جست‌وجوی استاد و پژوهشگر

Ahmed Abdelatyمشاهده پروفایل

استادیار

• Infrastructure Asset Management
• Preconstruction Services
• Data Analytics in Construction Management
• +۴ مورد دیگر

Ahmed Abdelaty is an Assistant Professor of Civil and Architectural Engineering at the University of Wyoming's College of Engineering and Physical Sciences. His research focuses on infrastructure asset management, data analytics in construction management, and digital project delivery. He holds a Ph.D. from Iowa State University and M.S./B.S. degrees from Cairo University. Abdelaty's work integrates emerging technologies like BIM, UAVs, and machine learning into construction safety and efficiency solutions. Education: B.S. Construction Engineering and Management, Cairo University (2010) M.S. Civil Engineering, Cairo University (2013) Ph.D. Civil Engineering (Construction), Iowa State University (2017) Research Interests: Abdelaty explores innovative approaches to preconstruction services, pavement management systems, and data-driven decision-making in construction. His work emphasizes practical frameworks for local agencies, including dynamic pavement treatment evaluation and enhanced life cycle cost analysis. Recent studies involve 3D photogrammetry for safety training and UAV-based infrastructure assessment. Publications: His recent articles address UAV limitations, BIM integration with game engines, and deep learning for energy consumption prediction. Themes include digital transformation in construction, risk mitigation, and regulatory challenges in emerging technologies. Advising & Grants: While specific student advisees are not listed, his research aligns with funded studies on project scheduling, adverse weather modeling, and social media analytics in construction. No awards are explicitly noted in the provided materials. Labs/Teams: Abdelaty collaborates on projects involving the University of Wyoming's Civil Engineering department and external partners like Iowa State University, focusing on data-driven construction solutions.

Michael Möllerمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Inverse Problems
• +۲ مورد دیگر

Michael Möller is a Professor at the University of Siegen, leading the Lehrstuhl für Computer Vision within the Institute for Vision and Graphics. His research spans computer vision, machine learning, and optimization, with applications in medical imaging and computational geometry. Research Interests: Focuses on solving inverse problems in imaging, neural architecture optimization, and quantum-hybrid computational methods. His work integrates deep learning with traditional optimization for scalable solutions in 3D reconstruction and image analysis. Publications: Recent articles explore advancements in neural architecture search robustness, quantum annealing for shape matching, and data-driven regularization techniques for CT reconstruction, reflecting cross-disciplinary applications.

Fu Huمشاهده پروفایل

استاد

• Trauma Care
• Medical Informatics
• Critical Care Monitoring
• +۵ مورد دیگر

Fu Hu, Ph.D., is a Professor in the Department of Anesthesiology at the University of Maryland School of Medicine. He holds secondary appointments in Epidemiology & Public Health and Surgery. His research focuses on trauma care, predictive analytics, telemedicine, and medical informatics, with an emphasis on leveraging continuous vital signs and machine learning to improve patient outcomes. Key areas include traumatic brain injury monitoring, blood transfusion prediction, and critical care systems design. Education: B.S. in Electrical Engineering (Shanghai University, China, 1984), M.S. and Ph.D. in Computer Science (University of Maryland Baltimore County, 1992 and 2013). Research interests span medical AI, surgical workflow optimization, and telemedicine platforms. His work integrates advanced computational methods with clinical data to address challenges in trauma and critical care. Recent efforts include developing algorithms for real-time patient monitoring and decision support systems. He has led/co-investigated multiple grants on topics like non-invasive monitoring, trauma patient outcomes, and telemedicine. His publications highlight innovations in predictive modeling, medical imaging analysis (e.g., retinal disease detection), and clinical informatics. Notable collaborations include studies on automated ICP monitoring and the ONPOINT project for trauma care.

