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Daan Christiaens is a tenure track lecturer at KU Leuven's Faculty of Medicine and Faculty of Engineering Sciences. He is affiliated with the Department of Electrical Engineering (ESAT) and Department of Imaging & Pathology, serving as a member of the Medical Imaging Division and the KU Leuven Brain Institute (LBI). His academic responsibilities include membership in the Faculty Councils of Engineering Sciences and Medicine. His research focuses on: Inverse problems in medical imaging reconstruction Neuroimaging techniques for brain analysis Advanced quantitative MRI methodologies Diffusion-weighted imaging for microstructural assessment Dr. Christiaens' recent publications (2023-2025) demonstrate a consistent focus on diffusion MRI innovations, including novel reconstruction algorithms, neonatal brain development mapping, and clinical applications for neurodegenerative disorders. Key technical themes include motion correction, multi-shell modeling, and AI-enhanced image processing, while clinical applications span Alzheimer's disease, cerebral palsy, and autism research. He leads significant research projects including: MRI reconstruction with dynamic field monitoring (2024-2028) Compressed sensing for microstructure imaging (2022-2026) Neonatal diffusion MRI network connectivity analysis (2024-2028) As a core developer of the MRtrix3 software framework for medical image processing, he contributes to essential tools in neuroimaging research.

• Inverse problems and reconstruction
• Neuroimaging
• Quantitative MRI
• +1 more

Robin Lemmens is a Professor at the Faculty of Medicine, KU Leuven, and leads the Laboratory of Neurobiology (VIB-KU Leuven). He serves as head of Academic Consultants Training in Specialist Medicine and Program Director of the POC Medical Specialist in Training. His affiliations include the LBI - KU Leuven Brain Institute and membership in multiple institutional committees including the Metaforum Steering Committee and Faculty Council of Medicine. Dr. Lemmens' research focuses on stroke neurology with particular expertise in neuroimaging, cerebrovascular diseases, and endovascular therapy. His work spans ischemic stroke management, anticoagulation therapy, intracerebral hemorrhage, and advanced imaging techniques for stroke diagnosis and treatment planning. Current research explores AI applications in stroke imaging, optimal timing of anticoagulation after stroke, and endovascular therapy in extended time windows. Analysis of recent publications reveals a strong emphasis on translational stroke research with significant contributions to clinical trial methodology. Key themes include validation of imaging biomarkers, optimization of thrombolytic and anticoagulant therapies, and development of AI tools for stroke lesion segmentation. His work frequently appears in high-impact journals including Stroke , Neurology , and The Lancet . Dr. Lemmens supervises research projects and students, with recent supervision including Oosterbos, C. on peroneal nerve entrapment research. His grant portfolio includes multiple active projects totaling millions in funding, with recent grants focusing on secondary stroke prevention, comprehensive stroke management systems, and AI for stroke outcome prediction. He leads the Laboratory of Neurobiology at VIB-KU Leuven, which collaborates extensively with international stroke research networks. Current research directions include developing patient-centered stroke care pathways, advanced imaging techniques for early ischemic changes, and personalized stroke diagnostics and treatment protocols.

• Stroke Neurology
• Neuroimaging
• Cerebrovascular Diseases
• +4 more

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

Jesse Davis is a Professor at the Department of Computer Science , KU Leuven , actively contributing to the Machine Learning group and the Sports Analytics Lab . He is part of the Faculty of Engineering Science and the Leuven.AI Institute . Ph.D. in Computer Sciences from University of Wisconsin-Madison (2007) M.S. in Computer Sciences from University of Wisconsin-Madison (2005) B.A. in Computer Science from Williams College (2002) His research focuses on machine learning, data mining, big data analytics, and sports analytics, with significant work in: Transfer learning and Markov logic networks Anomaly detection and semi-supervised learning Medical NLP and biomechanical data analysis Soccer performance metrics and tactical analysis His recent work explores spatio-temporal data analysis in sports and explainable AI for medical applications, with collaborations spanning finance, healthcare, and semiconductor manufacturing. Notable scientific awards include: Best Paper Award (Applied Data Science Track) at KDD 2019 Best Technical Paper Award at Intelligence Analysis Workshop He advises numerous PhD and Master's students in areas like: Football analytics Tree ensemble compression Medical question-answering systems Biomechanical load prediction His lab develops tools such as: GSSL for Markov network structure learning TODTLER for transfer learning Alchemy system for Markov logic networks

