جست‌وجوی استاد و پژوهشگر

Minh-Quan Tranمشاهده پروفایل

پژوهشگر

• Distribution System Engineering
• Distributed Energy Resource Engineering
• State Estimation
• +۵ مورد دیگر

Minh-Quan Tran is a University Researcher at Eindhoven University of Technology (TU/e) within the Department of Electrical Energy Systems, Faculty of Electrical Engineering. His work focuses on modern power distribution systems, renewable energy integration, and grid stability solutions, contributing to UN Sustainable Development Goals in sustainable energy. He maintains an ORCID profile (0000-0001-6430-7333) and has accumulated 99 Scopus citations for his 20 research outputs, with publication activity increasing from 2 works in 2019 to 7 in 2023. His key research interests include: Distribution System Engineering (100% fingerprint strength) Distributed Energy Resource Engineering (71%) State Estimation (64%) Voltage Stability (51%) Microgrid Control (40%) System State Monitoring (47%) Recent publications (2022-2024) reveal strong trends in AI-driven grid management, particularly Model Predictive Control and Graph Neural Networks for voltage stability and state estimation. Real-world case studies feature campus networks (e.g., Chalmers University), emphasizing practical validation of control strategies for low-voltage systems with high renewable penetration. Scientific Awards: No awards or honors are documented in the provided materials Advising and Grants: Tran lists no formal PhD/Master's students in the repository. His research involves IoT platforms, digital twins, and real-life demonstrations for flexibility optimization but lacks explicit grant details or funding acknowledgments in the extracted text. Labs and Teams: He operates within TU/e's Electrical Energy Systems research group, specializing in active distribution network monitoring, microgrid stability, and distributed resource control. Collaborations span international institutions as visualized in the repository's network map, with recent joint work involving Vietnamese and European universities.

• Machine Learning
• Computer Vision
• Signal Processing
• +۳ مورد دیگر

Chenguang Liu is a researcher at Institut Polytechnique de Paris , France, with a focus on computational methods, machine learning, and control systems. His work bridges theoretical and applied research across multiple domains including computer vision, signal processing, and operations research. Key research areas: Machine Learning, Computer Vision, Computational Physics, Control Theory Notable publication trends: Develops novel algorithms for parallel computing, peridynamic modeling, and real-time systems Recent contributions include Bayesian neural networks for gas-bearing prediction, multi-agent reinforcement learning for UAV swarms, and domain adaptation techniques in object detection. He collaborates extensively with researchers in electrical engineering and applied mathematics disciplines.

Stefan Brüggenwirthمشاهده پروفایل

استاد

• Cognitive Radar
• Radar Systems
• Machine Learning
• +۵ مورد دیگر

Prof. Dr.-Ing. Stefan Brüggenwirth serves as a Professor at Ruhr University Bochum within the Faculty of Electrical Engineering and Information Technology, specifically leading the Cognitive Sensors department. His institutional address is Cognitive Sensors, Postbox ID 37, Universitätstraße 150, D-44801 Bochum, with departmental contact via sekretariat@est.rub.de. He maintains an active research profile with continuous publications in IEEE journals and major radar conferences, demonstrating his leadership in integrating artificial intelligence with radar technology. Brüggenwirth's research centers on cognitive radar systems, where he pioneers the application of machine learning techniques to enhance radar capabilities. His work spans neural network architectures for SAR target recognition, reinforcement learning for radar resource management, and explainable AI methods for radar applications. He investigates both theoretical foundations and practical implementations, with research addressing military applications, autonomous vehicle navigation, and aerospace systems. His department connects with related research areas including plasma technology and microwave systems within the faculty's ecosystem. Analysis of his publication trends reveals a distinct progression from early cognitive systems for UAVs (2010-2013) to increasingly sophisticated AI-radar integration (2015-present). Recent work emphasizes explainable AI techniques (grad-CAM, LIME, SHAP), neural network robustness, and quality of service frameworks for radar resource management. His 2024 publications demonstrate cutting-edge applications of YOLO and complex-valued networks for target recognition and signal denoising. Brüggenwirth has contributed to significant collaborative projects including SPERI (super-resolution and target identification), PolRad (polarimetric radar technology for European defense), and KI-ROJAL. His special issue contributions in IEEE Aerospace and Electronic Systems Magazine (2020) highlight his role as a thought leader in cognitive radar. While specific grant details aren't provided, his extensive project involvement suggests successful funding acquisition across defense, aerospace, and autonomous systems domains. The Cognitive Sensors department at Ruhr University Bochum serves as his primary research base, with his work intersecting with related groups in Learning Technical Systems, Medical Engineering, and Photonics & Terahertz Technology. His research has practical applications in defense systems, autonomous driving (RADAR SLAM), and aerospace, particularly in hypersonic plasma signature measurement. He maintains connections with international researchers including S. Z. Gurbuz and M. Rangaswamy for collaborative book chapters.

