جست‌وجوی استاد و پژوهشگر

Anders Fribergمشاهده پروفایل

استاد

• Music Communication
• Music Performance Modeling
• Music Perception
• +۵ مورد دیگر

Anders Friberg is a Professor at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science (EECS) and the Department of Speech, Music and Hearing. His research focuses on music communication, perception, and modeling, encompassing topics such as music performance, expressive timing, and the interplay between music and emotion. He leads projects in computational models for music analysis, audio signal processing, and the neuroscience of music. His work bridges musicology, computer science, and cognitive science. Research Interests: Music perception and modeling Expressive performance analysis Audio feature extraction Music emotion and crossmodal perception Neuroscience of singing and vocal production Key Contributions: Development of the KTH rule system for musical performance Studies on jazz swing rhythms and ensemble timing Computational models for accent salience and dynamics Investigations into insula connectivity in trained singers Publications highlight interdisciplinary approaches, with recent work on vocal sound imitation models, child-computer interaction in music learning, and neural correlates of musical expression.

• Machine Learning
• Computer Vision
• Multimodal Learning
• +۷ مورد دیگر

Yunhui Guo is an Assistant Professor in the Department of Computer Science at the Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas. His research focuses on advanced machine learning techniques including multimodal learning, continual learning, audio-visual recognition, and domain adaptation. He explores challenges in model robustness, cross-modal interactions, and efficient training strategies for deep neural networks. Key research areas include: Developing robust multimodal models for video entailment and dynamic 3D human reconstruction Improving audio-visual segmentation and sound separation through novel adaptation frameworks Advancing continual learning methods to handle domain shifts and out-of-distribution data Creating submodular optimization strategies for active learning in 3D object detection His recent work emphasizes real-world applications like medical image analysis (skin cancer sub-typing), robotics (LiDAR segmentation), and secure AI systems (model watermarking). The research also addresses foundational AI topics such as model uncertainty quantification and adaptive predictive systems. Publications focus on cutting-edge areas like multimodal LLM adaptation, hierarchical out-of-distribution detection, and bimodal online adaptation techniques. Current projects explore the intersection of multimodal perception and lifelong learning systems.

• Auditory Cognition
• Neuroscience
• Psychoacoustics
• +۱۱ مورد دیگر

Luc Arnal and Diane Lazard are Research Officers at the Institut Pasteur in Paris, where they co-lead a research team focused on auditory cognition and communication. Their work integrates psychoacoustics, neuroimaging (fMRI, EEG, intracranial electrophysiology), and computational modeling to investigate how the human brain processes verbal, nonverbal, and musical signals. Their research addresses fundamental and clinical questions related to hearing loss and neurodegenerative conditions such as Alzheimer's disease. Their research interests center on the neural mechanisms underlying speech and music processing, with a particular focus on predictive coding and neural oscillations. They study how the brain adapts to sensory deficiencies, including long-term cortical reorganizations in response to hearing loss. A key area of investigation is the impact of 'rough' sounds on both classical and non-classical (limbic and subcortical) auditory pathways, and how this processing is altered in neurodevelopmental and neurodegenerative disorders. They also explore audio-visual integration, particularly lip-reading abilities in hearing-impaired individuals, using behavioral and neuroimaging methods to understand the neural basis of multisensory perception and its developmental constraints. Their recent publications reveal trends in auditory neuroscience, particularly the role of neural rhythms in perception and the brain's adaptive strategies in hearing impairment. Their work bridges fundamental cognitive neuroscience with translational applications for diagnosing and treating hearing disorders. They mentor PhD students and are involved in major research projects such as AUDIOGENAGE, which aims to develop precise audiological tests for age-related hearing loss, and cross-functional studies on brain connectivity diseases. Their team operates within the broader research ecosystem of the Institut Pasteur, collaborating on interdisciplinary projects that connect auditory neuroscience with public health and disease mechanisms.

