جست‌وجوی استاد و پژوهشگر

• Computer Vision
• Deep Learning
• Semantic Segmentation
• +۳ مورد دیگر

Dr. Guangliang Cheng is an Associate Professor in the Department of Computer Science at the University of Liverpool. His research focuses on deep learning, computer vision, and perception algorithms with applications in remote sensing, medical imaging, and autonomous systems. Prior to his current role, he served as a vice research director in the Autonomous Driving Group at SenseTime and completed postdoctoral research at the Aerospace Information Research Institute, Chinese Academy of Sciences. Ph.D. in Pattern Recognition from the National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CASIA) Postdoctoral Researcher at Aerospace Information Research Institute, Chinese Academy of Sciences (2017–2019) Dr. Cheng’s research integrates computer vision and deep learning to address challenges in semantic segmentation, domain adaptation, and robust detection. Recent work explores wavelet-based multimodal fusion for remote sensing and attention-guided architectures for medical imaging. His 2025 publications span journals like GIScience & Remote Sensing and Knowledge-Based Systems , emphasizing scalable solutions for geospatial and biomedical applications. In 2025, Dr. Cheng’s article trends highlight remote sensing semantic segmentation, cross-domain medical imaging, and drone-based fire detection. His collaborations span institutions such as SenseTime, Chinese Academy of Sciences, and University of Liverpool teams, focusing on frequency-domain fusion, attention mechanisms, and GPU optimization. As a supervisor, Dr. Cheng seeks highly motivated PhD students to join projects supported by scholarships including the Centres for Doctoral Training (CDT) and Duncan Norman Scholarship. He serves as Module Co-ordinator for COMP338: Computer Vision (2024–2025) and actively reviews for top-tier journals and conferences.

• Earth Observation
• Synthetic Aperture Radar (SAR)
• Remote Sensing
• +۶ مورد دیگر

Dr. Armando Marino is a Senior Lecturer in Earth Observation at the University of Stirling’s Department of Biological and Environmental Sciences since 2018. He holds an MSc in Telecommunication Engineering (2006, Universita’ di Napoli) and a PhD in Polarimetric SAR Interferometry (2011, University of Edinburgh). His research focuses on synthetic aperture radar (SAR) for environmental monitoring, including maritime pollution, forest degradation, agricultural productivity, and coastal erosion. Education: MSc Telecommunication Engineering, Universita’ di Napoli ‘Federico II’ (2006) PhD in Remote Sensing, University of Edinburgh (2011) Marino develops machine learning algorithms for SAR data analysis and conducts fieldwork with custom-built radar systems. He collaborates with institutions like ESA, JAXA, and NASA, leading projects such as PlasticSurf (microplastic detection) and MoLaDy (ALOS-4 land monitoring). His work integrates optical and SAR satellite data for flood mapping and vegetation analysis. He has received accolades including the RSPSoc Best PhD Thesis (2011) and University of Stirling’s Outstanding Collaborator award (2022). Current projects involve £180,000+ in funding for radar-based environmental solutions. Scientific Awards: Best PhD Thesis 2011 (RSPSoc) Outstanding PhD Thesis (Springer Verlag) Outstanding Collaborator 2022 (University of Stirling) Marino’s methodologies combine SAR polarimetry, computer vision, and environmental field measurements. He actively mentors interdisciplinary teams and contributes to global initiatives on climate hazard mitigation.

• Atmospheric Science
• Remote Sensing
• Radar Meteorology
• +۷ مورد دیگر

Anthony Illingworth is a Professor in the Department of Meteorology at the University of Reading, UK, where he leads research in atmospheric remote sensing, radar and lidar technologies, and weather forecasting. His work is central to major satellite missions such as EarthCARE and WIVERN, and he collaborates extensively with European and international meteorological agencies. Education and Background: While specific degrees are not listed in the provided text, his long-standing academic career and leadership in advanced meteorological research suggest a PhD in atmospheric physics or a related field, likely from a UK institution. His research interests span radar meteorology , cloud physics , satellite remote sensing , precipitation measurement , boundary layer dynamics , and numerical weather prediction . He focuses on improving observational techniques using ground- and space-based sensors to enhance forecast accuracy. His work integrates physics-based models with real-world data from instruments such as Doppler radars, lidars, and polarimetric sensors. The publication trends from 2015 to 2025 reveal a consistent focus on satellite-based wind and cloud observations (e.g., WIVERN and EarthCARE), calibration of remote sensing instruments, and data assimilation for weather models. His articles frequently address technical challenges in radar signal interpretation, wind profiling in extreme weather, and the use of ground networks to validate and improve forecasts. A recurring theme is the development and validation of new methodologies for extracting atmospheric parameters from remote sensing data. Scientific Awards: Advising and Grants: While no students or grants are explicitly listed, his frequent senior authorship and leadership in large collaborative projects (e.g., FRANC, EarthCARE) suggest active supervision of PhD students and postdoctoral researchers, as well as success in securing major research funding from agencies such as the UK Met Office, ESA, and NERC. Labs and Teams: Illingworth is closely associated with the atmospheric remote sensing group at the University of Reading, which operates advanced radar and lidar systems. He collaborates with the European Centre for Medium-Range Weather Forecasts (ECMWF), CNRS in France, and the CloudSat science team, indicating strong institutional partnerships and team-based research in operational and satellite meteorology.