Bin Zhangمشاهده پروفایل

دانشیار

• Prognostics and Health Management
• Fault Detection and Isolation
• Failure Prognosis
• +۸ مورد دیگر

Dr. Bin Zhang is an Associate Professor in the Department of Electrical Engineering at the Molinaroli College of Engineering and Computing, University of South Carolina. With over 20 years of experience in prognostics and health management, intelligent systems and control, and robotics, he has established himself as a leading researcher in battery management systems, power electronics, and fault-tolerant control systems. Dr. Zhang's educational background includes: Ph.D. in Electrical Engineering from Nanyang Technological University, Singapore M.E. in Mechanical Engineering from Nanjing University of Science and Technology, China B.E. in Mechanical Engineering from Nanjing University of Science and Technology, China His research focuses on active approaches to achieve intelligent smart systems with self-situational-awareness and self-adapting capabilities. Primary interests include prognostics and health management (PHM), which covers fault detection and isolation, failure prognosis, and fault tolerance; robotics and unmanned systems; intelligent systems and control; and dynamic systems design, modeling, simulation and control. His work integrates physics-based models with data-driven techniques and computational intelligence, including pattern recognition and machine learning. Analysis of Dr. Zhang's recent publications reveals a strong emphasis on battery modeling (particularly lithium-ion batteries), power electronics control (including fractional order delay and virtual variable sampling techniques), and deep learning methods (including graph neural networks, deep residual convolutional neural networks, and deep belief networks). His research spans multiple application domains including power grids, batteries, aircraft, helicopters, and manned/unmanned vehicles. Dr. Zhang serves as Associate Editor for prestigious journals including IEEE Transactions on Industrial Electronics, IEEE Transactions on Systems, Man, and Cybernetics: Systems, and Neurocomputing. He is a Senior Member of IEEE and a member of ASME. As director of the Resilient Systems Laboratory, Dr. Zhang advises numerous graduate students working on cutting-edge research in battery technology, power cable insulation, and control systems. His lab is equipped with advanced facilities including an 8-channel ARBIN BT-Smart battery testing system, power electronics control systems, cable/wire testing systems, rotating machinery testing systems, and unmanned vehicles including quadrotors and hexacopters.

• Machine Learning
• Bioinformatics
• High Performance Computing
• +۵ مورد دیگر

Daniel Peralta Cámara is a Postdoctoral Researcher at Ghent University's Faculty of Engineering and Architecture, Department of Information Technology (EA05). His work spans Machine Learning , Bioinformatics , High Performance Computing , and Biometrics , with a focus on Single-Cell Data Analysis , Image Cytometry , and Missing Value Handling . Affiliation: Ghent University Academic Rank: Researcher Research Disciplines: Data Mining, Parallel Computing, Bioinformatics, High Performance Computing His research designs scalable machine learning frameworks for biological and engineering applications, including SCIP for morphological profiling and MSDeepAMR for antimicrobial resistance prediction. He explores advanced techniques like Polar Encoding for missing data and Fuzzy Rough Sets for classification uncertainty, contributing to one-class classification and novelty detection through Python libraries like Fuzzy-rough-learn 0.2 . Key Trends: Hybrid CNN-LSTM for sports analytics Time-series feature selection in healthcare UWB/IMU wearable integration for animal monitoring Dr. Peralta supervises PhD candidates like Maxim Lippeveld (2025) and Oliver Urs Lenz (2023). His collaborations span Ghent University researchers including Eli De Poorter , Chris Cornelis , and Yvan Saeys . Applications: Badminton strategy analysis Goat activity classification White blood cell identification

Shaon Bhatta Shuvoمشاهده پروفایل

استادیار

• Deep Learning
• Machine Learning
• Reinforcement Learning
• +۴ مورد دیگر

Dr. Shaon Bhatta Shuvo is an Assistant Professor at the University of Windsor's School of Computer Science. He holds a Ph.D. from the University of Windsor (2020–2024), an M.Sc. from South Asian University (2013–2015), and a B.Sc. from Noakhali Science and Technology University, Bangladesh. Research Interests: Deep Learning & Reinforcement Learning: Development and application of advanced algorithms for computer vision and natural language processing. AI-Based Decision Support Systems: Designing AI-driven systems for healthcare and social network analysis. Modelling and Simulation: Multi-agent simulations and mathematical modeling for complex systems, including pandemic preparedness and healthcare optimization. Research Trends in Publications: His work focuses on AI-driven solutions for healthcare (e.g., pandemic modeling, PPE demand prediction), social network analysis (link/node classification, knowledge graphs), and computational epidemiology. He emphasizes hybrid simulation models and data-driven approaches for real-world challenges. Awards: No scientific awards explicitly mentioned. Advising & Grants: No student advisees or grants listed. Labs/Teams: Not specified in available information.

