جست‌وجوی استاد و پژوهشگر

• Medical Imaging
• Deep Learning
• Cardiac Ultrasound
• +۷ مورد دیگر

Dr. Qingjie Meng is a Researcher in the Department of Computing at Imperial College London, affiliated with the Faculty of Engineering. Their work focuses on advancing AI-driven medical imaging technologies, particularly in cardiac ultrasound, fetal anomaly detection, and MRI analysis. Meng's research emphasizes privacy-preserving data solutions and generative models for healthcare applications. Research interests include deep learning for medical image synthesis, motion tracking in MRI, and real-time AI applications in ultrasound screening. Recent work explores foundational models like EchoFlow for cardiac imaging and SACB-Net for medical registration tasks. Publications highlight trends in generative AI for medical data, robust segmentation techniques, and multi-view cardiac tracking. Meng's contributions address challenges in fetal anomaly detection and respiratory motion correction in MRI scans. No awards or advising roles are explicitly listed in the provided text. Their work contributes to labs focused on biomedical AI and healthcare informatics, though specific lab affiliations are not detailed.

• Machine Learning
• Computer Vision
• Signal Processing
• +۶ مورد دیگر

Giacomo Boracchi is a researcher at the Polytechnic University of Milan , focusing on machine learning, computer vision, and signal processing. His work spans anomaly detection, change detection in data streams, 3D imaging, and biomedical applications. Key Collaborations : Diego Carrera, Luca Magri, Cesare Alippi Industries : Embedded systems, medical imaging, environmental monitoring Recent research explores adaptive Kalman filtering for battery estimation, zero-shot anomaly detection, and explainable AI for vision-language models. Publications highlight applications in waste sorting, histological data generation, and cardiac monitoring. His technical focus includes convolutional networks, ensemble learning, and domain adaptation. Boracchi's work bridges theoretical advancements with real-world systems in fraud detection, semiconductor manufacturing, and wearable health devices.

Haidar Sametمشاهده پروفایل

پژوهشگر

• Power Systems
• Electrical Engineering
• Renewable Energy
• +۴ مورد دیگر

Haidar Samet is a Researcher in the Department of Electrical Engineering at Eindhoven University of Technology, focusing on power systems, electric arc furnaces, and fault detection. He previously held academic roles at Shiraz University, including Professor (2018–2020), Head of Department of Power and Control (2017–2019), Associate Professor (2014–2018), and Assistant Professor (2008–2014). His research bridges electrical engineering fundamentals with practical applications in renewable energy and grid reliability. Research Interests: Electric Arc Furnace Modeling and Optimization Fault Detection in Power Systems and Photovoltaic Systems Grid Protection Mechanisms Renewable Energy Integration and Smart Grid Technologies Power Quality Analysis Publications highlight advancements in arc fault modeling, transformer protection algorithms, and renewable energy system optimization. His work frequently addresses challenges in grid stability, energy efficiency, and predictive maintenance. No scientific awards or grants were explicitly mentioned in the provided materials.

Mohammad Amir Owrangiمشاهده پروفایل

دانشیار

• Radiation Oncology
• Medical Physics
• Brachytherapy
• +۳ مورد دیگر

Mohammad Amir Owrangi serves as Associate Professor in Radiation Oncology at Baylor College of Medicine (Houston) and University of Texas Southwestern (Dallas), with prior appointments as Assistant Professor at both institutions and the University of Toronto. His expertise bridges medical physics and clinical radiation oncology, focusing on advancing precision radiotherapy through technological innovation. His academic foundation includes a BSc and MSc in Physics and Radiation-Medicine Engineering from Shiraz University (Iran), followed by a PhD in Biomedical Engineering from The University of Western Ontario (Canada), and a CAMPEP-accredited residency in Therapeutic Radiologic Medical Physics at University of Michigan Health System. Dr. Owrangi's research centers on solving critical challenges in image-guided radiotherapy, particularly MRI-based treatment planning for gynecological cancers. His work pioneers synthetic CT generation using deep learning to eliminate CT simulation needs, optimizes brachytherapy applicators for cervical cancer, and develops metal artifact reduction techniques. Recent publications demonstrate increasing integration of AI methods to improve dosimetric accuracy and workflow efficiency in radiation therapy. Analysis of his 15 most recent publications (2017-2024) reveals three dominant research thrusts: (1) MRI-only radiotherapy workflows with synthetic CT generation using deep learning/GANs, (2) brachytherapy optimization for cervical cancer through applicator design and dosimetry, and (3) artifact reduction in multi-modal imaging. His work consistently targets clinical translation, with 70% of recent publications focusing on gynecological applications. Scientific Awards: No awards explicitly mentioned in source materials Academic advising and research grant details were not provided in available documentation. His collaborative network spans Baylor College of Medicine, UT Southwestern, University of Toronto, and international institutions including Shiraz University and University of Western Ontario, with frequent co-authorship among medical physicists and radiation oncologists. While specific laboratory infrastructure isn't detailed, his research requires advanced capabilities in MRI/CT image processing, Monte Carlo simulation, and deep learning development, suggesting access to high-performance computing resources and clinical imaging datasets at his affiliated institutions.

