جست‌وجوی استاد و پژوهشگر

Guy De Tréمشاهده پروفایل

دانشیار

• Computational Intelligence
• Data Quality
• Decision Support Systems
• +۷ مورد دیگر

Guy De Tré is an Associate Professor at the Department of Telecommunications and Information Processing within Ghent University's Faculty of Engineering and Architecture. He leads the Database, Document and Content Management (DDCM) research group and focuses on computational intelligence in information systems, with expertise in bi-polarity handling, uncertainty modeling, and multi-valued logic systems. Primary Affiliation: Ghent University Research Focus: Data quality, fuzzy querying, spatio-temporal modeling Key Contributions: Foundational work in possibilistic databases and explainable AI His research combines theoretical and applied approaches to information management systems. Theoretical work includes: Bipolarity and uncertainty handling in databases Multi-valued logic frameworks Interval B-tree indexing for possibilistic data Applied research spans: NoSQL database optimization Decision support systems Contextualized machine learning 3D/4D modeling for geological resources Recent publications show increasing focus on explainable AI, with multiple works on contextualized support vector machine classification and orthographic similarity measures for graph-based data representations. His work bridges database theory with practical applications in data quality assessment, medical informatics, and cultural heritage projects like the Byzantine Book Epigrams database. Research Group: Leads the DDCM group at Ghent University, specializing in: Database management innovation Content modeling techniques Fuzzy logic implementations Temporal data indexing Intelligent information systems

Filip De Turckمشاهده پروفایل

استاد

• Resilient Networks
• Anomaly Detection
• Large-Scale Data Processing
• +۳ مورد دیگر

Filip De Turck is a Professor at Ghent University's Department of Information Technology , leading the network and service management team. With a PhD in Engineering (2002) and a visiting postdoc at Columbia University (2003), his research focuses on resilient softwarized networks , anomaly detection , and adaptive large-scale data processing . He has supervised 38 PhD students and currently mentors 8, with an h-index of 40 and over 8952 citations. Research Interests : Resilient networks, anomaly detection, edge computing, Kubernetes orchestration, SDN, and adaptive data processing. Education : Master in Electrical Engineering (1997), PhD in Engineering (2002) from Ghent University. Leadership : Co-authored 180 Web of Science journal papers and 500+ peer-reviewed works, leading the IDLab network and service management team. Publications : Recent studies span reinforcement learning for container orchestration, haptic-enabled VR systems, and knowledge graphs for network security. Impact : Innovations in cloud-native edge computing, volumetric video delivery, and federated learning for intrusion detection.

• Neural-Symbolic Artificial Intelligence
• Statistical Relational Artificial Intelligence
• Automated Data Science
• +۳ مورد دیگر

Giuseppe Marra is an Assistant Professor at the Faculty of Engineering Science and affiliated with the Declarative Languages and Artificial Intelligence (DTAI) unit at KU Leuven , Belgium. His research focuses on Neural-Symbolic Artificial Intelligence , integrating symbolic reasoning with neural networks to enhance transparency and reasoning capabilities in AI systems. Current affiliations: Department of Computer Science, DTAI (Leuven) Roles: Promotor in multiple projects (2022-2028), Council member of Faculty of Engineering Science His research portfolio includes projects on: Relational Concept-Based Models (2025-2028) Neuro-Symbolic AI scalability (2024-2026) Deep Statistical Relational Learning (2022-2024) Publications demonstrate expertise in Neuro-Symbolic AI , with applications in: Reinforcement learning safety (probabilistic logic shields) Graph neural network optimization (subgraph mining) Concept-based memory reasoning (interpretable architectures) Markov logic network extensions (quantified logic, temporal models) He contributes to teaching courses on: Artificial Intelligence (H0O18A) Reinforcement Learning (H0O23A) Machine Learning (H0T25A)

Thibaut Septonمشاهده پروفایل

پژوهشگر

• Human-Computer Interaction
• Multimodal Interfaces
• Wearable Technology
• +۳ مورد دیگر

Thibaut Septon is a researcher affiliated with the Namur Digital Institute at the University of Namur . His work focuses on Human-Computer Interaction , Augmented Reality , and Wearable Technology , with recent publications on vehicular XR environments and multimodal interface development. Active in multimodal interaction and semantic search applications Key contributions to gesture recognition and data visualization in interactive systems His research spans Wearable Devices , Augmented Reality , and Interactive Computing from 2021 to 2025, emphasizing practical solutions for user interface challenges in emerging technologies. Activities include participation in conferences like ACM IUI , ESANN , and EICS from 2022 to 2025.