• Parallel and Distributed Systems
• Cloud Computing
• Computer and Communication Networks
• +۱۲ مورد دیگر

Deniz Turgay Altılar is a Professor in the Department of Computer Engineering at Istanbul Technical University (ITU), Faculty of Computer and Informatics. He has been an active academic since 1988, progressing from Research Assistant to Associate Professor in 2013 and later achieving the rank of Professor. His work is centered on distributed systems, cloud computing, and computer networks, with expanding interests in AI applications for agriculture and healthcare. Education Bachelor of Science, Control and Computer Engineering, Istanbul Technical University (1984–1988) Master of Science, Computer Engineering, Istanbul Technical University (1988–1992) Research experience at Queen Mary and Westfield College, University of London (1998–2002) His research interests include parallel and distributed systems, cloud computing, secure computation, deep learning, and cybersecurity. He applies these to interdisciplinary domains such as corn yield prediction, radar identification, and medical diagnostics using ECG signals. His recent work emphasizes efficient and secure AI systems. The publication trends show a strong focus on distributed deep learning, straggler mitigation, secure multiparty computation, and hardware-based attack detection. His work bridges theoretical computer science with real-world applications in agriculture, medicine, and national security. Recent articles highlight innovation in lightweight AI models, privacy-preserving computation, and cross-layer system design. Scientific Awards No specific awards are mentioned in the provided text. He actively supervises students and leads major research projects, including those on cache side-channel attacks in multi-tenant clouds, distributed OpenCL platforms, real-time scheduling, and molecular communication in nano-networks. These projects reflect sustained external funding and leadership in cutting-edge computing domains. His research group collaborates across disciplines and institutions, contributing to both national and international scientific communities. Labs and Teams : While not explicitly named, his role as Principal Investigator on multiple projects suggests leadership in a research lab focused on distributed systems, cybersecurity, and applied AI at ITU.

• Neuromuscular Control
• Motor Learning
• Exercise Physiology
• +۷ مورد دیگر

Benedikt Lauber is a Senior Lecturer and Researcher at the University of Fribourg, within the Faculty of Science and Medicine, Section of Medicine. He is affiliated with the Department of Neurosciences and Movement Sciences (NMS), where he conducts research on motor control, neuromuscular adaptations, and exercise physiology. His work bridges neuroscience, sports science, and rehabilitation, with a focus on cortical inhibition, blood flow restriction training, and tendon biomechanics. His research interests include: Neuromuscular control and cortical inhibition Blood flow restriction and resistance training adaptations Tendon morphology and mechanical properties Neuroplasticity induced by exercise and neuromodulation Motor learning and interference in dual-task performance Biomechanics in sports such as ski jumping and choral singing Analysis of his recent publications (2019–2023) reveals a strong focus on the neural and biomechanical effects of different training modalities. A recurring theme is the comparison between low-load blood flow restriction and high-load resistance training, particularly regarding tendon adaptations. Another major line of research involves transcranial direct current stimulation (tDCS) and its interaction with exercise-induced neuroplasticity. His work also extends into applied domains such as choral singing biomechanics and ski jumping imitation validity, demonstrating interdisciplinary reach. Benedikt Lauber actively collaborates with a core group of researchers including Wolfgang Taube, Adam Gollhofer, Christoph Centner, and David König. His publications appear in high-impact journals such as Scandinavian Journal of Medicine & Science in Sports , Frontiers in Physiology , and Neuropsychologia .

