جستجوی استاد راهنمای دانشگاهی

Tomas Norton is a Senior Lecturer at the Faculty of Bioscience Engineering, Department of Biosystems at KU Leuven. He is actively affiliated with multiple research units including the Division of Bio-Environmental Control, the KU Leuven Brain Institute (LBI), and the KU Leuven Institute for Integration of Micro- and Nano-scale Technologies (LIMNI). His work is centered at the Animal and Human (A2H) research unit located at Castle Park Arenberg. Dr. Norton's research focuses on precision livestock farming with particular expertise in computer vision, machine learning, and sensor technologies applied to animal monitoring. His current projects span sustainable agriculture, animal vocalization analysis, and bio-response monitoring systems across multiple species including poultry, pigs, and buffalo. He serves as promotor on numerous research initiatives funded through 2028-2029 that address critical challenges in sustainable food production while improving animal welfare. His recent publications demonstrate a strong trend toward AI-driven solutions for animal monitoring, with particular emphasis on behavior recognition, health assessment through sound analysis, and robotic management systems. These works appear primarily in agricultural technology journals with a focus on practical implementation of advanced computational methods in farming contexts. As an academic leader, Dr. Norton serves on the Faculty Council of Bioscience Engineering, the Doctoral Committee for Bioscience Engineering, and the Departmental Council for Biosystems. He teaches courses including Modelling of Biosystems, Bioresponse Measurements and Process Control, and Sustainable Precision Livestock Farming, integrating his research expertise directly into his educational practice. His laboratory work within the A2H unit represents a multidisciplinary approach that bridges engineering, computer science, and animal science to develop practical technological solutions for modern agricultural challenges, with particular emphasis on sustainability and welfare improvements in livestock production systems.

• Precision Livestock Farming
• Animal Monitoring Systems
• Machine Learning Applications in Agriculture
• +۵ 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

Nikolaos Tsiogkas is an Assistant Professor in the Declarative Languages and Artificial Intelligence (DTAI) group at KU Leuven's Faculty of Engineering Technology, Department of Computer Science. His research focuses on robotics, artificial intelligence, and autonomous systems, with particular emphasis on cognitive reasoning, knowledge representation, and sensor fusion for robotic navigation. Projects: Promotor of initiatives like 'Harnessing Robotics for Safe Agriculture' (2024-2028) and 'ROSANA: Robust Semantic Navigation in Orchards' (2022-2027); co-promotor in demining robotics and multi-arm manipulation research. Research: Combines symbolic AI with robotics, exploring knowledge graphs for explainable navigation, reinforcement learning frameworks, and computationally efficient free-space detection algorithms.

• Robotics
• Artificial Intelligence
• Autonomous Systems
• +۴ مورد دیگر

Prof. Estefanía Serral Asensio is an Associate Professor at the Faculty of Economics and Business (FEB) at KU Leuven , with a primary affiliation to the Information Systems Engineering Research Group (LIRIS) in Brussels. She holds a highly international and interdisciplinary academic profile, having previously served as an Assistant Professor at Eindhoven University of Technology (2018), led the Semantic Knowledge Representation and Integration research group at the Technical University of Vienna (2012–2014), and contributed to the ProS Research Center at the Technical University of Valencia (until 2012). PhD in Computer Science (2011) Master in Software Engineering, Formal Methods, and Information Systems (2008) 5-year Bachelor in Computer Science (2006) Her research focuses on Internet of Things (IoT) , Business Process Management , and context-adaptive systems , with methodological expertise in Model-Driven Development , Conceptual Modeling , and ubiquitous systems . Key projects include Novel Process Mining Techniques for Discovering IoT-enhanced Business Processes (2022–2025), Novel Sustainability-Driven IoT Prescriptive Analytics for Improving Irrigation Practices in Fruit Trees (2021–2024), and foundational work on Runtime Evolution of IoT Processes (2018–2020). Her publications span top-tier venues like CAiSE , ER , SOSYM , and Internet of Things Journal . She teaches courses in ICT Strategy and Architecture , ICT Management , and Research Methodologies in Business Information Systems Engineering , contributing to academic programs at KU Leuven.