• Remote Sensing
• Photogrammetry
• Civil Engineering
• +۹ مورد دیگر

Dr. Maria-Valasia Peppa is a researcher at Newcastle University's School of Civil Engineering and Geosciences . Her work focuses on remote sensing, photogrammetry, and geospatial technologies, with applications in hydrodynamic modeling, landslide monitoring, and urban infrastructure analysis. Key Research Areas: UAV-based photogrammetry, glacial lake dynamics, digital elevation models (DEMs), and environmental data uncertainty. Recent Trends: Integration of deep learning with CCTV for traffic analysis, calibration of multisensor UAV systems, and real-time flood nowcasting frameworks. Collaborations: Active partnerships with Professors Jon Mills, Claire Walsh, and international institutions in Africa and Nepal. Applications: Cultural heritage mapping, water security strategies, and urban air quality modeling.

• Gait Analysis
• Parkinson's Disease
• Wearable Technology
• +۳ مورد دیگر

Dr. Lisa Alcock is a Researcher at Newcastle University, specializing in gait analysis, wearable technology, and neurodegenerative disorders, particularly Parkinson’s disease. Her work focuses on developing and validating algorithms for mobility assessment, leveraging digital health technologies. She collaborates extensively with interdisciplinary teams, including the Mobilise-D consortium, to advance real-world gait monitoring and clinical outcomes. Recent projects include studies on environmental discrimination using machine learning, patient-centric visualization of mobility data, and drug efficacy analysis through wearable sensors. Her research interests span biomechanics, motor function assessment, and the integration of wearable devices into clinical practice. She has contributed to over 50 peer-reviewed publications, emphasizing methodological advancements in gait detection, turning algorithms, and sensor validation. Collaborations with institutions worldwide highlight her commitment to bridging technological innovation with clinical needs. Key contributions include the development of energy-based composite indices for Parkinson’s disease and novel imaging techniques for gait neuroanatomy. Her work often addresses challenges in real-world applicability, ensuring technologies are validated in diverse settings. Current efforts focus on enhancing patient collaboration in outcome visualization and improving accessibility through context-aware systems.

Gareth Robertsمشاهده پروفایل

دانشیار

• Landscape Fire Monitoring
• Earth Observation Data Analysis
• Fire Emissions Estimation
• +۳ مورد دیگر

Dr. Gareth Roberts is an Associate Professor at the University of Southampton, affiliated with the School of Geography. His research focuses on leveraging optical and thermal Earth Observation (EO) data to monitor landscape fire activity, quantify fire emissions, and assess environmental and health impacts. He has led projects like UAV4VAL (validating Sentinel-2/Gaofen-6 data using UAVs) and contributed to initiatives such as the CEOS Land Product Validation Fire Disturbance Theme and the GOFC-GOLD Fire Implementation Team. Key research projects include validating fire radiative power (FRP) retrievals using thermal camera imagery and analyzing wildfire trends in CONUS. Roberts has also explored the impact of landscape fires on air quality and collaborated with agencies like the Food and Agriculture Organization (FAO) on agroclimate projects. He currently supervises PhD student Abdullah Bin Abdulwahed in Geography. His work spans remote sensing methodologies, satellite data validation, and interdisciplinary applications in ecology, climate, and public health. He is actively involved in training the next generation of researchers through PhD supervision and academic collaborations.

• VHDL hardware synthesis
• Novel Microprocessor Architectures
• Stack-based processors
• +۳ مورد دیگر

Christopher Crispin-Bailey is a Senior Lecturer in the Department of Computer Science at the University of York. He holds a BEng and PhD from Teesside University. His research focuses on VHDL hardware synthesis, novel microprocessor architectures, stack-based processors, optimization of stack-oriented object code, multimedia server architectures, and bus arbitration methods. Career highlights include roles as a Teaching Research Fellow (1996–1997), Senior Lecturer at the University of Teesside (1997–1999), and Lecturer at the University of York since 1999. He currently holds the title of Senior Lecturer and is part of the Real-Time and Distributed Systems research group. His contributions span hardware design, embedded systems, and biomedical signal processing. He contributes to departmental roles as an Exam Paper Checker and Union Rep. His work emphasizes practical applications in both academic and industry contexts, with a focus on optimizing hardware and software systems for efficiency and scalability.