Xuefeng Weiمشاهده پروفایل

دانشیار

• Neural Engineering
• Neural Prosthetics
• Computational Neuroscience
• +۳ مورد دیگر

Xuefeng Wei is an Associate Professor in the Department of Biomedical Engineering at The College of New Jersey. He earned his Ph.D. in Biomedical Engineering from Duke University in 2009. His research focuses on neural engineering, neural prosthetics, and computational neuroscience, with specialized expertise in deep brain stimulation technologies and neural interface design. Research Focus: Dr. Wei's work bridges computational neuroscience and biomedical device innovation. Key areas include: Development of novel electrode geometries to minimize tissue damage during neural stimulation High-frequency stimulation paradigms for neural modulation and desynchronization Computational modeling of sodium channel dynamics and axonal block mechanisms In-vivo validation of stimulation systems in hippocampal and thalamic networks Publication Trends: His 15 most recent articles (2005-2020) demonstrate consistent focus on optimizing neural stimulation through: Electrode design innovations (fractal, recessed, and segmented configurations) Advanced stimulation paradigms (time-varying, sinusoidal, high-frequency) Multiscale validation (computational models, rat hippocampus, nonhuman primates) Applications in epilepsy management and arousal regulation Honors: Support of Scholarly Activities (SOSA) Award (2023) Academic Service: Faculty Senate Representative (2021-2024) Strategic Plan Implementation Committee (2021-Present) PRC Committee Member (2022, 2024) Committee on Student and Campus Community (2025)

• 3D Shape Analysis
• Graph-Based Machine Learning
• Computer Vision
• +۴ مورد دیگر

Andrea Torsello is a researcher at the University of York, specializing in 3D shape analysis, graph-based machine learning, and quantum computing applications in computer vision. His work bridges theoretical and applied domains, focusing on pattern recognition, network thermodynamics, and remote sensing. PhD from University of York (2004) Published extensively in journals like IEEE Transactions and Pattern Recognition Research interests include: Quantum-inspired graph analysis 3D reconstruction techniques Manifold learning for complex networks Thermodynamic modeling of time-evolving systems Key contributions involve: Quantum walk-based graph similarity measures k-Anonymity for graph data Physics-driven CNN models for ocean wave reconstruction Game-theoretic approaches to shape matching

• Power Electronics
• Renewable Energy Systems
• Electrical Machines
• +۳ مورد دیگر

Tomasz Lerch serves as a Lecturer in the Department of Power Electronics and Automation of Energy Conversion Systems at AGH University of Science and Technology's Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering in Kraków. He actively participates in the Discipline Council for Automation, Electronics, Electrical Engineering and Space Technologies. His research spans power electronics, renewable energy integration, and electrical machine design with emphasis on grid stability and power quality. Key interests include grid-tied converter systems, islanding detection methods for distributed generation, permanent magnet motor optimization (particularly transverse flux and cogging machines), and power quality analysis in modern LED lighting and photovoltaic installations. His methodological approach frequently combines electromagnetic modeling with experimental validation. Analysis of his 2021-2025 publications reveals consistent focus on renewable energy grid integration challenges. Dominant themes include rapid voltage fluctuation compensation using hardware-in-the-loop techniques, hybrid islanding detection combining phasor measurement units with artificial intelligence, and thermal/power density optimization in novel motor designs. His work demonstrates strong industry relevance through experimental validation of control algorithms for grid-connected systems.

Ahmed Rezineمشاهده پروفایل

مدرس ارشد

• Computer Science
• Software Verification
• Machine Learning
• +۷ مورد دیگر

Ahmed Rezine is a Senior Lecturer and Associate Professor at the Department of Computer Science (IDA) , specifically within the Software and Systems (SAS) division at Linköping University . His research focuses on software verification , machine learning , and cybersecurity , particularly in the context of deep neural networks and embedded systems . Research Trends: His recent publications, such as VNN: Verification-Friendly Neural Networks (2024) and Trojans in Instruction Sets (2024), highlight his work at the intersection of AI robustness and hardware security . Earlier studies (2023-2017) emphasize formal verification of concurrent systems and GPU architectures , reflecting a consistent focus on parameterized systems and security analysis . Colleagues & Collaborations: Rezine collaborates with researchers in the Software and Systems group, part of the Wallenberg Autonomous Systems Program (WASP) . His work intersects with AI IDA and Embedded Systems (ESLAB) , focusing on autonomous systems and real-time processing .