• Internet of Things
• Business Process Management
• Model-Driven Development
• +۷ مورد دیگر

Francisco Jara Avila is a Researcher at Vrije Universiteit Brussel's Faculty of Engineering Sciences within the Engineering Technology department, specializing in wind energy systems and machine learning applications. His work focuses on improving wind turbine performance monitoring and predictive capabilities. His research interests center on wind turbine analytics , where he develops advanced methodologies for power prediction, fault detection, and performance optimization. Using techniques like deep learning, Gaussian process regression, and signal processing, his work addresses critical challenges in wind farm operations including uncertainty quantification, spatial correlation analysis, and drivetrain health monitoring. His publication record shows a clear trajectory in wind energy analytics, with increasing sophistication from basic fault detection (2023) to sophisticated uncertainty-aware power prediction frameworks (2025). His work consistently bridges theoretical machine learning approaches with practical wind farm operational challenges. While no specific awards are listed in the available information, his publications in reputable journals like IOP Journal of Physics: Conference Series and Energies demonstrate recognition in the wind energy research community. As a researcher focused on technical development rather than academic mentoring, there's no indication of student supervision activities in the available information. His work appears to be conducted within the Acoustics & Vibrations Research Group, collaborating with researchers like Helsen, Verstraeten, and Nowe on wind energy analytics projects.

• Wind turbines
• SCADA systems
• Wind power
• +۳ مورد دیگر

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.

• Neonatal Neurology
• Electroencephalography (EEG)
• Brain Development
• +۴ مورد دیگر

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

• Artificial Intelligence
• Health Informatics
• Data Analytics
• +۹ 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

Chris Develder is an Associate Professor with the research group IDLab in the Department of Information Technology (INTEC) at Ghent University - imec, Ghent, Belgium. He received his MSc degree in computer science engineering and PhD in electrical engineering from Ghent University in Jul. 1999 and Dec. 2003 respectively (as a fellow of FWO). He has held research visitor positions at UC Davis, CA, USA (Jul.-Oct. 2007) and at Columbia University, NY, USA (Jan. 2013 - Jun. 2015). Prof. Develder leads two research teams within the Internet Technology and Data Science Lab (IDLab): one focused on converting text to knowledge (NLP, primarily information extraction using machine learning), and another on data analytics and machine learning for smart grids. His Text-to-Knowledge (T2K) research group has a strong track record in information extraction across various domains including news, human resources, and biomedical applications, as well as text classification tasks such as sentiment analysis. More recently, the group has expanded into conversational agents and generative models for educational applications. With his team, Prof. Develder has published over 200 papers in international journals and conferences including EMNLP, CoNLL, EACL, ACL, ECIR, CIKM, WSDM, WWW, and NIPS. His recent publications demonstrate a growing focus on practical applications of NLP in healthcare (personality style recognition from speech, biomedical adverse drug event extraction), education (question generation, gap-filling exercises), and labor market analysis (career path prediction, skill extraction). His work bridges theoretical advances in machine learning with real-world applications across multiple domains. Prof. Develder actively supervises PhD students and has co-supervised numerous successful doctorates, including Maarten De Raedt (2024), Semere Kiros Bitew (2024), Yiwei Jiang (2024), Amir Hadifar (2023), and Klim Zaporojets (2022). His former students have gone on to work at organizations including EarlyTracks, Zoom, Nokia Bell Labs, Clarivate, and various academic institutions.

• Natural language processing
• Information extraction
• Smart grid
• +۲ 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

Ilias El Makrini is a postdoctoral researcher at Vrije Universiteit Brussel (VUB), affiliated with both the Federated Labs AI and Robotics and the Applied Mechanics department within the Faculty of Engineering. His research spans multiple interdisciplinary domains focused on human-robot interaction, ergonomics, and industrial automation. His primary research interests include Robotics (100%), Ergonomics (60%), Robot Mathematics (30%), Industrial plants (28%), Manipulators (28%), Sliding mode control (19%), Industry applications (17%), and Reinforcement learning (16%). His work centers on developing sustainable human-centered robotics systems that optimize operator well-being while maintaining production efficiency. Recent publications reveal a strong focus on practical applications of robotics in industrial settings, human-robot collaboration safety systems, operator well-being assessment using specialized sensors, and rehabilitation technologies. His research demonstrates a clear trajectory toward creating more intuitive, safe, and efficient human-robot collaborative environments, particularly in manufacturing contexts. El Makrini actively participates in multiple funded research projects including Brubotics (sustainable human-centered robotics), Wellficiency (operator well-being optimization), AUTOCRAFT, XRtwin_SBO, and ILSF 2024 (Ergonomics 2.0 with exoskeletons). He has co-authored 41 research outputs and contributed to 3 datasets, with a notable h-index of 12 and 638 citations. His collaborative work includes organizing events like the I Love Science Festival (2024) and the Opening of Flanders Make AugmentX lab (2024), demonstrating active engagement in both academic and public outreach activities within the robotics community.

