جست‌وجوی استاد و پژوهشگر

Charalampos Orfanidisمشاهده پروفایل

استادیار

• Embedded Systems Engineering
• Internet of Things
• Low Power Wide Area Networks
• +۷ مورد دیگر

Charalampos Orfanidis is an Assistant Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), specializing in Embedded Systems Engineering. His research focuses on Internet of Things (IoT), low-power wireless networks, wearable systems, and network performance optimization, with strong alignment to sustainable technological development. His research interests center on enabling efficient, intelligent, and secure embedded systems for real-world applications. Key areas include Low Power Wide Area Networks , Wearable Systems , 5G network performance , and reinforcement learning for industrial IoT . His work emphasizes energy efficiency, user experience, and robustness in mobile and distributed systems. The recent publications highlight a strong trend in applying machine learning and AI techniques to solve practical communication challenges in IoT and 5G environments. Topics span from detecting mobile jammers in LoRa networks to optimizing scheduling in industrial IoT using hierarchical reinforcement learning, indicating a focus on intelligent, adaptive network solutions. Scientific Contributions: Principal Investigator of the research project: Low-Cost Acoustic Networks for Underwater Communication Active contributor to international collaborative studies on 5G performance and IoT security He collaborates extensively with researchers across Europe and contributes to high-impact peer-reviewed venues. While no formal advising list is provided, his role as a PI and co-author on multiple student-involved publications suggests mentorship activity. His work bridges theoretical innovation and practical deployment in next-generation communication systems.

• Autonomy of robots and vehicles
• Motion planning
• Optimal motion control
• +۶ مورد دیگر

Björn Olofsson is an Associate Professor and Senior Lecturer in the Department of Automatic Control at Lund University's Faculty of Engineering. He also serves as the Director of First and Second Cycle Studies and is a Project Manager. He is affiliated with major research initiatives including ELLIIT (the Linköping-Lund initiative on IT and mobile communication) and WASP (Wallenberg AI, Autonomous Systems and Software Program). His academic affiliations span Lund University and Linköping University, where he was appointed Docent in 2020. He holds an M.Sc. in Engineering Physics and a Ph.D. in Automatic Control, both from Lund University. His academic journey reflects a strong foundation in engineering and control systems. His research focuses on the autonomy of robots and vehicles, with emphasis on motion planning and optimal motion control. He explores applications in ground vehicles, unmanned aerial and surface vehicles, and industrial robotics. His work intersects with key global challenges, including sustainable transport and digitalization, aligning with UN Sustainable Development Goals related to technology and health. The 15 most recent publications analyzed show a consistent trend in autonomous systems, predictive control, and robotics. Topics include uncertainty-aware motion planning, human-robot collaboration, maritime autonomy, and learning-based control. The research integrates AI, machine learning, and advanced control theory, applied across aerial, marine, and terrestrial domains. Björn actively supervises multiple PhD students and has led numerous research projects, such as ELLIIT B14 and the Center for Construction Robotics. He is involved in organizing academic events like Robotics Week for Schools and manages the RobotLab LTH infrastructure. He has taught a range of courses including Applied Robotics, Autonomous Vehicles, and graduate-level courses on motion planning and optimal control. He also supervises Master’s theses in Automatic Control and Vehicular Systems.

• Applied Cryptography
• Image Encryption
• Post-Quantum Cryptography
• +۵ مورد دیگر