Ana Pacheco Pascagazaمشاهده پروفایل

پژوهشگر ارشد

• Remote Sensing
• Vegetation Analysis
• Land Cover Assessment
• +۷ مورد دیگر

Dr. Ana Pacheco Pascagaza is an Honorary Research Fellow in the Department of Geography at the University of Leicester. Her research focuses on remote sensing applications for environmental monitoring, particularly in the areas of vegetation analysis, land cover change, and fire risk assessment. She is currently accepting PhD students and actively involved in research projects related to UK environmental systems. Dr. Pacheco Pascagaza's educational background includes: Doctor of Philosophy (PhD) in Assessing Forest Degradation in Bajo Calima – Colombia from Multi-Frequency and Multi-Temporal Synthetic Aperture Radar (SAR) from the University of Leicester (awarded January 29, 2021) Master in Science from the University of Leicester (awarded January 29, 2014) Her primary research interests center around the application of remote sensing technologies to environmental challenges. Dr. Pacheco Pascagaza specializes in Synthetic Aperture Radar (SAR) data analysis, vegetation monitoring, land cover classification, and peatland ecosystem assessment. Her work frequently incorporates machine learning techniques for change detection and real-time environmental monitoring. Much of her recent research focuses on UK-specific environmental issues, particularly in upland moorlands and the South Pennines region. Dr. Pacheco Pascagaza's publication record demonstrates a strong focus on practical applications of remote sensing for environmental management. Her recent work shows increasing emphasis on fire risk assessment and vegetation recovery monitoring, with several publications specifically addressing fuel mapping in UK landscapes. Her research combines multiple remote sensing modalities and temporal approaches to create dynamic monitoring systems that can inform environmental policy and management decisions. Dr. Pacheco Pascagaza is currently involved as a Researcher in the active project "Toward a UK fire danger rating system: Understanding fuels, fire behaviour and impacts" (January 2020 - September 2025), which has received attention from policy sources and academic readership. This project focuses on UK vegetation, fire behavior, land cover, and peatland systems.

• Remote Sensing
• Environmental Science
• Climate Change Monitoring
• +۵ مورد دیگر

Professor Kevin Tansey is a Professor of Remote Sensing at the University of Leicester's School of Geography, Geology & The Environment. His work focuses on advancing Earth observation techniques for environmental monitoring, including drought forecasting, peatland hydrology, wildfire science, and agricultural yield estimation. He specializes in integrating satellite data (Sentinel, MODIS, Landsat) with machine learning to address global challenges like climate change and ecosystem degradation. Research interests span remote sensing applications in agriculture, forest ecology, and hydrology. Key areas include developing fusion frameworks for multisensor data, improving crop yield models using deep learning, and quantifying peatland subsidence through InSAR techniques. He leads projects like ESA’s WorldPeatland initiative for global peatland mapping and collaborates on wildfire research across historically and emerging fire-prone regions. Publications highlight innovations in vegetation stress indices (VTCI), evapotranspiration datasets, and oil spill detection in tropical forests. His work bridges technical advancements (e.g., transformer networks, CNN-Transformer hybrids) with practical applications for sustainability. Current efforts emphasize enhancing global burned area mapping and improving biomass estimation in African savannahs through machine learning-driven feature selection. Key Projects: ESA WorldPeatland, GLOBBIOMASS, Fire_cci Technical Expertise: SAR/InSAR, hyperspectral imaging, deep learning frameworks Global Collaboration: Partnerships in China, Malaysia, Ecuador, and Africa Grants and funding support interdisciplinary approaches to climate change impacts, including carbon storage quantification and loss-and-damage analysis. His lab develops open-source tools like the Pyeo package for near-real-time forest cover change detection.

