جستجوی استاد راهنمای دانشگاهی

Ojleska Latkoska Vesna is a Researcher at the Institute of Automation and Systems Engineering within the Faculty of Electrical Engineering and Information Technologies at the Ss. Cyril and Methodius University in Skopje. She holds a BSc in Electrical Engineering from the same university (2004). Her career includes roles such as Junior Assistant (2007–present) and Associate-Demonstrator (2005–2007) at the institute, alongside a stint as a programmer at VIGOUR spol. S r.o. in Prague, Czech Republic (2004–2005). Her research focuses on Fuzzy Logic , Machine Learning , Robotics , and Biomedical Engineering . Key areas include fuzzy logic-based algorithms for electrical load management, epilepsy detection using neuro-fuzzy systems, robotics path planning, and feature reduction techniques in biomedical signal analysis. She explores interdisciplinary applications, blending control systems with renewable energy optimization and human-machine interaction. Her publications reflect a trend toward algorithmic innovation in healthcare diagnostics, robotics, and industrial automation. Notable contributions include comparative analyses of heliostat field control algorithms and experimental studies on electrostatic discharge effects in networked systems. She is affiliated with the Institute of Automation and Systems Engineering, contributing to both theoretical research and practical implementations in automation.

• Fuzzy Logic
• Machine Learning
• Robotics
• +۳ مورد دیگر

Mustafa Suphi Erden is an Associate Professor in the School of Engineering & Physical Sciences at Heriot-Watt University, UK. He holds a joint appointment with the Institute of Sensors, Signals & Systems. His research focuses on physical human-robot interaction, assistive robotics, medical robotics (e.g., laparoscopy, surgical training, and wearable exoskeletons), and industrial automation (e.g., underwater robotics, laser alignment, and micro-assembly). He leads projects such as AILap (EPSRC grant) and DeMILIO, emphasizing capturing human expertise to enhance robotic systems. Education: B.S., M.S., Ph.D. in Electrical & Electronics Engineering (Middle East Technical University, 2006) Postdoctoral roles at Delft University, École Polytechnique Fédérale de Lausanne, and others Recipient of the European Union Marie Curie Intra-European Fellowship (2012-2014) Research Interests: Medical robotics for surgery and rehabilitation; industrial robotics for dangerous tasks (e.g., underwater inspection, laser alignment); human skill capture for automation. His work aligns with UN SDGs, particularly sustainable industrialization and healthcare access. Recent Projects: Development of a robotic surgery training system, waste collection robots for trains, and exoskeletons for spasticity assessment. Key collaborations include the Surgical Skills Centre at Dundee University and industrial partners like RSSB and Atomic Energy Authority. Awards: Best Student Paper Award (ICRA 2025), Research Team of the Year (2022) Labs/Equipment: Manages facilities like the Robotarium, including laparoscopy training setups and NIRS brain monitoring systems. Future Work: Expanding underwater robotic manipulation (AEROSUB EU Project), enhancing surgical training platforms, and advancing assistive exoskeleton technologies.

• Physical Human-Robot Interaction
• Medical Robotics
• Industrial Robotics
• +۵ مورد دیگر

