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Peter Lambert is a full-time Associate Professor at Ghent University – imec (Belgium), affiliated with the Internet Technology and Data Science Lab (IDLab) where he coordinates the MEDIA research team since 2013. His academic background includes a Master's degree in Science (Mathematics) and Applied Informatics from Ghent University, followed by a Ph.D. in Computer Science from the same institution in 2007. Prior to his current role, he served as a Technology Developer at Ghent University (2010-2013). Lambert's research focuses on: Multimedia signal processing and visual communication systems Computer graphics and computational geometry Augmented and virtual reality (AR/VR) technologies Video compression and perceptual quality assessment Multimedia security and digital watermarking His recent publications (2024-2025) demonstrate strong emphasis on real-time multimedia systems, VR/AR applications, perceptual quality metrics, and multimedia security. Research trends include deep learning-based video forensics, light field rendering optimizations, perceptual hashing techniques, and adaptive video streaming solutions. As leader of the IDLab-MEDIA team, Lambert oversees research on emerging visual media formats with applications in immersive experiences. The team develops technologies like OpenDIBR (real-time light field renderer) and maintains datasets such as SILVR (Synthetic Immersive Large-Volume Plenoptic Dataset).

• multimedia signal processing
• visual communication
• computer graphics
• +2 more

Peter Schelkens is a Professor at the Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel (VUB). He holds additional roles including Department Chair and Head of Research Group, focusing on technology transfer and innovation in electronics and informatics. His research spans fundamental signal processing, holography, medical imaging, and standardized multimedia coding frameworks like JPEG Pleno. Education and Academic Background: Postdoctoral Fellowship (2002–2011) funded by the Research Foundation – Flanders (FWO). His work bridges theoretical advancements with applied domains such as eHealth, bio-informatics, and cultural heritage preservation. Research Interests: Holography and digital signal processing dominate his focus, including holographic compression, Fourier-based techniques, and light field imaging. Strategic projects involve error-resilient coding, computer architectures (e.g., GPU/GPGPU), and quality assessment metrics. His applied research addresses medical imaging, 3D media broadcasting, and immersive technologies. Article Trends: Recent work emphasizes holographic video codecs (e.g., INTERFERE), high-throughput hologram generation, and JPEG Pleno standardization. He explores computational methods for 3D metrology and deep learning applications in hologram optimization. Scientific Awards: Gauss Award (2000), ERC Consolidator Grant (2014), Best Associate Editor Award (2014), and multiple industry accolades. Grants/Projects: Leads major initiatives like the SRP-Onderzoekszwaartepunt LSDS (2022–2027) and GEAR (2021–2025), focusing on health tech and learning-based systems. Labs/Teams: Active in ETRO, the interdisciplinary research group at VUB, collaborating globally on holography, multimedia standards, and biomedical imaging systems.

• Holography
• Image and Video Coding
• Digital Signal Processing
• +6 more

Brent De Weerdt is a doctoral student and researcher at the Vrije Universiteit Brussel, affiliated with the Faculty of Engineering and the Electronics and Informatics department. His research focuses on interpretable deep learning, distributed source coding, generative AI, and AI explainability. He holds a Master of Science in Electrical Engineering from the same institution (2021). Current projects include the FWOSB140 DUST project, exploring interpretable and efficient deep unfolding sparse transformers for multimodal image processing and generation. He has published in top-tier venues like IEEE Transactions on Signal Processing and IEEE conferences, with contributions to sparse recovery, trajectory anomaly detection, and layered coding. De Weerdt actively engages in peer-review activities for journals such as IEEE Transactions on Multimedia and conferences like the European Signal Processing Conference. His work bridges theoretical signal processing with practical AI applications, emphasizing interpretability and efficiency in neural architectures.

• Interpretable Deep Learning
• Distributed Source Coding
• Generative AI
• +3 more

Hannes Mareen is a Postdoctoral researcher at Ghent University's Faculty of Engineering and Architecture, specifically within the Department of Electronics and information systems (EA06), and is also affiliated with IMEC. His research spans multimedia security, image and video processing, and digital forensics, with a focus on watermarking techniques, deepfake detection, and media authentication. Dr. Mareen's research interests primarily focus on multimedia forensics and digital content security . His work addresses critical challenges in media authentication, including developing robust watermarking techniques that withstand geometric transformations, creating methods for deepfake video detection using advanced vision transformer architectures, and building tools for identifying AI-generated image manipulations. He has made significant contributions to perceptual hashing techniques and the analysis of compression fingerprints for forgery detection. His research has practical applications in copyright protection, content authentication, and combating misinformation through advanced media analysis tools. The recent publication trends reveal a strong focus on leveraging deep learning and vision transformers for multimedia security applications. Dr. Mareen's work demonstrates increasing sophistication in addressing complex challenges like geometrically robust watermarking, deepfake detection in video content, and the development of comprehensive datasets for training forgery detection systems. His research bridges theoretical advances with practical implementations, particularly evident in his work on real-time video delivery systems and applications in sports rehabilitation through virtual reality. Dr. Mareen has established a productive research trajectory with consistent high-impact publications in top conferences and journals. His collaborative network is extensive, with frequent co-authorship with Glenn Van Wallendael and Peter Lambert, indicating strong research group dynamics within Ghent University's multimedia security research team. His work spans both theoretical contributions and practical implementations, with several publications demonstrating real-time systems and applications.