• Visual Perception
• Cognitive Psychology
• Computational Aesthetics
• +۳ مورد دیگر

Johan Wagemans is a full professor at the Faculty of Psychology and Educational Sciences, KU Leuven, leading the Laboratory for Experimental Psychology. He actively contributes to DigiSoc, LBI, and Leuven.AI institutes. Research Focus: Interdisciplinary work at the intersection of visual perception, computational aesthetics, and AI-mediated art. Key themes include perceptual organization, mid-level vision, and the application of machine learning to aesthetic assessment. Current projects cover symmetry detection, cross-cultural aesthetics, AI-generated art, and neural modeling of perceptual processes Recent publications emphasize computational tools, dataset development, and Gestalt principles in modern perception research Methodological Innovations: Pioneering techniques in deep learning, psychophysics, and naturalistic shape analysis to bridge cognitive science and technological applications.

Dries Benoitمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Marketing Modeling
• Customer Relationship Management
• +۵ 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.

Dirk Deschrijverمشاهده پروفایل

دانشیار

• Data analytics
• Machine learning
• Time series
• +۳ مورد دیگر

Dirk Deschrijver is an Associate Professor in the Department of Information Technology at Ghent University, affiliated with the Internet Technology and Data Science Lab (IDLab). He has held positions as a senior researcher at iMinds/imec (2014-2016) and an FWO post-doctoral research fellow at Ghent University (2008-2014). His academic background includes two PhD degrees: in Computer Science from the University of Antwerp (2007) and in Engineering from Ghent University (2012). His educational background includes: PhD in Computer Science, University of Antwerp, Belgium (2007) PhD in Engineering, Ghent University, Belgium (2012) Deschrijver's research focuses on data analytics, machine learning, time series analysis, and surrogate modeling . He develops advanced algorithms for applications in microwave engineering, biomedical signal processing, and industrial monitoring. His work bridges theoretical machine learning with practical engineering problems, particularly in the areas of Bayesian optimization, Gaussian processes, and interpretable AI models. Analysis of his recent publications (2023-2025) reveals a strong trend towards interdisciplinary applications. He applies machine learning to diverse fields including microwave filter design, respiratory and cardiac monitoring, and industrial predictive maintenance. A notable focus is on physics-informed modeling and uncertainty quantification, especially using kernel-based methods and Bayesian approaches. His work consistently emphasizes model interpretability and reliability, which is critical for healthcare and safety-critical systems. His scientific awards include: Marie Curie Fellowship (2005) at Eindhoven University of Technology FWO post-doctoral research fellowship (2008-2014) at Ghent University As a senior researcher and associate professor, Deschrijver has been involved in multiple research projects funded by competitive grants, including the Marie Curie and FWO fellowships. His current work at IDLab is supported by imec and likely involves collaborative grants with industry and EU programs. He has co-authored numerous high-impact publications and has contributed to open-source tools such as the Complex Vector Fitting toolbox. He is a core member of the Internet Technology and Data Science Lab (IDLab), a leading research group at Ghent University and imec focusing on data science, machine learning, and internet technology. The lab collaborates extensively with industry and academia on projects spanning telecommunications, health, and smart systems.

Jan Fostierمشاهده پروفایل

پژوهشگر

• Bioinformatics
• High Performance Computing (HPC)
• Big Data Analytics
• +۳ مورد دیگر

Jan Fostier is a researcher at Ghent University, affiliated with the Internet Technology and Data Science Lab (IDLab). His work focuses on High Performance Computing (HPC) and Big Data Analytics in life sciences and bioinformatics, leveraging supercomputers and GPUs to accelerate genomic data processing. He collaborates across disciplines with bioscience engineers, biologists, medical doctors, and industry partners like Janssen Pharmaceutica and Agilent. His research interests include: Bioinformatics Sequencing Data Analysis High-Performance Computing Stochastic Modelling Genomic Data Compression Recent publications highlight advancements in approximate pattern matching, de Bruijn graph construction, and nanopore signal analysis. Awards include an Outstanding Presentation Award (2015) and an Outstanding Oral Poster Presentation Award (2015). He has developed tools like Halvade , Jabba , and BLSSpeller for genomic applications.

