جست‌وجوی استاد و پژوهشگر

• Wireless Communications
• Massive MIMO
• UAV Networks
• +۵ مورد دیگر

David Gesbert is a Professor at Eurecom in Sophia Antipolis, France. His research focuses on advanced wireless communication systems, including Massive MIMO, UAV networks, federated learning, and 6G technologies. He has contributed extensively to channel prediction, beamforming optimization, and machine learning applications in wireless networks. His work spans theoretical analysis and experimental validation, addressing challenges in aerial communication, energy efficiency, and network intelligence. Key areas include UAV-aided localization, cooperative satellite-RIS beamforming, and environment-aware communication frameworks. Research interests emphasize wireless systems, signal processing, and machine learning integration. Notable projects include optimizing UAV trajectories for IoT networks, enhancing 5G connectivity for aerial highways, and developing efficient federated learning strategies for distributed networks. His contributions bridge theoretical advancements with practical implementations, particularly in dynamic environments and future communication architectures. Core Expertise: 5G/6G, Massive MIMO, UAV Networks, Federated Learning Key Projects: Channel quality prediction via ML, RIS-enabled beamforming, global channel charting Impact: Over 400 publications, with focus on scalability and real-world applicability

• French Language Practice
• Romance Studies
• Computer Vision
• +۳ مورد دیگر

Viridiane Fay is a researcher in the Institute of Modern Languages and Romance Studies at the University of Würzburg , specializing in French language practice and applied computational methods. During lecture periods, she holds office hours every Tuesday from 1 p.m. to 2 p.m., with appointments available during semester breaks. Role: French Editor, Language Practice Location: Am Hubland, 97074 Würzburg Contact: viridiane.fay@uni-wuerzburg.de Her research spans two distinct domains: French language pedagogy and computer vision/AI . While her institutional affiliation focuses on Romance Studies, her publication record reveals expertise in: Medical and underwater image processing Transformer-based architectures Federated learning and network optimization Watermarking and security algorithms Multi-modal AI applications Neuroscience signal analysis Recent work includes lightweight segmentation models (e.g., ContextFormer), drone/aerial imagery detection frameworks (CH-YOLO-Lite), and privacy-preserving AI systems (Find). Despite limited biographical details in the scrape, her 30+ publications since 2017 suggest significant technical contributions.

• Marine Science
• Environmental Science
• Remote Sensing
• +۴ مورد دیگر

Jason McIlvenny is a Research Fellow at the University of the Highlands and Islands (UHI), affiliated with the Environmental Research Institute (ERI), Aquaculture Research Network, and Energy Innovation Team. His research focuses on marine and environmental science, particularly tidal stream energy systems, remote sensing technologies, and ecological impacts of renewable energy infrastructure. Research Interests Unmanned aerial vehicles (UAVs) for environmental monitoring Tidal hydrodynamics and marine renewable energy Remote sensing applications in coastal ecosystems Environmental impacts of wind and tidal energy systems Biophysical interactions between seabirds and tidal currents Key Contributions Developed methodologies for automated detection of surface turbulence features in tidal streams Pioneered low-cost graphical user interfaces for drone imagery processing Investigated CO2 flux dynamics in blanket bogs during drought conditions Awards & Recognition While no specific awards are listed, his work has been widely cited and featured in interdisciplinary journals such as Sensors and Ecohydrology . Advisory & Collaborative Work McIlvenny has supervised 2 research projects and collaborated with organizations like the Nuclear Decommissioning Authority on legacy particles research. His work aligns with UN Sustainable Development Goals related to climate action and responsible consumption of energy resources.

• Machine Learning
• Computer Vision
• Artificial Intelligence
• +۴ مورد دیگر

Manuel Felipe Muciéntes Molina is a Professor of Computer Science and Artificial Intelligence at the Research Center on Intelligent Technologies (CiTIUS), University of Santiago de Compostela (USC). He holds a PhD from USC (2002) focusing on fuzzy logic-based control systems for autonomous robots. His research emphasizes machine learning applications in computer vision, process mining, robotics, and healthcare. He teaches courses like Statistical Learning, Supervised Machine Learning, and Deep Learning. Key Projects: RAI4P: Responsible AI for Process Mining 2.0 QC4SUSTEX: Authentication and security of sustainable textiles CAREBOT: Intelligent robotic units for hospital care Research Interests: Machine learning, computer vision (object detection, tracking), process mining, robotics navigation, and healthcare AI applications. Labs/Teams: Leading the Grupo GSI (Systems Intelligence) within CiTIUS, focusing on intelligent systems and AI-driven solutions.

