جست‌وجوی استاد و پژوهشگر

Asaad Faramarziمشاهده پروفایل

استاد

• Computational Geotechnics
• Machine Learning Applications in Civil Engineering
• Geophysical Sensing
• +۴ مورد دیگر

Professor Asaad Faramarzi is a Professor of Civil and Geotechnical Engineering at the University of Birmingham's School of Engineering, serving as Head of the Department of Civil Engineering. He specializes in computational geotechnics, machine learning applications for infrastructure modeling, and geophysical sensing. His research focuses on buried infrastructure assessment, quantum sensor integration, and offshore geotechnics. Education: PhD in Geotechnical Engineering (2011) MSc in Structural Engineering (2006) BSc in Civil Engineering (2004) His research interests include developing novel numerical methods for sensing technologies, poroelasticity modeling, and energy geotechnics. He leads projects like the ISCF Ground and Underground Infrastructure Damage Evaluation (GUIDE) and the Quantum Technology Mapping (QT-MIBA) initiative. He has published over 100 papers and serves on editorial and conference committees, including as Chair of the 25th UKACM Conference on Computational Mechanics (2017). Awards: Fellow of the Higher Education Academy (2014) Prof. Faramarzi's work bridges AI-driven infrastructure modeling with practical engineering challenges, emphasizing quantum technology applications and sustainable solutions for urban underground systems. His grants include EPSRC funding for quantum sensing and structural health monitoring.

Jelena Ninicمشاهده پروفایل

دانشیار

• Computational Mechanics
• Artificial Intelligence
• Civil Engineering
• +۷ مورد دیگر

Dr. Jelena Ninic is an Associate Professor in Digital Engineering at the University of Birmingham's School of Engineering since 2022. Her research focuses on computational methods, AI, and BIM for structural and geotechnical engineering. She holds a PhD in Computational Mechanics (2015, Ruhr University Bochum) and a Marie Curie Fellowship (2016, University of Nottingham). She is an Associate Editor of Tunnelling and Underground Space Technology , UK representative for ISSMGE TC222, and a member of UKACM's Executive Committee. Her work integrates finite element simulations, machine learning, and digital twins for infrastructure lifecycle management. Research interests include process-oriented simulations, digital twins, and risk assessment in tunnelling, construction, and transportation. She has pioneered multi-level BIM parametrization and BIM-to-IGA workflows. Awards include FHEA (2020) and Marie Curie SATBIM Fellowship (2016). Recent articles explore Bayesian optimization for tunnels, meshless methods for buried structures, and sustainability-driven infrastructure resilience. Education: PhD in Computational Mechanics (2015) Dipl.-Ing. in Structural Engineering (2008) Professional Roles: Associate Editor, Tunnelling and Underground Space Technology Core member, ISSMGE TC222 UKACM Executive Committee Grants & Advising: Supervises PhD students in computational mechanics and BIM applications Co-chair of international conferences (e.g., CTTU 2020) Labs/Teams: Develops automated tunnel damage reporting systems and digital twin frameworks Collaborates on global-local optimization for aircraft structures

James Prittsمشاهده پروفایل

استادیار

• Computer Vision
• Underwater Robotics
• 3D Geometry
• +۵ مورد دیگر

Dr. James Pritts is a computer vision researcher currently working at the Institute of Computer Science, Kiel University, within the Marine Data Science group. He is affiliated with the Faculty of Engineering and contributes to interdisciplinary marine research through the KiTE early-career fellowship program. His work bridges robotics, computer vision, and ocean science, focusing on enabling autonomous underwater vehicles (ROVs) to operate effectively in challenging, low-visibility environments. His research interests include: Underwater visual localization and mapping Robust 3D reconstruction in turbid waters Geometric calibration of camera systems Sensor fusion (camera, IMU, DVL) Non-rigid reconstruction (e.g., jellyfish) Autonomous monitoring of marine ecosystems Dr. Pritts' work supports applications in marine biology, underwater archaeology, and infrastructure inspection. He develops algorithms that allow robots to assess their own perception accuracy, enabling reliable long-term monitoring of seagrass beds and fragile marine species. His research is deeply collaborative, involving partnerships with GEOMAR and MBARI, and aligns with Kiel University’s priority research areas such as Kiel Marine Science and Ocean Health. He has previously held research roles at NASA, DARPA, and Facebook Reality Labs, and was an Assistant Professor at the Ukrainian Catholic University. His academic journey reflects a strong focus on geometric computer vision and real-world deployment of complex systems. Notable scientific contributions include advancing calibration techniques for underwater optics and developing robust localization methods under refractive distortions. His work often requires solving multiple vision challenges simultaneously, such as refraction, non-rigid deformation, and sparse features. Dr. Pritts is actively involved in hands-on robotics, working closely with hardware engineers to deploy ROVs in coastal waters near Kiel. He emphasizes practical utility in research and is driven by the principle of being useful. He holds a PhD from the Czech Technical University in Prague (2020) and has conducted postdoctoral research at Chalmers University and the Czech Institute of Informatics, Robotics & Cybernetics.

