جست‌وجوی استاد و پژوهشگر

Stefan Balkeمشاهده پروفایل

پژوهشگر

• Music Information Retrieval
• Deep Learning
• Jazz Music Analysis
• +۳ مورد دیگر

Stefan Balke is a Researcher at the AudioLabs Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), focusing on wind music research and Music Information Retrieval (MIR). Previously, he held a PostDoc position at the Institute of Computational Perception, JKU Linz (2018–2019) and later worked as a Data Scientist and Team Lead in industry. He temporarily served as a professor at Hochschule Weserbergland in 2023/24. Education: PhD (Dr.-Ing.) in MIR from FAU (2018), Electrical Engineering studies at Leibniz Universität Hannover (2008–2013). Research Interests: Music Information Retrieval, Deep Learning, Jazz and Wind Music Analysis, Dataset Development. His work includes creating datasets like ChoraleBricks (wind music) and JSD (jazz structure analysis), and tools like trackswitch.js for audio visualization. Grants & Projects: Secured 35k€ funding for his orchestra through the 'Engagiertes Land' program (2024). Collaborated on exhibits such as 'The Listening Machine' (Ars Electronica) and 'Con Espressione!' (mathematics of music exhibition). Contributions: Active on GitHub with repositories for datasets (choralebricks, jsd) and tools. Co-developed web-based audio tools and contributed to open-source projects like Sonic Visualiser and librosa.

• Computer Vision
• Machine Learning
• Image Classification
• +۳ مورد دیگر

Jalila Filali is a Researcher at Université Laval, affiliated with the Computer Vision and Systems Laboratory. Her work focuses on advancing computer vision techniques through ontology integration and deep learning models, with applications in environmental monitoring and wildlife tracking. She holds a post-doctoral position and specializes in image classification, object detection, and semantic annotation systems. Her research interests include developing ontology-driven systems for image annotation, comparing machine learning models like HMAX and Bag-of-Visual-Words for large-scale classification, and applying YOLOv5 frameworks to analyze wildlife behavior via camera-collar videos. She has contributed to projects involving environmental information extraction in northern Quebec ecosystems. Key technical trends in her publications include hybrid ontology-machine learning architectures, classifier fusion techniques, and the integration of visual features with semantic knowledge bases. She has explored both theoretical advancements in computer vision algorithms and practical implementations in ecological monitoring contexts. Jalila Filali's work is supported by the Computer Vision and Systems Laboratory at Université Laval, where she contributes to interdisciplinary projects merging artificial intelligence with ecological research.

Pedro Pablo Lucas Bravoمشاهده پروفایل

پژوهشگر ارشد

• Music Technology
• Robotics
• Artificial Intelligence
• +۵ مورد دیگر

Pedro Pablo Lucas Bravo is a Doctoral Research Fellow at the University of Oslo's Department of Informatics (IFI), affiliated with the RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion within the Faculty of Mathematics and Natural Sciences. His research focuses on developing interactive music systems using autonomous agents and swarm intelligence, with applications in Extended Reality (XR), Spatial Audio, and robotics. His Ph.D. project explores automatic tempo synchronization in human-machine systems through swarms of autonomous agents, aiming to create emergent musical behaviors in virtual, physical-virtual, and physical platforms. Key technologies include XR, spatial audio synthesis, motion capture, and robotic platforms. Bravo has contributed to projects like the XR Human-Swarm Interactive Music System and the MusicLab Copenhagen Dataset. His work bridges computer science, music technology, and artificial intelligence, with publications in venues such as IEEE ACSOS, NIME, and SMC. He holds a focus on interdisciplinary collaboration, leveraging swarmalator systems for self-organizing compositions and exploring cross-disciplinary applications of embodied oscillators in music performance.

Melodee Woodمشاهده پروفایل

مدرس ارشد

• Digital History
• Historical Newspapers
• Computational Analysis in Humanities
• +۳ مورد دیگر

Melodee Wood is a Senior Lecturer in Digital History at Loughborough University, where she has been since 2015. Her academic journey includes a PhD from the University of Glasgow and undergraduate studies at Clark University. Prior to Loughborough, she held roles such as Principal Lecturer at Sheffield Hallam University and teaching fellowships at the University of Warwick. Her work emphasizes open research, computational methods in humanities, and the intersections of digital archives with historical narratives. Wood’s research focuses on digital history, particularly analyzing historical newspapers, transnational exchanges, and the application of computational techniques to historical data. She advocates for open access publishing and collaborates with journals like Frontiers in Digital Humanities and the Open Library of the Humanities . Her editorial roles include History Editor for these platforms and membership in the Advisory Board of British History Online . Her publications span topics like digitized newspapers, textual analysis methodologies, and the role of print media in shaping cultural identities. She actively engages with academic communities via social media, emphasizing collaborative and accessible scholarship.