• Machine Learning
• Data Mining
• Big Data
• +4 more

Frederik Temmermans is a Paid Visiting Professor in the Electronics and Computer Science department at Vrije Universiteit Brussel (VUB), Brussels, Belgium. His research focuses on digital media standards, image processing, and trustworthiness assessment of digital content. He actively participates in various research projects related to health technologies and signal processing systems. Dr. Temmermans' research expertise spans multiple interconnected domains: Standardization of digital media formats (100% focus) Metadata management for digital assets (90% focus) Medical imaging applications including tissue engineering (72%) and biopsy analysis (71%) Application programming interfaces and system interoperability (60% and 56% respectively) Image retrieval systems for medical diagnostics His recent work centers on developing standards and methodologies for assessing the trustworthiness of digital media, particularly focusing on fake media detection and verification frameworks. Temmermans has been instrumental in the JPEG committee's efforts to establish standards for media trust and provenance tracking, with multiple publications in 2024 addressing these critical issues in the era of deepfakes and digital misinformation. He is actively involved in multiple research projects including SRP74 on self-learning signal processing systems (2022-2027), GEAR for future health technologies (2021-2025), and Tech4Health initiatives (2024-2025). His work bridges theoretical research with practical applications in medical imaging, digital media verification, and next-generation communication technologies including 6G standardization efforts.

• Standardization
• Digital Media
• Image Retrieval
• +5 more

Prof. Dr. Paul Scheunders is a Professor at the Vision Lab, University of Antwerp, specializing in image processing and machine learning with applications in remote sensing. He co-founded the Vision Lab in 1992 and has led research in hyperspectral imaging, focusing on spectral unmixing and material characterization. Research Interests: Model-based and statistical techniques for hyperspectral image analysis, moisture content estimation in soils and materials, corrosion monitoring, and spectral unmixing algorithms. Teaching: Courses in mathematical methods for physics, classical field theory, and artificial neural networks. Equipment: Utilizes VNIR/SWIR hyperspectral cameras and spectrometers for close-range applications. Recent Article Trends: 2024-2025 publications emphasize advanced unmixing methods (e.g., Bézier surfaces, dual-feature networks), moisture/water content estimation, and drone-based corrosion detection using hyperspectral data.

• Image Processing
• Machine Learning
• Remote Sensing
• +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

Bert Van hauwermeiren is a researcher affiliated with the Department of Electronics and Informatics at the Faculty of Engineering, Vrije Universiteit Brussel. His work focuses on signal processing, computer science, and data compression, particularly in the context of point cloud compression and multimedia systems. PhD candidate in Electronics and Informatics. Prominent research output includes a 2025 publication on non-uniform voxelisation in Sensors . Peer-review activities include evaluations for the IEEE 26th International Workshop on Multimedia Signal Processing (2024) and IEEE Transactions on Image Processing (2024–2025). No scientific awards or grants are explicitly mentioned in the text.

• Signal Processing
• Computer Science
• Point Cloud Compression
• +3 more

Evangelia Tsiligianni is a postdoctoral researcher in the Department of Electronics and Informatics at Vrije Universiteit Brussel , Belgium. Her work focuses on deep learning, image processing, and computer vision, particularly for multimodal image restoration and super-resolution tasks. Active in deep learning and neural networks . Specializes in image resolution , compressed sensing , and medical imaging . Her recent research explores graph regularization for depth map super-resolution and interpretable deep learning in financial and healthcare domains. She has presented at conferences like ICASSP, MICCAI, and EUSIPCO.

• Deep Learning
• Image Processing
• Computer Vision
• +3 more

Wesley De Neve is an Associate Professor working at both the Internet Technology and Data Science Lab (IDLab) at Ghent University in Belgium and the Center for Biotech Data Science at the Ghent University Global Campus (GUGC) in Korea. His academic career spans computer science and biotech data science, with significant contributions to machine learning and multimedia analysis. Dr. De Neve received his M.Sc. in Computer Science (2002) and Ph.D. in Computer Science Engineering (2007) from Ghent University, Belgium. His research focuses on developing novel compression and machine learning approaches for analyzing multimedia content (text and video) and biotech data (omics data and medical images). His work bridges computer science with biological applications, particularly in genomic data analysis and medical imaging. He has made significant contributions to splice site prediction, translation initiation site prediction, and hashtag recommendation systems. An analysis of his recent publications (2023-2025) shows a strong focus on applying deep learning to medical imaging and genomic data analysis. His work increasingly incorporates vision transformers, diffusion models, and multimodal approaches to solve problems in healthcare AI, particularly in surgical video analysis, cancer screening, and protein structure prediction. There's a clear trend toward developing more interpretable models for medical applications. Dr. De Neve is actively involved with the Internet Technology and Data Science Lab (IDLab), where he leads research at the intersection of computer science and biotechnology. His team works on applying advanced machine learning techniques to solve real-world problems in healthcare and multimedia analysis.