Dr. Xianwen Kong is an Associate Professor at the Institute of Mechanical, Process and Energy Engineering within the School of Engineering and Physical Sciences at Heriot-Watt University. He is an ASME Fellow and member of the Edinburgh Centre for Robotics. Dr. Kong has authored over 200 publications including a Springer monograph on parallel manipulators (with translations in Russian and Chinese) and holds two U.S. patents. He has secured multiple research grants from EPSRC and Innovate UK focusing on parallel robots and reconfigurable manufacturing systems. Research Focus Dr. Kong's research specializes in creative design methodologies for parallel and reconfigurable robots with applications spanning advanced manufacturing, renewable energy, and biomedical engineering. He developed systematic approaches for parallel manipulator design and pioneered disassembly-free reconfigurable parallel manipulators. Current projects include: Machine Learning-empowered Responsive Manufacture Of Industrial Laser Systems (EPSRC EP/V051164/1) Multiscale digital twin-driven smart manufacturing system (EP/T024844/1) Research Publication Trends Analysis of recent publications reveals strong focus on: type synthesis of variable-DOF mechanisms, kinematic analysis of parallel manipulators, reconfigurable robotic systems, and industrial applications including precision assembly and optical system alignment. Computational methods feature prominently with geometric modeling, screw theory, and machine learning approaches applied to mechanism design and robotic perception. Honors and Recognition 2024 ASME Leonardo Da Vinci Award 2020 ASME A.T. Yang Memorial Award in Theoretical Kinematics 2012 ASME Freudenstein/General Motors Young Investigator Award Multiple best paper awards at international robotics conferences Professional Service Dr. Kong serves as associate editor for leading journals including Mechanism and Machine Theory , IEEE Robotics and Automation Letters , and ASME Journal of Mechanisms and Robotics . He has chaired major conferences including ASME IDETC and organized the ASME/IEEE ReMAR conferences. As an elected member of ASME's Mechanisms and Robotics Committee, he contributes to the advancement of kinematic research.

• Parallel Robots
• Reconfigurable Robots
• Mechanism Design
• +۵ مورد دیگر

Marco Santello is the Fulton Professor of Neural Engineering at Arizona State University's School of Biological and Health Systems Engineering within the Ira A. Fulton Schools of Engineering. He directs the Neural Control of Movement Laboratory and previously served as Harrington Endowed Chair and Director of his department (2012–2022). He leads the NSF Industry/University Cooperative Research Center BRAIN and the Science and Technology Center in Human Performance. His research focuses on human performance, motor control, haptics, and rehabilitation engineering, employing techniques like TMS, EEG, and virtual reality. Education: B.Sc. Kinesiology, Università di L'Aquila, Italy (1990) Ph.D. Neurophysiology of Motor Control, University of Birmingham, UK (1995) Postdoctoral training, University of Minnesota (1995–1999) Research Interests: His work explores sensorimotor integration, multi-digit grasping, and neural control mechanisms in conditions like Parkinson's disease and carpal tunnel syndrome. He investigates how tactile feedback and neuromodulation (e.g., TMS, tFUS) influence motor performance. Grants & Leadership: Supported by NIH, NSF, DARPA, Mayo Clinic, and Google. Leads interdisciplinary initiatives in neurotechnology and rehabilitation engineering. Serves on editorial boards for Neuroscience , Biomedical Engineering , and others. Labs & Teams: Director of the Neural Control of Movement Lab and collaborative centers focused on human performance and neurotechnology. His work bridges robotics, neuroscience, and clinical applications to advance prosthetics and rehabilitation strategies.

• Neural Engineering
• Motor Control
• Haptics
• +۳ مورد دیگر

Prof. Daniel Quevedo is a Professor in Cyberphysical Systems at the School of Electrical Engineering and Robotics, Queensland University of Technology (QUT). He previously led the Chair in Automatic Control at Paderborn University, Germany. He holds a PhD from the University of Newcastle (Australia) and degrees from Universidad Técnica Federico Santa María (Chile). His research focuses on networked cyberphysical systems, cybersecurity, and model predictive control, with over 240 publications and three patents. He has attracted over $2M in research funding and is a Fellow of the IEEE. His work emphasizes cross-disciplinary collaboration in fields like power electronics, telecommunications, and behavioral economics. Educations: PhD in Automatic Control (2005), University of Newcastle, Australia M.Sc. and B.Eng. (2000), Universidad Técnica Federico Santa María, Chile Research Interests: Prof. Quevedo’s work bridges theoretical systems control with practical applications. Key areas include networked cyberphysical systems, cybersecurity (e.g., deception attacks, privacy preservation), and energy-efficient control strategies. His contributions span event-triggered control, encrypted cooperative systems, and power converter optimization. He collaborates globally with institutions like ABB, TU Berlin, and Indian Institute of Technology Bombay. Grants & Awards: Notable grants include DFG-funded projects on network-informed control and ARC grants on predictive control. His 2018 IEEE Axelby Award highlights his impact on systems control theory. He has also held visiting professorships and editorial roles for journals like IEEE Transactions on Automatic Control. Teaching & Supervision: At Paderborn University, he modernized control systems curricula, integrating blended learning and industry-relevant projects. He has supervised over 10 PhD/MSc theses and mentored postdoctoral researchers in areas like cyber-physical security and distributed control. Labs & Teams: He is affiliated with QUT’s Centre for Robotics, focusing on interdisciplinary robotics and cyberphysical systems research.