Hani HAMDANمشاهده پروفایل

استاد

• Machine Learning
• Signal Processing
• Intelligent Communication
• +۵ مورد دیگر

Hani Hamdan is a Professor of Electrical Engineering and Computer Science at CentraleSupélec, part of Université Paris-Saclay. He holds a PhD from Université de Technologie de Compiègne and has held roles including Research Engineer at CETIM, Researcher at CNRS, and Assistant Professor at Sorbonne-Paris-Nord. His research focuses on machine learning, signal processing, robotics, and telecommunications, with contributions to biomedical engineering and sustainable technologies. He leads the COMEDY and MODESTY research groups within the L2S laboratory. Education: PhD in Systems and Information Technologies, Université de Technologie de Compiègne (2005) MSc in Industrial Control, Université Libanaise/UTC (2001) Engineering Diploma in Electricity & Electronics, Université Libanaise (2000) Research Interests: Machine learning for healthcare and environmental applications Signal processing in robotics and communication systems Intelligent communication networks (FSO, IoT security) Data-driven analysis of biomedical systems and civil infrastructure Key Contributions: Innovative clustering algorithms for big data (e.g., bin-EM-CEM) FSO communication system optimizations Robotics rehabilitation systems using ML-enhanced biomechanics Awards: Best Paper Award for Big Data clustering (2018) Outstanding conference organization award (DeSE 2017) CIFRE doctoral scholarship (2002-2005) Lab Affiliations: Laboratoire des signaux et systèmes (L2S), collaborating with CNRS and CentraleSupélec on interdisciplinary projects in signal processing and automated systems.

Tarek Elderiniمشاهده پروفایل

مدرس

• Robotics
• Autonomous UAVs
• Control Systems
• +۴ مورد دیگر

Tarek Elderini is an Instructor at the School of Electrical Engineering and Computer Science, University of North Dakota (UND), where he has held this position since August 2019. He earned his Ph.D. in Electrical Engineering (2021) and M.Sc. in Control Engineering (2013) from UND and the Arab Academy for Science, respectively. His research focuses on Robotics, Autonomous UAVs, Control Systems, Computer Vision, and Machine Learning, with notable contributions to indoor navigation systems and UAV localization. Education: Ph.D. in Electrical Engineering (2021), University of North Dakota M.Sc. in Electrical & Control Engineering (2013), Arab Academy for Science, Technology & Maritime Transport (AASTMT) B.Sc. in Electrical and Control Engineering (2009), AASTMT Research Interests: Elderini’s work spans autonomous systems, including wheeled robots, UAVs, and underwater ROVs. He develops Bayesian models for space occupancy mapping, path planning, and sensor-based navigation. His recent projects include a robotic system for long-term care facility cleaning, leveraging fluorescence imaging for pathogen detection. Awards: Dean’s Outstanding Faculty Award (2022) Professor of the Year (2022) 1st place in IEEE Red River Valley Graduate Competition (2018) Teaching & Grants: He teaches courses like Control Systems and Embedded Systems. As Co-PI, he secured a $40K grant for a robotic cleaning system under the CARES Act. His prior roles include Graduate Teaching/Research Assistantships at UND (2015–2019).

