جستجوی استاد راهنمای دانشگاهی

Tim Van de Cruys is a Senior Lecturer at the Faculty of Arts, KU Leuven, serving as Head of the Centre for Computational Linguistics (CCL). He maintains significant affiliations with LECTIO (KU Leuven Institute for the Study of the Transmission of Texts, Ideas and Images), Leuven.AI (KU Leuven Institute for Artificial Intelligence), and LILI (KU Leuven Interdisciplinary Language Institute). His work bridges computational linguistics, artificial intelligence, and humanities research with practical applications across multiple disciplines. Dr. Van de Cruys specializes in computational semantics and creative language generation, with particular expertise in applying NLP techniques to historical and classical texts. His research spans multiple domains including: Natural Language Processing for ancient languages (Latin, Ancient Greek) Computational approaches to lexical and compositional semantics Large language models and their applications in humanities research Creative language generation and human-AI collaboration Named entity recognition and disambiguation in historical contexts Non-autoregressive modeling for sequential generation tasks His recent publications demonstrate a strong focus on applying cutting-edge NLP techniques to humanities challenges, particularly in processing ancient languages. He frequently employs transformer models to address named entity recognition, word sense discrimination, and semantic analysis in low-resource language contexts. His work consistently bridges formal linguistic theory with practical computational applications, creating valuable tools for digital humanities scholars. As promotor and co-promotor on numerous research projects extending through 2029, Dr. Van de Cruys supervises PhD students working at the AI-humanities intersection. His current major projects include "Living Corpora" (exploring human-AI collaboration in digital humanities), "Stochastic processes and non-autoregressive models for sequential generation," and "NIKAW" (exploring knowledge networks from classical antiquity). These projects demonstrate his commitment to advancing both theoretical understanding and practical applications of computational linguistics. He teaches various courses including Computational Linguistics, Scripting Languages, Programming for Humanities, Computational Creativity, and AI for Humanities, training students to work at this critical interdisciplinary crossroads. His leadership of the Centre for Computational Linguistics positions him at the forefront of computational linguistics research in Belgium, where he continues to expand the boundaries of what's possible at the intersection of language, computation, and humanistic inquiry.

• Computational Linguistics
• Natural Language Processing
• Computational Semantics
• +۵ مورد دیگر

Prof. Jan De Beenhouwer is a faculty member at the University of Antwerp, affiliated with the Department of Physics and the imec Vision Lab. His research focuses on advanced computational imaging techniques, particularly in X-ray tomography, phase contrast imaging, and reconstruction algorithms for medical and industrial applications. His primary research interests include: Development of novel X-ray imaging methodologies like edge illumination phase contrast Advanced CT reconstruction algorithms for sparse-view and dynamic systems Integration of deep learning with tomographic reconstruction Industrial applications including defect detection and material characterization Biomedical imaging such as bone structure analysis and tissue modeling Analysis of recent publications (2024-2025) reveals strong emphasis on: Innovations in phase contrast imaging hardware and simulation tools Advanced reconstruction techniques for motion compensation and sparse data AI-powered approaches for industrial inspection and biomedical research Development of open-source tools (CAD-ASTRA) for the tomography community He leads research at imec Vision Lab, focusing on both fundamental imaging physics and practical applications. The lab collaborates extensively with industrial partners on non-destructive testing solutions.

• X-ray Imaging
• Computed Tomography
• Image Processing
• +۳ مورد دیگر

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
• +۵ مورد دیگر

Prof. Dieter De Witte serves as a Professor at Ghent University, dedicating 50% of his time to the Internet Technology and Data Science Lab (IDLab) while simultaneously contributing 50% to the Royal Museums of Fine Arts Belgium (RMFAB) in Brussels through a FED-tWIN mandate from Belspo. At RMFAB, he spearheads the strategic overhaul of digital infrastructure toward FAIR-compliant and data-driven systems, while at Ghent University he collaborates with Prof. Steven Verstockt on applied AI projects across heritage, mental healthcare, and education domains. His academic foundation includes a Master's in Engineering Physics from Ghent University (2008) followed by doctoral research on Big Data technologies and FAIR data for life sciences. Prior to returning to academia in 2021, he gained industry experience as an AI consultant and team lead at Telenet and Ordina. De Witte's research centers on AI-driven transformation of cultural heritage through FAIR data publication , collection enrichment , and intuitive querying interfaces . His technical expertise spans multimodal algorithms, image segmentation, pose estimation, large language models (LLMs), and semantic technologies including SPARQL and IIIF. Current projects focus on human-in-the-loop AI systems that combine diverse AI building blocks for practical heritage applications. Analysis of his 15 most recent publications reveals a clear trajectory from early bioinformatics work (2007-2018) on genomic motif discovery and life sciences data infrastructure toward contemporary cultural heritage applications (2023-2024). Recent outputs demonstrate innovative fusion of pose estimation, linked data frameworks, and multimodal AI for museum contexts, highlighting increasing specialization in AI enrichment of digital collections while maintaining core expertise in FAIR data principles. His FED-tWIN grant enables critical knowledge transfer between academic research and cultural heritage institutions, supporting development of next-generation digital infrastructure at RMFAB. Current projects involve creating AI tools for intuitive collection exploration and systematic enrichment of heritage assets through advanced computational methods. De Witte operates within Ghent University's Internet Technology and Data Science Lab (IDLab), participating in interdisciplinary teams developing applied AI solutions. His work bridges technical innovation with practical implementation in cultural institutions, focusing on sustainable, interoperable systems that enhance public access to digital heritage collections through cutting-edge AI interaction paradigms.