Juan G. Restrepoمشاهده پروفایل

استاد

• Complex Networks
• Synchronization
• Nonlinear Dynamics
• +۵ مورد دیگر

Juan G. Restrepo is a Professor in the Department of Applied Mathematics at the University of Colorado Boulder, where he maintains an active research program in complex systems and network science. His work bridges theoretical mathematics with applications in physical, biological, and social systems. Professor Restrepo's research focuses on complex network dynamics, particularly synchronization phenomena, nonlinear dynamics and chaos, spread of information and disease in social networks, and criticality in brain networks. His work spans theoretical foundations of network science while addressing real-world applications from cardiac dynamics to social contagion processes. He has made significant contributions to understanding higher-order interactions in networks through hypergraphs and simplicial complexes, revealing new synchronization phenomena and dynamical behaviors. Analysis of his recent publications reveals a strong trajectory toward higher-order network structures, with increasing focus on hypergraphs, simplicial complexes, and topological approaches to network dynamics. His work connects mathematical theory with applications in neuroscience, quantum systems, epidemiology, and social dynamics, demonstrating the unifying power of network science across disciplines. Recent papers show growing integration of machine learning techniques with traditional dynamical systems approaches. Professor Restrepo maintains an active research group investigating fundamental questions in network dynamics while exploring applications to real-world systems. His work on criticality in neural networks and resource transport mechanisms has implications for understanding brain function and developing more efficient networked systems.

Erivelton Nepomucenoمشاهده پروفایل

دانشیار

• Computer Arithmetic
• Chaotic Cryptography
• Green Computing
• +۶ مورد دیگر

Erivelton Nepomuceno is an Associate Professor at Maynooth University's Faculty of Science & Engineering , affiliated with the Hamilton Institute and Centre for Ocean Energy Research . He holds a PhD in Electrical Engineering from UFMG (2005) and has held visiting positions at Imperial College London, Saint Petersburg Electrotechnical University, and City, University of London. Educational Background BEng, UFSJ (2001) PhD, UFMG (2005) Research Interests Computer Arithmetic Chaotic Cryptography Green Computing Ocean Energy Sustainable Circuits and Systems System Identification His recent publications focus on computational chaos, reinforcement learning applications, and sustainable energy systems. His work bridges chaos theory , cybersecurity , and renewables , with a strong emphasis on energy transition and finite-precision arithmetic challenges. Scientific recognitions include Senior Member of IEEE and Chair-Elect of IEEE Technical Committee on Nonlinear Circuits and Systems . He has served as Deputy Editor-in-Chief for multiple IEEE journals and currently holds associate editor roles. Erivelton supervises 5 active PhD students and has advised 7 PhD completions. His funded projects include studies on hybrid wind-wave energy control (€587,975.18), green computing (€3,700), and chaotic system simulation (€7,500). He leads the Hamilton Institute's research group on computational chaos and sustainable systems.

• Wireless Communications
• Signal Processing
• MIMO Systems
• +۴ مورد دیگر

Cédric Le Ruyet is a prominent researcher at the Conservatoire National des Arts et Métiers (CNAM) , affiliated with the CEDRIC Laboratory. His work spans Wireless Communications , Signal Processing , and 5G/6G Network Design , with a focus on Filter Bank Multicarrier (FBMC) systems, MIMO , and Cognitive Radio . He collaborates extensively with institutions like CEA-LETI and CNAM . His research interests include Channel Modeling , Resource Allocation , Beamforming , and Machine Learning in wireless contexts. He has contributed to Reconfigurable Intelligent Surfaces (RIS) for beam optimization and SCMA/OFDM codebook design. Recent publications highlight his work on QAM-FBMC systems, Rayleigh Fading Models , and 5G Network Coexistence . His articles often explore Distributed MIMO , Low-Complexity Equalizers , and Indoor Positioning via Channel State Information (CSI) . He has authored numerous IEEE Transactions and conference papers, with technical reports for projects like ANR POSEIDON and NF-PERSEUS . His methodologies integrate Genetic Algorithms , Lattice-Reduction , and Deep Learning for next-gen wireless systems.