Tanya Vladimirovaمشاهده پروفایل

استاد

• Embedded Systems
• Artificial Intelligence
• Space Systems Engineering
• +۵ مورد دیگر

Professor Tanya Vladimirova is a Professor of Electronic and Computer Systems Engineering at the University of Leicester, affiliated with the School of Engineering's Mathematical Sciences department. Her research focuses on custom computing for edge intelligence, embedded systems design, and space-based applications. She pioneered innovations in satellite computing systems, including reconfigurable on-board computers, fault-tolerant designs, and FPGA-accelerated image processing. Her work integrates AI, digital signal processing, and hardware acceleration for applications in autonomous vehicles, spacecraft, and IoT. Key achievements include the first FPGA-based on-board hyperspectral image compression systems, fault-tolerant distributed computing frameworks for satellites, and self-healing distributed intra-satellite systems. Current projects explore machine learning for satellite reliability and autonomous vehicle vision systems. Prior to Leicester, she led a globally recognized group at the Surrey Space Centre, collaborating with Surrey Satellite Technology Limited (SSTL). Her research spans satellite miniaturization, wireless sensor networks for planetary exploration, and radiation-hardened computing. Her publications address challenges in satellite data processing, fault tolerance, and embedded AI. Notable contributions include novel algorithms for landmine detection, radiation mitigation strategies for FPGAs, and adaptive middleware for distributed space systems. Her work bridges theoretical computing with practical aerospace engineering, emphasizing resilience and efficiency in hostile environments.

Pablo Rodolfo Baldivieso Monasteriosمشاهده پروفایل

مدرس

• Control Systems
• Distributed Control
• Model Predictive Control
• +۳ مورد دیگر

Dr. Pablo Rodolfo Baldivieso Monasterios is a Lecturer in AI and Data Science at the School of Electrical and Electronic Engineering, University of Sheffield. He holds a Ph.D. in Robust Distributed Control (2018) from the same university, an MSc in Control Systems from the University of Sheffield, and degrees in Electronic Engineering (Escuela Militar de Ingeniería, Bolivia) and Pure Mathematics (Universidad Mayor de San Andrés, Bolivia). His research focuses on control systems, distributed control, model predictive control (MPC), and their applications in power systems and energy networks. He has contributed to advancements in DC microgrids, nonlinear control architectures, and coalitional MPC frameworks. Dr. Baldivieso Monasterios has held postdoctoral roles at the Control and Power Systems (CAPS) laboratory and the University Technology Centre (UTC) at the University of Sheffield. His work integrates AI with engineering challenges, emphasizing robustness and scalability in cyber-physical systems. His recent publications (2021–2024) explore neural network integration with MPC, decentralized control for meshed networks, and plug-and-play features in coalitional systems. Education: Ph.D. in Robust Distributed Control, University of Sheffield (2018) MSc in Control Systems, University of Sheffield Bachelor's Degree in Electronic Engineering, Escuela Militar de Ingeniería, Bolivia Bachelor's Degree in Pure Mathematics, Universidad Mayor de San Andrés, Bolivia Teaching: ACS234: Systems Engineering Mathematics II ACS6124: Multisensor and Decision Systems His research interests span distributed control systems, MPC optimization, and AI-driven solutions for energy infrastructure. He has collaborated on projects involving DC microgrids, peer-to-peer negotiation frameworks, and autonomous cyber-physical systems.

Yuanbo Nieمشاهده پروفایل

مدرس

• Control Theory
• Optimization-based Control
• Aerospace Systems
• +۳ مورد دیگر

Dr. Yuanbo Nie is a Lecturer in Control and Systems Engineering at the School of Electrical and Electronic Engineering, University of Sheffield, and serves as the Employability Lead for the Global Engineering Challenge. He holds a Ph.D. in Aeronautics from Imperial College London (2021), with previous roles including a postdoctoral position at Rolls-Royce and research at the German Aerospace Center (DLR). His research focuses on numerical methods for dynamic optimization, optimization-based control, and aerospace systems control, particularly in trajectory optimization, energy management for aircraft, and upset recovery simulation. Education: MSc in Aerospace Engineering, Delft University of Technology MSc in Advanced Computational Methods, Imperial College London Ph.D. in Aeronautics, Imperial College London (Thesis: Numerical Optimal Control with Applications in Aerospace) Research Interests: Dr. Nie’s work emphasizes developing advanced numerical techniques for dynamic optimization, including integrated residual methods and optimization-based control strategies. He explores applications in aerospace systems, such as optimal flight trajectory design, hybrid-electric aircraft energy management, and next-generation flight simulators. His methods aim to bridge theoretical rigor and practical implementation for engineers. Professional Activities: He is a member of IEEE, the IFAC Optimal Control Technical Committee, and the EPSRC Automatic Control Engineering Network. His teaching includes courses on multisensor systems and aerospace system modeling. Labs & Groups: Involved in the Control Theory research group and collaborations with Rolls-Royce’s University Technology Centre.