Shahbaz Khan is a researcher and Ph.D. candidate in Computing (Cyber Security and AI) at Edinburgh Napier University, UK. He holds B.S. and M.S. degrees in Electronics and Electrical Engineering from NFC IET and HITEC University, Pakistan. He previously served as a Lecturer in Electrical Engineering at HITEC University for eight years. His research focuses on applied cryptography, image encryption, post-quantum cryptography, and intelligent healthcare systems. He is a member of the Centre for Cybersecurity, IoT and Cyberphysical Systems at Edinburgh Napier University. Education: Bachelor of Science (B.S.) in Electronics and Electrical Engineering, NFC Institute of Engineering & Technology (NFC IET), Pakistan Master of Science (M.S.) in Electronics and Electrical Engineering, HITEC University, Taxila, Pakistan Research Interests: Image Encryption Techniques (e.g., chaos-based, DNA computing, and hybrid models) Post-quantum cryptographic schemes Federated learning in healthcare and IoT Cybersecurity in industrial IoT and edge computing AI-driven medical diagnostics (e.g., EEG-based MDD detection, skin disease classification) Notable Projects: SafeNet: Carnegie-funded project (2024-2025) to enhance IoT network security through robust cryptographic methods. Development of encryption algorithms like VisCrypt , PermutEx , and Noise-Crypt for secure image transmission. AI models for decentralized EEG-based mental health detection and federated learning in skin disease diagnosis. Awards: None explicitly mentioned in available texts. Grants: £13,381 from the Carnegie Trust for the SafeNet project. Labs/Teams: Active member of the Centre for Cybersecurity, IoT and Cyberphysical Systems at Edinburgh Napier University.

Ville Kyrkiمشاهده پروفایل

استاد

• Intelligent Robotic Systems
• Computer Vision
• Robotics Manipulation
• +۷ مورد دیگر

Professor Ville Kyrki is a Full Professor at Aalto University's School of Electrical Engineering, leading the Intelligent Robotics research group. His work focuses on intelligent robotic systems, particularly addressing challenges in imperfect knowledge and uncertain sensory data. Key research areas include computer vision, tactile sensing, robotic manipulation, and machine learning applications in robotics. Education details are not explicitly provided in the text, but his academic trajectory includes a progression from Associate Professor (appointed 2012) to Full Professor at Aalto University. Research interests span feature extraction, visual servoing, sensor fusion, and planning under uncertainty. He has pioneered methods for deformable object manipulation, including data-driven grasp synthesis and imitation learning approaches for dynamic tasks like cloth folding. His work integrates reinforcement learning with domain adaptation techniques to bridge simulation-to-real gaps. Awards include the 2022 Best Paper Award and a 2024 nomination for the Best Safety, Security, and Rescue Robotics Paper. His contributions address critical challenges in robotics such as safe human-robot collaboration and autonomous decision-making in dynamic environments. Lab activities center on the Intelligent Robotics group, which develops advanced systems for industrial automation, autonomous navigation, and human-centric robotics. Current research emphasizes scalable solutions for UAV localization, multi-agent coordination, and robust policy learning in uncertain conditions.

• Intelligent Vehicles
• Traffic Technologies
• Advanced Driver Assistance Systems (ADAS)
• +۷ مورد دیگر

Iván García Daza is a Professor at the Department of Automática, University of Alcalá, Spain. He leads research in Intelligent Vehicles and Traffic Technologies (INVETT group), focusing on autonomous systems, digital twins, and ADAS technologies. His work integrates computer vision, deep learning, and human-vehicle interaction analysis. Education: PhD in Systems Engineering from University of Alcalá (2011), with a thesis on "Driver Fatigue Detection via ADAS Fusion" supervised by Rafael Barea Navarro and Luis Miguel Bergasa Pascual. Research interests include autonomous vehicle algorithms, real-time sensor fusion, virtual reality simulations for human-factor testing, and ecological conservation projects. His recent work emphasizes digital twin technology to bridge simulation-to-reality gaps in autonomous systems. Publications span autonomous driving validation frameworks, LiDAR-based navigation, and ecological studies. Notable contributions include VR-based human-vehicle interaction testing and synthetic data generation for ADAS systems. Labs/Teams: Director of the INVETT lab at the University of Alcalá, collaborating on EU-funded projects involving automotive industries and environmental agencies.

Milica D. Jovanovicمشاهده پروفایل

استادیار

• Wireless Sensor Networks
• Networks-on-Chip
• Communication Protocols
• +۵ مورد دیگر

