Yasmine Akaichi is a Researcher at the University of Namur (UNamur) affiliated with the Faculty of Computer Science and the Namur Digital Institute. She leads the project FILP: Federated Learning using Inductive Logic Programming (2023–present), focusing on advancing machine learning methodologies. Her research emphasizes interdisciplinary applications of computational intelligence. She actively participates in academic events, including the Women and Girls in Science symposium (February 2025) and the Grascomp Doctoral Day (September 2024), showcasing her commitment to promoting science and academic collaboration. Her work bridges theoretical foundations of AI with practical implementations in federated learning and logic programming.
Marc Denecker is a Professor at the Department of Computer Science, KU Leuven, affiliated with the Faculty of Engineering Science and the Declarative Languages and Artificial Intelligence (DTAI) research group. He contributes to KU Leuven's Institute for Artificial Intelligence (Leuven.AI). Current promotor for projects like A Category-Theoretic Perspective on Approximation Fixpoint Theory (2024-2028) and Proof systems for first-order logic extended with inductive definitions (2023-2027). Co-promotor for AI in Industry: Learning and Reasoning for Automation (2021-2025) and IMPULS-AI-2021 (2021-2024). His research focuses on formal logic, knowledge representation, and artificial intelligence. Recent work includes epistemic logic for decision-making under uncertainty, approximation fixpoint theory, justification theory, and symmetry-based satisfiability optimization. Publications span venues like AAAI, SAC, and LPNMR conferences, as well as journals such as Artificial Intelligence . Key subfields include logic programming, inductive definitions, model expansion, and constructive knowledge formalization. His teaching involves courses on complex systems (G0B23A/H0N05A), knowledge representation (H02C3A), automata (G0P84A), and logic for computer science (G0T42D).
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.