• Artificial Intelligence
• Machine Learning
• Constraint Programming
• +۴ مورد دیگر

Siegfried Nijssen is a Professor at the Department of Computer Science within the Faculty of Engineering Science at KU Leuven. He is a core member of the Declarative Languages and Artificial Intelligence (DTAI) research group at the Arenberg campus and affiliated with Leuven.AI, the university-wide Institute for Artificial Intelligence. His academic position is designated as 'professor BOF', reflecting a specialized research-focused appointment. Nijssen's research centers on the integration of declarative programming paradigms with machine learning, particularly through constraint programming frameworks. Key focus areas include interpretable rule learning, neural-symbolic integration, and constraint-based optimization for combinatorial problems. His work bridges theoretical computer science with practical applications in bioacoustics, pandemic response modeling, and network analysis, emphasizing transparency and reliability in AI systems. Analysis of his 2021-2024 publications reveals a strong trajectory toward interpretable AI, with significant contributions like RL-Net (combining neural networks with rule-based reasoning) and novel approaches to NP-hard optimization using structured perceptrons. His research consistently targets the intersection of symbolic reasoning and statistical learning, addressing critical challenges in constraint imposition, model explainability, and stochastic optimization. Nijssen currently leads two major research initiatives: 'Declarative Languages for Imposing Constraints on Machine Learning Models' (2025-2027) and the long-term 'Declarative Programming for Machine Learning (DeclaLearn)' project (2025-2035), demonstrating sustained leadership and funding in his specialized domain. These projects extend his foundational work on constraint-based machine learning frameworks. Based at the DTAI research group, Nijssen contributes to KU Leuven's AI ecosystem through collaborative research in logic programming, constraint solving, and data mining. His work with Leuven.AI positions him at the forefront of institutional efforts to advance trustworthy and constraint-aware artificial intelligence systems.

María del Carmen Benavides Cuéllarمشاهده پروفایل

استاد

• Systems Engineering and Automation
• Cybersecurity
• IoT Network Security
• +۶ مورد دیگر

María del Carmen Benavides Cuéllar is a Professor at the Department of Electrical, Systems and Automation Engineering within the College of Industrial, Informatics and Aerospace Engineering at the University of Leon. Her research focuses on Systems Engineering and Automation, with particular emphasis on Cybersecurity, IoT Networks, Ontology-Based Knowledge Representation, and Intelligent Distributed Systems. Doctorate in Control Engineering (University of Leon, 2009) Currently leads research in Cybersecurity and IoT Her recent publications span topics from Cybersecurity in IoT 5G networks to Medical AI applications for Parkinson's disease severity analysis. She has contributed significantly to LiDAR-based navigation systems and Social Network Analysis in healthcare contexts. Key research areas include: Ontology-based knowledge representation for control engineering Distributed intelligent systems and middleware development Biomedical applications of deep learning Social media analysis using NLP techniques She has supervised multiple research projects at the intersection of Computer Science and Healthcare, with notable works in nursing management and adolescent alcohol consumption studies. Her lab SECOMUCI focuses on cybersecurity and knowledge management in cyber-physical systems.

• 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.

• Acoustics
• Psychoacoustics
• Spatial Audio
• +۷ مورد دیگر

Iain McGregor is an Associate Professor at the School of Computing Engineering and the Built Environment, Edinburgh Napier University. His work bridges acoustics, psychoacoustics, and human-computer interaction to advance audio technologies in mixed reality and IoT environments. Current Research Focus Designing auditory contracts for smart devices Developing ethical sound frameworks Creating perceptually adaptive audio systems His recent publications explore: 2025: Auditory contracts in IoT 2025: Adaptive sound ethics 2025: Perceptually stabilizing audio 2023: Spatial audio in metaverse Scientific Grants: £99,711 Royal Academy of Engineering (2024-2025) £24,999 Innovate UK (2024-2026) Multiple AHRC grants (2022-2025) PhD Supervision: Dynamic robotic personality adaptation (2024) Adaptive game sound design (2022) Exosolar planetary sonification (2015-2021)