Bernie Tiddemanمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Data Analysis
• +۳ مورد دیگر

Bernie Tiddeman is a Professor in the Department of Computer Science at Aberystwyth University. His research focuses on computer vision, machine learning, and their applications in fisheries technology, anomaly detection, and data analysis. He has led projects funded by DEFRA, the European Maritime and Fisheries Fund, and the Welsh Government, addressing challenges in environmental monitoring, automated data capture, and heritage preservation. His work contributes to UN Sustainable Development Goals related to sustainable fisheries and responsible consumption. Key research interests include deep learning for object detection (e.g., crabs and lobsters), reinforcement learning in non-stationary environments, and neural architecture search. He collaborates with researchers like Dr. Seb Gregory Dal Toe and Muhammad Iftikhar on fisheries data automation and computer vision systems. Notable projects include the Automated Video-Based Capture of Crustacean Fisheries Data and the Improved Shellfish Data Collection Project. His recent publications (2023-2025) highlight advancements in anomaly detection, crustacean identification, and low-power hardware solutions. Tiddeman has supervised over a dozen PhD candidates and actively participates in educational initiatives like the Wales Collaborative for Learning Design.

• Event-Driven Control
• Cyber-Physical Systems
• Robotics
• +۶ مورد دیگر

Sylvain Durand Chamontin serves as an Associate Professor at INSA Strasbourg, affiliated with the ICube research laboratory (UMR 7357) and the AVR (Automation, Vision, Robotics) team. His teaching encompasses advanced automation (anti-windup, Smith predictor, LQ control), embedded systems/IoT, motorization/axis control, linear automation (state feedback, observers), and sequential automation (GRAFCET, GEMMA) for electrical engineering, mechatronics, and mechanical engineering students across 2nd–5th year programs. His research centers on frugal design and control of embedded cyber-physical/robotic systems under resource constraints, with a dedicated focus on non-periodic sampling and event-driven techniques . Key domains include event-driven control architectures, dynamic vision sensor-based visual servoing, aerial robotics (UAVs/aerial manipulators), and swarm robotics. This work systematically reduces computational load, communication overhead, and energy consumption while maintaining robust performance in resource-limited environments—critical for embedded implementations in drones and cyber-physical systems. Analysis of Durand's 15 most recent publications (2022–2025) reveals a dominant trend in event-driven control for robotics, increasingly integrating machine learning for adaptive tuning. His work targets practical applications in aerial robotics, including UAV stabilization under ground effects, elastic-suspension aerial manipulation, and event-based visual servoing. A strong emphasis on frugality permeates techniques like non-periodic sampling and resource-aware control strategies, directly addressing hardware limitations in embedded platforms. Durand mentors award-winning PhD students including M. Pivert (Best Student Paper Award, IFAC Robotics 2025), T. Paul (i-PhD Innovation Contest 2022), and A. Yiğit (Best PhD Award in French Robotics 2021). He leads multiple ANR-funded projects: e-VISER (event-driven visual control, 2018–2021), DexterWide (cable robots, 2015–2018), and current initiatives eSWARM (modular UAVs, 2023–2025), muteSWARM (acoustic swarm control, 2023–2027), STRAD (street art drone, 2022–2026), TIR4sTREEt (urban micro-climatology, 2022–2026), and dark-NAV (GPS-denied navigation, 2021–2025). Within ICube's AVR team, Durand drives laboratory development of the dextAIR robot (omnidirectional aerial manipulator with elastic suspension) and embedded control systems for cable-driven parallel robots and swarm robotics. His experimental work emphasizes real-time implementation, energy efficiency, and frugal engineering principles—translating theoretical event-driven control into hardware solutions for resource-constrained robotic applications.

Gianluca Ciattagliaمشاهده پروفایل

استادیار

• Radar Signal Processing
• Automotive Engineering
• Aerospace Engineering
• +۳ مورد دیگر

