جست‌وجوی استاد و پژوهشگر

Craig Hancockمشاهده پروفایل

استاد پژوهشی

• Geospatial Engineering
• Structural Health Monitoring
• Global Navigation Satellite Systems (GNSS)
• +۳ مورد دیگر

Dr. Craig Hancock is a Research Professor in Geospatial Engineering with 15 years of research experience in Surveying and Geodesy. His expertise spans GNSS error mitigation, structural monitoring, and geospatial techniques for digital construction. He has supervised 10 PhD students and published over 80 academic papers. Education: BSc and PhD in Surveying/Geomatics Key Projects: Principal Investigator for projects on GNSS error mitigation, structural health monitoring, and marine economy technology. His research focuses on three core areas: GNSS error categorization and mitigation (particularly ionospheric effects), structural and environmental change monitoring, and geospatial data acquisition for BIM and digital construction. Recent work includes improving 3D modeling accuracy, UAV-based GNSS spoofing detection, and BIM-enabled facility management in healthcare infrastructure. His articles explore topics like sensor optimization, structural dynamics, and geospatial data fusion. Grants include £150k for bridge deformation studies and £9k for ionospheric error analysis. He actively contributes to teaching and enterprise initiatives, integrating geospatial technologies with industry needs.

• Human Activity Recognition
• Wearable Sensors
• Machine Learning
• +۴ مورد دیگر

Thomas Ploetz is an Adjunct Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology. His work focuses on sensor-based human activity recognition, wearable computing, and computational behavior analysis with applications in healthcare, smart homes, and education. He leads interdisciplinary research integrating machine learning, IoT systems, and human-centered design. His research interests include improving activity recognition robustness through synthetic data generation, developing explainable AI for time-series analysis, and advancing healthcare technologies like diabetic foot ulcer monitoring systems. He explores ethical implications of wearable sensing in diverse populations, including underrepresented groups. Key contributions include IMUTube (virtual sensor data generation), ProxiCycle (cyclist safety monitoring), and DISCOVER (smart home activity recognition framework). His work addresses challenges in data scarcity, domain adaptation, and long-term system maintenance in pervasive computing environments. He has been awarded grants such as the GVU/IPaT Research and Engagement Grant (2020), and his research appears in top venues like UbiComp and ISWC. He actively contributes to the wearable computing community through conference organization and editorial work.

Wout Josephمشاهده پروفایل

استاد

• electromagnetic field exposure assessment
• in-body electromagnetic field modelling
• electromagnetic medical applications
• +۱۲ مورد دیگر

Wout Joseph is a Professor in the domain of Experimental Characterization of wireless communication systems at Ghent University (Belgium), where he has been working since October 2009. He is also an IMEC Principal Investigator since 2017. His research is conducted within the wireless, acoustics, environment & expert systems (WAVES) research unit at the Department of Information Technology (INTEC). Dr. Joseph was born in Ostend, Belgium on October 21, 1977. He received his M.Sc. degree in electrical engineering from Ghent University in July 2000. From September 2000 to March 2005 he was a research assistant at the Department of Information Technology (INTEC), where his scientific work focused on electromagnetic exposure assessment around base stations for mobile communications related to health effects. This work led to his Ph.D. degree in March 2005. Professor Joseph's research expertise spans multiple domains within wireless communications and bioelectromagnetics. His primary research interests include electromagnetic field exposure assessment, in-body electromagnetic field modeling, electromagnetic medical applications, propagation for wireless communication systems, IoT, antennas and calibration. He also specializes in wireless performance analysis, industry 4.0 applications, wireless localization, and Quality of Experience metrics. His work is particularly notable for its focus on dosimetric studies in the radiofrequency range, where his research is ranked first in number of peer-reviewed studies. His research has practical applications in wireless network planning, occupational safety, and public health policy related to electromagnetic fields. His extensive publication record (over 886 publications with an h-index of 45 in ISI Web of Science and 66 in Google Scholar) demonstrates a clear trajectory from fundamental electromagnetic field measurements to applied research in industrial wireless networks and bioelectromagnetic applications. Recent work shows a strong emphasis on 5G exposure assessment across multiple European countries, millimeter-wave channel modeling, and the application of machine learning techniques to exposure assessment and wireless localization. EBEA council board member (2015-2018) EBEA board member at large (2019) Bioelectromagnetics Society board member (2022) Bioelectromagnetics Society board member (2024) 24 research awards Professor Joseph leads significant research efforts in electromagnetic field exposure assessment, with particular emphasis on developing measurement methodologies and computational models for real-world exposure scenarios. His work bridges theoretical electromagnetic modeling with practical applications in wireless communications and bioelectromagnetics. His research group within the WAVES unit is highly active in both theoretical and experimental aspects of wireless communications and bioelectromagnetics, with current projects focusing on 5G exposure assessment across Europe, millimeter-wave channel modeling for data centers and industrial environments, and the development of novel exposure assessment methodologies using advanced signal processing and machine learning techniques.

• Photovoltaics
• Solar Energy Systems
• Silicon Solar Cells
• +۵ مورد دیگر

Jef Poortmans is a Visiting Professor at KU Leuven, Belgium, specializing in photovoltaic technologies and solar energy systems. His research spans multiple applications including conventional solar installations, agrivoltaics, vehicle-integrated photovoltaics, and tandem solar cell configurations. Affiliated with the Electa department at KU Leuven, he maintains an active research profile with numerous publications extending into 2025. His research interests focus on advancing photovoltaic technology across multiple dimensions. Poortmans investigates thermal modeling to improve energy yield predictions, develops lightweight PV modules for vehicle integration, explores agrivoltaic systems that combine agriculture with solar energy production, and works on next-generation perovskite and tandem solar cell technologies. His work often addresses practical implementation challenges including reliability under various environmental conditions, mechanical integration requirements, and performance optimization for specific applications. Analysis of his recent publications reveals a strong emphasis on practical implementation challenges of photovoltaic systems. His work spans fundamental materials science (particularly for perovskite and thin-film technologies), system integration challenges (especially for vehicle applications), and innovative approaches to land use optimization through agrivoltaics. A recurring theme is addressing reliability and performance issues under real-world operating conditions rather than ideal laboratory settings. Poortmans frequently collaborates with researchers across multiple institutions, indicating strong industry and academic connections within the photovoltaics community. His work appears in high-impact journals including Solar Energy Materials and Solar Cells, Scientific Reports, and Advanced Functional Materials, demonstrating recognition within the field. While specific grant information isn't detailed in the provided materials, his extensive publication record across diverse photovoltaic applications suggests successful funding acquisition for multiple research projects. His involvement in PhD theses supervision indicates active mentorship of next-generation researchers in the photovoltaics field. His research group appears to focus on bridging fundamental photovoltaic science with practical engineering applications, particularly addressing the reliability and integration challenges that prevent wider adoption of solar technologies in non-traditional applications like vehicles and agricultural settings.

Marc Olanoمشاهده پروفایل

دانشیار

• Computer Graphics
• Real-Time Rendering
• Graphics Hardware
• +۴ مورد دیگر

Marc Olano is an Associate Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC), and serves as the Associate Dean of Academic Programs and Learning in the College of Engineering and Information Technology. He leads the Computer Science Game Development Track and co-directs the VANGOGH lab. His research focuses on interactive 3D computer graphics, programmable shading, graphics hardware, and surface appearance modeling, with contributions to foundational graphics technologies like procedural shading and normal mapping. Research Interests: Olano’s work spans real-time rendering, GPU algorithms, texture compression, and procedural shading. He has pioneered techniques such as LEAN mapping and variable bitrate texture compression, significantly impacting game development and real-time graphics. His research often explores the intersection of hardware capabilities and algorithmic innovation, with applications in medical visualization, visualization of scientific data, and haptic interaction. Key Contributions: Olano’s accomplishments include pioneering procedural shading on graphics hardware, developing homogeneous rendering techniques, and advancing normal mapping. His work on GPU-based curvature estimation and BT volumes for volume rendering exemplifies his focus on leveraging GPU parallelism for real-time visualization challenges. He has also contributed to standards in shading languages and GPU programming. Teaching & Mentorship: Olano teaches courses in computer graphics, game development, and advanced computer architecture. He mentors students in independent studies and has advised numerous MS theses exploring topics like GPU random number generation, volume rendering, and soft shadow algorithms. His students’ work often bridges theoretical research and practical GPU implementations. Labs & Projects: The VANGOGH lab under his co-direction focuses on advanced visualization and graphics research, including real-time rendering techniques, GPU algorithms, and interactive data visualization. His research collaborations span industry partners like Firaxis Games, contributing to titles such as Civilization V through texture compression innovations.

Yelva Roustanمشاهده پروفایل

مدرس

• Air Quality Modeling
• Atmospheric Chemistry
• Pollutant Deposition
• +۳ مورد دیگر

Yelva Roustan is a Lecturer at CEREA (joint laboratory between École Nationale des Ponts et Chaussées and EDF R&D) since 2005, specializing in air quality modeling . Her research focuses on multiscale modeling approaches, source-concentration relationships, and pollutant deposition processes. She teaches at the National School of Bridges and Roads, sharing knowledge from her research in atmospheric transport and environmental science. Her work addresses urban pollution challenges, including the impact of low-emission vehicles on air quality, urban tree effects, and pollutant dynamics in street networks. She has developed the MUNICH street-network model and contributed to projects like EURODELTA for multi-pollutant analysis across Europe. Her methods integrate data assimilation, inverse modeling, and Bayesian techniques to improve emission source reconstruction and model accuracy. Key contributions include studies on radioactive release scenarios (e.g., Fukushima, 106Ru event) and urban infrastructure impacts on pollution. Her research bridges atmospheric science with engineering solutions for sustainable urban environments. She holds a PhD in Environmental Engineering from École Nationale des Ponts et Chaussées (2005) and an HDR in Numerical Modeling of Atmospheric Pollutants (2020).

Rynson W.H. Lauمشاهده پروفایل

استاد

• Computer Graphics
• Computer Vision
• Deep Learning
• +۳ مورد دیگر

Rynson W.H. Lau is a Professor of Computer Science at City University of Hong Kong (CityU), leading research in Computer Graphics, Computer Vision, and Deep Learning. He holds an Honorary Professorship at Swansea University. Previously, he served on faculties at Durham University and The Hong Kong Polytechnic University. His work focuses on advancing graphics and vision techniques, including deep learning applications for graphics/vision problems, with publications in top venues like SIGGRAPH, CVPR, and NeurIPS. He has received the Adobe Research Gift (2023) and the Springer Nature Editorial Contribution Award (2025) for his editorial contributions to the International Journal of Computer Vision . Education: B.Sc. (First-class Honors) in Computer Systems Engineering from University of Kent Ph.D. in Computer Science from University of Cambridge Research Interests: Computer Graphics: Focused on 3D reconstruction, rendering, and real-time performance capture. Computer Vision: Specializing in saliency detection, object recognition, and low-light scene enhancement. Deep Learning: Developing generative models and diffusion-based frameworks for graphics and vision tasks. Editorial Roles: Editorial Board Member, International Journal of Computer Vision and IET Computer Vision . Guest Editor for special issues in journals like ACM Transactions on Internet Technology and IEEE Transactions on Multimedia. Teaching: 2024/25 Academic Year: CS4185: Multimedia Technologies and Applications CS4188/CS5188: Virtual Reality Technologies and Applications Research Team: Advises over 20+ students and collaborates internationally. Recent projects include AI-driven VR systems for healthcare and advanced 3D content generation using diffusion models.

Peter Suttonمشاهده پروفایل

دانشیار

• Engineering Education
• Embedded Computing Systems
• Reconfigurable Computing
• +۳ مورد دیگر

Associate Professor Peter Sutton is an academic at the University of Queensland (UQ), holding roles as Deputy Head of School (Teaching and Learning) and Associate Professor in the School of Electrical Engineering and Computer Science. His research focuses on Engineering Education, Embedded Systems, Reconfigurable Computing, and Electronic Design Automation. He has contributed to curriculum design, remote lab management during the pandemic, and hardware-software co-design for embedded systems. Sutton completed his undergraduate studies at UQ and earned advanced degrees at Carnegie Mellon University, with over three decades of experience in computer systems research and education. Education Bachelor of Science, University of Queensland Bachelor (Honours) of Engineering, University of Queensland Masters of Science (Coursework), Carnegie Mellon University Doctor of Philosophy, Carnegie Mellon University Research Interests Sutton’s work spans engineering pedagogy, embedded system design, and reconfigurable computing. Recent projects include adapting hands-on labs for remote learning during the pandemic and optimizing FPGA-based architectures for data compression and encryption. His contributions to cache optimization and multiprocessor systems highlight his expertise in hardware-software integration. Publications His 50+ publications cover topics like FPGA implementations of neural networks, code compression techniques for VLIW processors, and embedded system design tools. Notable contributions include frameworks for reconfigurable system-on-chip development and methods to enhance debugging practices in post-novice students. Labs & Teams He collaborates within UQ’s School of Electrical Engineering and Computer Science, contributing to research groups focused on embedded systems and engineering education innovation.

• Antennas
• Applied Electromagnetics
• Computational Electromagnetics
• +۲ مورد دیگر

Giuseppe Vecchi is a Full Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino , Italy. He leads the Applied Electromagnetics research group and contributes to projects in computational electromagnetics, metamaterials, and biomedical applications of electromagnetic fields. He has been a IEEE Fellow since 2010 and serves on PhD college committees for Electrical, Electronic, and Communications Engineering. Research Interests : Antennas, Applied and Computational Electromagnetics, Metamaterials, Microwave Imaging for medical applications, Nuclear Fusion Reactor Physics. Scientific Leadership : Principal Investigator for projects like METEOR, MTSA, and RESOLVED-K, focusing on terahertz generation, metasurface antennas, and real-time temperature mapping in hyperthermia. Awards : IEEE Fellow (2010), recognizing his contributions to electromagnetic simulations and antenna design. Students : Supervises PhD candidates in advanced antenna engineering, computational electromagnetics, and biomedical applications, including Owais Khan, Francesco Lattanzio, and Sara Paknezhad Panahi. Patents : Holds multiple patents in antenna diagnostics, encrypted metasurface antennas, and microwave soil disinfection systems.

Elham Raminمشاهده پروفایل

پژوهشگر

• Wastewater Treatment
• Computational Fluid Dynamics
• Water Resource Recovery
• +۵ مورد دیگر

Elham Ramin is a Researcher at the Department of Chemical and Biochemical Engineering at the Technical University of Denmark (DTU), affiliated with the Center for Energy Resources Engineering (CERE) and the Process and Systems Engineering Centre (PROSYS). Her work contributes to multiple UN Sustainable Development Goals, particularly in clean water and sanitation, affordable and clean energy, and industry innovation. Dr. Ramin completed her PhD at DTU (2010-2014) with research focused on modeling water quality in sewer-WWTP systems. Her academic journey demonstrates a strong foundation in environmental process engineering with applications to real-world water treatment challenges. Her research interests span wastewater treatment optimization, Power-to-X applications for water resource recovery, industrial symbiosis in water management, and biomanufacturing process modeling. She specializes in computational fluid dynamics, activated sludge modeling, and one-dimensional simulation models for wastewater treatment plants. Her work bridges environmental engineering with sustainable resource management, focusing on practical solutions for water-energy nexus challenges. Analysis of her recent publications reveals a strong trend toward integrating sustainable energy solutions with water treatment processes, particularly Power-to-X technologies. Her research portfolio shows increasing focus on digitalization of water resource recovery facilities and cross-sectoral industrial symbiosis for optimal resource utilization. The work demonstrates strong interdisciplinary connections between environmental engineering, chemical process modeling, and sustainable development. Dr. Ramin has participated in significant research projects including ERASE (Evaluation of Resource recovery Alternatives in South African water) and GECKO (Green and Circular Innovation for Kenyan Companies), demonstrating international collaboration and application of research to diverse water management contexts. Her work has generated substantial academic interest with multiple publications receiving significant downloads and reader engagement on platforms like Mendeley. She is actively involved with research centers including CERE and PROSYS at DTU, contributing to interdisciplinary teams focused on energy resource engineering and process systems optimization. Her collaborations extend to pharmaceutical industry applications, particularly in vaccine manufacturing development and digital twin implementation for bioprocesses.

Chris Leeمشاهده پروفایل

استاد

• Transportation Engineering
• Traffic Safety
• Driver Behavior Analysis
• +۵ مورد دیگر

Professor Chris Lee is a faculty member in the Department of Transportation Science and Engineering at the University of Windsor's Faculty of Engineering. His research focuses on advancing transportation safety through the analysis of driver behavior, traffic flow dynamics, and the integration of emerging technologies like autonomous vehicles and machine learning. Key areas include collision risk prediction, driver vigilance assessment, and the development of advanced car-following models. He has contributed to initiatives such as the Transportation Science and Engineering scholarship program, supporting student research in innovative technologies like driving simulators for lane change behavior studies. His work bridges engineering and human factors, addressing challenges such as driver response to autonomous systems, heavy vehicle traffic management, and cross-cultural automotive design. Lee's interdisciplinary approach leverages data analytics, physiological signals, and machine learning to solve real-world transportation problems. His research has implications for policy-making, infrastructure design, and vehicle safety standards. Lee has collaborated extensively on projects analyzing crash precursors, variable speed limits, and the impact of ITS (Intelligent Transportation Systems) on safety. His publications span over two decades, demonstrating a commitment to both academic rigor and practical applications in transportation engineering. Notable contributions include refining car-following models, studying driver aggression, and evaluating the effectiveness of traffic management strategies.

Youngchul Raمشاهده پروفایل

دانشیار

• Combustion Modeling
• Computational Fluid Dynamics (CFD)
• Internal Combustion Engines
• +۴ مورد دیگر

Dr. Youngchul Ra is an Associate Professor in the Department of Mechanical and Aerospace Engineering at Michigan Technological University. He holds a PhD from MIT (1999) and degrees from Seoul National University. His expertise includes computational fluid dynamics (CFD), combustion modeling, chemical kinetics, and alternative fuel research. His work focuses on advanced combustion strategies like Gasoline Compression Ignition (GCI), engine CFD code development, and high-performance computing. Education: PhD in Mechanical Engineering, Massachusetts Institute of Technology (1999) Masters and Bachelors in Mechanical Engineering, Seoul National University Research Interests: Developing multi-component fuel models for real-world applications Optimizing six-stroke GCI engines with advanced valve technologies Reducing emissions via combustion control and injection strategies Parallel computing techniques for large-scale engine simulations Recent work emphasizes oxygenated fuels in GCI engines and parametric studies of combustion efficiency. His CFD models are validated against experimental data for accuracy. His research has led to advancements in low-temperature combustion and emission reduction without explicit awards listed. He collaborates on engine design optimization and fuel formulation projects.

James F. O'Brienمشاهده پروفایل

استاد

• Computer Graphics
• Computer Animation
• Physical Simulation
• +۷ مورد دیگر

Dr. James F. O'Brien is a Professor of Computer Science at the University of California, Berkeley, affiliated with research centers including the Berkeley Artificial Intelligence Research Lab (BAIR) and the Visual Computing Lab (VCL). His research focuses on computer graphics, animation, physical simulation, and image forensics, with applications in film, gaming, and virtual reality. O'Brien pioneered destruction modeling techniques used in over 200 films and games, earning an Academy Award in 2015. He holds leadership roles in tech companies like Juice Labs and Get Klothed, and has advised on patent litigation cases. Education: PhD in Computer Science (Georgia Tech, 2000), MS (Georgia Tech, 1997), BS (Florida International University, 1992). Research Interests: Computer Animation & Simulation Image/Video Forensics Human Perception of Motion VR/AR Privacy & Motion Data Machine Learning Applications Awards: Recipient of the 2015 Academy Award for Technical Achievement, ACM Distinguished Scientist (2009), and MIT TR-35 Innovator (2004). His work spans over 50,000+ VR user studies and groundbreaking contributions to cloth simulation and destruction modeling. Current Projects: Exploring ethical implications of extended reality (XR) motion data, developing privacy-preserving VR systems, and advancing AI-driven animation techniques.

• Humanitarian Operations
• Air Traffic Management
• Logistics Optimization
• +۷ مورد دیگر

Dr. Jose Escribano is a Lecturer in Aviation & Logistics at the Department of Civil and Environmental Engineering within the Faculty of Engineering at Imperial College London. His research focuses on humanitarian logistics optimization, AI-driven airspace management, and urban resilience strategies. He holds a First Class Honours bachelor’s degree (2015) and a PhD (2021) from Imperial College London. Dr. Escribano is affiliated with the Centre for Transport Engineering and Modelling and the Transport Systems and Logistics Project D-Risk SHIFT. His academic qualifications include a BEng in Engineering and a PhD in Civil Engineering, both from Imperial College London. His professional affiliations include the Institution of Civil Engineers, Chartered Institute of Logistics and Transport, and the American Institute of Aeronautics and Astronautics. He has received the 2023 Transportation Research Board Best Paper Award and a JSPS Fellowship for urban evacuation modelling. Dr. Escribano’s research integrates stochastic modelling, machine learning, and simulation to address challenges in humanitarian response, UAV coordination for disaster relief, and airspace safety. His work emphasizes endogenous value-of-information analysis and the application of cutting-edge technologies to enhance societal resilience. He has collaborated with the United Nations World Food Programme on UAV deployment models for humanitarian contexts. His recent publications span topics like air traffic network resilience, autonomous vehicle optimization, and last-mile delivery mechanisms. He advises doctoral candidates in transportation systems, logistics, and air traffic management, offering opportunities for PhD research in these domains.

• Transportation Engineering
• Intelligent Transportation Systems (ITS)
• Wrong-Way Driving Countermeasures
• +۱۵ مورد دیگر

Haitham Al-Deek is a Professor in the Department of Civil, Environmental, and Construction Engineering at the University of Central Florida's College of Engineering and Computer Science. He leads the Intelligent Transportation Systems and Data Analytics Lab and has over 32 years of experience in transportation engineering, planning, and operations. His work is nationally recognized, particularly in freeway operations and intelligent transportation systems (ITS). Ph.D., Civil Engineering-Transportation Engineering, University of California, Berkeley (1991) M.S., Civil Engineering-Transportation Engineering, University of California, Berkeley (1987) B.S., Civil Engineering (with Honors), University of California, Berkeley (1985) Dr. Al-Deek's research focuses on wrong-way driving countermeasures, connected and automated vehicles, traffic safety, and data analytics. He pioneered innovative ITS solutions for detecting and preventing wrong-way driving, including the development of a high-success-rate detection system in partnership with the Central Florida Expressway Authority (CFX). His work extends to freight transportation, electronic toll collection, and sustainable transportation systems. He has also contributed to safety performance functions and driver behavior modeling. His recent publications highlight advanced methodologies in network screening for crash modeling, the use of crowdsourced data (e.g., Waze) for incident detection, optimization of wrong-way driving countermeasures, and benefit-cost analyses of safety technologies. These works reflect a strong trend toward data-driven, real-time, and cost-effective solutions in transportation safety and operations. Scientific awards and recognitions include: TRB Chairman Award (2018, 2012) Multiple TRB Best Paper Awards (Freeway Operations and Regional TSM&O, 2023–2003) TRB Best Student Paper Awards (2022, 2019, 2018, 2017) UCF Excellence in Research Award (2018) UCF Researcher of the Year (1999) Distinguished Researcher, UCF College of Engineering (2003) Dr. Al-Deek has supervised 15 Ph.D. students and 29 M.S. theses and has secured over $10.3 million in research funding from agencies including FDOT, TRB, USDOT, and CFX. He serves as a technical editor for TRR and associate editor for the Journal of Intelligent Transportation Systems. He also chaired key TRB paper review subcommittees and is an active professional engineer in Florida. He leads the Intelligent Transportation Systems and Data Analytics Lab, which focuses on real-world applications of ITS, data warehousing, and advanced analytics for transportation safety and efficiency.