• Biotech
• Deep learning
• Machine learning
• +4 more

Professor Bart De Moor is a full professor at the KU Leuven Faculty of Engineering Sciences, affiliated with the Department of Electrical Engineering (ESAT) and the STADIUS Center for Dynamic Systems, Signal Processing, and Data Analysis. His research spans mathematical engineering, system theory, and biomedical data science, with significant contributions to Machine Learning Medical AI Time Series Analysis High-Dimensional Data Optimization underpinned by an ERC Advanced Grant and the Order of the Crown (2025). His recent work includes AI-driven clinical decision support systems ( BJOG , 2025), spatial omics pipelines for pancreatic cancer ( Cancer Research , 2025), and energy demand forecasting ( Applied Energy , 2025). He has supervised over 93 PhD students and founded 9 spin-offs, including Health House and Athumi .

• Mathematical Engineering
• System Theory and Control
• Numerical Optimization
• +4 more

Sofie Rummens is a Lecturer at the Faculty of Medicine , KU Leuven , affiliated with the Department of Development and Regeneration and the Locomotor and Neurological Disorders unit. Her work focuses on medical imaging, musculoskeletal research, and neurological rehabilitation. Key roles: Lecturer, Co-promoter, Co-supervisor Collaborations: International Society for the Study of the Lumbar Spine, KU Leuven research teams Research interests : Structural and functional analysis of the lumbar multifidus, subsynovial tissue in carpal tunnel syndrome, and comparative studies on foot drop treatments. She utilizes MRI and ultrasound for muscle and nerve assessments. Recent publications : Highlighted by advancements in 3D ultrasound reliability, MRI-based muscle quantification, and clinical trials on surgical vs conservative treatments for nerve-related pathologies. Teaching activities : Involved in courses on Anatomy , Musculoskeletal Rehabilitation , and Clinical Problem-Solving Skills within physical medicine curricula.

• Locomotor Disorders
• Neurological Rehabilitation
• Musculoskeletal Anatomy
• +3 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

Bruno Cornelis is a postdoctoral researcher at Vrije Universiteit Brussel's Electronics and Informatics school, with a focus on biomedical signal processing, digital holography, and deep learning applications. His work spans both fundamental and applied research projects. Education : Master's thesis on Quantization-based Watermarking (2007) and Digital Holographic Image Processing (2007) Research Interests include: Deep Learning for Image Restoration and Biomedical Applications Sparse Signal Representations in Holography and Anomaly Detection Photoplethysmography (PPG) Signal Analysis Art Classification via Mathematical Signal Models Recent Publication Trends show a shift towards real-time traffic data disaggregation, PPG-based health monitoring, and deep learning architectures for image reconstruction. His work often bridges theoretical signal processing with practical biomedical/transportation applications. Honors : BARCO/FWO Master Thesis Award (2007) Collaborations involve EU-funded initiatives like GEAR and Tech4Health, with cross-disciplinary teams in Belgium and China. He has presented at IEEE conferences on transportation systems (ITSC 2022) and medical imaging (DATE 2025).

• Digital Holography
• Image Processing
• Deep Learning
• +5 more

Ingrid Gielen is a researcher at the Faculty of Veterinary Medicine of Ghent University (UGent), specializing in Veterinary Radiology , Comparative Orthopedics , and Diagnostic Imaging . Her work bridges Canine Neurology , Equine Trauma , and Bone Density Analysis , with a focus on advanced imaging techniques like CT and MRI. Key Affiliations Ghent University (UGent) Faculty of Veterinary Medicine Collaborations with Henri van Bree, Luc Van Ham, Jimmy Saunders, and others Research Interests revolve around musculoskeletal disorders in animals , spinal diseases in dogs , and CT/MRI applications . She investigates elbow dysplasia , subchondral bone density , and imaging accuracy in both canine and equine contexts. Publications highlight her expertise in diagnostic imaging trends , including CT for head trauma , MRI for spinal herniation , and bone density studies . Her work often compares radiography vs. CT and explores orthopedic pathologies . Advising and Collaboration : Gielen has promoted 8 PhD students, including Billy Dries, Walter Dingemanse, and Kaatje Kromhout. She collaborates extensively with UGent co-authors (243 with Henri van Bree) on veterinary imaging and orthopedic research .

• Veterinary Radiology
• Comparative Orthopedics
• Diagnostic Imaging
• +2 more