• Acoustics
• Finite Element Analysis
• Ultrasonics
• +۷ مورد دیگر

Marc Arnela Coll is an Assistant Professor at the Engineering Department of La Salle Digital Engineering School, where he actively contributes to research in acoustics, vibration control, and sustainable building technologies. His work spans interdisciplinary domains, integrating engineering, environmental science, and human comfort in architectural contexts. His research interests focus on acoustics , finite element analysis , ultrasonics , and sustainable materials , with applications in noise and vibration reduction, sound insulation, and eco-friendly construction. He explores innovative solutions such as acoustic black holes, recycled concrete walls, and ultrasonic waste classification. The trends in his recent publications (2022–2025) reflect a strong emphasis on both theoretical and applied acoustics, with increasing integration of sustainability and human-environment interaction. His work appears in high-impact journals such as Applied Acoustics , Waste Management , and Chaos, Solitons and Fractals , covering topics from urban soundscapes to biomedical voice modeling. Scientific Awards: No scientific awards listed in the provided text. Advising and Grants: While no formal students are listed, Marc Arnela Coll leads and participates in multiple funded research projects. He serves as Principal Investigator (PI) for projects such as Bettair V and Aria , and as a key researcher in initiatives like ABHSSYS and Inhabiting Gaudí . These projects are supported by internal grants, research contracts, and collaborative agreements, reflecting strong institutional and interdisciplinary support. Labs and Teams: He is a core member of the Human-Environment Research group at La Salle Digital Engineering School, collaborating closely with researchers such as O. Guasch, R. M. Alsina-Pagès, and M. Freixes Guerreiro. His work is deeply embedded in team-based, project-driven research environments focusing on acoustic innovation and sustainable design.

• 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.

• Digital Signal Processing
• Spectral Analysis
• Pattern Recognition
• +۷ مورد دیگر

Maria Ragousi is a faculty member in the Department of Electrical and Electronic Engineering at the University of West Attica, affiliated with the School of Engineering. She holds a PhD in Digital Signal Processing and Pattern Recognition from NTUA (1994), an MSc from the University of Virginia (1990), and a Diploma in Electrical Engineering from NTUA (1987). Her academic role is as a Lecturer, focusing on courses such as Digital Signal Processing, Biomedical Signal Processing, and Educational Technologies. Education: PhD (1994): Digital Signal Processing & Pattern Recognition, NTUA MSc (1990): Digital Signal Processing, University of Virginia Diploma (1987): Electrical Engineering, NTUA Her research interests span Digital Signal Processing (DSP), Spectral Analysis, Pattern Recognition (including neural networks and genetic algorithms), Biomedical Signal Processing, Wireless Sensor Networks for environmental monitoring, and E-Learning technologies. She has contributed to over 30 peer-reviewed publications, including work on EEG-based person identification, environmental sound classification, and educational data mining. Recent research emphasizes integrating machine learning into education and developing tools for environmental acoustic monitoring. She has collaborated on projects like the ESOUNDMAPS initiative, deploying wireless sensor networks for real-time environmental sound mapping. Her work also explores active learning methodologies in physics education and visualization of e-learning platform data. Maria advises courses at both undergraduate and postgraduate levels, including specialized programs in medical signal analysis and smart electronic systems. She actively participates in academic governance and has led several educational innovation projects funded by national and international grants.