Gianluca Ciattaglia is an Assistant Professor (RTDa) in Electrical and Electronics Measurements at the Department of Electrical and Electronic Engineering, Università Politecnica delle Marche, Italy. Previously, he served as a Research Fellow at the same institution from 2022 to 2024 and worked at Ferrari S.p.A. as an Electronic Support Engineer for Formula 1 test teams (2018–2022). He holds a B.Sc. (2014), M.Sc. (2017), and Ph.D. (2021) in Electronic Engineering and Information Engineering from Università Politecnica delle Marche. His research focuses on radar-based measurement techniques for industrial, automotive, and aerospace applications, including radar signal processing, vibration analysis, and sensor fusion. He has contributed to projects involving drone detection, elderly telemonitoring, and automotive radar optimization. His work bridges theoretical advancements in signal processing with practical implementations in safety, health, and environmental monitoring. Publications span radar-based physiological sensing, autonomous drone identification, and mmWave radar applications in structural health monitoring. His recent work emphasizes machine learning integration with radar data for improved accuracy in activity recognition and environmental control systems. No scientific awards are explicitly listed, but his contributions to automotive and aerospace radar technologies reflect significant industry-academia collaboration. Professional activities include advising on radar sensor deployments and collaborating with automotive firms like Ferrari. His lab focuses on developing edge-node systems for real-time monitoring and smart environmental solutions. Current projects include optimizing radar configurations for energy efficiency and enhancing indoor air quality through smart ventilation systems.

FRANCESCO FERRACUTIمشاهده پروفایل

پژوهشگر

• Control Systems
• Robotics
• Brain-Computer Interfaces
• +۶ مورد دیگر

Prof. Francesco Ferracuti is a Researcher at the Department of Information Engineering (Università Politecnica delle Marche, Ancona, Italy). His work focuses on advanced control systems, robotics, brain-computer interfaces, and energy management. Key research themes include fault-tolerant control, autonomous navigation, and human-machine interaction through wearable sensors. Research interests span across: Control Engineering (MIMO systems, PID control, Model Predictive Control) Robotics (mobile robotics, UAVs, smart wheelchairs) Neuroengineering (EEG-based BCI, cognitive workload assessment) Energy Systems (renewable integration, home energy management) Data-Driven Methods (machine learning, system identification) Publications emphasize innovative solutions like EEG-driven obstacle avoidance and thermal stress monitoring for smart wheelchairs, demonstrating interdisciplinary approaches combining robotics with biometrics. His work on fault detection in multirotors and energy optimization algorithms highlights practical applications in both industrial and healthcare domains. Current projects include the E-MOTIVE initiative for mobility solutions via affective computing and BCI. His lab develops cutting-edge algorithms for cyber-physical systems security and predictive maintenance in industrial equipment.

Ennio Gambiمشاهده پروفایل

دانشیار

• Telecommunications
• Radar Systems
• IoT and Sensor Networks
• +۳ مورد دیگر

Prof. Ennio Gambi is an Associate Professor at the Polytechnic University of Marche , affiliated with the Department of Information Engineering within the Faculty of Engineering. His research focuses on telecommunications, radar systems, biomedical engineering, and machine learning applications . He is actively involved in projects related to IoT sensor networks, drone surveillance, and contactless health monitoring. Prof. Gambi's academic contributions span physical layer authentication , radar-based physiological sensing , and automotive radar technology . He collaborates on initiatives such as the OPENCARE system for elderly care and has expertise in signal processing for biomedical applications. His office hours are held weekly at the Department of Information Engineering, and he maintains an active research profile through publications and projects. Key research areas include: Radar-based human activity monitoring Cybersecurity in UAV communications IoT-enabled healthcare systems Machine learning for sensor data analysis His work bridges telecommunications engineering with practical applications in healthcare, transportation, and environmental monitoring.