Massimiliano Di Lucaمشاهده پروفایل

دانشیار

• Human Perception
• Multisensory Integration
• Temporal Perception
• +۷ مورد دیگر

Massimiliano Di Luca is an Associate Professor at the University of Birmingham, affiliated with both the School of Psychology and the School of Computer Science. His research focuses on understanding the mechanisms of human perception through psychophysical methods, computational modeling, and immersive technologies, particularly in the dynamic and temporal aspects of multisensory integration. He has made significant contributions to the study of virtual reality, haptics, and 3D shape perception. PhD in Cognitive Science (Brown University, 2006) Laurea in Psychology (Università di Trieste, 2000) Di Luca’s research spans human perception, haptics, and computational modeling, emphasizing the temporal dynamics of multisensory processing. His recent work includes the ARME project , Virtual Reality Lab , and collaborations on wearable haptic devices. He has developed models for perceptual recalibration, synchronization in musical ensembles, and tactile feedback optimization in XR systems. His publications highlight trends in virtual reality applications, haptic technology, and multisensory perception, with keywords like 3D shape , augmented reality , Bayesian modeling , and wearable haptics . Notable projects include TEXT: Text Entry for XR Trove and ARME . Scientific Awards: Turing Fellow (2021-2023), Fellow of the InterContinental Academia UBIAS (2021-2023). Di Luca mentors PhD, MSc, and BSc students in psychology and computer science. His grants include funding from EPSRC, BBSRC, Google, and UKRI for projects like ARME , Perception of Materials , and iCURE . He leads the Virtual Reality Lab and has contributed to the InterContinental Academia and Alan Turing Institute .

Tomislav Stankovskiمشاهده پروفایل

پژوهشگر

• Neuroscience
• Biomedical Engineering
• Dynamical Systems
• +۴ مورد دیگر

Dr. Tomislav Stankovski is a Visiting Researcher in the Department of Physics at Lancaster University. His research spans interdisciplinary domains, focusing on dynamical systems, neuroscience, and biomedical engineering. Key areas include neural cross-frequency coupling, cardiorespiratory interactions, and synchronization mechanisms in complex networks. Recent publications highlight his work on cortico-muscular connectivity in Parkinson’s disease, time-varying interactions in coupled systems, and applications of Bayesian inference in physiological dynamics. His research integrates physics and computational methods to analyze biological and medical data, particularly in neurophysiology and cardiovascular systems. Neuroscience Biomedical Engineering Dynamical Systems Signal Processing Physics Computational Biology His scientific contributions emphasize understanding synchronization, cross-frequency coupling, and emergent behaviors across physical, biological, and medical contexts. No specific awards or student advisement details are provided in the available texts.

Trevor Crawfordمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Aging Research
• Neuropsychology
• +۱۰ مورد دیگر

Professor Trevor Crawford is a Professor of Cognitive Neuroscience and Aging at the Department of Psychology , Lancaster University , with over 30 years of experience. He serves as Deputy Director of the Crawford Neuropsychology Research Lab , which focuses on age-related neurological disorders. Alzheimer's Disease Parkinson's Disease Schizophrenia Dyslexia Visual Attention & Face Perception Neurovascular Dynamics His research combines eye-tracking paradigms , EEG , and fNIRS to study cognitive control mechanisms across healthy aging and pathological states. Recent work includes collaborations with Oxford University on Parkinson’s disease, Chinese institutions on long COVID impacts, and Physics Department colleagues on neurovascular synchronization. Article trends since 2025 show focus on biomarker development through eye movement analysis, neurovascular coherence in Alzheimer’s, and auditory-cognitive links in Parkinson’s. His lab also explores visual expertise as a protective factor against perceptual illusions. Notable collaborations include Professor Aneta Stefanovska (Physics), Dr. Juliane Bjerkan , and Professor Chris Plack . Media coverage spans 50+ press mentions since 2012, including Newsweek and Daily Mail features on early Alzheimer’s detection via eye tests.