• Robotics
• Human-Robot Interaction
• Ergonomics
• +۷ مورد دیگر

Prof. Dr. Fabian DI FIORE is a Lecturer and Research Coordinator at the Department of Computer Science, Faculty of Sciences, Hasselt University. He is affiliated with the Expertise Center for Digital Media (EDM) and leads research in Computer Graphics, Virtual Reality, and Extended Reality (XR). His roles include coordinating research projects and teaching courses such as Operating Systems, Computer Animation & Simulation, and Computer Graphics & Visual Computing. His research focuses on modeling and simulation, computer vision, and digital media applications. Notable projects include XR innovations for manufacturing, digital support for industrial processes, and collaborative virtual environments for education. He coordinates 16 active research projects, emphasizing XR infrastructure and applied technologies in industry and healthcare. Prof. DI FIORE supervises PhD candidates in Extended Reality applications, such as Psychomotor Task Assistance and Training using XR. He is involved in education initiatives like the International interdisciplinary project and the International School program. His work bridges academic research and practical applications, with a focus on innovation in digital media and educational technology. His teaching responsibilities include coordinating courses on operating systems, computer animation, and project management. He is a co-owner of key training components such as Computer Architecture and International interdisciplinary projects, reflecting his dual role in education and research leadership.

• Computer Graphics
• Computer Vision
• Virtual Reality
• +۴ مورد دیگر

Sam Verboven serves as Assistant Professor within the Department of Business Technology and Operations (BUTO) at the Vrije Universiteit Brussel (VUB), based at the Brussels Humanities, Sciences & Engineering Campus (PL5.4.30, Pleinlaan 2). His teaching portfolio includes core courses such as Fundamentals of Data Science, Information Systems Strategy and Management, IT Modeling, and Introduction to Informatics and SCM, reflecting his integration of technical and business perspectives. Verboven's research program centers on context-aware analytical methodologies that harmonize machine learning innovations with operational realities across finance, healthcare, HR, and marketing domains. His primary expertise lies in causal machine learning and multi-task learning frameworks, specifically designed to incorporate domain expertise and business requirements into algorithmic development. This approach enables the creation of decision-support systems that maintain technical rigor while addressing organizational constraints. Analysis of his recent publications (2023-2025) reveals consistent emphasis on causal inference methodologies, fairness-aware decision optimization, and legally aligned machine learning architectures. His work spans high-impact applications including mortgage loan pricing, organ transplantation logistics, financial compliance systems, and sustainable energy forecasting, demonstrating how machine learning can be adapted to meet regulatory and operational demands across diverse sectors. Scientific awards: No awards or fellowships documented in available materials. Advising and grants: Source information does not specify doctoral students, master's advisees, or externally funded research projects. Laboratory affiliations: No dedicated research labs or specialized teams mentioned in provided content.

• Causal Machine Learning
• Multi-task Learning
• Data Science
• +۴ مورد دیگر

Laurent Vanbever is an Associate Professor at ETH Zürich and heads the Networked Systems Group (NSG). He specializes in network programmability, Internet routing, and network security, aiming to make networks more performant and manageable. Previously, he held a postdoctoral position at Princeton University and earned his PhD from the University of Louvain. Education: PhD in Computer Science, University of Louvain (2012) Postdoctoral Researcher, Princeton University (2012–2014) MSc in Computer Science, University of Louvain (2008) MSc in Management, Solvay Brussels School (2010) Research Interests: His work bridges theory and practice, focusing on network programmability, routing protocols, and sustainable networking. Recent projects include NetFabric, an ETH spin-off for network observability, and studies on energy-efficient router design (e.g., Fantastic Joules ) and BGP reconfiguration ( Taming the transient ). Key contributions include xBGP, Magnifier, and SP-PIFO. Awards: Best Paper Award at HotCarbon '24 SIGCOMM 2015 Best Paper Award Multiple IETF/IRTF Applied Networking Research Prizes Advising and Labs: Supervises PhD students (e.g., Benjamin, Weiran) and manages the NSG lab. Collaborates with NetFabric, offering student projects in network observability and sustainability. Labs & Teams: Leads the NSG and co-founded NetFabric, focusing on next-gen network observability using AI. Active in teaching Communication Networks and Advanced Topics in Communication Networks .

• Network Programmability
• Internet Routing
• Network Verification
• +۵ مورد دیگر

Dr. Maarten Slembrouck is a postdoctoral researcher affiliated with Ghent University, working across the Faculty of Medicine and Health Sciences (Department of Movement and Sports Sciences) and the Faculty of Engineering and Architecture (Department of Electronics and Information Systems). His research focuses on computer vision, 3D reconstruction, and sensor fusion for sports analytics and human motion analysis.

• Computer Vision
• 3D Reconstruction
• Sports Data Science
• +۳ مورد دیگر