Abderrahim Halimiمشاهده پروفایل

دانشیار

• Statistical Signal Processing
• Bayesian Inference
• Hyperspectral Imaging
• +۲ مورد دیگر

Dr. Abderrahim Halimi is an Associate Professor and Royal Academy of Engineering Research Fellow at Heriot-Watt University's School of Engineering and Physical Sciences. His research focuses on Bayesian statistical methods for signal and image processing, with applications in remote sensing, single-photon imaging, and medical imaging. His current projects develop algorithms for depth imaging, hyperspectral unmixing, and X-ray data quantification. He serves as Associate Editor for Digital Signal Processing and holds senior membership in IEEE, contributing to signal processing theory and multisensor systems.

• Data Visualization
• Data Analysis
• Biotelemetry
• +۳ مورد دیگر

Dr. Mark Holton is a Research Officer at Swansea University, affiliated with the School of Biosciences, Geography and Physics, and the Colleges of Engineering and Science. His work focuses on data logging systems, sensor circuit design, and biotelemetry technologies, particularly for animal monitoring and Human-Computer Interaction (HCI) devices. He has contributed to projects involving animal behavior analysis, environmental framing studies, and the development of algorithms for big data visualization and categorization. His research spans disciplines such as ecology, marine biology, and biomechanics, with applications in wildlife conservation, animal welfare, and technology integration. Key research areas include: animal movement tracking via accelerometers and magnetometry, biotelemetry for behavioral studies, and sensor-based solutions for non-invasive wildlife tagging. Holton has collaborated on projects analyzing the effects of tourism on whale sharks, reptile behavior, and the impact of environmental cues on animal navigation. His work often bridges engineering and ecology, emphasizing practical applications such as multisensor collar design and orientation sphere visualization tools. Notable publications highlight innovations in dead-reckoning algorithms, energy expenditure modeling in terrestrial animals, and the use of angular velocity metrics for metabolic analysis. While no formal awards are listed, his contributions to biotelemetry and animal behavior research have been widely cited in ecological and engineering journals. Holton’s research extends to HCI, including tactile feedback systems and sustainable technology prototyping with everyday materials.

Sarah Cangمشاهده پروفایل

مدرس ارشد

• Digital Healthcare
• Wellbeing
• Ageing Society
• +۵ مورد دیگر

Dr. Sarah Cang is a Senior Lecturer (Education) in Mathematics and Statistics at Brunel University London, within the College of Engineering, Design and Physical Sciences. She has extensive experience in both industry and academia, having worked for a leading UK software company, a UK Government Research Laboratory, Central Government Department as Senior Statistician, and at Exeter University and Bournemouth University prior to joining Brunel. Her educational background includes: PhD in Applied Mathematics (UK) MSc in Mathematics (Distinction) (UK) BSc (Hons) in Mathematics (First Class) (China) PG Cert in Research Degree Supervision (UK) PG Cert in Education Practice (UK) (Fellowship of the Higher Education Academy) PG Cert in Computer Software (UK) Dr. Cang's research focuses on Digital Healthcare & Wellbeing, particularly for the ageing society, promoting healthy living for senior citizens. She applies advanced techniques in Artificial Intelligence (data mining, machine learning, pattern recognition) and Big Data to address challenges in activity recognition, assistive technologies for dementia care, and tourism demand forecasting. Her work bridges mathematical statistics with real-world applications in health and tourism. Analysis of her recent publications reveals a strong emphasis on interdisciplinary research at the intersection of healthcare technology and data science. Key trends include the development of wearable sensor systems for elderly activity monitoring, the application of machine learning for feature selection and classification in multi-sensor environments, and innovative approaches to tourism demand forecasting using copula-GARCH models and ensemble methods. Her work consistently targets improving quality of life for the elderly through technological solutions. Her scientific achievements have been recognized with: World's Top 2% scientists by Stanford University (2020-present) Fellowship of the Higher Education Academy Dr. Cang has supervised numerous PhD students on topics ranging from activity recognition systems for assisted living to tourism demand forecasting and robotic control. She has secured substantial research funding as Principal Investigator, including multiple EU projects such as H2020-MSCA-ITN (€2.7M), CHARMED (€2.2M), Erasmus Mundus cLINK (€2.5M), FUSION (€3.05M), SMOOTH (€0.9M), and RABOT (€310.8K), all addressing challenges in digital health tourism and elderly well-being. She collaborates with researchers including Dr. Fang Wang, Mr. Tianhao Wang, Dr. Mayo Adetoro, Mr. Amir Ashrafi, and Dr. Zoi Krokida on projects related to digital healthcare, ageing society, and forecasting, forming a dynamic research team focused on innovative solutions for societal challenges.