جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Computer Science
• Game Design
• +۳ مورد دیگر

Cristiana Pacheco is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science. Her research focuses on artificial intelligence applications in video game design, particularly in modeling believable agent behavior. She holds an affiliation with the university's Peter Landin research facility and is reachable at c.pacheco@qmul.ac.uk. Her work bridges computer science and game design, emphasizing predictive models, Monte Carlo methods, and time-continuous believability assessment. Recent research explores agent behavior pipelines and co-evolution strategies in multi-player game environments. Key contributions include frameworks for evaluating agent realism and optimizing gameplay dynamics. Though no awards or grants are explicitly listed, her publications indicate sustained engagement with AI-driven gaming challenges. She has not listed formal advisees but contributes to the school's academic and research activities through visiting collaborations.

Teresa Brunsdonمشاهده پروفایل

مدرس ارشد

• Time Series Analysis
• Compositional Data
• Data Science
• +۳ مورد دیگر

Dr. Teresa Brunsdon is a Senior Lecturer in the Department of Statistics at the University of Warwick, part of the Faculty of Science. She holds a PhD in Time Series Analysis of Compositional Data from the University of Southampton (1997) and has over three decades of academic experience, including 31 years at Sheffield Hallam University before joining Warwick in 2019. Education: BSc in Mathematics Sciences, Loughborough University MSc in Applied Statistics, University of Southampton PhD in Time Series Analysis of Compositional Data, University of Southampton Research Interests: Time Series Analysis Compositional Data Analysis Machine Learning Applications (e.g., interpretable classifiers) Statistical Methodology Education Technology in Data Science Teaching: Delivers core modules such as Introduction to Statistical Practice (ST952) and Designed Experiments (with Advanced Topics) (ST305/ST410) at Warwick. Publications: Focuses on time series methodologies, compositional data, and applications in social media and language processing (e.g., Sinhala documents). Recent work explores ethical AI and interpretable machine learning. Affiliations: Office: MSB 2.11 Email: teresa.brunsdon@warwick.ac.uk Office Hours: Monday 16:30–17:30 and Thursday 11:30–12:30

Natalie Harveyمشاهده پروفایل

پژوهشگر

• Atmospheric Transport Processes
• Volcanic Ash Modeling
• Forecast Verification
• +۳ مورد دیگر

Dr. Natalie Harvey is a Senior Research Scientist at the Department of Meteorology, University of Reading. She specializes in atmospheric transport processes, volcanic ash dispersion modeling, and uncertainty quantification in natural hazard forecasts. Her work focuses on improving volcanic ash forecasting through advanced model techniques and satellite data integration. She is affiliated with projects like R4Ash, IMPALA, and RACER, addressing volcanic hazards and climate-related risks. Her research interests include boundary layer dynamics, remote sensing applications, and decision-making under uncertainty. She has contributed to over 30 peer-reviewed articles, analyzing volcanic eruptions such as Raikoke 2019 and Grímsvötn 2011. Her methods enhance forecast accuracy by integrating ensemble meteorology and source inversion techniques. Notably, she explores how AI models compare to traditional physics-based approaches in weather prediction, highlighted in a 2024 study on Storm Ciarán. Dr. Harvey holds a PhD from the University of Reading (Boundary-layer type classification and pollutant mixing) and has developed classification algorithms for atmospheric layers using Doppler lidar. She collaborates with institutions like the Met Office and has received international recognition for her work in volcanic ash transport and dispersion modeling.

Angela Daveyمشاهده پروفایل

عضو هیئت علمی

• Radiation Therapy
• Lung Cancer
• Medical Imaging
• +۳ مورد دیگر

Angela Davey is a Research Associate in the Division of Cancer Sciences at The University of Manchester, affiliated with the Manchester Cancer Research Centre. She holds a Master of Physics (2013–2017) and a PhD focused on identifying imaging biomarkers for personalized radiotherapy in lung cancer treatment under supervisors including McWilliam, Faivre-Finn, and van Herk. Her research focuses on optimizing radiation therapy through advanced imaging techniques, analyzing dose effects on patient outcomes, and improving pediatric oncology protocols. Key areas include radiation-induced toxicity, dose distribution optimization, and voxel-based analysis of clinical data. Her recent work explores interactions between radiation dose and heart function in lung cancer survival, as well as minimizing dentofacial radiation exposure in children. She has contributed to multi-center studies on reirradiation scenarios and developed machine learning methods for medical image enhancement. Awards: ICCR Rising Stars Oral Presentation Competition 1st Place (2019) Winter Institute of Medical Physics Early Career Scholarship (2020) Advising & Grants: Supervised her own PhD thesis; her work aligns with Sustainable Development Goals related to health and innovation. She collaborates internationally on pediatric radiotherapy protocols and radiomics applications, contributing to clinical guidelines and treatment planning advancements.