• Predictive Maintenance
• Machine Learning
• Fault Detection
• +۵ مورد دیگر

Dr. Lilach Goren Huber is a Senior Lecturer and R&D Projects Leader at the ZHAW School of Engineering, specializing in predictive maintenance and data-driven solutions for industrial systems. She leads multiple projects focusing on AI applications in anomaly detection (e.g., wind turbines, solar power plants), physics-informed machine learning for sensor error correction, and fault prognostics under data scarcity. Her work bridges academic research with industrial implementation, emphasizing scalable deep learning frameworks for commercial fleets and energy infrastructure. Research interests include predictive maintenance strategies, machine learning for industrial IoT, and hybrid prognostics combining domain knowledge with AI. She has published extensively in journals like International Journal of Prognostics and Health Management and Journal of Big Data , with a focus on renewable energy systems, SCADA data analysis, and cross-domain transfer learning. Current Projects: Physics-informed ML for elastomer sensors, end-to-end fault prognostics for power grids, and hybrid prognostics research. Past Contributions: Developed decision support systems for laser cutting machines, optimized hydroelectric maintenance schedules, and created risk-based frameworks for Swiss national road safety equipment. She actively contributes to the Expert Group Smart Maintenance and serves as a project leader in the ZHAW-PARC initiative. Her work addresses technical challenges like data contamination, sensor calibration errors, and real-world implementation barriers in industrial AI adoption.

• Deep Learning
• Medical Imaging
• Artificial Intelligence
• +۷ مورد دیگر

Peter Eggenberger Hotz is a researcher at the Zurich University of Applied Sciences (ZHAW) , affiliated with the School of Engineering . His work bridges Artificial Intelligence , Medical Imaging , and Computational Modeling . As a team member in projects like Bio-Hybrid Hierarchical Organoid-Synthetic Tissues (Bio-HhOST) and DIR3CT: Deep Image Reconstruction through X-Ray Projection-based 3D Learning , he focuses on Deep Learning applications in Computed Tomography and Radiation Therapy .

Konstantinos Kyriakopoulosمشاهده پروفایل

مدرس ارشد

• Cybersecurity
• Artificial Intelligence
• Machine Learning
• +۴ مورد دیگر

Dr. Konstantinos Kyriakopoulos is a Senior Lecturer in Cyber Intelligence, specializing in cybersecurity challenges and AI-driven solutions for converged Information and Operational Technology infrastructures. His work integrates machine learning with cyber-physical systems to enhance decision-making under uncertainty, with a focus on resilient AI algorithms and autonomous defense mechanisms. He has contributed to projects funded by Dstl, Innovate UK, and industry partners, addressing Industry 4.0 security, smart vehicles, electromagnetic attacks, and smart buildings. His research interests include adversarial machine learning, evidence theory for uncertainty modeling in ML, and autonomous cyber defense systems. He has developed novel approaches using reinforcement learning, hidden Markov models, and Bayesian optimization. His work emphasizes creating ML algorithms resistant to adversarial attacks while improving cybersecurity resilience in dynamic environments. Dr. Kyriakopoulos has also explored applications of AI in educational technology, such as automated assessment of spoken English proficiency. His recent publications focus on federated learning security, causal reinforcement learning, and anomaly detection techniques. He has experience in translating research into practical solutions, including business model pitches to investors in Canary Wharf for threat modeling tools. His contributions span over 40 peer-reviewed articles, with notable work in network intrusion detection, attack prediction via HMMs, and wavelet-based compression for network measurements. His expertise bridges academia and industry, addressing both theoretical advancements and real-world implementation challenges.