Milica D. Jovanovic is an Assistant Professor in the Department of Electronics at the Faculty of Electronics, University of Niš, Serbia. She has been with the institution since 2013, progressing from assistant trainee to her current academic rank, and holds a PhD earned in 2017 from the same faculty. PhD, Faculty of Electronics, University of Niš (2017) MSc, Microsystems Design and Engineering, Faculty of Electronics, University of Niš (2011) BSc, Computer Engineering and Informatics, Faculty of Electronics, University of Niš (2003) Her research centers on wireless sensor networks, networks-on-chip, and communication protocols, with a focus on MAC layer design, contention resolution, and indoor localization. She employs signal processing, fuzzy logic, and optimization techniques to enhance network performance and reliability. Her recent publications reflect a strong trend in improving communication efficiency and robustness in constrained environments such as wireless sensor networks and on-chip interconnects. Key themes include tone-based contention resolution, multi-channel MAC protocols, deflection routing, and UWB-based localization, placing her work at the intersection of embedded systems, networking, and signal processing. She is actively involved in one national and one international research project, contributing to both theoretical and applied aspects of electronic systems. Her work has appeared in high-impact journals including The Journal of Supercomputing , Expert Systems with Applications , and Computer Communications . She has collaborated extensively with researchers such as Igor Z. Stojanovic, Sandra Djosic, and Goran Lj. Djordjevic. While no formal advisees are listed, her involvement in doctoral studies and research projects suggests mentorship roles within her lab and department.

Dragan B. Zivanovicمشاهده پروفایل

استاد

• Metrology and Measurement Technology
• Electrical Measurement Systems
• Signal Processing
• +۷ مورد دیگر

Dr. Dragan B. Zivanovic is a Full Professor at the Faculty of Electronics in Niš, University of Niš, Serbia, where he has been a long-standing academic in the Department of Electrical Engineering and Computer Science. His expertise lies in Metrology and Measurement Technology, with a focus on industrial instrumentation, power quality, and embedded measurement systems. PhD in Metrology and Measurement Techniques (2006), Faculty of Electronics, Niš Master’s in Metrology and Measurement Technology (1996), Faculty of Electronics, Niš Bachelor’s in Electrical Engineering and Computer Science (1990), Faculty of Electronics, Niš His research focuses on the design and optimization of measurement systems, including thermocouple linearization, power quality signal generation, and sensor signal processing. He has developed practical instrumentation adopted by international companies such as EADS Astrium and BMW, bridging academic research with industrial innovation. His work emphasizes precision, reliability, and real-world applicability in electrical and industrial metrology. The recent publications highlight a consistent trend in advancing measurement accuracy and system robustness in electrical engineering contexts. His work spans from fundamental sensor linearization to complex signal generation for power quality analysis, reflecting a deep integration of theory and practical engineering. Key themes include signal conditioning, embedded control, and industrial compliance testing. Dr. Zivanovic has contributed significantly to engineering education through textbooks and laboratory manuals, shaping curricula in measurement and instrumentation. He actively supervises students and collaborates on national and international research projects. As a consultant and contract designer, he continues to influence industrial measurement solutions globally. His affiliations and projects reflect strong industry-academia collaboration. Notable partners include: EADS Astrium GmbH Bayerische Motorenwerke AG (BMW) Löcher GmbH Mattson Thermal Products GmbH MAN Technologie AG He has also led the development of internationally adopted measurement systems and transducers, such as the Carbo100E transducer and RF ion engine testing systems.

• Microelectronics
• Energy Harvesting
• Wireless Sensor Networks
• +۷ مورد دیگر

Zoran D. Prijic is a Full Professor at the Department of Microelectronics, Faculty of Electronics, University of Niš, Serbia. He has been a faculty member since earning his PhD in 1993 and was promoted to Full Professor in 2004. His academic work is deeply rooted in microelectronics, with a focus on energy harvesting, sensor systems, and semiconductor reliability. Education: PhD in Microelectronics, Faculty of Electronics, University of Niš (1993) Master's in Microelectronics, Faculty of Electronics, University of Niš (1990) Bachelor's in Electronic Components, Faculty of Electronics, University of Niš (1987) His research spans energy harvesting for wireless sensor nodes using thermoelectric and photovoltaic methods, PCB-integrated sensors , and reliability of power MOSFETs under stress and radiation. He explores thermal cutoffs, capacitive sensing, and CMOS-based photodiodes, contributing significantly to low-power and robust electronic systems. His work often bridges theoretical modeling with practical implementation in industrial and embedded environments. The recent publications indicate a strong trend in self-powered sensor systems , particularly using ambient thermal and light energy. A secondary but consistent theme involves device degradation mechanisms like NBTI and radiation-induced damage in power transistors—critical for aerospace and high-reliability applications. His integration of energy harvesters into PCBs reflects innovation in system miniaturization and sustainability. Scientific Awards: No awards explicitly mentioned in the provided text. Advising and Grants: While no formal students are listed, Zoran D. Prijic has collaborated extensively with researchers such as A. Prijić, Lj. Vračar, and D. Danković. He is currently involved in one national research project, indicating ongoing funding and research activity. His work in engineering education also suggests mentorship in curriculum development and laboratory instruction. Labs and Teams: Though not explicitly named, his research is conducted within the Department of Microelectronics at the Faculty of Electronics, likely involving a research group focused on semiconductor devices, sensors, and energy systems. The repeated collaborations suggest a well-established team working on energy harvesting, device reliability, and PCB-based systems.