Jun Liمشاهده پروفایل

مدرس

• Data Analytics
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Dr. Jun Li is a Lecturer in Mathematics and Computer Science at Cranfield University's Centre for Computational Engineering Sciences. His expertise spans Data Analytics, Machine Learning, Deep Learning, and Mathematical Modelling applied to diverse domains including aerospace, urban planning, and cybersecurity. BSc in Computer Science from QingDao University MSc in Software Engineering from LondonMet University PhD in Neural Networks from LondonMet University Dr. Li's research focuses on integrating Machine Learning with High Performance Computing and Mathematical Modelling. Key areas include: Large-scale Neural Network simulations in Neuroscience Deep Learning for aircraft engine defect detection via computer vision Neural Network language modeling with noise resilience Transport flow analysis using Complex Networks Urban agglomeration formation and imbalanced economy recovery modeling His recent publications highlight interdisciplinary applications of Machine Learning in transport systems, aerospace diagnostics, and cybersecurity. These works emphasize data-driven methods, GANs, and predictive analytics for real-world challenges. Current grants include funding from China's National Key Research and Development Program (2016YFC0503506) and CAS's Bingwei Excellent Young Scientists Programme (2015RC202). Dr. Li supervises MSc and PhD students in Artificial Intelligence, Machine Learning, and Big Data analytics. He collaborates with Cranfield's Centre for Computational Engineering Sciences, leading projects like: Airport service quality evaluation using Skytrax reviews Urban land-use transport modeling for policy impact analysis Imbalanced economic recovery frameworks post-disasters

Boyu Kuangمشاهده پروفایل

پژوهشگر ارشد

• Semantic Segmentation
• Object Detection
• Weakly Supervised Learning
• +۹ مورد دیگر

Dr. Boyu Kuang is a Research Fellow in Computer Vision and Artificial Intelligence at Cranfield University, affiliated with the Centre for Computational Engineering Sciences. His work bridges academic research with industrial applications in aviation, robotics, and energy systems. Current role: Research Fellow in Computer Vision and AI Key affiliations: Centre for Computational Engineering Sciences Research interests include semantic segmentation , object detection , weakly and self-supervised learning , multi-modal perception , and vision-language foundation models . His methodology emphasizes robust AI systems for low-resource industrial environments , with applications in aviation maintenance , robotics , and energy infrastructure monitoring . He leads the Artificial Intelligence and Machine Learning module at Cranfield. Key activities include the UKRI, ATI, and Airbus-funded ONEHeart project on autonomous systems and intelligent inspection. Collaborations span Stanford University , King’s College London , Civil Aviation University of China , and industry partners like Airbus and Leidos . Publications focus on vision-language models , multi-modal perception , and industrial AI . As an Editorial Board Member for Discover Artificial Intelligence (Springer Nature), he contributes to academic governance. Peer review experience includes 60+ journal manuscripts for venues like IEEE Transactions on Image Processing and Elsevier Neural Networks . Facilities utilized include DARTeC and AIRC .

• Laser Doppler Anemometry
• Thermography-Based Diagnostics
• Vision and Photogrammetry in Inspection
• +۱ مورد دیگر

Prof. Sławomir Zator holds the position of University Professor at the Department of Safety Engineering and Technical Systems under the Faculty of Production Engineering and Logistics at Opole University of Technology. His research focuses on advanced diagnostics techniques using laser Doppler anemometry, thermography, and vision-based methods for infrastructure and energy systems. He also explores energy management strategies in buildings, emphasizing smart home integration with renewable energy sources like photovoltaics and heat pumps. His work bridges theoretical advancements with practical applications in power line safety, industrial equipment monitoring, and sustainable energy solutions. Research interests include: Laser-based flow measurement innovations Non-destructive evaluation of boiler tubes and power line components Machine vision applications for structural diagnostics Optimization of building energy systems Recent publications highlight trends in smart grid energy scheduling, icing risk mitigation for power infrastructure, and hybrid energy storage integration. His work often features cross-disciplinary approaches combining thermal imaging, computational modeling, and automation technologies. Prof. Zator maintains an active research profile through collaborations with international institutions and contributes to technical education via course development in safety engineering and metrology.

