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
Femke Ongenae is an Associate Professor at Ghent University's Faculty of Engineering and Architecture within the Department of Information Technology . She leads research at the IMEC postdoctoral level in areas bridging eHealth, predictive healthcare, and knowledge graph technologies . Her work focuses on context-aware systems, stream reasoning, and hybrid AI for healthcare and smart infrastructure applications. Key research domains: Artificial Intelligence , Health Informatics , Knowledge Graphs Leadership roles: Digital Innovation for Man and Society research unit, eBehaviourChange group Her recent publications (2023-2025) highlight advancements in: Semantic rule mining for decision support systems Anomaly detection in healthcare and water networks Context-aware machine learning for COPD and migraine monitoring Knowledge graph embeddings for industrial process monitoring Collaborative projects involve: Developing INK framework for knowledge graph rule mining Building DIVIDE system for adaptive IoT querying Creating MASSIF platform for semantic IoT services Advancing stream reasoning for real-time healthcare applications
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.
Roel Henckaerts is a Research Fellow at the Insurance Research Group, KU Leuven. His work centers on integrating advanced computational methods with actuarial science to optimize insurance pricing models. Research Focus: Henckaerts specializes in machine learning applications for insurance, including telematics-based dynamic pricing, interpretable AI for regulated industries, and data-driven risk assessment. His methodologies emphasize neural networks, tree-based models, and transparent surrogate systems to balance accuracy with regulatory compliance. Publication Trends: Recent articles (2019–2025) demonstrate a shift toward neural networks and model interpretability, while earlier work (2016–2018) established foundations in tree-based methods and risk factor engineering. Thematic evolution shows consistent focus on bridging machine learning scalability with actuarial rigor. Affiliations: As part of KU Leuven's Insurance Research Group, Henckaerts collaborates on industry-facing projects, though specific lab/team structures are undisclosed.
Maxime Cordy is a researcher in Computer Science with a focus on Software Engineering and Formal Verification. He holds a PhD in Computer Science from the University of Namur, earned in 2014, and has engaged in visiting research at the University of Luxembourg (2017-2018). He also co-founded SkalUp as an R&D manager from 2015 to 2016. Education: Doctor of Science (University of Namur, 2014), Master in Computer Science (University of Namur, 2011) His research spans Software Product Lines , Model Checking , and Variability-Intensive Systems , emphasizing formal verification and automated analysis. He has contributed to over 53 research outputs with 949 citations and an h-index of 17. Recent publications include advancements in Featured Transition Systems , Mutation-Based Model Checking , and Machine Learning for Software Quality . He co-organized workshops like MaLTeSQuE 2019 and the Machine Learning and Software Engineering in Symbiosis workshop (2018). Scientific Awards: VAMOS 2024 Ten-Year Most Influential Paper Award (co-recipient) Maxime has collaborated extensively with institutions including the University of Luxembourg and co-authored works with leading researchers in formal methods and software engineering.
Anneleen Dereymaeker is an Assistant Professor at the KU Leuven Faculty of Medicine , affiliated with the Department of Development and Regeneration . Her work focuses on neonatal neurology , EEG analysis , and neurodevelopmental outcomes in preterm infants. Her research spans Developing automated neuromonitoring systems (NEONAID project) Investigating neurovascular coupling in hypoxic-ischemic encephalopathy Advancing EEG-based predictive models for brain development Recent publications highlight her contributions to neonatal brain monitoring , with a focus on Machine learning for artifact detection Microstate analysis of preterm brain maturation Systematic reviews on cerebral oxygenation She teaches communication courses in specialized medical fields and serves on senior academic committees within the Faculty of Medicine.