• Artificial Intelligence
• Data Science
• FAIR Data
• +۳ مورد دیگر

Dr. Nicholas Francken serves as a Postdoctoral Researcher at the Imec-Vision Lab, University of Antwerp, Belgium, specializing in advanced x-ray imaging techniques with a focus on inline phase contrast systems using edge illumination setups. His interdisciplinary background bridges astrophysics, data science, and computational imaging. His educational foundation includes: Master of Science in Astrohysics & Astronomy (2018) from the Catholic University of Leuven Completion of the Lcie Entrepreneurship Academy (2018) Francken's research centers on developing high-performance computational frameworks for x-ray imaging, leveraging GPU-accelerated ray tracing to simulate complex optical phenomena. His work addresses critical challenges in phase contrast imaging, with applications spanning materials science and non-destructive testing. The integration of physics-based modeling with cutting-edge computing infrastructure defines his methodological approach. His 2022 publication demonstrates expertise in translating optical theory into practical simulation tools using NVIDIA's OptiX engine, revealing trends toward real-time computational imaging solutions. This work contributes to evolving subfields including GPU-accelerated physics simulations and advanced tomography techniques. Within the Imec-Vision Lab ecosystem, Francken actively participates in research collaborations while supporting academic instruction through exercise sessions for physics and field theory courses, demonstrating commitment to both research innovation and educational development.

• X-ray Imaging
• Phase Contrast Imaging
• Computational Imaging
• +۳ مورد دیگر

Maarten Vergauwen is a Professor at KU Leuven's Faculty of Engineering Technology, where he serves as head of Geomatics and campus chair for Science, Engineering and Technology Group across Ghent and Aalst Campuses. His institutional roles include head of the Division Digital and Sustainable Civil Engineering, Subdivision Geomatics Ghent, and contact person for Geomatics. His research focuses on the intersection of geomatics, computer vision, and artificial intelligence with applications in heritage documentation, 3D modeling, and infrastructure analysis. Key research thrusts include BIM automation, digital twin development for sewer systems, UAV-assisted bridge inspections, and AI-driven cultural heritage preservation. His work leverages advanced techniques in point cloud processing, transformer networks, and semantic segmentation to solve complex spatial problems. Vergauwen actively supervises graduate students on topics ranging from SCAN-to-BIM automation to digital epigraphic documentation of archaeological sites. His research is supported by numerous grants including CHAI: Cracking built Heritage with AI, Deep Sewer, Foundation Models for EO Big Data, and UAV-assisted bridge inspections projects. Member of East and West Flanders Workplace Safety Committee Member of Bureau ADS and Board of Faculty of Engineering Technology Observer of KU Leuven Student Services Council, Brugge He teaches courses including Geomatics 1 & 2, Fotogrammetrie, GIS, Topografie, and 3D-modelleren across KU Leuven's engineering programs.