Jonathan F. Kominskyمشاهده پروفایل

استادیار

• Cognitive Science
• Developmental Psychology
• Vision Science
• +۳ مورد دیگر

Jonathan F. Kominsky is an Assistant Professor of Cognitive Science at Central European University in Vienna, serving as Principal Investigator (PI) of the Causal Cognition Lab and co-PI of the CEU Cognitive Development Center. His research focuses on understanding how the mind represents causal relationships and employs tools from vision science, cognitive psychology, and developmental psychology to explore causal perception and its development. He created PyHab , an open-source system for infant gaze coding and stimulus presentation, enhancing reproducibility in developmental studies. Education: Ph.D. in Developmental Psychology from Yale University (2016). Research Interests: Causal perception, counterfactual reasoning, causal representations in infancy, and the interplay between causality and memory. His work bridges perceptual and cognitive mechanisms, examining how causal judgments form and influence decision-making and social evaluation. Labs/Teams: Leads the Causal Cognition Lab and collaborates with the CEU Cognitive Development Center. His projects emphasize methodological rigor and interdisciplinary approaches to developmental research.

• Machine Learning
• Natural Language Processing
• Computational Social Media
• +۵ مورد دیگر

Jerry Spanakis is an Assistant Professor at Maastricht University with dual affiliations: the Department of Advanced Computing Sciences (Faculty of Science and Engineering) and the Maastricht Law+Tech Lab (Faculty of Law). His roles include leading the EU Horizon project VOXReality, researching for NSMD/HumanAds/RegTech4AI initiatives, and serving as a technical expert for the European Commission’s e-enforcement academy. He coordinates MaastrichtNLP (NLP research group) and participates in the Open Science Community Maastricht. Education: PhD in Computational Intelligence (2007–2012) from the National Technical University of Athens, School of Electrical & Computer Engineering. Research Focus: Social Machine Learning: Developing responsible AI systems for societal challenges, including interpretable models for consumer protection and regulatory compliance. Computational Social Media: Analyzing social media data to detect online harms (e.g., misleading ads, content moderation failures) and model user behavior. Structuring Unstructured Data: Semantic organization of legal texts, social media, and multimodal data for applications in law, aviation, and public health. Publication Trends: Jerry's recent work (2023–2025) emphasizes NLP innovations for legal and regulatory domains, multilingual information retrieval, and ethical AI frameworks. Key themes include Large Language Model applications in law, influencer marketing compliance, and cross-lingual neural machine translation. Scientific Awards: None reported. Advising & Grants: Jerry supervises 7 PhD candidates and 100+ Master’s/Bachelor’s students in NLP, machine learning, and social computing. He leads the €2.8M EU project VOXReality (voice-driven XR interactions) and contributes to NWO/Philips grants on mental health analytics. Current grants focus on: AI-driven legal process automation (RegTech4AI) Dark pattern detection in e-commerce (NSMD) Influencer marketing transparency (HumanAds) Labs & Teams: Jerry founded MaastrichtNLP, a university-wide NLP research group, and co-leads the Law+Tech Lab, which develops computational tools for legal compliance. His teams collaborate with Deloitte, the European Commission, and healthcare institutions on applied AI projects.

Miroslav Minovicمشاهده پروفایل

استاد

• HCI
• Multimedia
• Computer Networks
• +۳ مورد دیگر

Miroslav Minović is a Full Professor and Head of the Laboratory for Multimedia Communications at the University of Belgrade's School of Business Administration. He holds a PhD in Information Technology and has extensive experience in academia and research. His roles include Director of the Laboratory for Multimedia Communications and guest editor for the International Journal of Engineering Education . Education: PhD in Information Technology (2010), University of Belgrade M.Sc. in Information Technology (2002), University of Belgrade Visiting Researcher at University of the Balearic Islands (2015) and EBS Business School (2013) Research Interests: Human-Computer Interaction (HCI), Multimedia Systems, Biometric Technologies, Blockchain Applications, Computer Networks, and Gamification in Education. Notable Projects: TRAILER EU Project (2012–2013): Focused on informal learning recognition AI4WorkplaceSafety (2020–2022): AI-driven workplace safety solutions Blockchain Technology Research: Explored applications beyond cryptocurrencies Publications: Over 50 peer-reviewed articles in journals like IEEE Multimedia , Computers in Human Behavior , and Sensors . Key themes include gamification ecosystems, biometric systems, and e-learning analytics. Awards & Memberships: Member of IEEE and ACM National Contact Point for Serbia, European Association for Biometrics Grants & Labs: Directed multiple EU-funded and Serbian government projects Lab focuses on multimedia communications, blockchain, and biometric technologies