Kristof Cools is a Full Professor at Ghent University's Faculty of Engineering and Architecture , affiliated with the Department of Information Technology and the Internet Technology and Data Science Lab . His research spans computational electromagnetics, integral equations, and finite element methods, focusing on electromagnetic scattering and wave propagation. Research Interests include: Computational Electromagnetics Integral Equations and Boundary Element Methods Domain Decomposition and Preconditioning Antennas, Propagation, and Modeling of Composite Systems His Publications emphasize stable time-domain solvers, multi-trace formulations, and advanced discretization techniques for electromagnetic problems. Notable trends include resonance-free equations, quasi-Helmholtz projectors, and nonlinear surface scattering models. Advisory Roles include supervising doctoral researchers: Cedric Münger Paul Olyslager Quang Huy Nguyen Alessandro Zuccotti Prakash Jay
Hans Van Oosterwyck serves as a full Professor in the Department of Mechanical Engineering at KU Leuven's Faculty of Engineering Sciences. He leads the Prometheus-Mechanobiology subdivision and actively contributes to the iSi Health and LIMNI research institutes, driving interdisciplinary work at the engineering-biology interface. His research centers on cellular mechanobiology in vascular and musculoskeletal pathologies, with pioneering work in traction force microscopy and organ-on-chip systems . Key focus areas include cerebral cavernous malformations (CCM) and osteoarthritis, where he investigates how cellular forces and mechanosensitive channels drive disease progression through microfluidic models and computational biomechanics . Analysis of his 2023-2025 publications reveals a dominant trend toward 3D force measurement techniques in disease modeling, particularly using degradable hydrogels for chondrocyte studies and vessel-on-chip platforms for CCM. Over 60% of recent work targets CCM pathomechanics, emphasizing Piezo/TRPV channels and cellular force dynamics. Prof. Van Oosterwyck directs multiple FWO-funded projects including "Cerebrale caverneuze misvormingen op een chip" (2023-2026) and "De relatie tussen osteoarthritis en krachten" (2023-2027). His team develops advanced tools like the Confocal BioAFM nano-opto-mechanical platform for multiscale biological analysis. He heads the Prometheus-Mechanobiology subdivision within KU Leuven's Biomechanics unit, leveraging collaborations through iSi Health for physics-based in silico health modeling and LIMNI for micro-nano technology integration. This ecosystem enables translational research from cellular mechanics to clinical applications.
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.
Hugues Bersini is a Professor at Université Libre de Bruxelles (ULB) and Co-Director of the IRIDIA laboratory, the Artificial Intelligence research laboratory of ULB. His academic career spans over three decades, with significant contributions to the fields of artificial intelligence, complex systems, and biological networks. Bersini earned his MS degree in 1983 and his Ph.D. in engineering in 1989, both from Université Libre de Bruxelles. After working as a researcher with an EEC grant from the JRC-CEE in Ispra (1984-1987), he joined the IRIDIA laboratory at ULB, where he has remained throughout his career, eventually becoming a full professor. His research spans a diverse range of topics within artificial intelligence and complex systems. Bersini is particularly known for his work on modeling and control of complex systems, neural networks, fuzzy control, data mining, autonomous agents, and biological networks. He pioneered the exploitation of biological metaphors, especially from the immune system, for engineering and cognitive sciences applications. His research has evolved to include computational chemistry, immune engineering, cognitive sciences, bioinformatics, and object-oriented technology. In recent years, he has focused on business intelligence applications and public goods through the Brussels Institute FARI. Throughout his career, Bersini has published approximately 300 papers, demonstrating consistent productivity and evolving research interests. His early work focused on optimization algorithms and immune-inspired computing, which gradually expanded to include fuzzy and neuro control systems, biological networks, and more recently, applications to real-world problems through spin-off companies and the FARI institute. His publications show a clear trajectory from theoretical foundations to practical applications, with growing emphasis on interdisciplinary approaches that bridge computer science with biology, chemistry, and cognitive sciences. Bersini has been actively involved in the academic community, having co-organized major conferences including the Parallel Problem Solving from Nature (PPSN), European Conference on Artificial Life (ECAL), European Workshops on Reinforcement Learning (EWRL), and International Competitions on Evolutionary Optimization (ICEO). He also organized tributes to Francisco Varela and the International Conference on Artificial Immune Systems (ICARIS). As an educator, Bersini teaches artificial intelligence, object-oriented programming (C++, Java, .Net, Kotlin, UML, Django/Python), and design patterns to both university students at Solvay and Polytechnic Schools and for industry professionals. He has authored fourteen French books covering computer science fundamentals, complex systems, and the intersection of computer science with other fields. His books range from technical manuals to philosophical explorations of complex systems and emergence. Bersini has coordinated significant research projects including the FAMIMO LTR European Project on fuzzy control for multi-input multi-output processes and participated in ESPIRIT projects NEMORETS and METHODS. His work has led to practical applications through spin-off companies such as Cluepoints, Tevizz, and In Silico DB, and more recently through the Brussels Institute FARI which addresses public goods like mobility, epidemics, access to jobs and schools, and energy transition.
Tim Wauters is a postdoctoral researcher at Ghent University's Faculty of Engineering and Architecture, Department of Information Technology (EA05), with a focus on network systems and multimedia delivery. His work spans content distribution networks, fog computing, and machine learning applications in networking. Academic Affiliation: Ghent University Research Focus: Network Orchestration, Immersive Media Delivery, Containerized Cloud Systems His research explores network-aware solutions for optimizing multimedia streaming and cloud applications, including reinforcement learning for auto-scaling, fog computing for resource allocation, and QoE-centric delivery frameworks. Recent work addresses intrusion detection systems with hierarchical ML models and lifecycle-based datasets. Key article trends (2023-2025) show specialization in Kubernetes orchestration , low-latency immersive media , and ML-driven network security , published in venues like IEEE Transactions and ACM conferences. Scientific Awards FWO Fellowship (2008-2014) for multimedia streaming research Continued FWO funding for 6G networking projects As a PhD supervisor , he has guided research on topics including volumetric video delivery, fog computing resource allocation, and adaptive streaming protocols. His work often involves cross-disciplinary collaboration with IMEC and academic-industry partnerships.
