جستجوی استاد راهنمای دانشگاهی

• Wind turbines
• Condition monitoring
• Reinforcement learning
• +۴ مورد دیگر

Timothy Verstraeten is a postdoctoral researcher at the Vrije Universiteit Brussel , affiliated with the Engineering Technology Acoustics & Vibrations Research Group . His work bridges Wind Energy and Machine Learning , focusing on optimization, condition monitoring, and control systems for wind farms. Role: Researcher in acoustics, vibrations, and intelligent monitoring Key Projects: IOF Gear (2018–2023), Robust Fleet-Dedicated Reinforcement Learning (2017–2020) Research Interests include: Wind turbine operations (power prediction, balancing schemes) Reinforcement learning for hybrid energy systems and fleet control Condition monitoring using Gaussian processes and anomaly detection Deep learning for predictive maintenance and wake loss modeling Scientific Contributions show a focus on Wind turbines (100% Scopus), Farms (58%), and Reinforcement learning (37%), with recent publications on uncertainty quantification and hybrid energy systems. Award: ICTAI Best Student Paper (2018) He has supervised research proposals and presented at conferences like ACM E-Energy and Wind Energy Science, emphasizing control systems , power optimization , and structural health monitoring .

Herman Bruyninckxمشاهده پروفایل

استاد

• Robotics
• Mechatronics
• Bayesian Sensor Processing
• +۱۰ 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.

• Life Cycle Engineering
• Ecodesign
• Sustainable Manufacturing
• +۴ مورد دیگر

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.

• Durability of brittle construction materials
• Structural resilience of existing buildings
• Non-destructive testing (NDT) techniques
• +۳ مورد دیگر

Els Verstrynge is an Associate Professor at the Department of Civil Engineering, Faculty of Engineering Science, KU Leuven. Her work focuses on the durability of brittle construction materials and structural resilience of existing buildings , particularly those of architectural heritage. She leads interdisciplinary research projects utilizing multi-scale numerical modeling, advanced non-destructive testing (NDT), and 4D X-ray tomography to study concrete and masonry degradation. Research emphasizes reinforcement corrosion , fatigue fracture , and climate-induced degradation Develops Bayesian updating techniques for corrosion assessment and AI-driven structural analysis (CHAI project) Supervises PhD research on fracture mechanics and degradation processes Her work bridges experimental and computational methods, contributing to conservation strategies for aging infrastructure and innovative reuse of building materials.

Wilfried Philipsمشاهده پروفایل

استاد

• Computer Vision
• Real-time Computer Vision
• Video Quality Improvement
• +۴ مورد دیگر

Wilfried Philips is a senior full professor at Ghent University and scientific lead of imec's Center of Excellence on Image Processing and Sensor Fusion. He leads the Image Processing and Interpretation (IPI) research group at imec-UGent and Ghent University's valorization consortium iKnow, which has created 9 spin-off companies including Senso2Me (IoT services for elderly care). His research focuses on real-time computer vision and video quality improvement for industrial applications, with key domains spanning automotive sensing, industrial inspection, machine learning, and video surveillance. The IPI group's work has directly generated three commercial spin-offs through applied research in these areas. Dr. Philips' publication portfolio demonstrates significant impact across remote sensing, medical imaging, and biological computer vision, with multiple highly-cited papers including a 2014 Best Paper Award-winning study on hyperspectral-LiDAR fusion. His honors include: Best Paper Award, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2014) Senior Member, IEEE Fellow, Belgian-American Educational Foundation As a translational research leader, he has successfully bridged academic innovation and industrial implementation through multiple technology spin-offs and industry partnerships.

Silvère Bonnabelمشاهده پروفایل

استاد

• Systems and control
• Robotics
• Machine learning
• +۵ مورد دیگر

Silvère Bonnabel is a Professor at École des Mines ParisTech (Mines ParisTech), a prestigious engineering school in France. He holds a Research Habilitation HDR (2014) and a PhD (2007) from Mines ParisTech, along with advanced degrees in financial mathematics and engineering. His professional experience includes roles as Professor at the University of New Caledonia (2019-2022), Visiting Fellow at the University of Cambridge (2017), and Assistant Professor at Mines ParisTech (2009-2016). Bonnabel’s research focuses on systems and control, robotics, machine learning, and navigation, with notable contributions to invariant Kalman filtering and optimization on manifolds. He has authored/co-authored over 100 articles, 9 patents, and achieved an h-index of 39. Notable awards include the 2024 George N. Saridis Award and 2021 European Control Award. His work bridges theoretical advancements with industrial applications, including collaborations with companies like Safran, Thales, and Manitowoc. Bonnabel has supervised numerous PhD students, contributing to breakthroughs in inertial navigation, radar tracking, and autonomous systems. Education: 2014: HDR in Mathematics (Paris VI Sorbonne) 2004-2007: PhD, Mines ParisTech 2003-2004: Master in Financial Mathematics (Paris XII) 2001-2004: Engineering Degree, Mines ParisTech Industrial Collaborations: Partnerships with Safran, Thales, Manitowoc, and startups like OFFROAD, focusing on crane control, inertial navigation, and autonomous systems. Editorial Roles: Associate Editor for IEEE Control Systems Magazine, European Control Conference, and Systems & Control Letters. His research emphasizes geometric control theory, stochastic algorithms, and sensor fusion, with applications to robotics, aerospace, and industrial automation. Bonnabel’s work on invariant extended Kalman filters has been commercialized in products like Safran’s inertial navigation systems.

• Modelling dynamic and ultrasonic response of complex structures
• Design optimisation of multifunctional structures and metamaterials
• Structural health monitoring (SHM)
• +۲ مورد دیگر

Dimitrios Chronopoulos is an Associate Professor in the Department of Mechanical Engineering at KU Leuven , Faculty of Engineering Technology. His research focuses on structural health monitoring, design optimization of composite and metamaterial structures, and wave propagation analysis for damage prognosis. Unit: Mecha(tro)nic System Dynamics (LMSD), Ghent and Aalst Campuses Research Affiliations: Leuven Centre for Affordable Sustainable Technology, LIM - KU Leuven Institute for Mobility His work integrates physics-informed machine learning and computational modeling to address challenges in predictive maintenance of smart composite systems, including aircraft and wind turbine components. Recent projects emphasize digital twins, Bayesian frameworks, and ultrasonic sensor networks for real-time damage assessment. Publications highlight trends in deep learning for SHM , topological phononic crystals , and multi-scale damage detection using hybrid models. He actively supervises students in advanced ultrasonic monitoring and predictive analytics.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Jan Lemeire is an active researcher at Vrije Universiteit Brussel (VUB), affiliated with the Department of Electronics and Informatics within the Faculty of Engineering. Based in Brussels, