Find Academic Supervisors

• Event-Driven Control
• Cyber-Physical Systems
• Robotics
• +6 more

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 CiattagliaView profile

Assistant Professor

• Radar Signal Processing
• Automotive Engineering
• Aerospace Engineering
• +3 more

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 FERRACUTIView profile

Researcher

• Control Systems
• Robotics
• Brain-Computer Interfaces
• +6 more

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 GambiView profile

Associate Professor

• Telecommunications
• Radar Systems
• IoT and Sensor Networks
• +3 more

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 WilsonView profile

Associate Professor

• Coastal Ocean Physics
• Wave Dynamics
• Sediment Transport
• +6 more

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
• +3 more

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 AlbabaView profile

Researcher

• Deep Reinforcement Learning
• Computer Vision
• Natural Language Processing
• +3 more

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
• +3 more

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.

Daniele CirilloView profile

Researcher

• Structural Geology
• Seismotectonics
• Fault System Analysis
• +5 more

Dr. Daniele Cirillo is an academic affiliated with the DiSPUTer department at Ud'A University in Chieti Scalo, Italy. He specializes in structural geology and seismotectonics with a focus on fault systems, 3D geomodelling, and geohazard assessment. His research integrates fieldwork, remote sensing, and advanced software tools like ArcGIS and drone photogrammetry. Education: Bachelor's (2006) and Master's (2009) in Geological Sciences from the University of Calabria, followed by a PhD in Structural Geology from the University of Chieti (2014). He completed postgraduate studies at Glasgow University (2010). Research interests span tectono-stratigraphic evolution, seismogenic zoning, and active fault analysis in the Apennines and Calabrian regions. Notable projects include 3D fault modelling of the Crati Valley and seismic hazard assessments following the 2016 Central Italy earthquakes. He collaborates with institutions like CRUST (Interuniversity Center for 3D Seismotectonics) and has authored over 50 peer-reviewed papers. Teaching includes courses on structural geology, digital mapping, and GIS. His work often combines field data with geophysical techniques to understand earthquake mechanisms and geological deformation processes. Publications emphasize interdisciplinary approaches, blending geological mapping with advanced imaging technologies. Key contributions include UAV-based rockfall analysis and fault segmentation studies of major historical earthquakes.

Ivica KostanicView profile

Associate Professor

• Wireless Communications
• Wireless Multimedia Communications
• Wireless Sensor Networks
• +7 more

Dr. Ivica Kostanic is an Associate Professor in the Department of Electrical Engineering and Computer Science at Florida Institute of Technology (FIT), serving as Director of the Wireless Center of Excellence (WiCE). He holds affiliations with both the College of Engineering and Science and the WiCE, leading research in wireless communication systems, sensor networks, and mobile analytics. His work emphasizes practical applications in cellular, microwave, and satellite communication. Research Focus: WiCE research spans wireless sensor networks (WSN), quality of service (QoS) optimization, LTE/5G network analysis, and hurricane monitoring via WSN. Key projects include the Mobile Video Quality Prediction (MVQP) initiative, which evaluates video streaming over cellular networks, and sensor network deployments in diverse terrains. Collaborations with industry partners enhance practical relevance. Publications & Awards: Over 50 peer-reviewed publications highlight contributions to WSN performance modeling, video quality metrics, and LTE optimization. Notable recognition includes a Best Paper Award at WCECS 2014 for subjective video quality assessment in LTE networks. Recent work extends to 5G beamforming, UAV communication systems, and spatial mobility analysis. Education & Mentorship: Teaches graduate courses in RF propagation, communication theory, and wireless systems. Advises numerous PhD and MS students, many contributing to WiCE projects. His curriculum development includes the Wireless Systems and Technology specialization, emphasizing theoretical and applied skills. Labs & Facilities: Oversees two labs: the WiCE Research Lab (equipped with CAD tools like Agilent ADS) and the Microwave Lab (featuring anechoic chambers and network analyzers). Software tools like SimWiser and HSN Client aid WSN simulation and deployment analytics.

• Autonomous Systems
• Robotics
• Computer Vision
• +5 more

Asanka Perera is a Lecturer in Mechatronic Engineering at the University of Southern Queensland. His research focuses on robotics, autonomous systems, computer vision, and adversarial learning. He is affiliated with the Centre for Agricultural Engineering and the Centre for Future Materials, and holds an ORCID ID: https://orcid.org/0000-0003-4021-3943. Education: BScEEEng, University of Peradeniya (2008) MScience Industrial Automation, Moratuwa University (2015) PhD, University of South Australia (2020) Professional Memberships: Engineers Australia, Australian Computer Society (ACS), IEEE, Australian Association of Uncrewed Systems (AAUS) His research interests span autonomous vehicles, field robotics, intelligent robotics, and computer vision applications. He has supervised doctoral and master's students on topics like swarm robot localization, adversarial machine learning, and biomedical robotics. His work includes developing algorithms for robust perception in autonomous systems and non-invasive medical monitoring using drones and thermal imaging. Key contributions include reviews on UAV-UGV collaboration, adversarial robustness in LiDAR systems, and biomimetic navigation techniques inspired by celestial orientation. His research bridges robotics, computer vision, and healthcare technologies, emphasizing real-world applications in autonomous systems and medical diagnostics.

