جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Signal Processing
• Quantum Computing
• +۴ مورد دیگر

Andreas Spanias is a Professor at Arizona State University specializing in Machine Learning , Signal Processing , and Quantum Computing with applications in renewable energy, healthcare, and wireless networks. His research includes Photovoltaic Fault Detection , Quantum Machine Learning , and Real-Time Energy Monitoring . Key research areas: Machine Learning, Quantum Computing, Renewable Energy Systems, Medical Imaging, Wireless Sensor Networks. Notable collaborations: Glen S. Uehara, Cihan Tepedelenlioglu, Sunil Rao, Jayaraman J. Thiagarajan. Recent Publications (2025–2024) focus on quantum machine learning for photovoltaic fault classification, Bayesian optimization in circuit design, and real-time solar array monitoring. Trends include quantum algorithms for signal processing, energy-efficient ML, and educational innovations in quantum computing. Educational Initiatives include REU programs in Quantum Machine Learning and integrating ML into signals and systems courses. He leads international collaborations like the ASU-DCU Sensors and ML Workforce Development Program.

• Biomedical Engineering
• Automotive Systems
• Computer Vision
• +۳ مورد دیگر

Martin Glavin is a Professor at the National University of Ireland, Galway, affiliated with the Department of Electronic & Electrical Engineering within the College of Engineering and Informatics. His research focuses on cutting-edge areas such as biomedical engineering, automotive systems, and computer vision. Key contributions include advancements in image processing for low-light environments, driver gaze analysis for cyclist safety, and optimizing automotive camera systems. He has collaborated extensively with co-authors like Edward Jones and Darragh Mullins, yielding over 70 peer-reviewed publications since 2002. His work spans journals like IEEE Transactions and conferences such as ICC and ICARCV. Research interests include safety-critical object detection for autonomous vehicles, microwave imaging for medical diagnostics, and 5G-enabled V2X communication systems. Glavin's interdisciplinary efforts bridge signal processing, machine learning, and engineering applications, addressing real-world challenges in healthcare and transportation.

• Computer Vision
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Xiaoming Wang is a Professor at Shaanxi Normal University's School of Computer Science, specializing in computer vision, machine learning, and artificial intelligence. His research spans multiple domains including object detection, federated learning, and industrial process modeling. He maintains active collaborations with researchers across China and internationally, particularly with Yongmeng Liu, Chuanzhi Sun, and Shitong Wang. Dr. Wang's research interests focus on developing advanced AI models for practical applications. His work in computer vision includes improving object detection systems like YOLO variants for security and industrial applications. In machine learning, he has made significant contributions to federated learning with privacy preservation techniques. His industrial applications research addresses real-world problems in manufacturing, aerospace, and process control systems. Analysis of Dr. Wang's recent publications reveals a strong emphasis on practical AI applications with industrial relevance. His work shows consistent innovation in adapting established AI techniques like LSTM networks, transformers, and attention mechanisms to solve specific domain challenges. The publications demonstrate a clear trajectory toward more efficient, privacy-preserving, and real-time AI systems applicable across multiple sectors. Dr. Wang actively mentors students and junior researchers, with multiple co-authored publications featuring individuals likely to be his advisees. His collaborative approach is evident through numerous multi-institutional projects and consistent research partnerships. While specific grant information isn't detailed in the publication record, the scope and consistency of his work suggest substantial research funding support.

• Computer Vision
• Natural Language Processing
• Computational Thinking in Education
• +۳ مورد دیگر

Jiyoung Kim is a Researcher in computer science, with a focus on computer vision , natural language processing , and geospatial data analysis . She has collaborated extensively across disciplines, particularly in robotics , educational technology , and signal processing . Her research interests include: Developing advanced diffusion models for high-fidelity talking head generation . Creating automated pipelines for detoxifying Korean language in AI systems. Applying deep learning to haze removal and indoor navigation for accessibility. Exploring 6G communication through computer vision-aided beamforming . Recent publications highlight trends in multi-task learning , unsupervised segmentation , and geospatial knowledge graphs . Her work bridges theoretical and applied domains, from hardware design to educational interventions for computational thinking in early childhood.