İshak Paçalمشاهده پروفایل

دانشیار

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

İshak Paçal is an Associate Professor at the Department of Computer Hardware, Faculty of Engineering, İğdır University. His research focuses on deep learning applications in medical imaging, including cancer detection, polyp identification, and neurological disorder analysis. He holds a doctorate from Erciyes University (2017-2022), a master's from the University of Nottingham (2015-2016), and a bachelor's from Harran University (2009-2013). Education: Ph.D., Computer Engineering, Erciyes University M.Sc., Electronic Communication and Computer Engineering, University of Nottingham B.Sc., Computer Engineering, Harran University Research Interests: Deep learning in medical diagnostics Applications in MRI, CT, and endoscopic imaging Neural networks for tumor and lesion classification Computer vision for agriculture and infrastructure Recent Contributions: Developed U-Net-based frameworks for brain stroke segmentation Pioneered hybrid ConvNeXt models for skin cancer detection Advanced transformer architectures for lung cancer diagnosis Grants & Projects: Leading a Tübitak-supported project on automated cancer detection Conducted MRI-based cerebral vascular occlusion research Labs/Teams: Medical AI Lab (focusing on endoscopic and radiological image analysis) Computer Vision Applications Group

• Artificial Intelligence
• Machine Learning
• Anomaly Detection
• +۳ مورد دیگر

Dr. Victoria J Hodge is a Research Fellow in the Department of Computer Science at the University of York, affiliated with the Centre for Assuring Autonomy (CfAA). She holds a PhD in Computer Science from the University of York and has industry experience in software architecture for medical diagnostics and industrial anomaly detection systems. Her research focuses on artificial intelligence (AI), anomaly detection, machine learning, and safety assurance for autonomous systems, particularly in robotics. Her work emphasizes through-life safety assurance of AI-driven autonomous systems, including robotics platforms operating in uncertain environments. She has authored over 70 publications across disciplines like AI, machine learning, robotics, and safety engineering. Notable projects include SCALOFT (drone safety analysis) and ASUMI (UAV safety assurance in mining). Her research trends show a strong emphasis on interdisciplinary applications of AI, with recent focus on autonomous systems in high-risk environments (e.g., mining, healthcare) and real-time decision support systems. Collaboration with industry ensures practical impact in domains like medical diagnostics and industrial automation.

• Robotics
• Artificial Intelligence
• Human-Robot Interaction
• +۶ مورد دیگر

Juan Carlos Gonzalez Victores is an Associate Professor at the Department of Systems Engineering and Automation, Universidad Carlos III de Madrid (UC3M), actively leading research in the Robotics Lab. His work spans multiple disciplines including Computer Science, Medicine, and Robotics and Industrial Informatics. Research interests focus on: Human-Robot Interaction with applications in neurorehabilitation and accessibility Autonomous Systems for smart cities and underground infrastructure inspection Deep Learning and Reinforcement Learning for robotic manipulation Medical Robotics with emphasis on surgical and diagnostic applications Recent publications highlight trends in: Garment-agnostic manipulation techniques Neurorehabilitation systems using synthetic data Human-like sketching via Deep Q-Learning Safety analysis of AI-human organizational interfaces Contact: juancarlos.gonzalez@uc3m.es | jcgvicto@ing.uc3m.es

• Structural Engineering
• Civil Engineering
• Robotics
• +۳ مورد دیگر

Hamish Dow is a Research Associate at the University of Strathclyde , affiliated with the Faculty of Engineering and the Department of Civil and Environmental Engineering . His work focuses on integrating hardware and software innovations for image-based defect detection in concrete and steel structures, with applications in nuclear facilities, tunnels, and bridges. Specializes in Structural Health Monitoring Active in Robotics and Computer Vision Principal Investigator for ALICS projects Research trends include automation , photometric imaging , and defect quantification for civil infrastructure. His work bridges machine learning and sensor technology for industrial applications. 2025: Best Poster Award at Strathclyde CEE PhD Poster Conference 2025: 1st Place in IOM3 National Young Persons' Lecture Competition 2024: Nuclear Institute Scotland YGN Speaking Competition Winner Has lectured on Structural Mechanics & Materials and Computer Programming at Strathclyde partner universities in China, and collaborates closely with industry partners for field trials.