• Geomatics
• Computer Vision
• Artificial Intelligence
• +۷ مورد دیگر

Francis Wyffels is an early career professor at Ghent University, affiliated with the Internet Technology and Data Science Lab (IDLab). His expertise spans machine learning, robotics, and computer science education, with significant contributions to European research projects like FP7 AMARSi on robot control. Wyffels' research focuses on robot control, embodied cognition, and (deep) reinforcement learning, applied to agricultural robotics, cloth manipulation, and human-robot interaction. He actively promotes realistic public perspectives on robotics through educational initiatives, countering societal fears about automation. His work bridges theoretical neural networks with practical applications in precision agriculture and educational tools. Recent publications (2024-2025) reveal three dominant trends: agricultural robotics (e.g., intrarow weeding, crop monitoring), deformable object manipulation (e.g., cloth handling), and computational thinking education. These reflect his commitment to solving real-world problems while advancing robotic capabilities in unstructured environments. Scientific awards received: UGent Hermes award for public/scientific engagement and knowledge transfer Google RISE award Wyffels leads research within IDLab, coordinating robot-related work packages in international projects. His approach combines technical innovation with public dissemination, emphasizing robotics' societal benefits in productivity and convenience. Current projects include agricultural automation frameworks and emotion-conveying social robots. He directs the Internet Technology and Data Science Lab (IDLab), where his team develops systems for precision farming, robotic manipulation, and educational tools. Recent work includes DIY social robot platforms and compliant control systems for soft-body insertion, demonstrating IDLab's focus on accessible, real-world robotics solutions.

• Robot control
• Embodied cognition
• Reinforcement learning
• +۵ مورد دیگر

Joeri Lenaerts is a Researcher at Vrije Universiteit Brussel (VUB) in Belgium, affiliated with the Integrated Intelligence Lab. His work focuses on the intersection of Physics and Artificial Intelligence, particularly using neural networks to advance Nanophotonics and applying physical principles to enhance AI interpretability. Research interests include: Inverse design of nanophotonic components via deep learning Disentangled representations in β-VAEs Oscillatory loss landscapes in photonic design Physics-informed AI transparency His recent publications demonstrate trends in computational physics, AI-driven optical design, and interpretable machine learning. Scientific recognition includes the FWO Aspirant Mandate. Joeri teaches Advanced Mathematics for Business Economics at VUB, covering infinite sequences, differential equations, and Wolfram Language programming. He earned a Master in Physics from VUB (2020), conducted an Erasmus exchange at EPFL, and contributed to open-source projects like beta-VAE on GitHub.

• Machine Learning
• Nanophotonics
• Artificial Intelligence
• +۶ مورد دیگر

Matthieu Boone is an Associate Professor at Ghent University's Faculty of Sciences, Department of Physics and Astronomy, where he leads research in advanced X-ray imaging techniques. He serves as head of the Centre for X-ray Tomography (UGCT), a core facility specializing in micro-CT and related technologies. His work bridges physics, engineering, and multiple application domains including materials science, cultural heritage, and biomedical research. Dr. Boone's research focuses on developing novel X-ray imaging methodologies, particularly in micro-computed tomography, phase-contrast imaging, and hyperspectral X-ray techniques. His expertise spans image processing , detector development , and reconstruction algorithms for both laboratory and synchrotron-based systems. Current projects include time-resolved 3D X-ray imaging, lab-based hard X-ray ptychography, and hyperspectral speckle-based X-ray imaging. Analysis of his recent publications reveals a strong trend toward multimodal imaging approaches that combine spectral, phase-contrast, and conventional absorption-based techniques. His work increasingly focuses on 4D imaging (3D plus time) for dynamic processes, quantitative analysis methods, and applications in diverse fields from cultural heritage preservation to advanced materials characterization. The research demonstrates a clear trajectory toward making synchrotron-quality techniques accessible in laboratory settings. As a supervisor and collaborator, Dr. Boone has mentored numerous doctoral researchers across multiple projects funded by the Research Foundation - Flanders (FWO), European programs, and industrial partnerships. His grant portfolio includes major projects like 'Solving barriers to time-resolved 3D x-ray imaging' (FWO, 2025-2027) and 'Towards lab-based hard X-ray ptychography' (FWO, 2024-2027). He leads the Centre for X-ray Tomography (UGCT), a core facility that serves as a hub for advanced X-ray imaging research at Ghent University. The facility supports numerous interdisciplinary projects and maintains strong collaborations with European synchrotron facilities. Under his guidance, the center has developed specialized capabilities in hyperspectral X-ray imaging, dual-phase grating interferometry, and time-resolved micro-CT.

• X-ray tomography
• Micro-CT imaging
• Image processing
• +۵ مورد دیگر

Ben Caljon is an active Researcher at Vrije Universiteit Brussel in the Department of Genetics, Reproduction and Development within the School of Clinical Sciences. With an h-index of 243 and 35 research outputs spanning from 2012 to 2024, his work demonstrates significant scholarly impact in medical genetics and related fields. Dr. Caljon's research interests center around mitochondrial DNA, mitochondrial genome analysis, genetic mutations, Klinefelter Syndrome, and exome sequencing. His work spans multiple disciplines within medical genetics, with particular focus on reproductive genetics, oncogenetics, and diagnostic applications of genomic technologies. His fingerprint analysis shows strong concentration in Mitochondrial DNA (100%), Mitochondrial Genome (87%), and Mutation research (50%). Recent publication trends indicate active involvement in neurodevelopmental disorders, cancer genetics, reproductive medicine, and immunotherapy research. His 2024 publications include work on NSRP1-associated neurodevelopmental disorders and intracranial immunotherapy for glioma, demonstrating breadth across clinical genetics and oncology. Dr. Caljon actively participates in academic conferences and workshops, including the Belgian Society of Human Genetics meetings and specialized workshops on comparative genomics and epigenetics. His collaborative network extends across multiple research institutions, with significant contributions to datasets related to cancer predisposition and reproductive genetics.