Jun Liuمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Digital Health
• +۳ مورد دیگر

Jun Liu is a Professor and Chair of Digital Health at Lancaster University's School of Computing and Communications. He holds a PhD from Nanyang Technological University (NTU), an MSc from Fudan University, and a BEng from Central South University. His research focuses on computer vision, machine learning, and digital health, with notable contributions to image segmentation, AI security, and multimodal learning. He leads the VL Group (https://wp.lancs.ac.uk/vl/) and supervises 5 postgraduate students. Education: PhD (NTU), MSc (Fudan), BEng (Central South University). Research Interests: His work spans AI-driven healthcare solutions, adversarial defense in vision-language models, and advanced computer vision techniques. He has pioneered methods like LLaFS++ for few-shot image segmentation and developed frameworks to combat jailbreak attacks in large models. Publications: Over 123 peer-reviewed articles, including high-impact work in IEEE Transactions and ACM journals. Recent trends focus on AI-generated content analysis, robust model security, and cross-modal learning. Awards: Best Paper Awards (PREMIA 2016/2019), IEEE VSPC Rising Star (2024), Stanford Top 2% Scientists. Editorial Roles: Senior Area Editor (IEEE T-IP), Associate Editor (IEEE T-CASVT, ACM Computing Surveys). Grants: Principal Investigator for the 'Unrestricted Gift' project (2025-2028). Labs/Teams: Leads the Vision & Learning (VL) research group, collaborating on digital health innovations and AI ethics challenges.

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

Weitong Cai is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science. He specializes in teaching advanced topics like Deep Learning and Computer Vision at the postgraduate level. His research focuses on computer vision applications, including video moment retrieval, image segmentation, and cross-modal learning. He has contributed to advancements in temporal feature refinement, semantic localization, and robust image super-resolution techniques. Research Interests: His work bridges theoretical machine learning methodologies with practical applications in multimedia systems and dynamic scene analysis. Key areas include: Video moment retrieval using hybrid learning frameworks Camouflaged object segmentation via prompt-agnostic methods Multi-domain adaptation in video analysis Efficient screen content coding Publication Trends: Recent work emphasizes mitigating modality imbalance in cross-modal tasks (2025), improving temporal localization accuracy (2024), and robust image processing under complex degradations (2020-2019). His research spans both foundational algorithms and real-world applications like RGB-D SLAM in dynamic environments. Advising & Grants: No specific grants or advisee名单 listed. Active in collaborative research with industry through the School's qTech Programme. Labs/Teams: Participates in the School's Computer Vision and Machine Learning research groups, contributing to the qTech innovation ecosystem.

• Artificial Intelligence
• Machine Learning
• Audio Signal Processing
• +۳ مورد دیگر

Shubhr Singh is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science, based in the Peter Landin building (Room CS 403). His research focuses on artificial intelligence, machine learning, audio signal processing, and their applications in bioacoustics and financial technology. He has contributed to advancements in graph neural networks (GNNs) for audio identification and classification, few-shot learning for bioacoustic event detection, and AI-driven innovations in banking systems. His work spans interdisciplinary areas such as perceptual music similarity metrics, intelligent audio control systems, and agricultural studies on crop growth optimization. Singh collaborates on international challenges like the DCASE series, exploring cutting-edge methods in sound event detection and domain adaptation. Key research trends in his publications include leveraging GNNs for complex audio analysis tasks, addressing data scarcity via few-shot learning, and integrating AI into real-world applications like neobanks and agricultural sustainability. His articles reflect a blend of theoretical advancements and practical implementations across diverse domains.

Carlos Vianna Lordeloمشاهده پروفایل

استاد مهمان

• Audio Signal Processing
• Machine Learning
• Music Information Retrieval
• +۳ مورد دیگر

Carlos Vianna Lordelo is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science. His research focuses on Audio Signal Processing and Machine Learning, particularly in music-related applications such as source separation, instrument recognition, and deep learning architectures. He contributes actively to music information retrieval through projects like harmonic-percussive source separation and dataset development for folk music analysis. Key research interests include developing algorithms for music audio processing, adversarial domain adaptation, and CNN-based models. His work integrates theoretical advancements with practical applications in audio engineering and multimedia systems. Lordelo's GitHub repository showcases contributions to instrument prediction, source separation techniques, and open-source tools for music research. Notable projects include the 'source-separation-AHS' repository and collaborations on datasets like 'Tap & Fiddle'.