Huan Nguyenمشاهده پروفایل

استاد

• Digital Twin Modeling
• Machine Learning
• Wireless Communications
• +۵ مورد دیگر

Prof. Huan Nguyen is a Professor of Digital Communication Engineering at Middlesex University, affiliated with the Faculty of Science and Technology's Design Engineering & Mathematics department. He leads the London Digital Twin Research Centre and the 5G/6G & IoT Research Group. He holds a PhD from the University of New South Wales (2007) and a BSc from Hanoi University of Science & Technology (2000). Research Interests: Digital twin modeling, machine learning for disaster management and structural health monitoring, 5G/6G communication systems, and IoT. His work focuses on smart factories, critical infrastructure resilience, and healthcare applications like stroke care via digital twins. Grants & Leadership: Secured £2.5M+ in grants, including projects on quantum-assisted digital twinning (QuanDT, £93K), cognitive twins for Dubai manufacturing (£270K), and stroke care in Vietnam/Indonesia (StrokeDT, £99.8K). Served as PI or Co-I on 15+ grants from EPSRC, British Council, and industry partners. Teaching: Currently teaches Digital Communication Systems and Major Project modules. Previously instructed courses in digital communications, wireless systems, and telecommunications at Middlesex and other institutions. Awards & Roles: Senior Fellow of the HEA (2019), Senior IEEE Member, and Chair of the Vietnamese Intellectual Society in the UK/Ireland. Editor for KSII Transactions on Internet and Information Systems and REV Journal on Electronics and Communications . Labs & Teams: Leads research groups focused on digital twins, 5G/6G, and IoT. Collaborates with global partners like VinGroup, Vietnamese universities, and UK institutions on Industry 4.0 and healthcare innovation.

Kevin Englehartمشاهده پروفایل

استاد

• Biomedical Engineering
• Prosthetics and Orthotics
• Myoelectric Control Systems
• +۴ مورد دیگر

Kevin Englehart is a Professor in the Department of Electrical and Computer Engineering at the University of New Brunswick and currently serves as the Associate Dean of Graduate Studies. He holds a PhD and is a Professional Engineer (PEng). His primary affiliation is with the Institute of Biomedical Engineering, where he conducts groundbreaking research on advanced prosthetic control systems. Englehart's work focuses on improving the usability and adaptability of myoelectric prostheses through machine learning, signal processing, and biomechanical modeling. He previously served as Director of the Institute of Biomedical Engineering, demonstrating leadership in fostering interdisciplinary research. His research interests include electromyography (EMG) signal analysis, human-machine interfaces, and the application of artificial intelligence in healthcare technologies. Englehart's publications span over two decades, with a strong emphasis on real-time control systems, noise reduction techniques, and user-centric design principles for assistive devices. His academic contributions extend to the development of novel training paradigms for prosthetic users and the mitigation of technical challenges such as electrode shift and signal instability. Englehart collaborates extensively with clinical and engineering teams to translate theoretical advancements into practical solutions for individuals with physical disabilities. His office is located at R.N. Scott Hall 219 in Fredericton, New Brunswick.

Paul Savaمشاهده پروفایل

استاد

• Seismic Imaging
• Planetary Geophysics
• Wavefield Tomography
• +۵ مورد دیگر

Paul Sava is a Professor and C.H. Green Chair of Exploration Geophysics at the Colorado School of Mines, serving as Department Head of Geophysics since 2019. He holds a PhD in Geophysics from Stanford University (2005), an MSc from Stanford (1998), and an Engineer Geophysicist degree from the University of Bucharest (1995). His research focuses on seismic and radar wavefield imaging, planetary geophysics, and infrastructure monitoring. Notable contributions include advancements in wave-equation migration and planetary radar tomography. Education: PhD in Geophysics, Stanford University, 2005 MSc in Geophysics, Stanford University, 1998 Engineer Geophysicist, University of Bucharest, 1995 Research interests emphasize wavefield-based methods for subsurface imaging, planetary resource exploration, and autonomous data acquisition. Recent work explores seismic imaging of asteroids and Mars, leveraging orbital radar systems. His publications span seismic inversion, microseismic monitoring, and elastic wavefield tomography. Awards include the Reginald Fessenden Award (2007), T.K. Young Leadership Award (2020), and multiple Top 30 presentations at SEG Annual Meetings. He leads the Center for Wave Phenomena and actively collaborates with NASA and industry consortia on grants totaling over $6 million. Advising and grants involve mentoring over 30 students and postdocs, with grants focused on planetary radar imaging, seismic FWI, and Mars subsurface analysis. Key projects include the Planetary Orbital Radar Processing and Simulation System (PORPASS) and autonomous seismic exploration systems. Labs/Teams: Directly leads the Center for Wave Phenomena and the Seismic Imaging Team at CWP, advancing open-source tools like Madagascar for reproducible geophysical research.

Fabien Moutardeمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Computer Vision
• +۸ مورد دیگر

Fabien Moutarde is a Full Professor and Director of the Center for Robotics at MINES ParisTech (PSL University, Paris, France). He holds a PhD in Physics and an Habilitation to Direct Research in Engineering Sciences. Dr. Moutarde coordinates French engineering education at ParisTech_Shanghai (SPEIT) in China. Research Areas Deep Learning & Reinforcement Learning Computer Vision for Intelligent Vehicles Collaborative Robotics Traffic Analysis & Forecasting Human Gesture Recognition Recent Article Trends His work focuses on autonomous driving using Deep Reinforcement Learning, pedestrian trajectory prediction with spatio-temporal attention, and multi-modal localization techniques combining vision with Wi-Fi. Key applications include urban traffic analysis, collaborative robotics, and end-to-end driving systems. Leadership & Teaching Co-created specialized Machine Learning courses Pioneered Deep Reinforcement Learning lectures Led French-Chinese academic coordination Former UML/Java curriculum developer Publications With 28 h-index, his research includes 30+ IEEE/ACM publications on autonomous vehicles, gesture recognition, and traffic mining. Representative conferences: CVPR, NeurIPS, IROS, ITSC.

Fumie Costenمشاهده پروفایل

مدرس

• Computational Electromagnetics
• Ultra-WideBand Systems
• Microwave Imaging
• +۲ مورد دیگر

Dr. Fumie Costen is a Lecturer in the School of Electrical and Electronic Engineering at the University of Manchester, specializing in computational electromagnetics and medical imaging applications. With a research career spanning ATR Laboratories and Kyoto University, she develops advanced algorithms for electromagnetic wave modeling. Education: PhD in Informatics, Kyoto University MEng, Kyoto University BEng, Kyoto University Her research integrates electromagnetic theory with machine learning, focusing on: Ultra-wideband systems and FDTD methods Microwave imaging for medical diagnostics GPU-accelerated electromagnetic simulations Precision agriculture applications Recent publications demonstrate her pivot toward biomedical applications, using convolutional neural networks for brain tumor analysis, stroke classification, and age prediction from MRI data. This work bridges electromagnetic expertise with clinical diagnostics. Awards: ATR Excellence in Research Award (1996) Best Paper Award at International Conference (2000) Supervision and Collaboration: Supervised 25+ PhD students in electromagnetics and computing Collaborates internationally with RIKEN, ESPCI, and Kyushu University Leads projects on medical image classification and electromagnetic sensor systems

Krikor Ozanyanمشاهده پروفایل

استاد

• Sensor Systems
• Tomography Imaging
• Signal Processing
• +۳ مورد دیگر

Krikor Ozanyan is Professor of Control of Sustainable Energy Networks at the University of Manchester's Department of Electrical and Electronic Engineering. He directs research in sensing technologies, tomography imaging, and signal processing for industrial applications. Research Focus: Develops wearable inertial sensors for gait recognition, fluorescence monitoring systems, THz tomography, and advanced signal processing algorithms using deep learning convolutional neural networks. Awards: Recipient of the IEEE Sensors Council Lifetime Contribution Award (2023) and Best Student Paper Award at IDEAL 2019. Projects: Leads multidisciplinary teams on combustion species imaging diagnostics and non-intrusive engine exhaust measurement systems.