• Mitochondrial DNA
• Mitochondrial Genome
• Mutation
• +۷ مورد دیگر

Luc Martens is a Full Professor at Ghent University, Belgium, and principal investigator at imec. Since 1991, he has led the WAVES (Wireless, Acoustics, Environment & Expert Systems) research group within the Department of Information Technology. His dual affiliation drives innovation at the intersection of academic research and industrial wireless technology development. Martens' research centers on wireless communication systems with emphasis on smart wireless channels, wireless body area networks, and human exposure to electromagnetic fields. He pioneers Industry 4.0 applications and personalized recommender systems, extending into energy consumption optimization and wireless localization. His work bridges electrical engineering, computer science, and biomedical applications through practical implementations in health monitoring and industrial IoT. Analysis of his 2021-2025 publications reveals three dominant trajectories: (1) advanced recommender systems for physical activity interventions using micro-randomized trials; (2) rigorous EMF exposure assessment for 5G/6G networks through hybrid simulation-measurement frameworks; and (3) animal monitoring applications leveraging accelerometers for equine gait analysis and colic detection. This interdisciplinary approach consistently integrates machine learning with domain-specific challenges. As head of the WAVES research group, Martens maintains strong international collaborations with UC Berkeley, ETH Zurich, and Télécom ParisTech. The group's work on wireless channel modeling, EMF safety, and personalized systems has produced over 400 peer-reviewed publications with tangible impact on wireless standardization and healthcare technology.

• Recommender Systems
• Wireless Communication
• Industry 4.0
• +۴ مورد دیگر

Dries Benoit is an Associate Professor in Data Analytics at Ghent University, specializing in Bayesian Statistics and its applications in business engineering. He teaches advanced courses in Bayesian Statistics, Statistical Modeling & Datamining, and Pricing & Revenue Management. Additionally, he serves as a visiting professor at Université de Namur and IESEG School of Management, where he contributes cross-institutional expertise in statistical modeling. Primary Affiliation : Ghent University Secondary Affiliations : Université de Namur, IESEG School of Management Research Focus : Dries Benoit's work bridges methodological advancements in Bayesian statistics with real-world applications across business domains. Core areas include marketing modeling, customer relationship management (CRM), learning analytics, and operations research. His interdisciplinary collaborations extend to medicine, energy, and education sectors, leveraging data science to solve complex problems in churn prediction, demand forecasting, and multimodal analysis. Publications & Research Trends : His recent publications highlight: Machine learning techniques for question difficulty estimation Uncertainty quantification in industrial AI Transformer-based models for ordinal regression Churn prediction in business-to-business contexts Deep learning applications in cultural analytics Ensemble methods for robust classification These works emphasize interpretable modeling, cross-domain adaptability, and balancing technical precision with business strategy applications.

• Bayesian Statistics
• Marketing Modeling
• Customer Relationship Management
• +۵ مورد دیگر

Thomas Demeester is an Associate Professor at Ghent University's Faculty of Engineering and Architecture, Department of Information Technology (EA05), and a Postdoctoral Researcher at IMEC. He leads the Internet Technology and Data Science Lab research unit and has supervised over 25 doctoral researchers across diverse AI topics. His work bridges theoretical AI research with practical applications in healthcare, business analytics, and human-computer interaction. His research spans artificial intelligence with particular focus on natural language processing, machine learning, neural networks, and computer vision. Demeester's work shows strong interdisciplinary connections between theoretical AI development and practical applications in healthcare diagnostics, business analytics, educational technology, and human-robot interaction. His recent publications demonstrate expertise in language model alignment, multimodal learning, synthetic data generation, and efficient model architectures. Analysis of his recent publications reveals a strong focus on practical AI applications with emphasis on interpretability, efficiency, and multimodal integration. His work spans healthcare AI (medical data analysis, diagnostics), business applications (bankruptcy prediction, labor market analysis), educational technology (question generation, assessment), and human-AI interaction (emotion analysis, social robotics). The publications show consistent innovation in neural network architectures, particularly with Hopfield networks and diffusion models. As an academic supervisor, Demeester has mentored numerous PhD students across multiple research domains, with notable projects in language model adaptation, entity extraction, multimodal interaction, and medical AI applications. His research is supported by multiple grants from the Research Foundation - Flanders (FWO) and other funding bodies, particularly in the areas of neural networks, natural language processing, and healthcare AI. His laboratory work focuses on the Internet Technology and Data Science Lab, where his team develops cutting-edge AI solutions with emphasis on practical deployment. Current research directions include multimodal large language models, neuro-symbolic AI approaches, efficient model training techniques, and applications of AI in healthcare and business domains.