Vilelmini Kalabratsidouمشاهده پروفایل

عضو هیئت علمی

• Wearable Computing
• Biosignals
• Cognitive Computing
• +۳ مورد دیگر

Vilelmini Kalabratsidou is an Adjunct Lecturer at the University of the Aegean’s Department of Product and Systems Design Engineering on Syros. Her work bridges cognitive computing, wearable technology, and performing arts, focusing on designing interfaces that augment human bodily signals (e.g., heart activity) into interactive experiences. She holds a PhD in Cognitive Computing from Rutgers University (USA) and a dance degree from the Athens Conservatory. Education: PhD in Computer Science (Cognitive Computing), Rutgers University, 2018 MSc in Electronic & Computer Engineering, Technical University of Crete, 2012 Dance Diploma, Athens Conservatory Dance Department Research Interests: Wearable biosensors, real-time biofeedback systems, embodied interaction in dance, data-driven artistic creation, and neuroplasticity interfaces. Her projects often involve collaborative art-science initiatives like the Transition to 8 project, blending social data with music/visual art. Key Contributions: Developed co-adaptive interfaces using heart rate data to steer dance performances and pioneered methods for analyzing bodily signals during social interactions (e.g., sociodrama studies). Her work has been showcased at conferences like Movement and Computing (MOCO) and published in journals like Wearable Technologies and Sensors . Awards: Qualcomm Innovation Fellowship Finalist (2016), Student Entrepreneurship Grand (2011 for a one-button communication system). Labs & Collaborations: Collaborated with Rutgers’ Sensory-Motor Integration Lab, Athena Research Center, and the University of Peloponnese. Active in transdisciplinary projects fusing art, tech, and psychology.

Sapountzaki Galiniمشاهده پروفایل

استادیار

• Greek Sign Language (GSL) Grammar
• Deaf Education
• Educational Technology for Sign Languages
• +۳ مورد دیگر

Sapountzaki Galini is an Assistant Professor at the Department of Special Education, University of Thessaly, specializing in Greek Sign Language (GSL). She has held this position since January 2012 and teaches courses on GSL linguistics and methodology. Her work focuses on language acquisition in deaf communities, educational accessibility, and technology-enhanced learning platforms for sign languages. Education: PhD in Linguistics (2005) from the University of Bristol Diploma in GSL Interpreting (1997) Bachelor’s in Philology (1994) from the Athens School of Philosophy Research Interests: Dr. Sapountzaki’s work bridges theoretical linguistics and applied education. She explores grammar structures in GSL, particularly tense/aspect markers, and advocates for bilingual education for deaf students. Her projects include developing e-learning platforms like SL-ReDu for GSL recognition and assessment, emphasizing 3D modeling and multimodal data fusion. She also examines the impact of language deprivation on deaf children and strategies for remote GSL instruction during crises like the pandemic. Grants & Advising: No specific grants listed, but her work is supported by projects like SYNENNOISI and DIANOIMA. She has advised on curricula for deaf education programs and collaborates with institutions like the Neuropsychology Laboratory and Pedagogical Institute. Her teaching spans special education teacher training and interpreter education. Labs/Teams: Collaborates with the Institute of Speech Processing (ISL) and the University of Thessaly’s Special Education Committee. Active in designing GSL video texts and accessibility tools for public schools.

Maher Salemمشاهده پروفایل

مدرس ارشد

• Cybersecurity
• Blockchain Technology
• Machine Learning
• +۴ مورد دیگر

Dr. Maher Salem is a Senior Lecturer (Assistant Professor) in Cybersecurity at King's College London, affiliated with the Department of Informatics within the Faculty of Natural, Mathematical & Engineering Sciences. He holds a PhD in Security Engineering from Kassel University (2014) and is a HEA Fellow. His research focuses on intrusion detection systems, cyber threat intelligence, machine learning-based security solutions, and blockchain applications in cybersecurity and finance. He has 15+ years of industrial experience with firms like AUDI AG and E-Plus, and has secured funding from the German Federal Ministry of Education and Research (BMBF). Dr. Salem leads the Computing Education Research Centre (CERC) at King's, advancing cybersecurity education through pedagogical tools and PhD supervision. His work bridges academia and industry, addressing challenges in online learning equity, cloud security, and automotive/IoT security via blockchain. Notable contributions include frameworks for threat intelligence detection, privacy-preserving cloud search, and decentralized transportation authentication systems. Education: PhD in Security Engineering (Kassel University, 2014) Affiliations: Former roles at Higher Colleges of Technology (UAE) and Fulda University of Applied Sciences (Germany) Labs/Teams: Security Hub (King's College), CERC His awards include the Emirates Skills First Rank Award (2017) and HEA Fellowship (2019). Research outputs span 25+ peer-reviewed articles, focusing on AI-driven penetration testing, blockchain applications, and cybersecurity education innovations. He collaborates globally on projects like dynamic cloud congestion management and APT detection frameworks.