Md Sultan MahmudView profile

Assistant Professor

• Precision Agriculture
• Computer Vision
• Artificial Intelligence
• +5 more

Md Sultan Mahmud is an Assistant Professor of Precision Crop Protection in the Department of Plant Pathology at the University of Georgia (UGA), with a courtesy faculty appointment in the School of Environmental, Civil, Agricultural, and Mechanical Engineering (College of Engineering). He holds a Ph.D. in Agricultural and Biological Engineering from Pennsylvania State University (2022), an M.Sc. in Agriculture (Engineering) from Dalhousie University (2019), and a B.Sc. in Agricultural Engineering from Hajee Mohammad Danesh Science and Technology University (2016). His research focuses on integrating computer vision, AI, robotics, and sensor technologies to develop precision solutions for plant disease and pest management. Key areas include smartphone applications, drone-based monitoring, early disease prediction models, and autonomous robotic systems for agricultural applications. He has received notable awards such as the Boyd-Scott Graduate Research Award (ASABE, 2022) and the Outstanding Dissertation Award from Penn State. Dr. Mahmud leads the Precision Crop Protection Laboratory and has secured grants from organizations like USDA NIFA, Southern SARE, and commodity boards. He actively publishes in journals like Sensors , Computers and Electronics in Agriculture , and AgriEngineering , with a focus on precision spraying systems, disease detection algorithms, and automation in crop production. Education: Ph.D., Agricultural and Biological Engineering, Penn State, 2022 M.Sc., Agriculture (Engineering), Dalhousie, 2019 B.Sc., Agricultural Engineering, Bangladesh, 2016 Research Themes: Computer vision for crop health monitoring AI-driven disease prediction models Autonomous robotic systems in agriculture Drone-based precision spraying Recent Grants: Optimizing Pecan Orchard Health via Drone Imagery (GA Commodity Board) Autonomous Laser-Based Weed Management (Southern SARE) UVC Light for Turfgrass Disease Control (USDA NIFA)

• Visualization
• Image Processing
• Fractal Geometry
• +3 more

Martin Turner is a Business Relationship Manager for Research IT at the University of Manchester, specializing in e-Science and computational infrastructure. He also serves as an Honorary Lecturer in Computer Science. His roles include project management for UKRI grants and leadership in initiatives like the Harwell Imaging Partnership and the Scientific Computing Division at STFC. Research interests span visualization, image processing, and fractal geometry, with notable contributions including a book on Fractal Geometry in Digital Imaging and over 120 peer-reviewed publications. He has supervised 21 PhD/MPhil students and contributed to edited volumes such as Geographic Visualization: Concepts, Tools and Applications . Key awards include a short-term Fellowship with British Telecom. His work integrates real-world applications with cutting-edge imaging technologies, emphasizing collaboration and longitudinal research support. Projects involve visualization frameworks, tomography, and UAV-based computer vision applications. Advising focuses on MSc and PhD levels, while grants and collaborations address imaging science, geology, and computational methods. He leads teams in developing tools like the Core Imaging Library and SIRF framework, advancing tomographic and multi-channel imaging techniques.

• Artificial Intelligence
• Machine Learning
• Edge Computing
• +7 more

Huan Liu is a Professor at Arizona State University's School of Computing, Informatics, and Decision Systems Engineering, with research spanning artificial intelligence, edge computing, and interdisciplinary applications. His work bridges theoretical advances with real-world implementations across healthcare, transportation, and environmental systems. His primary research interests include: Artificial Intelligence and Machine Learning for complex system optimization Edge-Cloud Computing architectures for IoT and vehicular networks Medical image analysis and clinical decision support systems Graph neural networks and multimodal learning Privacy-preserving federated learning frameworks Recent publications demonstrate strong emphasis on applied AI, with 15+ 2025 papers addressing UAV swarm intelligence, medical diagnostics, and sustainable transportation through novel algorithmic approaches. Publication trends reveal consistent leadership in: Developing efficient edge computing solutions for dynamic environments Creating medical AI tools with clinical validation Innovating in graph-based and multimodal learning Applying optimization techniques to logistics and environmental challenges His work frequently appears in top venues including IEEE Transactions, Bioinformatics, and AAAI.

• Neural Networks
• Medical Image Analysis
• Computer Vision
• +5 more

Zhigang Li is a Professor in the Department of Computer Science at South China University of Technology's School of Computer Science and Engineering. His research spans multiple technical domains with significant contributions to neural networks, medical imaging, computer vision, and sensor technologies. Recent collaborations include work with Northwestern Polytechnical University, Hong Kong University of Science and Technology, and various medical research institutions. Dr. Li's research interests focus on neural network architectures, particularly small-world and feedforward networks for system modeling and medical applications. His work bridges computer science with practical applications in healthcare (EEG analysis, schizophrenia detection, liver transplant allocation), environmental monitoring (wastewater treatment), and engineering systems (CMOS image sensors, UAV networks). His research demonstrates strong interdisciplinary connections between theoretical computer science and real-world problem solving. Analysis of his recent publication trends shows increasing focus on medical applications of AI, with significant work in brain functional network analysis, depression recognition, and schizophrenia detection. His technical contributions include novel neural network architectures, efficient sensor systems, and advanced signal processing techniques. The publications reveal a consistent pattern of high-quality output in top-tier journals across multiple disciplines. Dr. Li has received recognition through publications in prestigious venues including IEEE Transactions, Medical Image Analysis, and Expert Systems with Applications, though specific awards aren't documented in the provided bibliography. His work demonstrates significant impact across multiple fields, particularly in applying computational methods to healthcare challenges. His research program includes collaborations with medical researchers for brain imaging applications, electrical engineers for sensor development, and computer scientists for network architecture design. Current projects appear focused on multi-view brain network analysis, energy-efficient sensor systems, and medical AI applications with potential clinical impact.