• Cloud Computing
• Blockchain Technology
• Data Security
• +۵ مورد دیگر

Xiaojun Zhang is a Professor actively contributing to cloud computing, blockchain technology, data security, and educational technology. His work spans cybersecurity, signal processing, and wireless systems. Key Research Areas: Privacy-preserving data aggregation, machine learning for biomedical imaging, blockchain-based integrity auditing, and educational metacognition studies. Recent Article Trends (2022–2025): Focus on secure federated learning, data denoising algorithms, and blockchain applications in smart grids, healthcare, and education. Collaborations include institutions in China and international researchers.

• Sensor Systems
• Embedded Systems
• Ultrasound Imaging
• +۷ مورد دیگر

Stefan Johann Rupitsch is a Professor at the University of Freiburg, leading the Professorship for Electrical Measurement Technology and Embedded Systems since December 2020. He holds a prominent role in academic and industrial research collaborations, focusing on interdisciplinary fields such as sensor systems, biomedical engineering, and embedded technologies. His work bridges theoretical research with practical applications, including drug delivery systems, environmental monitoring, and disaster response technologies. **Education**: Information not explicitly provided in the text. **Research Interests**: His primary research areas include wireless sensor networks, ultrasound imaging for biomedical applications, magnetic nanoparticle localization, and energy-efficient embedded systems. He has pioneered advancements in magnetomotive ultrasound for drug targeting and developed innovative sensor platforms for industrial and environmental monitoring. His projects often integrate machine learning and robotics to enhance system precision and autonomy. **Projects**: As a project manager or coordinator, he oversees initiatives such as wireless microsensor probes for tree canopy monitoring (B1), cochlear implant sensing (CIAS), and disaster-site radar systems (avaRES). Recent efforts focus on sustainable sensor technologies for wear part optimization (Smart Belt) and gas infrastructure resilience (LEAKALERT). **Grants & Advising**: Rupitsch has supervised numerous theses in fields like sensor design, AI for building acoustics, and earthquake monitoring. He actively participates in reviewing academic works and industry projects, reflecting his leadership in technical communities. **Labs & Teams**: His research is conducted through collaborations with the Microsystems for Biomedical Imaging Laboratory and the Sustainability Performance Center, emphasizing interdisciplinary teamwork and real-world impact.

Stephanie Thevesمشاهده پروفایل

پژوهشگر

• Structure-learning and neural concept representations
• Neural mechanisms of relational reasoning
• Neuro-cognitive processing mechanisms underlying intelligence

Dr. Stephanie Theves is a Research Group Leader at the Max Planck Institute for Empirical Aesthetics. Her work focuses on understanding the neural mechanisms underlying intelligence, relational reasoning, and concept representation through cognitive neuroscience approaches. Department of Cognitive Neuropsychology Her research explores how the hippocampus and prefrontal cortex encode hierarchical concepts, construct cognitive maps, and support relational memory systems. Key findings demonstrate that neural representations prioritize conceptual relationships over raw feature distances, and that place-cell-like mechanisms in the brain modulate memory organization. Theves' recent publications examine category abstraction, spatial cognition, and beta-band activity modulation through sensory training. Her methodological approach combines neuroimaging with computational modeling to investigate neural processes. As part of the Max Planck Society, her work contributes to foundational knowledge in cognitive neuroscience while intersecting with interdisciplinary research themes in music, language, and computational auditory perception.

• Explainable AI
• Human-AI Interaction
• Audiovisual Affective Computing
• +۴ مورد دیگر

Dr. David Johnson is a Junior Independent Group Leader at Bielefeld University's Faculty of Engineering , leading the Human-Centric Explainable AI research group within CITEC. His work bridges Explainable AI with Audiovisual Affective Computing , focusing on high-stakes human-AI collaboration and industrial sound analysis. Current Position: Junior Independent Group Leader, Bielefeld University (since 2021) Previous: Postdoctoral Researcher at Fraunhofer Institute for Digital Media Technology (2019-2021) Education: PhD in Sound and Music Computing from University of Victoria (2019), MSc in Computing in the Arts from College of Charleston (2014) Research interests span Explainable AI , Human-AI Interaction , and Extended Reality applications, particularly in medical diagnostics and industrial sound processing. Recent publications highlight his work on trust dynamics in high-stakes AI and federated learning architectures . His collaborative projects include involvement in the TRR 318 'Constructing Explainability' initiative. While no formal awards are mentioned, his research has produced multiple publications and practical implementations in industrial and educational contexts.