• Condensed Matter Physics
• Computational Materials Science
• Artificial Intelligence in Materials Discovery
• +۱۱ مورد دیگر

Aldo H. Romero is the Eberly Family Distinguished Professor of Physics and Astronomy at West Virginia University, where he serves as Director of Research Computing . His research integrates computational materials science, artificial intelligence, and high-performance computing to advance understanding of condensed matter systems, including strongly correlated materials, 2D materials, and magnetic compounds. Education : Ph.D. in Physics and Chemistry from University of California, San Diego (1998) Awards : Eberly Family Distinguished Professorship Romero’s group specializes in Density Functional Theory (DFT) , Dynamical Mean Field Theory (DMFT) , and machine learning for materials discovery. They develop open-source tools like PyProcar , MechElastic , and ABINIT , focusing on properties such as electronic structure, elastic behavior, and magnetic interactions. Recent work explores chiral materials , AI-driven optimization , and high-throughput computational methods for material design. Key trends in publications include studies on chiral symmetry in kagome lattices, machine learning for material properties, symmetry-based structure prediction, and DFT+DMFT analysis of correlated systems. Collaborations span Germany, France, China, Spain, and Italy, with applications in energy storage, quantum technologies, and spintronics. Scientific contributions encompass: Development of Fireball and ABINIT codes Machine learning for Hubbard U parameter optimization Chiral material discovery via symmetry arguments Pioneering work on 2D Rashba systems Labs and teams include: AI WVU Discussion Group (founded by Romero) West Virginia University Computational Materials Group Collaborative efforts with international institutions Grants and proposals highlight initiatives like the NSF & NVIDIA Open Multimodal AI Infrastructure (OMAI) program and Spencer Foundation support for AI in education. The group works with over 20 graduate students and 4 postdoctoral researchers, emphasizing interdisciplinary training and global collaboration.

• Image Processing
• Machine Learning
• Medical Imaging
• +۳ مورد دیگر

Weiwei Cui is a Visiting Professor at the School of Electronic Engineering and Computer Science, Queen Mary University of London. She specializes in image processing, machine learning, and medical imaging applications. Her research focuses on advancing computational methods for medical diagnostics, particularly in dental imaging and ophthalmology. She teaches courses on Image Processing and Machine Learning, integrating theoretical foundations with practical applications in computer science and engineering. Her research contributions include developing novel datasets like Ctooth+ and CARDIAN, advancing semi-supervised learning techniques for medical image segmentation, and applying transformer networks to conversational AI. Key areas of expertise include deep learning frameworks, neural networks, and biomedical signal processing. She has published extensively on topics ranging from dental CT segmentation to real-time cardiovascular imaging analysis. Dr. Cui’s work bridges computer science methodologies with healthcare challenges, emphasizing practical solutions for diagnostic accuracy and clinical decision-making. Her datasets and benchmarking efforts contribute to reproducible research in medical imaging communities. She actively engages in interdisciplinary collaborations to address real-world healthcare technology challenges.

Remco Duitsمشاهده پروفایل

دانشیار

• Differential Geometry
• Mathematical Image Analysis
• Geometric Deep Learning
• +۲ مورد دیگر

Remco Duits is Associate Professor at Eindhoven University of Technology, leading research in mathematical image analysis and geometric deep learning. His work develops PDE-based methods on Lie groups for medical imaging, crack detection in infrastructure, and optimal transport. Key innovations include equivariant neural networks for homogeneous spaces and geodesic tracking algorithms for vascular analysis. Dr. Duits holds an ERC Starting Grant (2013) for Lie Group Analysis in Medical Imaging and an NWO VICI Grant (2021) for Geometric Learning. His team pioneers data-driven Cartan connections for vascular tree tracking and PDE-CNN architectures with axiomatic foundations.

John Ray Bergadoمشاهده پروفایل

مدرس

• Machine Learning
• Deep Learning
• Remote Sensing
• +۳ مورد دیگر

John Ray Bergado is a Lecturer and Researcher at the Department of Earth Observation Science (EOS), Faculty of Geoinformation Science and Earth Observation (ITC), University of Twente, The Netherlands. He holds a PhD in End-to-end predictive models for remote sensing applications (2020), an MSc in Geoinformation Science and Earth Observation (2016, with distinction), and a BSc in Geodetic Engineering (2013, magna cum laude). His research focuses on integrating machine learning and deep learning with geospatial technologies like remote sensing, photogrammetry, and UAVs to address environmental and urban challenges. Education: PhD, University of Twente, 2016–2020 MSc (Distinction), University of Twente, 2013–2016 BSc (Magna Cum Laude), University of the Philippines, 2009–2013 His work emphasizes applications such as wildfire prediction, UAV-based point cloud extraction, and land use classification. He has contributed to projects with institutions like NEO BV, RMIT (Melbourne), and SRDP Consulting Inc. His research aligns with UN SDGs, particularly in sustainable development through geospatial innovation. Key trends in his articles include deep learning for geospatial data analysis, UAV applications in disaster response, and multispectral image fusion. His methodologies often combine neural networks and attention mechanisms to enhance remote sensing outcomes. He has no listed awards but has received academic distinctions for his degrees. His advisory roles and grants are not explicitly mentioned, though his research collaborations span global institutions. He is affiliated with ITC’s EOS department, contributing to its mission in geoinformation science and earth observation.

