary professorship at the University of St Andrews School of Medicine. His seminal work has redefined the evolutionary understanding of cholera, identifying the '7PET' lineage of Vibrio cholerae as responsible for all major epidemics since the 1960s. Thomson earned his undergraduate degree in Microbiology and Microbial Technology from the University of Warwick in 1991, followed by a PhD in bacterial molecular biology from the same institution. He completed postdoctoral work at the University of Cambridge Biochemistry Department and L'Institut National des Sciences Appliquées (INSA) de Lyon, France, before joining the Sanger Institute in 1999 as a Senior Computer Biologist. He was promoted to Group Leader and faculty member in 2014 after serving as Principal Scientist. His research focuses on bacterial evolutionary biology and population dynamics, particularly in pathogens causing diarrheal and sexually transmitted diseases. Thomson's laboratory uses genomic approaches to investigate how bacterial genome variation relates to hosts, changes over time and geography, or responds to therapeutic interventions. His work on cholera shifted focus from environmental sources to human-to-human transmission, opening new intervention opportunities. He also made significant contributions to understanding Shigella infections with macrolide resistance, informing UK guidelines for managing sexually transmitted enteritis. Analysis of Thomson's recent publications reveals strong focus on genomic epidemiology of bacterial pathogens, antimicrobial resistance mechanisms, and innovative surveillance approaches. His work spans cholera, Shigella, Salmonella, Klebsiella, and syphilis research, with increasing integration of machine learning and mobile data for outbreak tracking, as demonstrated during his pandemic work establishing SARS-CoV-2 genomic surveillance in Bangladesh. Thomson is deeply committed to improving global health through long-term partnerships, equitable data sharing, and building research capacity in low and middle income countries. His laboratory has global policy influence, collaborating with the Pan-American Health Organization and WHO Global Taskforce for Cholera Control, where he serves on working groups and the Independent Review Panel for National Cholera Plans. He leads the Thomson Group which specializes in accessing genome data directly from clinical samples without culture. His research approach emphasizes multidisciplinary teams combining scientists, modelers, and clinicians with global reach. The group's work directly informs public health interventions and has shaped current cholera control strategies worldwide.