• Artificial Intelligence
• Natural Language Processing
• Machine Learning
• +۵ مورد دیگر

Matthew Blaschko is a Senior Lecturer BOF at KU Leuven, affiliated with the Department of Electrical Engineering (ESAT) within the Faculty of Engineering Sciences. He directs the KU Leuven ELLIS unit and serves as a fellow in the ELLIS Health program, part of the European Laboratory for Learning and Intelligent Systems. As a Core PI in the Flanders AI Research Program, he leads work packages for Decision Support Systems and Medical Imaging. He is also a member of the KU Leuven Institute for Artificial Intelligence and co-leads the working group on Machine Learning and Data Science. Habilitation (HDR) from École Normale Supérieure de Cachan Newton International Fellow at University of Oxford Dr. rer. nat. from Max Planck Institutes Tübingen (awarded by Technische Universität Berlin) M.S. from University of Massachusetts Amherst B.S. from Columbia University Blaschko's research focuses on machine learning, computer vision, and medical image analysis, with particular expertise in uncertainty quantification in deep neural networks and trustworthy AI for healthcare applications. His work bridges theoretical foundations with practical implementations, developing methods for calibration, uncertainty estimation, and efficient model deployment. He has made significant contributions to neural network architectures, loss functions, and evaluation metrics for medical imaging tasks, with applications spanning Alzheimer's disease research, surgical phase recognition, and ophthalmic image analysis. His recent publications reveal a strong emphasis on calibration methods, uncertainty quantification, and medical applications of AI. The research spans diverse areas including Alzheimer's disease analysis, Bayesian optimization, novel view synthesis, knowledge extraction from text, and surgical phase recognition. Many papers focus on improving model reliability and safety for healthcare applications, reflecting his commitment to developing trustworthy AI systems that can be deployed in clinical settings. Best Student Paper Award, ECCV 2008 Université Paris-Saclay STIC Doctoral School Best Scientific Contribution Award Best paper award, CVPR 2008 Best paper award, Benelearn 2014 Blaschko actively mentors numerous PhD and MSc students, with current advisees working on topics ranging from uncertainty in deep neural networks to medical image analysis and AI for healthcare. He leads multiple significant research projects including 'onzekerheid in diepe neurale netwerken' (2025-2029), 'Trustworthy AI for Medical Image Analysis and Computer Vision' (2025), and 'Van metingen naar biomarkers in medische beeldanalyse' (2024-2028). His research is supported by the Flanders AI Research Program and other substantial funding sources. As director of the KU Leuven ELLIS unit and a key member of the KU Leuven Institute for Artificial Intelligence, Blaschko leads a vibrant research team focused on machine learning and data science. His laboratory develops cutting-edge AI technologies with practical applications, particularly in healthcare. Technology from his research has been incorporated into MONA, software for ophthalmic image analysis, demonstrating the real-world impact of his work.

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +۶ مورد دیگر

Fabio Jose Ayres serves as an Adjunct Professor at Insper, contributing to the development of engineering programs in Computer Science and Computer Engineering. He holds a PhD in Electrical and Computer Engineering from the University of Calgary and bachelor's/master's degrees in Electrical Engineering from the Polytechnic School of the University of São Paulo (Poli-USP). His professional background includes a Postdoctoral Fellowship in Biomedical Engineering at the University of Calgary, research associate roles at the National Research Council of Canada, and software engineering positions at Opal-RT Technologies and Google. His educational qualifications: Bachelor's and Master's in Electrical Engineering, Polytechnic School of the University of São Paulo (Poli-USP) PhD in Electrical Engineering, University of Calgary, Canada Dr. Ayres' research integrates computer vision, machine learning, and high-performance computing to address challenges in image processing and information retrieval systems. His interdisciplinary work bridges electrical engineering fundamentals with modern computational techniques, particularly relevant to biomedical applications and large-scale software solutions developed during his industry tenure.

• computer vision
• machine learning
• image processing
• +۲ مورد دیگر