• Virtual and extended learning environments
• Multisensory interaction
• Human-computer interaction
• +۳ مورد دیگر

Dr. Jens Maiero is a postdoctoral researcher at the Leibniz Institute for Knowledge Media (IWM) in Tübingen, Germany, where he has led projects in the Multimodal Interaction group since 2023. His work focuses on designing and evaluating interactive virtual and augmented learning environments for AI-driven and collaborative educational contexts. His educational background includes a PhD in Computer Science from Brunel University London (2021), a Master of Computer Science from Bonn-Rhein-Sieg University of Applied Sciences (2009), and a Diploma in Mathematics from Stuttgart University of Applied Sciences (2006). Maiero's research centers on virtual and extended learning environments , multisensory interaction , and haptic interfaces , with applications spanning art museums, STEM education, teacher training, and cultural heritage. His projects frequently integrate motion capture, 3D modeling, and machine learning to create immersive educational tools that bridge physical and digital experiences. His 2024 publications reveal a strong emphasis on VR/AR applications in teacher education (KuMuS-ProNeD and MINT-ProNeD projects), with recurring themes in collaborative learning, multisensory design, and domain-specific adaptations for art, music, sports, and science. Key trends include cross-disciplinary approaches combining literature, dance, and chemistry with immersive technologies. As an active contributor to the Multimodal Interaction lab, Maiero co-organizes events like the KI-Fachtag and MINT-ProNeD Future Innovation Hub meetings, demonstrating leadership in knowledge transfer between research and educational practice.

• Medical Image Analysis
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Vajira Thambawita is a researcher at the University of Oslo's Department of Informatics, specializing in medical image analysis and AI-driven healthcare solutions. With over 90 publications since 2019, their work spans gastrointestinal endoscopy, reproductive medicine technology, and multimedia systems for clinical applications. Research focuses on medical image segmentation (polyp detection, sperm tracking), anomaly detection in time-series data, and multimodal analysis for clinical decision support. Key projects include the Medico Multimedia Task at MediaEval (sperm tracking), VISEM-Tracking dataset, and ImageCLEFmedical challenges for GI tract analysis. Their work bridges computer vision with clinical practice through collaborations with Oslo University Hospital and Simula Research Laboratory. Recent publications demonstrate strong trends in generative AI for medical data augmentation (SinGAN-Seg, PolypConnect), explainable AI for clinical validation , and multimodal integration (SoccerNet-Echoes). The research consistently targets real-world clinical problems with emphasis on transparency and robust evaluation. Thambawita actively contributes to major benchmark challenges including ImageCLEF, Medico, and Medical AI competitions, serving as organizer and participant in advancing evaluation standards for medical AI systems.

• Speech Signal Processing
• Speaker Verification
• Speech Emotion Recognition
• +۳ مورد دیگر

Professor Eliathamby Ambikairajah is a leading researcher at the University of New South Wales , specializing in speech signal processing, emotion recognition, and engineering education. His work spans multiple disciplines including Computer Science , Signal Processing , and Human-Computer Interaction , with collaborations across institutions in Australia and internationally. Research Focus: Speech Emotion Recognition and Ambiguity Modeling Transformer-based Speech Enhancement and Neural ODE Anti-Spoofing Systems for Speaker Verification Engineering Pedagogy and AI Integration in Education Publication Trends: Recent work explores adaptive audio front-ends , ambiguity-aware emotion prediction , and Transformer length generalization . Earlier studies focused on replay attack detection and GMM-HMM for blood pressure estimation . Awards & Grants: Not explicitly stated in the provided text. Advising: Mentored researchers in speech processing and engineering education, though specific students are not named.

• Machine Learning
• Internet of Things
• Biomedical Engineering
• +۷ مورد دیگر

Md. Mehedi Hasan is a researcher at Daffodil International University's Department of Computer Science and Engineering. With over 17 years of research activity, his work spans multiple domains including Internet of Things (IoT), machine learning, biomedical informatics, and environmental technology. Current affiliations include collaborations with institutions in Bangladesh, Japan, and Malaysia. Research areas: Machine Learning, IoT Security, Medical Imaging, Aquaculture Monitoring Key technologies: Deep Learning, Time Sensitive Networking, Kerberos Authentication Notable contributions: NAND Flash memory analysis, Pediatric health applications, Urbanization impact studies His 2025 publications focus on medical data preservation ( Comput. Biol. Medicine ), pedestrian risk assessment ( Complexity ), and toddler screen time management ( SoftwareX ). Recent work explores the intersection of environmental analysis with deep learning and cryptographic solutions for smart infrastructure. Scientific contributions include: Radiation tolerance analysis of neuromorphic systems (IRPS 2020) Smart meter security protocols (Earth Sci. Informatics 2024) Acoustic breathing phase detection (Proc. ACM IMWUT 2021)