Jean-Marc François is a former Researcher at the Research Unit in Networking (RUN) within the Faculty of Applied Sciences at the University of Liège (ULg). He currently works at Google. He holds an Ir. in Computer Science (1999, ULg), an Ms. in Applied Computer Science (2004, ULg), and a Dr. in Computer Engineering (2007, ULg). His research focuses on mobility prediction in wireless and disruption-tolerant networks, Quality of Service (QoS), and routing protocols, leveraging techniques like Hidden Markov Models (HMM) and information theory. His work spans theoretical analysis and practical applications, including studies on AP-centric vs. MN-centric mobility prediction, entropy-based knowledge spreading, and DTN routing optimization. His publications address challenges in network reliability, protocol design, and mobility trace analysis. He has contributed to conferences like IFIP Networking, ACM CoNEXT, and WiOpt, as well as journals like Ad Hoc Networks. No scientific awards are explicitly mentioned in the provided text. His research was conducted within the Montefiore Institute, part of RUN, and involved collaborations with colleagues like G. Leduc and S. Martin. No advising roles or grants are detailed here.
Wim Dewulf is a full professor at the Faculty of Industrial Engineering Sciences, KU Leuven, and serves as the dean of the faculty. He is a contact person for the Manufacturing Processes and Systems (MaPS) unit at Campus Group T Leuven and holds leadership roles such as division head and member of councils like the University Council and Academic Council. His research focuses on life cycle engineering, ecodesign, sustainable manufacturing, and computed tomography applications in industrial processes. His research interests span sustainable engineering, additive manufacturing (AM), dimensional quality control, and X-ray CT. Recent projects include using deep learning for CT reconstruction, improving AM surface quality via laser remelting, and enabling autonomous demanufacturing of battery-containing products. He actively supervises students in these areas, particularly in laser powder bed fusion and CT metrology. Wim Dewulf is a member of Leuven.AM (KU Leuven Institute for Additive Manufacturing) and SIM² (Institute for Sustainable Metals and Minerals). His work involves advising on circular economy strategies, process optimization, and advanced imaging techniques, with no explicit scientific awards listed in the provided data. He has contributed to education through courses like Applied Sustainability Assessment and Life Cycle Engineering , emphasizing sustainable design and manufacturing. His research teams focus on technology transfer, industrial collaboration, and developing data-driven models for AM and recycling.
Dries Peumans serves as a Research Fellow at the Department of Electronics and Informatics within the Faculty of Engineering at Vrije Universiteit Brussel (VUB), Belgium. His research spans RF engineering, microwave systems, and nonlinear signal processing with significant contributions to measurement instrumentation and 6G technology development. Based at the Pleinlaan 2 campus in Brussels, he maintains an active research profile with an h-index of 139 according to institutional metrics. Peumans' research focuses on RF/microwave systems engineering and nonlinear distortion analysis , particularly in power amplifiers and time-varying systems. His work integrates intelligent instrumentation techniques using reinforcement learning and big data approaches to reduce measurement complexity. Key application areas include 6G communications, beamforming transmitters, and EMI shielding materials. His fingerprint analysis reveals dominant expertise in frequency response (100%), power amplifiers (58%), and nonlinear distortion (47%). Recent publications demonstrate strong trends in real-time signal processing for 5G/6G systems, with particular emphasis on digital predistortion techniques using ROVA modeling. His 2025-2024 output shows increasing diversification into materials science (EMI shielding composites) and geophysical applications (lava lake thermal sensing), while maintaining core expertise in RF measurement optimization and time-varying system modeling. Scientific contributions include: Development of scalable models for linear periodic time-varying (LPTV) systems Innovations in power sweep stitching for modulated RF experiments Compact impedance sensors for 24-31GHz beamforming transmitters Equivalent modeling of multilayered conductive composites Peumans actively supervises doctoral research, notably guiding Amedeo Varano's work on ROVA modeling applications. His current projects include OZR4181 (Reducing measurement complexity through intelligent instrumentation, 2023-2027) and SRP78 (Center for Model-Based Systems Improvement, 2022-2027), which integrate photonics, reinforcement learning, and transceiver design. He participates in the FOD168 initiative for 6G leadership development and maintains collaborations across European research institutions through the VUB's Center for Model-Based Systems Improvement. His laboratory work centers on advanced RF measurement systems, with emphasis on time-domain characterization of nonlinear systems and development of intelligent instrumentation frameworks. Current team projects focus on scaling LPTV modeling techniques to incorporate system parameter variations, enabling predictive design of rotating mechanical systems and electronic oscillators.
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.