Greg Wilsonمشاهده پروفایل

دانشیار

• Coastal Ocean Physics
• Wave Dynamics
• Sediment Transport
• +۶ مورد دیگر

Greg Wilson is an Associate Professor in the College of Earth, Ocean, and Atmospheric Sciences (CEOAS) at Oregon State University, where he leads the Wilson Nearshore Research Group within the Coastal Imaging Laboratory (CIL). His research centers on the physics of nearshore coastal processes, including wave dynamics, sediment transport, and current systems, utilizing observational tools and probabilistic modeling approaches. Wilson specializes in acoustic remote sensing, data assimilation for bathymetry inversion, and rip current forecasting, with applications extending from beaches to rivers and inlets. His work emphasizes field measurements using Argus beach cameras, UAVs, and novel instrumentation to address coastal hazards and morphological changes through statistical and numerical frameworks. Analysis of his 2024-2025 publications reveals a pronounced focus on rocky shore dynamics, wave runup prediction on composite beaches, and innovative marine-bird-based environmental monitoring. These studies integrate multiscale remote sensing, field experiments, and modeling to tackle challenges in nearshore circulation, sediment transport, and wave energy dissipation. No scientific awards were mentioned in the provided text. Wilson actively recruits graduate and undergraduate researchers for projects involving surf zone modeling, data assimilation, and coastal hazard prediction. His collaborations with coastal engineers indicate applied research translating into practical coastal management solutions, though specific grant details were not provided. As part of the CIL, he utilizes advanced technologies including Argus imagery, marine radar, and biologging to study nearshore processes, with recent work emphasizing rocky shore environments and infragravity wave interactions.

• Robotics
• Computer Vision
• Autonomous Systems
• +۳ مورد دیگر

Jesus Pestana Puerta is a researcher at the Institute of Computer Graphics and Vision (ICG) within the Faculty of Computer Science and Biomedical Engineering at TU Graz. He holds a PhD in Automation and Robotics from the Technical University of Madrid (UPM). With over 10 years of experience, he specializes in Aerial Robotics, focusing on vision-based solutions for drone navigation, obstacle avoidance, and autonomous systems. His work bridges research and industry, including prototypes for delivery drones (Post AG) and inventory management systems. Key projects include Overview Obstacle Maps for safe drone navigation and Micro Aerial Projector for stabilized aerial displays. Research interests span visual-inertial odometry, GPS-denied navigation, and technology transfer. He has contributed to robotics competitions (IC CEA, IMAV, IARC) and published extensively in journals like Journal of Field Robotics and conferences such as IROS. His recent work explores cloud-based navigation systems, AI for waste sorting, and automotive embedded systems optimization. Collaborations include startups and industrial partners, emphasizing practical applications of robotics. He has supervised projects in sensor degradation detection, redundant systems, and UAV lifecycle monitoring. His contributions are documented in over 50 publications, with a focus on advancing drone autonomy and real-world deployment.

Mert Albabaمشاهده پروفایل

پژوهشگر

• Deep Reinforcement Learning
• Computer Vision
• Natural Language Processing
• +۳ مورد دیگر

Mert Albaba is a joint PhD student at ETH Zurich and the Max Planck Institute for Intelligent Systems (Tübingen), advised by Prof. Andreas Krause, Prof. Otmar Hilliges, and Prof. Michael Black. His research focuses on deep reinforcement learning, computer vision, and language models to model human behavior through generative approaches. He explores integrating vision-language models into imitation learning pipelines to enhance their generative capabilities. Affiliations: Max Planck Institute for Intelligent Systems (Primary), ETH Zurich Advisors: Andreas Krause, Otmar Hilliges, Michael Black Research Interests: Reinforcement learning and game theory for human behavior modeling Generative models for behavior representation Imitation learning with vision-language integration Key Projects: Development of frameworks like GT-DQN (RL + game theory) and SyNet (object detection for UAVs). Active contributor in repositories related to reinforcement learning, vision, and generative models on GitHub.

• Remote Sensing
• Agricultural Technology
• Robotics
• +۳ مورد دیگر

Dr. Péter Udvardy is an Associate Professor at Óbuda University. He is affiliated with the university's engineering and environmental science programs, located at the Budai Út Campus in Székesfehérvár. His research focuses on advanced technologies such as UAV applications, agricultural sensors, robotics, and environmental monitoring. He actively contributes to interdisciplinary fields like precision agriculture, remote sensing, and GIS-driven urban planning. His work combines theoretical and practical innovations, addressing challenges in renewable energy infrastructure inspection, livestock health monitoring via ingestible sensors, and educational robotics. Notable projects include UAV-based wind turbine assessment and bolus sensor development for dairy cattle. Udvardy emphasizes real-world problem-solving in industrial maintenance, data collection methodologies, and sustainable energy solutions. He maintains a consultation schedule on Mondays and can be reached via udvardy.peter@amk.uni-obuda.hu. His research outputs are publicly accessible via Google Scholar and institutional repositories.