• Networked Systems
• Cyberphysical systems
• Cyberphysical Security
• +۳ مورد دیگر

Fotios Lygerakis is a doctoral student and university assistant at the Chair of Cyber-Physical Systems, University of Leoben, Austria. He focuses on advancing machine learning and robotics, particularly in representation learning, visuotactile fusion, and robot learning through imitation and reinforcement learning. His work aims to enable robots to mimic human learning processes via abstraction and creativity, targeting tactile-rich manipulation tasks and human-robot interaction. Education: Pursuing a doctoral degree in Cyber-Physical Systems at the University of Leoben. Research Interests: Representation Learning and Visuotactile Fusion using self-supervised methods Robot Learning: Reinforcement Learning, Imitation Learning, Sim2Real Transfer Human-Robot Interaction and tactile-aware manipulation Recent Achievements: Received the Best Student Paper Award at the 2024 Ubiquitous Robotics Conference for his work on M2CURL. His research has been presented at venues like ICRA, IROS, and NYU. He is also a contributor to projects like MViTaC and CR-VAE. Engagement: Served on organizing committees for ECESCON 9 and PETRA 2021, and volunteered at TED AI Vienna 2024. Active in teaching as a Machine Learning lab assistant and through outreach initiatives like Neural Coffee Reading Group.

• Robotics
• Machine Learning
• Representation Learning
• +۳ مورد دیگر

Erlend Andreas Basso is a Postdoctoral Researcher in the Department of Engineering Cybernetics at NTNU, affiliated with the Faculty of Information Technology and Electrical Engineering. His research focuses on control systems, robotics, and marine engineering, with an emphasis on autonomous systems, guidance algorithms, and safety-critical control strategies. His work spans topics such as line-of-sight guidance for marine vehicles, adaptive hybrid feedback control, and robust hierarchical tracking for vehicle-manipulator systems. He has contributed to advancements in Kalman filtering for inertial navigation, obstacle avoidance in confined waters, and impedance control for compliant robot interactions. Basso’s publications reflect a strong focus on theoretical and practical aspects of control theory, including applications to underwater robotics, multirotor systems, and safety-aware navigation. His research often integrates machine learning and experimental validation, as seen in full-scale ship collision avoidance studies.

• Control Systems
• Robotics
• Marine Engineering
• +۳ مورد دیگر

Ari Visa is a Professor at the Faculty of Information Technology and Communication Sciences, Department of Computing Sciences at Tampere University. His research focuses on signal processing, radar systems, machine learning, robotics, and structural health monitoring. He has authored over 170 publications and contributed to projects in industrial automation, biomedical engineering, and sensor networks. His work spans applications such as data-driven ISAR imaging, fatigue analysis in aircraft structures, and aroma-based indoor localization. Notable contributions include radar clutter modeling, sensor fusion techniques, and algorithm development for industrial drilling operations. He has collaborated on EU projects like cHiPSet (High-Performance Modelling for Big Data) and holds a patent for an intelligent toothbrush monitoring device. Recent research trends emphasize fusion of data streams (e.g., radar/5G integration), machine learning for classification tasks (food scent analysis, metallic object detection), and real-time systems for manufacturing/health monitoring. His work bridges theoretical signal processing with practical industrial and biomedical applications. Key achievements include pioneering radar target characterization methods, developing kinematic models for heavy-duty manipulators, and advancing sensor-based localization techniques. Ongoing projects involve AI-driven predictive maintenance and multi-modal data analysis frameworks.