Daniele Salvatiمشاهده پروفایل

دانشیار

• Audio Signal Processing
• Acoustic Signal Processing
• Computer Audition
• +۱۳ مورد دیگر

Daniele Salvati is an Associate Professor in the Department of Mathematics, Computer Science and Physics at the University of Udine, Italy. He is actively involved in research, teaching, and editorial roles in leading signal processing journals. Associate Professor, University of Udine Ph.D. in Acoustic Source Localization, University of Udine (2012) Senior Member, IEEE Associate Editor, IEEE/ACM Transactions on Audio, Speech, and Language Processing Handling Editor, Signal Processing His research focuses on audio and acoustic signal processing , particularly in the areas of source localization, speaker identification, and computer audition. He integrates traditional signal processing with modern machine learning and deep learning techniques to develop robust systems for real-world environments. His recent publications demonstrate a strong trend toward end-to-end deep learning models using raw waveforms, beamforming optimization (especially diagonal unloading), and applications on mobile and drone-based platforms . His work spans both journal and conference venues, with a focus on IEEE and ISCA publications. Scientific recognition includes: National Scientific Qualification as Full Professor (IINF-05/A) Senior Member of IEEE Editorial board roles in top journals He mentors doctoral students as part of the Ph.D. program in Computer Science and Artificial Intelligence and collaborates widely with researchers such as Carlo Drioli and Gian Luca Foresti. His research is supported by publications in high-impact journals and participation in international conferences. He leads work in the G.L. Foresti's Lab, focusing on intelligent systems and signal processing. His work includes developing microphone array-based interfaces for music and human-computer interaction, acoustic scene analysis, and automated sensor configuration. Future directions likely involve further integration of AI with physical sensor systems, especially in autonomous and mobile contexts.

• Artificial Intelligence
• Machine Learning
• Cybersecurity
• +۵ مورد دیگر

Ehsan Sheybani is an Associate Professor in the Department of Information Systems & Decision Sciences at the University of South Florida. His work spans interdisciplinary research in Artificial Intelligence, Machine Learning, Cybersecurity, and Renewable Energy Integration. He has contributed to advancements in optimization algorithms, smart grids, and wireless communication systems. His recent studies focus on integrating nature-inspired algorithms for energy management, IoT applications for environmental monitoring, and cybersecurity solutions for smart infrastructure. Research interests include exploring next-generation computing technologies (e.g., 6G networks), developing robust energy systems for microgrids, and enhancing situational awareness through machine learning techniques. His work frequently addresses challenges in renewable energy integration, signal processing for communication systems, and leveraging data analytics for social and environmental impact. Dr. Sheybani’s publications highlight innovations in optimization methodologies (e.g., Grizzly Bear Fat Increase Optimizer), cybersecurity frameworks, and interdisciplinary approaches to education technology. Notable contributions include reviews on smart grid integration challenges and studies on emerging 6G architectures.