Guy Emersonمشاهده پروفایل

پژوهشگر ارشد

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +۳ مورد دیگر

Dr Guy Emerson is a Departmental Early-Career Academic Fellow and Affiliated Lecturer in the Department of Computer Science and Technology at the University of Cambridge. He also serves as a College Lecturer at Gonville & Caius College and Executive Director of Cambridge Language Sciences. His research focuses on computational linguistics, particularly functional distributional semantics, probabilistic models, and machine learning applications in natural language processing. He explores how computational models can illuminate linguistic knowledge and advance artificial intelligence. His work integrates formal linguistic theory with data-driven methods, addressing challenges like hypernymy detection, cross-linguistic syntax, and semantic representation. Recent projects include probing lexical semantics limitations, developing scalable distributional models, and leveraging visual data for semantic analysis. Key themes include natural language processing, machine learning, and cross-linguistic studies, with publications in top venues like SemEval workshops and computational linguistics journals. His contributions bridge formal syntax, probabilistic semantics, and scalable computational techniques, emphasizing both theoretical and applied outcomes. Emerson collaborates extensively on interdisciplinary initiatives through Cambridge Language Sciences, fostering connections between linguistics, computer science, and cognitive science. His research has implications for improving NLP systems, semantic modeling, and cross-linguistic computational frameworks.

Steven Herbertمشاهده پروفایل

مدرس

• Quantum Computing
• Quantum Algorithms
• Quantum Sensing
• +۳ مورد دیگر

Steven Herbert is an Affiliated Lecturer in the Department of Computer Science and Technology at the University of Cambridge, where he teaches the Part II Quantum Computing course. In addition to his academic role, he serves as Head of Quantum Algorithms at Cambridge Quantum and is a By-Fellow at Churchill College. His research focuses on quantum computing, quantum algorithms, and their applications in areas such as sensing, communications, and optimization. A central theme of his work involves developing and analyzing quantum algorithms for Monte Carlo integration, entanglement distribution, and network coding, with a particular emphasis on practical implementations on near-term quantum hardware. He has contributed to theoretical advancements regarding quantum-classical algorithm comparisons, noise-aware techniques, and reinforcement learning applications in quantum compilation. Herbert’s research also explores interdisciplinary applications of quantum computing, including data-centric engineering and scientific computing. He actively investigates the intersection of quantum technologies with machine learning, particularly in generative adversarial networks (GANs), and has pioneered methods to optimize quantum circuit design and resource utilization. His work often bridges foundational theory with practical hardware constraints, addressing challenges such as qubit routing, error mitigation, and algorithmic efficiency in restricted quantum architectures. Through his affiliation with Cambridge Quantum, he collaborates on real-world quantum computing projects, emphasizing the translation of theoretical insights into tangible applications. His contributions span academic publications, industry partnerships, and educational initiatives aimed at advancing quantum literacy and innovation.

Sam Nallaperuma-Herzbergمشاهده پروفایل

عضو هیئت علمی

• Machine Learning
• Artificial Intelligence
• Optical Communication Systems
• +۵ مورد دیگر

Dr. Sam Nallaperuma-Herzberg is an Assistant Research Professor at the University of Cambridge's Department of Computer Science and Technology. He holds prestigious fellowships including the Borysiewicz Interdisciplinary Fellowship, Bye Fellowship at Murray Edwards College, and the Schmidt/Accelerate Fellowship in AI for Science and Society. His research focuses on machine learning applications in optical communication systems, neural networks for medical diagnostics, and reinforcement learning for network optimization. Affiliations: Department of Computer Science and Technology, Murray Edwards College Key Research Themes: Machine Learning, AI for Healthcare, Optical Fiber Networks, Reinforcement Learning Algorithms His work bridges computational methods with real-world challenges in telecommunications and healthcare. Notable achievements include developing graph neural networks for insomnia modeling and optimizing ultra-wideband fiber-optic systems using deep learning. He has been recognized with multiple interdisciplinary research fellowships, reflecting his cross-cutting contributions. Research outputs span over 30 peer-reviewed articles since 2017, with recent emphasis on AI-driven solutions for optical network optimization and circRNA-RBP interaction prediction. His projects include the BrainTwin initiative applying AI to sleep disorder analysis. Awards: Borysiewicz Interdisciplinary Fellow Bye Fellow, Murray Edwards College Schmidt/Accelerate Fellow in AI for Science and Society Current research explores hybrid neural architectures for medical diagnostics and Gaussian process modeling for optical signal optimization. He collaborates extensively on EU-funded initiatives and industry partnerships in AI and telecommunications.