• Signal Processing
• Radar Systems
• Machine Learning
• +۵ مورد دیگر

Alice White is a Professor affiliated with the College of Engineering and Department of Mechanical Engineering at Boston University. She holds affiliations with the Division of Materials Science and the Photonics Center. Her research focuses on fabrication and packaging of optical and electronic devices, including novel optical devices for future exabit networks, additive manufacturing via high-resolution 3D printing, and silicon photonics integration using CMOS processes. Recent work includes neural interface devices and biocompatible microelectrodes. Education: PhD in Engineering from Harvard University. Professional roles emphasize interdisciplinary collaboration across materials science, photonics, and biomedical engineering. Research interests span optical device innovation, nanoscale fabrication, and biomedical microdevices. Recent publications highlight advancements in cardiac tissue engineering, programmable microrobots, and metamaterial scaffolds. Over 50 peer-reviewed articles demonstrate expertise in photonics, additive manufacturing, and biocompatible systems.

• Optical Device Fabrication
• Electronic Packaging
• Additive Manufacturing
• +۴ مورد دیگر

Professor Nick Pears is a faculty member in the Department of Computer Science at the University of York. He holds roles including Deputy Head of Department (Research) and Chair of the Departmental Research Committee. His academic career spans postdoctoral research at the University of Oxford, a Fellowship at the University of Cambridge, and progressive roles at York from Lecturer to Professor (since 2022). He holds a BSc in Engineering and a PhD in Robotics from 'Dunelm' (likely Durham University). His research focuses on 3D facial modeling, computer vision for autonomous systems, robotic manipulation, and medical imaging. He leads the Vision, Graphics and Learning research group and has contributed to advancements in morphable models, gaze estimation, and medical anomaly detection. His work bridges theoretical computer vision with practical applications in healthcare and robotics. Recent publications emphasize generative models for medical imaging, 3D face reconstruction techniques, and adaptive robotics. His research often intersects AI ethics, emphasizing safe and explainable systems for autonomous vehicles and surgical applications. He actively collaborates with clinicians and engineers to translate vision algorithms into real-world solutions. As a leader in academic research governance, he oversees York's REF submissions and departmental strategy, fostering interdisciplinary innovation. His lab maintains an open-access repository of 3D morphable models and benchmark datasets for robotic motion planning.

• 3D Face/Head Modeling
• Computer Vision for Self-Driving Vehicles
• Robot Manipulators
• +۴ مورد دیگر

Dr. Shanfeng Hu is an Assistant Professor in the Department of Computer and Information Sciences at Northumbria University, UK. He obtained his PhD from the same department in 2020 and joined the faculty as a Lecturer immediately after. During his PhD (2018-2020), he worked as a Senior Data Scientist in the financial sector. His research focuses on machine learning, causal reasoning, and mechanism design, with funded projects in advanced manufacturing (Innovate UK KTP), supply chain optimization (Digital Catapult, Innovate UK ISCF), and digital health (EU Erasmus+, EU MSCA). Education: PhD in Computer Science (2020) from Northumbria University, specializing in Metric Representations for Shape Analysis and Synthesis. Research interests include computer vision applications (e.g., defect detection, pose estimation), predictive maintenance, inventory management, health monitoring systems, and human-in-the-loop machine learning. He has collaborated on projects involving 3D printing (KTP-funded), steel surface analysis, and elderly health monitoring. His recent work addresses challenges in digital transformation for manufacturing, integrating predictive maintenance with inventory systems, and developing explainable AI for control systems. Projects are supported by Innovate UK, EU programs, and Digital Catapult. Lead researcher on Innovate UK KTP project: 3D printed Models Contributions to UN Sustainable Development Goals through education and healthcare projects Labs/Teams: Collaborates with industry partners in manufacturing, healthcare, and robotics sectors through funded initiatives.