Homa Nikbakhtمشاهده پروفایل

پژوهشگر ارشد

• Wireless Communication
• Information Theory
• Network Coding
• +۴ مورد دیگر

Homa Nikbakht is a Visiting Postdoctoral Research Associate at Princeton University, affiliated with the Weizmann Institute of Science. She operates within the realm of wireless communication and information theory, focusing on integrated sensing and communication (ISAC), network coding, and adversarial error correction. Role: Postdoctoral Fellow Affiliation: Weizmann Institute of Science Office: 305 Friend Center, Princeton University Adviser: H. Vincent Poor Her research centers on wireless communication systems, particularly addressing challenges in low-latency and ultra-reliable communication, stochastic network models, and finite blocklength regime analysis. She explores the integration of sensing and communication in multi-target environments and develops advanced coding strategies for adversarial channels. The 15 most recent articles highlight her work in ISAC systems, heterogeneous delay constraints, and network coding. Key trends include the application of reinforcement learning to ISAC, analysis of mixed-delay traffic in 5G/6G systems, and optimization of coding schemes for time-sensitive targets. As a visiting researcher at Princeton University's Friend Center, she contributes to the advancement of communication protocols and collaborates within the institute's research teams. Her work addresses critical challenges in future-generation networks and secure data transmission.

Xiaonan Sean Huangمشاهده پروفایل

استادیار

• Soft Robotics
• Tensegrity Robots
• Soft Materials
• +۷ مورد دیگر

Xiaonan (Sean) Huang is an Assistant Professor in the Department of Robotics at the University of Michigan, leading the Hybrid Dynamic Robotics Lab (HDR Lab). His research focuses on the design, control, and modeling of autonomous soft robots, particularly those employing tensegrity architectures and novel soft materials. Key areas include soft actuators, sensors, wearable devices, and integration of advanced control algorithms with SLAM and learning techniques. He emphasizes multifunctional materials and systems capable of operating in challenging environments. His lab, the HDR Lab, aims to revolutionize soft robotics through innovations in hardware and software, including shape-morphing actuators, stiffness-tunable materials, and onboard intelligence. Research spans robotics mobility, anomaly detection in vision systems, and collaborative 3D reconstruction. Notable projects include impact-resistant tensegrity robots and benchmarking frameworks for robust perception systems. Publications highlight advancements in tensegrity robotics, sensor fusion, and AI-driven design. While no awards are explicitly listed, his work is recognized through collaborations and high-impact journals/conferences. Advising and grant details are not provided in the text. Sean Huang’s lab fosters interdisciplinary innovation at the intersection of robotics, materials science, and AI, with applications in search-and-rescue, healthcare, and autonomous systems.

Talia Mooreمشاهده پروفایل

استادیار

• Robotics
• Animal Behavior
• Biomimetic Design
• +۴ مورد دیگر

Talia Moore is an Assistant Professor in Robotics at the University of Michigan, specializing in robot-animal interactions and the ecological, evolutionary, and control aspects of arrhythmic motion in animals and robots. Her research bridges robotics engineering with biological studies to create bio-inspired robots for ecological and evolutionary hypothesis testing. She leads the EMBiR lab, which focuses on designing robots to explore how traits influence survival in natural environments. Key research areas include modular robotics, biomimetic design, and the biomechanics of animal locomotion. Dr. Moore has pioneered studies on tail functions in mammals and robots, as well as the application of machine vision for behavioral analysis in animals like jerboas. Her work integrates computational modeling (e.g., ArborSim for musculoskeletal systems) with experimental robotics to address challenges in dynamic environments. Recent projects include developing the Robot of Theseus modular platform and exploring predator-prey dynamics through robotic mimicry. Her scientific accolades include the Robotics Faculty Award. She actively mentors undergraduate and graduate students in robotics and biomechanics through independent research opportunities. Collaborative efforts in open-source tools for natural history research and virtual expeditions aim to broaden student participation in ecological studies. Dr. Moore’s lab emphasizes interdisciplinary approaches, blending biomechanics, material science, and computer vision to advance robotics while deepening biological understanding. Current focuses include tail-assisted control mechanisms, soft robotics actuators (e.g., HASEL), and the evolutionary drivers of morphological adaptations in desert rodents and snakes.