• Artificial Intelligence
• Natural Language Processing
• Machine Learning
• +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.

• Micro-Electrical Discharge Machining (micro-EDM)
• Machine Learning Applications in Manufacturing
• Edge Computing
• +۵ مورد دیگر

Zequan Yao is a doctoral researcher at KU Leuven within the Manufacturing Processes and Systems (MaPS) unit. His work focuses on advancing micro-EDM and nano-manufacturing processes through digital monitoring and machine learning integration. Research Emphasis: Surface quality prediction, edge computing, and deep learning for precision machining Key Technologies: RF radiation sensing, multiphysical process analysis, and adaptive pulse classification Collaborations: Works with researchers like Ming Wu, Jie Qian, and Dominiek Reynaerts Recent publications highlight trends in machine learning applications for manufacturing diagnostics, CFRP composite machining challenges, and RF-based process signatures . His work bridges traditional machining with intelligent systems for quality control. Labs/Teams: Micro- & Precision Engineering group at KU Leuven Future Directions: Expanding edge computing for in-process optimization and interpretable AI frameworks

Luc De Raedtمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۳ مورد دیگر

Luc De Raedt is a Full Professor of Computer Science at KU Leuven, where he serves as the Director of Leuven.AI - KU Leuven Institute for Artificial Intelligence and Head of the Subdivision Declarative Languages and Artificial Intelligence within the Faculty of Engineering Science. He is also a Wallenberg Guest Professor at Örebro University's Center for Applied Autonomous Sensor Systems. Professor De Raedt's research focuses on the intersection of Artificial Intelligence, Machine Learning, and Logic Programming. His work spans probabilistic logic programming, neurosymbolic AI, statistical relational learning, and applications in various domains. He leads the Declarative Languages and Artificial Intelligence research unit (DTAI) at KU Leuven, which has been at the forefront of integrating symbolic and subsymbolic AI approaches. His research has evolved from traditional Inductive Logic Programming to Statistical Relational Learning and more recently to Neurosymbolic AI, which combines neural networks with symbolic reasoning for more transparent and trustworthy AI systems. His recent publications demonstrate a strong focus on neurosymbolic AI, with work spanning theoretical foundations as well as practical applications in areas like constraint learning, relational reinforcement learning, and probabilistic programming. His research integrates learning and reasoning in AI, with particular emphasis on creating systems that can both perceive patterns and perform logical reasoning. ERC Advanced Grant 2015 ERC Advanced Grant 2023 EurAI Fellow AAAI Fellow ELLIS Fellow Former IJCAI Trustee Elected Member of the Royal Flemish Academy of Belgium Wallenberg Guest Professor Professor De Raedt leads numerous research projects including "Bayesian Neurosymbolic AI for Trustworthy Machine Learning," "Neurosymbolic AI," "Theoretically Sound Neurosymbolic AI," and "Deep Probabilistic Logics." He is also involved in the Flanders Artificial Intelligence Research program (FAIR) as a work package leader. His research group includes PhD students and postdocs working on various aspects of neurosymbolic AI and probabilistic programming, with significant funding from European Research Council grants. He leads the Declarative Languages and Artificial Intelligence research unit (DTAI) at KU Leuven, which consists of multiple research teams focusing on different aspects of AI, machine learning, and logic programming. The unit has a strong international presence and collaborates with researchers worldwide, maintaining leadership in probabilistic logic programming through systems like ProbLog.

• Big Data Analytics
• Data Quality Management
• Business Process Management
• +۳ مورد دیگر

Wilfried Lemahieu is a full professor and Dean of the Faculty of Economics and Business (FEB) at KU Leuven. He leads FEB's academic and administrative operations, overseeing research centers, educational programs, and campus coordination across Leuven, Kortrijk, Brussels, and Antwerp. His academic roles include heading the Department of Decision Sciences and Information Management, where he focuses on big data analytics, data quality, and distributed systems. Research Interests: Big Data Storage & Integration Data Quality Management Business Process Analytics Machine Learning Applications in Labor Markets Software Architectures for Distributed Systems Recent Work Trends: His publications emphasize explainable AI for labor market predictions, churn prediction in telecom, and frameworks for improving data quality in manual processes. Collaborations with industry and academic partners highlight practical application of theoretical models. Leadership Roles: Dean of FEB since 2017 Vice Dean for Education (2013-2017) Member of University Council and Academic Council Chair of FEB Executive Committees Teaching: Leads courses on database management, software architectures, and research methodologies in business information systems.