• Autonomous Systems
• Unmanned Aerial Vehicles (UAVs)
• Artificial Intelligence
• +۵ مورد دیگر

Professor Jon Platts is the Dean of Cardiff School of Technologies and a Professor of Autonomous Systems at Cardiff Metropolitan University. He holds a BEng in Electrical and Electronic Engineering from the University of Bradford, an MSc in Aerosystems Engineering, and a PhD in Self-Organising Fuzzy Logic from Loughborough University. With a career spanning the UK Royal Air Force and industry roles at QinetiQ, he specializes in autonomous systems, UAVs, and cybersecurity. He is a Chartered Engineer and Fellow of the Institution of Engineering and Technology, with affiliations to the Institution of Measurement and Control and the International Council on Systems Engineering. His research focuses on advancing autonomous systems, IoT applications, cybersecurity in multinational operations, and ethical AI. Recent work includes smart water management, metaverse security, and sustainable hospitality technologies. He collaborates with organizations like Dstl, BAE Systems, and Thales UK, emphasizing cross-disciplinary and industry-academia partnerships. Key achievements include leading QinetiQ’s autonomy division, shaping defense research programs, and publishing widely on UAV control, AI ethics, and IoT. Awards include his IET Fellowship and Chartered Engineer status. His leadership drives innovation in Cardiff School of Technologies’ research centers like the Centre for Industry 4.0 and Blockchain Research.

• Medical Image Segmentation
• Motion Segmentation
• Visual Object Tracking
• +۹ مورد دیگر

Dr. Sandeep Singh Sengar is a Senior Lecturer in Computer Science at Cardiff Metropolitan University, United Kingdom. Prior to his current role, he served as a Postdoctoral Research Fellow at the Machine Learning Section of the Computer Science Department at the University of Copenhagen, Denmark. His academic journey includes a Ph.D. in Computer Science and Engineering from Indian Institute of Technology (ISM), Dhanbad, India, and an M. Tech. in Information Security from Motilal Nehru National Institute of Technology, Allahabad, India. Sengar actively engages in research collaboration and has organized special sessions and delivered keynote presentations at international conferences. Education: Dr. Sengar holds the following academic qualifications: Ph.D. in Computer Science and Engineering from Indian Institute of Technology (ISM), Dhanbad, India M. Tech. in Information Security from Motilal Nehru National Institute of Technology, Allahabad, India Research Interests: Dr. Sengar's research focuses on advanced applications of machine/deep learning and computer vision, particularly in healthcare diagnostics and cybersecurity. His work combines theoretical methodologies with practical implementations, including: Medical Image Segmentation (e.g., brain tumors, functional connectivity) Motion Segmentation and Tracking for surveillance and activity recognition Visual Object Tracking and Recognition in dynamic environments Video Compression techniques leveraging adaptive optimization Cybersecurity frameworks targeting DDoS attack mitigation Exploring Metaverse applications in healthcare and AI ethics Publications Trends: His recent articles emphasize interdisciplinary innovation, with a strong focus on healthcare AI and cybersecurity. Key themes include hybrid neural networks for medical imaging, entropy-driven frameworks for neurological disorders, and proactive network threat detection using deep learning. He also investigates emerging technologies like the Metaverse's role in healthcare systems and generative AI's broader applications. Scientific Awards: No scientific awards were explicitly mentioned in the provided texts. Advising & Grants: While Dr. Sengar has not listed formal advisees or grant details, his research mentorship is evident through collaborative projects and conference contributions. He actively participates in organizing special sessions and peer-review processes, fostering academic-industry partnerships. Labs & Teams: Though no specific labs are named, his work aligns with Cardiff Met's research centers and innovation initiatives, such as the PDR International Centre for Design and Research and ZERO2FIVE Food Industry Centre.

