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Miryam de Lhoneux is an Assistant Professor at the Department of Computer Science , Faculty of Engineering Science , KU Leuven . She is affiliated with interdisciplinary institutes including Leuven.AI and LILI (KU Leuven Interdisciplinary Language Institute). Active in Multilingual Natural Language Processing and Fair AI research Member of the Faculty of Engineering Science Council and Computer Science Department Council Her research focuses on neuro-symbolic methods , language adaptation , and typological diversity in NLP. Recent projects include: Neuro-Symbolic Methods for Fair Multilingual NLP (2023-2027), Graph-Based Instance Selection for Multi-Modal Data (2024-2028), and Unit of Representation in Multilingual Modeling (2025-2028). Her publications span topics including code-mixed data analysis , English bias in LLMs , and sociolinguistically informed interpretability . She teaches courses in Advanced Natural Language Processing and Language Engineering Applications .

• Natural Language Processing
• Multilingual Language Modeling
• Neuro-Symbolic AI
• +۴ مورد دیگر

Pieter Simoens is an Assistant Professor at Ghent University and affiliated with the imec research institute. He works at the intersection of distributed artificial intelligence, edge computing, and collective intelligence, with a focus on AI applications for resource-constrained environments and robotic systems. His research explores innovative approaches to machine learning deployment in heterogeneous infrastructures, task planning for IoT-integrated robotics, and modeling collective decision-making processes. He has contributed to frameworks like DIANNE for distributed deep learning and developed methods for cognitive modeling in reinforcement learning scenarios. With over 100 publications, his recent work spans adaptive neural networks, privacy-preserving surveillance, UAV hyperspectral data analysis, and computational fairness in AI systems. He leads research initiatives within the Internet Technology and Data Science Lab (IDLab) and contributes to educational programs in software engineering and applied machine learning. Responsible for courses on software engineering, mobile development, system design, and applied machine learning Active in edge computing and neuromorphic algorithms research Develops AI solutions for robotics, surveillance, and industrial IoT applications

• Distributed Artificial Intelligence
• Edge Computing
• Algorithms for Neuromorphic Computing
• +۲ مورد دیگر

Herman Bruyninckx is a Professor at the Faculty of Engineering Sciences , KU Leuven , where he also serves as Vice-Chair of the Department of Mechanical Engineering and head of the Robotics, Automation and Mechatronics (RAM) subdivision. His research focuses on integrating formally represented domain knowledge into robotic systems for real-time, self-explanatory, and certifiable control. He advocates for open standards and software engineering practices in robotics, with a career-long emphasis on knowledge-driven robotic systems over data-driven approaches.

• Robotics
• Mechatronics
• Bayesian Sensor Processing
• +۱۰ مورد دیگر

Ann Nowe is a Professor at the Department of Electronics and Informatics (ETRO) at Vrije Universiteit Brussel. Her research focuses on reinforcement learning, multi-agent systems, robotics, and energy management. She leads the Federated labs AI and Robotics, emphasizing interdisciplinary collaboration in artificial intelligence and sustainable robotics. With a prolific publication record (555+ outputs) and 152 active projects, her work spans theoretical advancements and applied solutions in AI ethics, explainability, and energy systems. Her research interests include AI security, neuro-symbolic integration, and federated robotics. Notable contributions include frameworks for explainable AI in multi-agent environments and predictive maintenance algorithms for wind turbines. She has received multiple awards, including the ALA Best Paper Award (2018) and Best Demonstration Award (2019). Dr. Nowe actively participates in conferences and workshops on trustworthy AI, co-organizing events like the World Conference on eXplainable AI (2025). Her work integrates ethical considerations and human-centric design principles across robotics, energy systems, and collective decision-making.

• Reinforcement Learning
• Artificial Intelligence
• Robotics
• +۳ مورد دیگر

Professor Katrien Beuls is a leading researcher at the Namur Digital Institute within the Faculty of Computer Science at the University of Namur. With over 69 research outputs and an h-index of 30, her work bridges artificial intelligence, computational linguistics, and cognitive science. She actively contributes to the United Nations Sustainable Development Goals through her research in human-inspired AI systems. Her research focuses on developing human-like language processing systems through computational construction grammar, neuro-symbolic approaches, and grounded language learning. Professor Beuls investigates how machines can learn language through situated interactions similar to human language acquisition, with particular emphasis on explainable AI systems that combine neural and symbolic approaches for better interpretability. Her work spans theoretical linguistics, cognitive modeling, and practical AI applications. Analysis of her recent publications reveals a strong trend toward integrating symbolic and neural approaches in language processing, with increasing focus on explainability, grounded language understanding, and construction grammar formalisms. Her research demonstrates consistent progression from theoretical computational linguistics toward practical applications in visual dialogue systems, recipe understanding, and language acquisition modeling. Professor Beuls leads significant research projects including CxG-LEARN (Syntactic-Semantic Operators for Machine Learning of Usage-Based Construction Grammars, 2024-2027), ARIAC by DigitalWallonia4.AI (Applications and Research for Trusted Artificial Intelligence, 2021-2027), and Meaning and Understanding in Human-Centric AI (2020-2024). These projects demonstrate her leadership in advancing construction grammar implementations and neuro-symbolic AI approaches. She has been actively involved in academic dissemination with 12 recorded activities including invited talks, conference organization (10th European Starting AI Researchers' Symposium), and workshop participation. Her media presence includes 11 contributions where she discusses topics like human-inspired language models, kindergarten-inspired AI, and the future of intelligent systems.

• Artificial Intelligence
• Computational Linguistics
• Construction Grammar
• +۵ 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)

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