Andreas Enqvistمشاهده پروفایل

استاد

• Nuclear Engineering
• Radiation Detection
• Neutron Spectroscopy
• +۱۲ مورد دیگر

Andreas Enqvist is a Professor in the Department of Materials Science and Engineering at the University of Florida, College of Engineering, where he leads the Enqvist Research Group (LOKE). The group is part of the Nuclear Engineering Program and actively contributes to national and international efforts in nuclear nonproliferation, safeguards, and radiation detection. They are members of the US Department of Energy's National Nuclear Security Administration (NNSA) Consortium for Monitoring, Technology, and Verification (NA-22) and collaborate with NASA on planetary protection research. Their research focuses on the detection and modeling of neutron and gamma-ray signatures from nuclear materials, particularly fresh and spent nuclear fuel. Key areas include nondestructive assay, neutron spectroscopy, detector development (especially He-4 scintillators), data fusion with vision systems, and simulation using tools like MCNPX-PoliMi and SCALE. The group conducts experiments at the University of Florida Training Reactor (UFTR) and the National Criticality Experiments Research Center (NCERC). The recent publications (2020–2025) reflect a strong emphasis on optimizing detector configurations, neutron scatter cameras, pulse shape discrimination using machine learning, dry cask monitoring, and advanced data fusion techniques. There is a clear trend toward integrating physics-based models with AI/ML methods and developing compact, field-deployable systems for nuclear security applications. Member, NNSA NA-22 Consortium for Monitoring, Technology, and Verification Collaborator, NASA Planetary Protection Research Program Principal Investigator, NEUP-funded projects The group mentors several students and researchers, including Cat, Brice, Sai, Jaylen, Nuo, and Joseph. They have secured funding from NNSA and DOE for projects related to nuclear safeguards, verification technologies, and training reactor applications. Their work bridges fundamental nuclear physics with practical engineering solutions for global security and space exploration. The Enqvist Research Group operates within the University of Florida’s Nuclear Engineering infrastructure, utilizing the UF Training Reactor and advanced simulation capabilities. They collaborate with national laboratories and other university teams within the NNSA consortium, forming a key part of a larger network dedicated to advancing nuclear monitoring technologies.

• Robotics
• Intelligent Systems
• Underwater Robotics
• +۳ مورد دیگر

Anthony Weir is a researcher at the Department of Electronics & Computer Engineering, University of Limerick, affiliated with the Centre for Robotics and Intelligent Systems. His work focuses on underwater robotics, control systems, and marine engineering applications. Role: Researcher Institution: University of Limerick Department: Electronics & Computer Engineering Research Interests: Specializing in Remotely Operated Vehicles (ROVs) and autonomous underwater systems , his research addresses challenges in offshore wind farm inspection, ship hull repair, and robotic manipulation. Key technologies include visual servoing , inverse kinematics , and friction stir welding . Recent Publication Trends (2023-2024): Recent work demonstrates advancements in autonomous visual inspection , path planning algorithms , and underwater repair systems . Collaborative projects with engineers like Santos, Omerdic, and Toal highlight applications in offshore wind turbines and damaged ship hulls. Collaborations: Active collaborations span marine robotics, control systems, and renewable energy engineering, with frequent contributions to IEEE-conference proceedings and journals like IEEE Access and Journal of Intelligent and Robotic Systems .