• Bioinformatics
• Health Informatics
• Affective Computing
• +۴ مورد دیگر

Dr. Stamos Katsigiannis is an Associate Professor in the Department of Computer Science at Durham University. His research focuses on bioinformatics, health informatics, affective computing, machine learning, and GPU computing applications. He holds a PhD in Computer Science from the National and Kapodistrian University of Athens (Greece), with prior roles including Postdoctoral Research Fellow and Lecturer at the University of the West of Scotland (2016-2020). Education: BSc (Hons) Informatics and Telecommunications, National and Kapodistrian University of Athens (Greece) MSc Computer Science, Athens University of Economics and Business (Greece) PhD Computer Science, National and Kapodistrian University of Athens (Greece) Research Interests: Bioinformatics, health informatics, affective computing (e.g., emotion recognition using EEG/ECG signals), machine learning applications in medical imaging and video quality, GPU-accelerated algorithms for biomedical data analysis, and biometric identification systems. His work bridges computational methods with healthcare, education technologies, and human-computer interaction. Advising & Students: Supervising postgraduate students in AI-driven healthcare, computer vision, and affective computing. Recent collaborations include projects on AI-generated content detection (De-Factify 4.0), chest X-ray image analysis (CLN network), and trajectory prediction using graph neural networks. Labs/Teams: Active in Durham's AI research groups, contributing to interdisciplinary projects in medical imaging, cybersecurity, and educational technology. Collaborates internationally in EU-funded initiatives and UK parliamentary AI governance consultations (AGENCY project).

Gemma Millerمشاهده پروفایل

پژوهشگر ارشد

• Ruminant Systems
• Greenhouse Gas Emissions
• Livestock Sensors
• +۲ مورد دیگر

Dr. Gemma Miller is a Research Fellow at Scotland's Rural College specializing in ruminant systems within the Beef & Sheep Research Centre. Her research focuses on greenhouse gas emissions measurement, mitigation strategies, and precision livestock technologies. Key research areas include: Measurement of GHG emissions from beef/sheep systems Proxy measures for GHG quantification Methane reduction through feed supplements Animal-mounted sensors for real-time monitoring Machine learning for carcass characteristic prediction Recent publications demonstrate focus on 3D imaging for carcass prediction, methane mitigation strategies, and precision livestock technologies. Article trends reveal integration of machine learning with livestock assessment and growing emphasis on practical emission reduction solutions. Current projects include: RESAS 22-27: Data Driven Innovations For Improved Sustainability AGROLIVESTOCK: Methane-reducing livestock feed supplements SmartCow: European cattle research infrastructure UltraGreenCow: Comprehensive GHG measurement for ruminants She supervises PhD research on precision farming technologies in beef production.

Dele Owodunniمشاهده پروفایل

مدرس ارشد

• Design and Manufacturing
• Computer-aided Design (CAD)
• Aerospace Manufacturing
• +۴ مورد دیگر

Dr. Dele Owodunni is a Senior Lecturer in Mechanical Engineering and Programme Leader of the MSc Mechanical Engineering at the University of Chester. He specializes in Design and Manufacturing, focusing on socially-responsible computerization of engineering processes based on law-like principles. His research includes developing taxonomies for design shapes and advancing sustainable manufacturing practices. Dr. Owodunni has secured over £100,000 in UK funding for research and enterprise activities. His work emphasizes improving design for manufacturing (DFM) in aerospace contexts, defect data management, and energy efficiency in machining. He has supervised PhD students and contributed to collaborative knowledge-sharing frameworks in manufacturing. His research interests span feature recognition in CAD/CAM, process optimization, and sustainability. Notable contributions include frameworks for micro-blogging in knowledge sharing and energy-efficient machining strategies. He actively engages in enterprise activities and has over 15 years of experience in UK academia and industry.