Bernard De Baetsمشاهده پروفایل

استاد

• Fuzzy Set Theory
• Machine Learning
• Cellular Automata
• +۳ مورد دیگر

Bernard De Baets is a Senior Full Professor and Head of the Department of Data Analysis and Mathematical Modelling at the Faculty of Bioscience Engineering, Ghent University. He leads the Knowledge-based Systems research unit KERMIT. With interdisciplinary expertise in mathematics, computer science, and knowledge engineering, his research bridges fundamental and applied domains in biological sciences. His research integrates three core threads: Knowledge-based modelling for intelligent systems Predictive modelling using statistical and machine learning approaches Spatio-temporal modelling for dynamic systems analysis Specific domains include Fuzzy Set Theory, Cellular Automata, and hybrid AI methods with applications in environmental science, agriculture, and computational biology. He has authored over 550 peer-reviewed journal papers (accumulating ~25,000 citations) and earned multiple best paper awards. As Editor-in-Chief of Fuzzy Sets and Systems and editorial board member for several journals, he significantly contributes to the scientific community. Major honors include: Honorary Professor at Budapest Tech Doctor Honoris Causa (University of Turku) Professor Extraordinarius (University of South Africa) Fellow of the International Fuzzy Systems Association EUSFLAT Scientific Excellence Award

• Life Cycle Assessment
• Biofuels
• Electromobility
• +۵ مورد دیگر

Anne Van Den Oever is a Scientific employee at the MOBI - Electromobility Research Centre within the Faculty of Engineering at Vrije Universiteit Brussel (VUB), Belgium, operating from the Department of Electrical Engineering and Power Electronics. Her research focuses on life cycle assessment methodologies applied to sustainable energy systems, with strong institutional ties to VUB's electromobility initiatives. Her core research interests include Life Cycle Assessment, Biofuels, Electromobility, and Environmental Impact Assessment, emphasizing quantitative analysis of renewable energy technologies. Recent work explores biofuel compatibility in bi-fuel vehicles, second-life battery economics, and environmental trade-offs in emerging solar cell technologies, reflecting an interdisciplinary approach bridging engineering and sustainability science. Publication trends reveal consistent expertise in environmental system analysis, with increasing focus on circular economy applications (e.g., battery reuse) and integration of machine learning for sustainability decision-making. Her 2024-2025 outputs demonstrate methodological innovation in life cycle impact assessment, particularly for ozone depletion and renewable fuel pathways. Scientific recognition includes: Best oral presentation award - Session 3B (2022) Best paper award - Environment (2022) Best Young Biomass Researchers Award (World Sustainable Energy Days 2021) Conference participation grant (2024) She contributes to major collaborative projects including the EU-funded REDIFUEL initiative (2018-2022) on renewable transportation fuels and currently chairs the Prospective LCA network, advancing life cycle assessment standards. Her grant portfolio supports conference participation and dataset development for environmental modeling. As a core member of MOBI's research ecosystem, she collaborates across VUB's sustainable mobility initiatives, with recent activities including school-level science communication (e.g., 'Red de planeet met wat je eet' talks) and media engagement on fuel efficiency technologies.

Jan Danckaertمشاهده پروفایل

استاد

• Photonics
• Lasers
• Optical Physics
• +۵ مورد دیگر

Jan Danckaert is a Professor of Physics and Vice Rector for Education and Student Affairs at Vrije Universiteit Brussel (VUB). He leads the Data Analytics Lab within the Department of Applied Physics and Photonics. His research spans photonics, semiconductor lasers, nonlinear dynamics, and ethical artificial intelligence. He has received the VUB-prijs Roger Van Geen (1998) for his work on semiconductor mode polarization behavior. Active in interdisciplinary projects, he co-leads initiatives like the EU-funded COMPASs 2.0 and FOR EU4All, focusing on human-centered AI and university governance reform. Education highlights include doctoral supervision in physics and engineering, with notable advisees including A. Vandervelde (prokaryotic toxin-antitoxin modules) and L. Appeltant (reservoir computing). His work bridges foundational physics with applied challenges like energy harvesting via metasurfaces and addressing algorithmic bias in AI systems. He maintains roles in academic governance, public lectures, and international conferences including the IEEE Photonics Society events.