Yang Chenمشاهده پروفایل

مدرس

• Composite Materials
• Computational Mechanics
• Materials Science
• +۵ مورد دیگر

Yang Chen is a Lecturer in the Department of Mechanical Engineering at the University of Bath, affiliated with multiple research centres including the Centre for Integrated Materials, Processes & Structures (IMPS) and the Centre for Regenerative Design & Engineering for a Net Positive World (RENEW). He holds a PhD from the Université Paris-Est (collaborating with French Alternative Energies and Atomic Energy Commission) and completed postdoctoral research at the University of Oxford. His research focuses on advanced techniques for data-rich experiments and simulations in heterogeneous materials, particularly fibre-reinforced composites applied in aerospace, automotive, and nuclear energy sectors. Key expertise includes FFT solvers, X-ray computed tomography, nonlinear mechanical modelling, and fluid flow in porous media. He has secured prestigious fellowships, including the EPSRC Future Composites Research Hub Innovation Fellowship and the Humboldt Fellowship. Teaching responsibilities include the ME20016 Solid Mechanics 3 course for mechanical engineering undergraduates. Current PhD supervision opportunities span topics like hydrogen storage composites, nuclear fusion materials, and nuclear waste management. He actively collaborates on projects such as HyFIVE (Hydrogen Storage) and explores advanced ceramic materials for nuclear energy. His work aligns with UN Sustainable Development Goals, emphasizing sustainable innovation and energy solutions. Notable contributions include developing physics-informed neural networks for resin flow prediction and machine learning-based emulators for constitutive modelling. His publications address fracture mechanics, computational homogenization, and material degradation under irradiation. He is open to supervising doctoral students through funded programs like ZENITH and GW4+ DLTP.

Yeong Shiong Chiewمشاهده پروفایل

مدرس ارشد

• Biomedical Engineering
• Mechanical Ventilation Protocols
• Artificial Intelligence in Healthcare
• +۵ مورد دیگر

Dr. Yeong Shiong Chiew is a Senior Lecturer at Monash University Malaysia's Malaysia School of Engineering. He holds a PhD in Mechanical Engineering from the University of Canterbury (New Zealand), specializing in model-based mechanical ventilation for respiratory failure patients. His research focuses on biomedical engineering, physiological modeling, and clinical trial design, with a strong emphasis on AI applications in healthcare and mechanical system control. He also serves as an Adjunct Senior Fellow at the University of Canterbury's Mechanical Engineering Department and holds leadership roles in global engineering organizations like the International Federation of Automatic Control (IFAC). Education: PhD in Mechanical Engineering, University of Canterbury (2013) MEng in Mechanical Engineering, Universiti Teknologi Malaysia (2010) BEng in Mechanical-Automotive Engineering, Universiti Teknologi Malaysia (2007) Research Interests: Model-based mechanical ventilation protocols AI-driven healthcare solutions Respiratory mechanics modeling Clinical decision support systems Medical device development Virtual reality in healthcare Recent Projects: Clinical Application of Respiratory Models (CARE Study) – evaluating ventilator-patient interaction AI-enhanced breast cancer risk modeling Real-time palm fruit ripeness detection via UAV High-resolution single-pixel imaging using GANs Awards: RINENG Young Investigator Award (2023) MUPA Supervisor of the Year (2020) PVC Excellence in Education Award (2019 Team) ITEX Gold Medal (2021) Grants & Supervision: Accepting PhD students in mechanical ventilation modeling, AI healthcare, and physiological systems Principal investigator on multiple interdisciplinary grants totaling over MYR 5M Supervised projects in ICU monitoring systems and medical device prototyping Labs & Teams: Leading the Model-Based Clinical Ventilation Research Group at Monash Malaysia Collaborator with the University of Liege's Biomedical Engineering Lab Part of the IFAC Technical Committee on Biological & Medical Systems

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.

• 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.