• Robotics
• Machine Learning
• Statistical Machine Learning
• +۷ مورد دیگر

Professor Fabio Ramos is a Professor of Robotics and Machine Learning at the University of Sydney's School of Computer Science. He holds a PhD from the University of Sydney (2008) and has held postdoctoral fellowships with the Australian Research Council (2008–2010 and 2012–2014). His research focuses on statistical machine learning for data fusion in robotics, environmental monitoring, healthcare, and mining automation. He leads projects in autonomous systems, including robotic navigation, terrain modeling, and human-robot interaction. Educations: B.Sc. and M.Sc. in Mechatronics Engineering, University of Sao Paulo (2001, 2003) Ph.D. in Robotics and Machine Learning, University of Sydney (2008) Research Interests: Professor Ramos' work addresses the integration of sensor data into actionable insights for autonomous systems. Key areas include: Statistical machine learning for large-scale data fusion Occupancy mapping and environment perception Autonomous navigation and path planning Healthcare robotics for patient monitoring Robot perception in unstructured environments Simulation-to-reality transfer in robotics Publications & Trends: His recent work emphasizes real-time robotic systems, including UAV path planning, differentiable simulators, and robust policy learning. Notable contributions include probabilistic trajectory optimization and Bayesian methods for uncertainty quantification. Awards: Sydney Accelerator Fellowship (2017) Supervisor of the Year (2017) Best Paper Awards at Robotics Science and Systems (2017) and IROS (2005) Grants & Labs: He leads the Sydney Institute for Robotics and Intelligent Systems (SIRIS) and collaborates with the Centre for Translational Data Science. His current projects include: Modeling geothermal energy potentials via geophysical data fusion Mobile health (mHealth) systems for mental health monitoring Differentiable simulators for robotic cutting and manipulation Teams: His research group includes students like Ethan HIRSCHOWITZ (Intelligent Reward Design) and Kunal OSTWAL (Curriculum Learning). Collaborations span robotics, healthcare, and environmental science domains.

Bryan Convensمشاهده پروفایل

پژوهشگر

• Robotics
• Control Systems
• Real-Time Systems
• +۳ مورد دیگر

Bryan Convens serves as a Researcher in the Department of Applied Mechanics at Vrije Universiteit Brussel, specializing in advanced robotics control systems with emphasis on safety, real-time performance, and human-robot collaboration. His work bridges theoretical control theory with practical applications in aerial robotics, prosthetics, and industrial automation, supported by active leadership in multiple research projects. Research interests focus on developing certified-safe control frameworks where Convens pioneers reference governor methodologies for constraint satisfaction in dynamic environments. His investigations span nonlinear model predictive control, learning-based adaptation for uncertainty handling, and energy-efficient actuation systems—particularly for prosthetic devices and multi-robot swarms. Current projects address resilient control under uncertainty (FWOTM1231), robotics adoption in industry (BRGRD68), and series-parallel elastic actuation for prosthetics (FWOSB33), demonstrating consistent innovation in safety-critical domains. Scientific contributions reveal an evolving trajectory toward integrating machine learning with classical control theory to enhance data efficiency while maintaining safety guarantees. Recent publications emphasize real-time feasibility, human-robot collaboration constraints, and robust perception techniques—indicating strategic movement toward deployable solutions for industrial and medical robotics applications. Scientific Awards Best Ph.D. thesis of 2023 by the Belgian National Committee for Theoretical and Applied Mechanics (NCTAM) FWO Travel Grant for Long Research Stay Abroad (2018) Public and jury prize: 2nd place (2020) Convens directs significant research funding through competitive grants including FWOTM1231 (€ project value unstated, 2024-2027), BRGRD68 (Policy-Based project, 2022-2025), and FWOSB33 (Fundamental Research, 2017-2020), focusing on resilient aerial robotics, digital twin integration, and prosthetic energy efficiency. His grant portfolio demonstrates strong alignment with Flanders' strategic priorities in robotics and AI, with active industry partnerships through AI Flanders initiatives. Mentorship activities include supervising junior researchers in complex project environments, though formal student lists aren't documented. He operates within VUB's robotics ecosystem through the Robotics & Multibody Mechanics research group, collaborating closely with Bram Vanderborght's team on actuation systems and Kelly Merckaert on safety frameworks. Current work leverages UWB/VIO sensor fusion and particle filtering techniques within active projects targeting 2025-2027 deliverables for multi-robot coordination in unstructured environments.