Tze Meng Lowمشاهده پروفایل

استاد پژوهشی

• Parallel and High Performance Computing
• Formal Methods
• Graph Algorithms
• +۳ مورد دیگر

Tze Meng Low is an Associate Research Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU), part of the College of Engineering. His research focuses on high-performance algorithms, formal methods, and hardware-software co-design, with an emphasis on achieving performance portability across architectures. He holds a Ph.D. and M.S. in Computer Science and dual B.A./B.S. degrees in Economics and Computer Science from the University of Texas at Austin. His research interests span parallel computing, graph algorithms, machine learning, and cyber-physical systems. He has contributed to projects like the DARPA BRASS initiative, collaborating on adaptive software systems for resource-challenged environments. His work also involves developing tools such as SMaLL and qLD, which address challenges in machine learning library instantiation and genomic analysis. Low has received the Dean’s Early Career Fellowship (2022) and led the $2.7M DARPA-funded BRASS project (2016–2020), supported by SpiralGen, Inc. and academic collaborators. His contributions include code generation frameworks like SPIRAL and advancements in linear algebra-based graph algorithms, emphasizing analytical models and automated code optimization. His research bridges theoretical formal methods with practical implementations, aiming to enhance software reliability and scalability in emerging domains. Collaborations include work on fault-tolerant coded computing and high-assurance systems for cyber-physical applications.

Alina Bialkowskiمشاهده پروفایل

مدرس ارشد

• Medical Imaging
• Biomedical Engineering
• Computer Vision
• +۶ مورد دیگر

Dr Alina Bialkowski is a Senior Lecturer at the School of Electrical Engineering and Computer Science , part of the Faculty of Engineering, Architecture and Information Technology at The University of Queensland. Her research focuses on interpretable machine learning and computer vision to enhance AI transparency and solve real-world challenges. Prior to joining UQ in late 2017, she held postdoctoral positions at University College London (2015–2017), where she studied human perception in driving, and Disney Research Pittsburgh (2014), analyzing team sports using spatiotemporal data. Dr Bialkowski earned her PhD and Bachelor of Engineering (Electrical Engineering) from Queensland University of Technology, Australia. Her doctoral research centered on group behavior analysis from visual and spatiotemporal data, with applications in sports analytics and intelligent surveillance systems. Her research interests span medical imaging (especially electromagnetic imaging of strokes), human attention modeling in driving, intelligent transport systems , surveillance systems , and sports analytics . She emphasizes explainable AI to bridge the gap between technical systems and human understanding, employing methods like feature visualization and attribution. Her work also explores sensors for non-invasive imaging and machine learning frameworks to ensure ethical AI. Dr Bialkowski has received significant recognition, including the Best Paper Prize at the 2017 IEEE Winter Conference on Applications of Computer Vision (WACV) . Her research has led to 6 international patents with collaborators such as Disney Research, Toyota Motor Europe, and The University of Queensland, focusing on electromagnetic imaging and AI-driven solutions. She actively contributes to interdisciplinary projects like The Lanyard Project (traffic sensor fusion) and co-authored a SmartSat CRC-funded research report on machine learning for satellites. Dr Bialkowski is available for supervision and advocates for AI systems that integrate human-centric principles into their design and evaluation.

Renato Pajarolaمشاهده پروفایل

استاد

• Computer Graphics
• Scientific Visualization
• 3D Modeling
• +۵ مورد دیگر

Prof. Dr. Renato Pajarola is the Head of the Visualization and MultiMedia Lab at the Department of Informatics, University of Zurich. His research focuses on computer graphics, scientific visualization, and geometric processing, with applications in 3D scanning, point cloud analysis, and real-time rendering. He leads a team developing advanced visualization techniques for high-dimensional data, parallel rendering frameworks, and interactive systems for complex datasets. Key research areas include: 3D reconstruction of indoor environments Tensor approximation for volume visualization Interactive ray tracing and point cloud processing Parallel rendering frameworks (e.g., Equalizer) Scientific computing and sensitivity analysis His recent publications emphasize: High-dimensional data exploration using tensor methods Efficient rendering techniques for large-scale point clouds Integration of citizen-reported weather data for environmental analysis Prof. Pajarola’s lab collaborates on projects like VIAN (visual annotation tool for film analysis) and Terrender (web-based terrain visualization). His Erdős number is 3, reflecting interdisciplinary research connections in mathematics and computer science.