• Machine Learning
• Causal Reasoning
• Mechanism Design
• +۷ مورد دیگر

Wael Mohammed is a Postdoctoral Researcher and Doctoral Researcher at Tampere University's Automation Technology and Mechanical Engineering department. His research focuses on advanced robotics, Industry 4.0, and digital twin architecture in manufacturing systems. He holds an MSc in Automation Engineering (2017) and a BSc in Mechatronics Engineering (2010) from the University of Jordan. His work emphasizes human-robot collaboration, cognitive validation of AI systems, and machine vision for food manipulation. Key contributions include methodologies for digital twin implementation in factories and ontology-driven semantic frameworks for manufacturing processes. Mohammed has contributed to projects like the FASTory digital twin initiative and REMODEL's cable manipulation research. Research interests span robotics applications in food production, energy-efficient automation, and semantic integration of industrial IoT systems. He has reviewed for IEEE Transactions on Industrial Informatics and co-created datasets on robotic grasping and assembly line optimization. Ongoing work explores large language models for HRC reliability and multimodal human-robot interfaces.

• Robotics
• Industry 4.0
• Digital Twins
• +۵ مورد دیگر

Francesco De Natale is a Full Professor at the Department of Information Engineering and Computer Science, University of Trento. His expertise spans Computer Vision, Digital Forensics, Signal Processing, and Multimedia Systems. He teaches courses including 'Fondamenti di Comunicazioni', 'Fondamenti di Elaborazione dei Segnali', and 'Tecnologie Multimediali', focusing on signal analysis, communication systems, and multimedia processing. Research Interests: Digital forensics (e.g., deepfake detection, image provenance), computer vision applications (3D haptic modeling, crowd behavior analysis), and embedded systems for IoT/AAL solutions. Key Projects: Developed the AUSILIA platform for assisted living and Neuroberry for pervasive EEG signaling. Contributed to datasets like WILD for synthetic image analysis. Recent Focus: Combining machine learning with forensic techniques for detecting media manipulation, and energy-efficient edge computing for drones/UAVs. His work bridges theoretical research with practical applications in healthcare, cybersecurity, and robotics. Over 50+ publications since 2012 highlight contributions to signal processing, multimedia forensics, and human behavior analysis.

• Computer Vision
• Digital Forensics
• Signal Processing
• +۵ مورد دیگر

Huapeng Wu is a Professor at the University of Windsor specializing in robotics, nuclear engineering, and control systems. His research focuses on advanced robotics for fusion reactor maintenance, remote handling systems, and intelligent control algorithms. He has secured significant funding, including a $383k grant for cybersecurity in autonomous vehicles (2020). Key projects include development of the CFETR remote handling system, deformation modeling of fusion manipulators, and EEG-based human-machine interfaces. Research interests span parallel mechanisms, cognitive load monitoring, and bionic robotics. Notable contributions include: Design of vacuum vessel assembly robots with anti-vibration features Development of neural network-based fault prediction frameworks Integration of digital twin technology for fusion maintenance systems His work combines mechanical engineering principles with AI-driven solutions for complex industrial challenges. Current efforts emphasize improving safety and precision in nuclear fusion environments through innovative robotic systems and predictive modeling.

• Robotics
• Nuclear Engineering
• Control Systems
• +۳ مورد دیگر

Dr. Ge Gao is a researcher at the Department of Informatics, University of Hamburg, affiliated with the CV Research Group. His research focuses on 6D object pose estimation, cognitive robotic vision, and deep learning applications in robotics. He holds a PhD in Informatics from the University of Hamburg, completed in 2021. Education: PhD in Informatics (2021), University of Hamburg. Previously conducted research on occlusion-resistant object pose estimation and saliency-guided segmentation techniques. Research emphasizes robotics perception, point cloud analysis, and multimodal sensor fusion. His work spans applications from robotic grasp planning to gait analysis using dynamic vision sensors. Key contributions include CloudAAE framework for 6D pose regression and saliency-driven supervoxel segmentation. Publications (2015–2021) address challenges in 3D object localization, multimodal data processing, and robotic vision systems. Active in conferences like ICRA, ECCV, and IROS. Labs/Projects: Involved in InteGreatDrones (drone-based robotics), Crossmodal Learning (CML), ahoi digital (crossmodal sensor data collection), and MACS (machine learning applications).

• 6D Object Pose Estimation
• Cognitive Robotic Vision
• Deep Learning
• +۵ مورد دیگر