• Robotics
• Computer Vision
• Industrial Automation
• +۴ مورد دیگر

Oleksandr Semeniuta is an active researcher specializing in robotics, computer vision, and industrial automation systems. His work demonstrates consistent contributions to the field with publications spanning from 2015 to 2022, indicating ongoing academic or research activity. His collaborations with researchers like Petter Falkman across multiple publications suggest established research partnerships in the robotics domain. Dr. Semeniuta's research interests center around practical applications of robotics and computer vision in industrial settings. His work encompasses event-driven architectures for data processing and robot control, stereo calibration methods for 3D reconstruction, and resource-aware machine learning for IoT security. His research shows a strong focus on developing practical frameworks and systems that address real-world challenges in manufacturing and industrial automation. The publication trends reveal a consistent focus on robotics systems engineering, with particular emphasis on vision-based solutions and event-driven architectures. His work bridges theoretical computer vision concepts with practical industrial applications, especially in automotive manufacturing and robotic deburring systems. The research demonstrates progression from foundational work on dataflow programming for vision systems to more complex implementations of machine learning on edge devices for security applications. While specific awards are not documented in the available publication records, the consistent output in reputable venues including IEEE conferences and PeerJ Computer Science indicates recognition within the academic community. His work appears in both journal and conference publications, suggesting a balanced approach to disseminating research findings through different academic channels. Dr. Semeniuta's collaborative network includes researchers from various institutions, with Petter Falkman appearing as a frequent co-author across multiple projects. This suggests participation in research teams focused on industrial robotics and computer vision applications. His work on frameworks like EPypes indicates contributions to software infrastructure that enables more efficient development of robotic systems and data processing pipelines.

Zoran Kapelanمشاهده پروفایل

استاد

• Urban Water Infrastructure
• Hydroinformatics
• Water Distribution Systems
• +۴ مورد دیگر

Professor Zoran Kapelan serves as Chair of Urban Water Infrastructure within the Department of Water Management at Delft University of Technology's Faculty of Civil Engineering and Geosciences. With over 30 years of experience in water engineering, he joined TU Delft in 2018 after leading the Water and Environment Group at the University of Exeter for 19 years, following a decade in international consultancy. His research spans smart water systems, hydroinformatics, and sustainable urban water infrastructure management. Key focus areas include AI-driven solutions for drinking water distribution (leakage detection, pressure management), sewer systems optimization (blockage prediction, sustainable drainage), asset management, and flood risk assessment. He pioneers methodologies integrating artificial intelligence, real-time control, and decision-making under uncertainty to address climate change adaptation and circular economy principles in water systems. Recent publications (2024-2025) reveal strong trends in deep learning applications for infrastructure monitoring (sewer crack detection, floating litter quantification), resource recovery assessment frameworks, and advanced modeling of non-Newtonian fluid dynamics in wastewater systems. His work increasingly emphasizes circular economy metrics, environmental risk assessment of novel materials, and co-developed sustainable solutions through serious gaming approaches. Editor, Journal of Water Resources Planning and Management (ASCE) Associate Editor, Water Resources Research Member, IWA Digital Water Steering Committee Member, External Scientific Advisory Council of KWR Prof. Kapelan has secured over €10 million in research funding across 50+ EU, RCUK, and industry grants. His supervision portfolio includes 40+ PhD students and 25+ postdocs, with significant industry impact through commercialized technologies like AI-based pipe burst detection systems deployed by United Utilities serving 7 million customers. He coordinates TU Delft's Urban Drainage and Water Management course while contributing to asset management and water transport curricula. The Urban Water Infrastructure group under his leadership maintains strong industry partnerships with water utilities globally, focusing on translating research into operational solutions for real-time monitoring, predictive maintenance, and climate-resilient infrastructure design.

Emad Samuel Malki Ebeidمشاهده پروفایل

استاد

• Robotics
• Embedded Systems
• Smart Grids
• +۳ مورد دیگر

Emad Samuel Malki Ebeid is a Professor at the Institute of Mechanical and Electrical Engineering, University of Southern Denmark, specializing in robotics, drone technology, and power line inspection systems. His work bridges embedded systems, FPGA acceleration, and smart grid applications. PhD in Computer Science, University of Verona (2014) Postdoc in Electrical and Computer Engineering, Aarhus University (2016) Research interests focus on robotics , UAV autonomy , and power line inspection , with a strong emphasis on embedded systems and magnetic gripper design . Articles highlight trends in mmWave radar , GPU/FPGA acceleration , and drone swarm coordination for infrastructure maintenance. Scientific contributions include: IEEE Senior Member PI and Coordinator roles in EU Horizon Europe projects (Robots4Green, SPADE) Development of AERIAL-CORE and Drones4Safety frameworks Supervision of PhD students V. D. Hoang , N. H. Malle , and F. F. Nyboe Lab and team involvement spans the Aalborg Robotics 10-year seminar and special sessions at the Euromicro DSD conference. Media features include TV 2 News and 2200 News (2025) on drone-based power line maintenance.