• Machine Learning
• Computer Vision
• Neural Networks
• +۵ مورد دیگر

Tahiya Chowdhury is an Assistant Professor of Computer Science at Colby College. Her research focuses on intersections of machine learning, computer vision, and human-centric AI applications. She teaches courses including CS166 (Computational Thinking: Computer Vision), CS232 (Computer Organization), and CS343 (Neural Networks). Her work emphasizes ethical AI, multimodal interaction analysis, and sustainable technology solutions. Research interests span feature reliability in open-source tools, autism interaction studies using non-verbal cues, and urban policy compliance monitoring via vision-language sensing. Her projects include semantic segmentation frameworks for environmental monitoring and frameworks for marine debris cleanup without human labels. She explores user-centric approaches in street view image quality ranking and child-centered AI ethics. Her publications from 2021-2025 reflect a trend toward applied AI in healthcare (e.g., Medbuds medication tracking), environmental sustainability (atoll imagery analysis), and pandemic-era communication studies. Despite prolific output, no scientific awards are listed in the provided materials. Advising and grant information are not documented here. Her work integrates hardware-software systems like the Maestro ambient sensing platform, emphasizing practical deployments in smart environments while addressing privacy challenges. Key themes include reducing labeling efforts in IoT systems and designing socially responsible AI interfaces for diverse user groups.

James Hopgoodمشاهده پروفایل

استاد

• Statistical Signal Processing
• Machine Learning
• Data Science
• +۵ مورد دیگر

James Hopgood is a Professor of Statistical Signal Processing at the University of Edinburgh's School of Engineering, leading the Institute for Data, Imaging, and Communications. He holds roles as Dean of Quality and Enhancement for the College of Science and Engineering and Director of the EPSRC/MoD CDT in Sensing, Processing & AI for Defence and Security (SPADS). His research focuses on statistical signal processing applications in medical imaging, acoustic sensing, and multi-target tracking. Hopgood earned his M.A., M.Eng., and Ph.D. from the University of Cambridge, joining Edinburgh in 2004. He has developed algorithms for Bayesian signal processing, acoustic source localization, and medical imaging, with over 100 publications. As Editor-in-Chief of the IET Journal of Signal Processing since 2011, he contributes to academic leadership. Teaching includes courses on signal processing, probability, and sensor networks, emphasizing visualization and real-world applications. Current projects span adaptive optical imaging for medical diagnostics and probabilistic graphical models for multi-target tracking. His work bridges academia and industry, collaborating with companies like Leonardo and Agilent Technologies. He supervises numerous PhD students and leads multidisciplinary initiatives in defense, healthcare, and autonomous systems. **Education**: PhD in Statistical Signal Processing, University of Cambridge (2001) MEng in Electrical and Information Sciences, University of Cambridge (1997) MA, University of Cambridge (2000) **Research Interests**: His work spans model-based Bayesian methods, acoustic source localization, medical imaging (e.g., fluorescence lifetime imaging), and multi-target tracking. Recent projects include optimizing UAV trajectories in dynamic environments and developing algorithms for distributed sensor networks. **Grants & Collaborations**: Funded by EPSRC/MoD, GSK, and Leonardo, his projects address defense sensing, drug development, and clinical imaging. Collaborations include Firefinch (software/data science) and the UDRC Summer School. **Labs/Teams**: Leads the Acoustics and Audio Group and the SPADS CDT, fostering innovation in sensing and AI for defense/security applications.