• Multimedia Forensics
• Image and Video Watermarking
• Deepfake Detection
• +6 more

Nikolaos Deligiannis is an Associate Professor at the Department of Electronics and Informatics (ETRO) of Vrije Universiteit Brussel (VUB). He holds a PhD in Engineering Sciences from VUB (2012) and a Diploma in Electrical & Computer Engineering from the University of Patras (2006). Previously, he was a Senior Researcher at University College London (2013-2015). His research focuses on interpretable machine learning, federated AI, and applications in image/video processing, computer vision, and natural language processing. He is an IEEE member and serves as an Associate Editor for the IEEE Transactions on Image Processing. He leads major projects such as the EU-funded 'Reinventing Multiterminal Coding for Intelligent Machines' (2025-2030) and 'ENACT: Environmental Effect on Health Care' (2025-2028). His work spans 202+ publications, including seminal papers on explainable AI and federated learning. Key awards include the EURASIP Best PhD Award (2017) and the Francqui Research Professorship (2024). Deligiannis has supervised 39 student theses, including master's works on distributed query systems, algorithmic trading, and water level forecasting. His research also addresses healthcare applications, such as developing a prognostic model for COVID-19 severity using thoracic CT scans. He actively participates in conferences and workshops, delivering talks on topics like graph-based explanations and deep learning optimization.

• Interpretable Machine Learning
• Explainable AI
• Federated AI
• +5 more

Adrian Munteanu is a Professor in the Department of Electronics and Computer Science at Free University of Brussels (Vrije Universiteit Brussel). He has been actively contributing to research in image processing, deep learning, and signal processing since 1995, with over 414 research outputs and an h-index of 29. His work spans diverse fields such as video compression, autonomous vehicle pose estimation, greenhouse gas emissions prediction, and real-time distributed video coding.

• Image Processing
• Deep Learning
• Signal Processing
• +6 more

Aleksandra Pizurica is a Professor in statistical image modelling at Ghent University, Belgium, affiliated with the Group for Artificial Intelligence and Sparse Modelling (GAIM). She serves as Senior Area Editor for IEEE Transactions on Image Processing (2016–) and Associate Editor for IEEE Transactions on Circuits and Systems for Video Technology (2016–), having previously held editorial roles at IEEE Transactions on Image Processing (2012–2016). Her educational background includes: Dipl. Ing. in Electrical Engineering, University of Novi Sad (1994) MSc in Telecommunications, University of Belgrade (1997) PhD in Engineering, Ghent University (2002) Prof. Pizurica's research centers on statistical modelling , probabilistic graphical models , and Bayesian inference , with significant contributions to sparse coding , signal/image processing , and machine learning . Her work bridges theoretical advances with applications in medical imaging, remote sensing, and cultural heritage preservation, particularly in image denoising, inpainting, and hyperspectral analysis. Analysis of her 15 most recent publications (2023–2025) reveals dominant themes in hyperspectral image processing (clustering/classification via model-aware deep learning), medical imaging (3D foot/ankle alignment, musculoskeletal segmentation), and cultural heritage (crack detection in paintings). Emerging trends include fairness in AI (skin color bias mitigation), scalable seabed mapping, and generative models for point cloud processing. Her notable recognition includes: Scientific Prize “de Boelpaepe” for 2013-2014 from the Royal Academy of Science, Letters and Fine Arts of Belgium While specific grant details and student advisement records aren't provided in available sources, her editorial leadership and research output indicate active supervision of graduate researchers. She leads initiatives in the Group for Artificial Intelligence and Sparse Modelling (GAIM), focusing on statistical image modeling and sparse representations for real-world applications. The GAIM research unit under her affiliation drives innovation in probabilistic modeling and machine learning, with projects spanning medical diagnostics, remote sensing, and digital art restoration, evidenced by recent publications in IEEE Transactions and high-impact journals.

• Statistical modelling
• Probabilistic Graphical Models
• Bayesian inference
• +5 more

Glenn Van Wallendael is an Associate Professor at Ghent University's Faculty of Engineering and Architecture , affiliated with the Department of Electronics and Information Systems . He leads research in video coding, digital watermarking, and immersive media technologies. Academic Focus: Video compression standards (HEVC, H.266), AI for multimedia, virtual reality Key Collaborations: iMinds, imec, European research consortia Research Interests include: Video compression algorithms (HEVC, SVC, MV-HEVC) Digital watermarking for copyright protection Machine learning applications in image/video analysis Quality of Experience (QoE) in immersive environments Recent Publications (2024-2025) show expertise in: Deepfake detection using vision transformers Medical image landmarking tools Lightweight geometric approximation methods AI-driven video quality assessment Doctoral Mentorship includes supervising: 2021: Hannes Mareen (video forensics) 2020: Vasileios Avramelos (light field coding) 2017: Johan De Praeter (adaptive video encoding)

• Video Coding
• Immersive Media
• Digital Watermarking
• +5 more