• Hardware Design
• FPGA Programming
• Parallel Computing
• +۵ مورد دیگر

Satnam Singh is a Professor at Newcastle University's School of Electrical and Electronic Engineering, UK. With a research career spanning over three decades from 1989 to present, Singh has established himself as a leading expert in hardware design, FPGA programming, and parallel computing systems. His research interests focus on hardware-software co-design , reconfigurable computing , and functional programming applications for hardware design. Singh has pioneered work in using functional languages like Haskell for hardware description and verification, particularly through his contributions to the Lava hardware description language. His work bridges theoretical computer science with practical hardware implementation challenges. Analysis of his publication history reveals a clear evolution from early work on formal verification and FPGA design in the 1990s, through substantial contributions to parallel programming models in the 2000s, to more recent applications of machine learning techniques in diverse domains including cheminformatics and sensory systems. His 2022-2025 publications demonstrate continued innovation in specialized processor programming, AI applications for olfactory systems, and health hazard classification using deep learning. Singh has maintained extensive collaborations throughout his career, notably with Krishna R. Pattipati (15 joint publications), Anuradha Kodali (8 publications), and David J. Greaves (5 publications), reflecting his ability to bridge theoretical computer science with practical engineering applications. His work spans multiple prestigious venues including FPGA, FCCM, ICFP, and IEEE Transactions on Systems, Man, and Cybernetics, demonstrating both theoretical depth and practical impact across computer architecture, programming languages, and applied machine learning domains.

Huayue Zhangمشاهده پروفایل

پژوهشگر

• Virtual Acoustic Environments
• Psychoacoustics
• Auditory Modeling
• +۷ مورد دیگر

Huayue Zhang is a Researcher at the Professorship of Audio Information Processing, Technical University of Munich. He holds a Master's in Architectural Technology from Harbin Institute of Technology (2019–2022) and a Bachelor's in Civil Engineering from Harbin University of Science and Technology (2014–2018). Since 2023, he has served as a Scientific Assistant at TUM. Research Areas : Virtual Acoustic Environments for Learning Spaces Psychoacoustics and Auditory Modeling Applications of Hearing Aids and Cochlear Implants Acoustic Monitoring and Virtual Acoustics Zhang’s work spans interdisciplinary fields, including deep learning for remote sensing , slope stabilization monitoring , and image processing techniques . His recent publications focus on neural network architectures for image denoising, SAR-based disaster assessment, and LiDAR semantic segmentation. Key Projects : HAPPAA: Exploring human auditory perception in acoustic environments Auralization: Sound-field simulation for virtual spaces Binaural Unmasking: Enhancing speech intelligibility in noise He collaborates with international teams on environmental monitoring, leveraging multi-source satellite data and drone imagery for flood and landslide assessments. His technical expertise includes signal processing and multimedia systems design.

• Haptic Interfaces
• Human-Computer Interaction
• Multimodal Interfaces
• +۵ مورد دیگر

Jeremy R. Cooperstock is a Professor at McGill University in Montreal, Canada, with a distinguished career spanning over three decades in human-computer interaction, haptics, and multimodal interface research. His work has established him as a leading figure in the development of practical haptic communication systems and accessibility technologies. Professor Cooperstock's research focuses on creating novel interfaces that enhance user experience through touch, sound, and visual feedback. His laboratory has pioneered significant advancements in vibrotactile feedback systems, telepresence technologies, and audio-haptic integration for improved user awareness and communication. His work bridges fundamental research in human perception with practical applications across multiple domains including accessibility, social interaction, and immersive environments. His recent publications demonstrate a strong trajectory in developing practical haptic applications for everyday use, with notable contributions to web accessibility frameworks, social touch interfaces, and context-aware haptic feedback systems. Cooperstock's research has evolved from foundational work in multimodal telepresence to increasingly applied research addressing real-world challenges in human communication and interaction. Throughout his career, Cooperstock has mentored numerous graduate students who have become significant contributors in the field. His collaborative approach is evident in his extensive publication record with students and colleagues across multiple institutions. His research group has consistently produced high-impact work published in top-tier venues including IEEE Transactions on Haptics, CHI, UIST, and WHC.