• Machine Learning
• Deep Learning
• Internet of Things (IoT)
• +۵ مورد دیگر

Shahid Awan is a Lecturer at the School of Computing, Engineering and Physical Sciences. He holds a Ph.D. in Computer Science, specializing in Machine Learning, with a focus on applying intelligent systems to United Nations Sustainable Development Goals (UNSDGs), particularly in circular economy, IoT-enabled smart agriculture, and health informatics. His work emphasizes innovation, as seen in projects like 'Cricket Video Scene Categorisation' (Rector’s Award for Innovation, 2018) and predictive models for electric demand and dengue fever spread. Education: Ph.D. in Computer Science. Professional Development: Completed the 'Innovation Strategy Course' by KTN UK and 'Leadership and Management Training Modules' from Ashorne Hill Management College, UK. Research Interests: Machine Learning applications, Deep Learning, IoT, and Data Science in healthcare, sustainability, and smart systems. His publications highlight contributions to blockchain security, medical imaging analysis, and education technology. Notable Projects: Led projects on electric demand forecasting (collaborating with National Grid) and dengue fever prediction (health department). Founded the circular economy startup Circulayo, focusing on innovation strategy. Scientific Awards: Rector’s Award for Innovation (2018). Advising and Grants: Accepting PhD students and secured funding for startups like 'Electrical Demand Forecasting' through Silicon Valley pitches. Collaborates with industry partners like Crickingif and the National Grid. Labs/Teams: Active in interdisciplinary teams addressing SDGs through tech solutions, though no specific lab names are provided.

Chamitha De Alwisمشاهده پروفایل

مدرس ارشد

• AI Security
• Cybersecurity
• Network Security
• +۵ مورد دیگر

Dr. Chamitha De Alwis is a Senior Lecturer in the School of Computer Science & Technology at the University of Bedfordshire, within the Faculty of Creative Arts, Technologies & Science. He holds a B.Sc. (First Class Honors) in Electronic and Telecommunication Engineering from the University of Moratuwa, Sri Lanka, and a Ph.D. in Electronic Engineering from the University of Surrey, UK. He is a Senior Member of the IEEE and a Fellow of the Higher Education Academy. Research Interests: Dr. De Alwis focuses on AI Security, Cybersecurity, Network Security, 5G/6G technologies, Blockchain, and Edge Computing. His work spans cutting-edge areas such as quantum technologies for next-gen networks, smart grid security, and healthcare applications of edge computing. He has secured competitive research grants and contributes to high-impact projects in collaboration with industry and academia. Teaching Areas: Cybersecurity, Network Security, and Mobile Communication. He actively supervises PhD students in these domains, including Mr. Mohammad Ahmad, Mr. Ahmad Kanbour, and Ms. Charithri Nanayakkara. Publications: His recent work explores Edge AI, 6G architectures, blockchain in energy systems, and eHealthcare innovations. Notable trends include integrating AI with edge computing for real-time applications and addressing security challenges in 6G networks. Awards/Honors: Fellow of the Higher Education Academy (2024) Senior Member, IEEE (2021) Grants & Collaborations: Secured multiple research grants supporting projects in Edge Computing, 6G technologies, and Smart Grid applications. Engaged with interdisciplinary teams in the Institute for Research in Applicable Computing (IRAC) and the Institute for Research in Engineering and Sustainable Environment (IRESE). Labs & Teams: Affiliated with the School’s research institutes, contributing to initiatives in robotics, AI, and sustainable engineering. Involved in collaborative projects with industry partners to advance edge computing and cybersecurity solutions.

Tamoor Shafiqueمشاهده پروفایل

مدرس ارشد

• Smart Cities
• Internet of Things
• Wireless Sensor Networks
• +۵ مورد دیگر

Tamoor Shafique is a Senior Lecturer at Staffordshire University's Digital, Tech, Innovation & Business school. With over 11 years of experience in engineering industry and academia, he has held roles such as Programme Leader for Electrical Engineering programmes validated by the University of Central Lancashire and Pearson. His international teaching experience includes positions at Xi'an University of Posts and Telecommunication (China) and Mirpur University of Science and Technology (Pakistan). He holds a PGCE from the University of Central Lancashire (2022), an MSc in Electrical Engineering from COMSATS Institute of Information and Technology (2013), and a BSc (Hons) in Electrical Engineering from Mirpur University of Science and Technology (2010). His research focuses on Smart Cities , IoT , Wireless Sensor Networks , and Energy Management . Recent work explores UAV trajectory optimization, energy-efficient IoT systems, and facial recognition technologies. He also investigates applications of AI in robotics, automation, and biomedical engineering. Key publications highlight advancements in UAV safety protocols (2025), EEG artifact removal via neural networks (2024), and energy hole mitigation in smart city infrastructure (2023). Earlier contributions include facial asymmetry analysis for medical diagnostics (2018) and smart grid energy management models (2016). Awards: Fellow of the Higher Education Academy Professional Memberships: Member of Institution of Engineering and Technology (MIET) He actively participates in IEEE, IET, and ETF initiatives, contributing to apprenticeship frameworks with the Institute of Apprenticeship and ESFA. His work bridges academic research with practical industrial applications in automation, control systems, and smart technologies.