Roeland J.F. Ordelmanمشاهده پروفایل

پژوهشگر

• Speech Technology
• Multimedia Retrieval & Recommendation
• Multimedia Research Infrastructures
• +۴ مورد دیگر

Roeland J.F. Ordelman is a Researcher and Innovation Manager at the Netherlands Institute for Sound and Vision, one of Europe's largest audiovisual archives. He holds a PhD (2003) focused on improving access to audiovisual archives via speech recognition technology. His expertise spans speech technology, multimedia retrieval, and infrastructure for digital humanities/social sciences. He leads projects like CLARIAH-PLUS (2019–2023) and coordinates 'Living Labs' for Media Innovation and Museum Interaction, collaborating with industry and user communities. Research interests include spoken conversational search, speech-based human-robot interaction, and content-based recommendation systems. Notable contributions include organizing MediaEval and TRECVid benchmark evaluations, and developing datasets like the NISV 81k Dutch TV Speech Corpus. His work bridges R&D to practical applications, emphasizing user-centric innovation in cultural heritage access.

• Digital Content Management
• Information Retrieval
• Semantic Analysis
• +۳ مورد دیگر

Nesrine Ben Mustapaha is a Researcher affiliated with the RIADI-GDL Laboratory at the National School of Computer Sciences in Tunisia. Her research focuses on managing heterogeneous digital content, addressing challenges in structuring knowledge across textual, graphic, and voice data to bridge semantic gaps in information retrieval. She emphasizes adaptive and intelligent systems for personalized information filtering. Her work has been presented at events such as the 15th IAPR International Workshop on Document Analysis Systems (DAS 2022) and the CICLing 2019 conference in La Rochelle. She has participated in numerous academic gatherings, including thesis defenses, workshops on AI applications, and scientific days organized by institutions like the MIRES Federation and the L3i laboratory. Key contributions include advancing socio-semantic search methodologies and exploring interdisciplinary approaches to data integration. Her involvement in initiatives like CircularSeas and NewsEye highlights her commitment to innovation in digital humanities and data-driven solutions.

• Network Science
• Evolutionary Dynamics
• Complex Systems
• +۵ مورد دیگر

Dr. Wei Zhang is a Researcher at the Complex Systems and Data Science group within The University of Sydney. Her work focuses on network science, evolutionary dynamics, and complex systems, with an emphasis on modeling social systems and analyzing meso-scale structures in social networks. She combines theoretical approaches, data-driven modeling, and web-based experimental methods to study emergent collective phenomena. Dr. Zhang holds a PhD in Network Science from ETH Zurich. Her research spans interdisciplinary topics including data science applications to complex systems and the detection of structural patterns in social networks. Recent projects include exploring domain adaptation techniques in machine learning and developing methods for 3D object detection and model compression. Her publications reflect expertise in computer vision, machine learning, and environmental science. Key themes include domain adaptation networks, 3D reconstruction, and LiDAR-based forestry analysis. While no specific awards are mentioned, her work demonstrates significant contributions to theoretical and applied data science. Dr. Zhang collaborates on projects involving interdisciplinary teams, though specific grants or lab affiliations are not detailed in the provided text. Her current research continues to bridge complex systems theory with modern data-driven methodologies.

Manling Liمشاهده پروفایل

استادیار

• Multimodal Learning
• Computer Vision
• Natural Language Processing
• +۶ مورد دیگر

Manling Li is an Assistant Professor of Computer Science at Northwestern University. Their research focuses on multimodal learning, vision-language models, and embodied agents. They explore challenges in spatial reasoning, knowledge hallucination in LLMs, and event understanding. Their work often bridges AI, computer vision, and natural language processing to advance autonomous agent capabilities and video-language understanding. Research interests prominently feature multimodal benchmarking (e.g., EmbodiedBench), video-language analysis (Hourvideo), and mental health applications via self-play systems like MentalArena. They investigate foundational issues like knowledge overshadowing in LLMs and develop techniques for 3D layout optimization (LayoutVLM). No scientific awards are explicitly listed in the provided information. No listed advisees or grants are currently available. Their contributions emphasize cross-disciplinary collaboration between computational models and real-world tasks, such as assembly instruction grounding and long-form video comprehension. Labs or team affiliations are not explicitly detailed in the provided information, though their work suggests involvement in AI/ML research groups focused on embodied intelligence and multimedia systems.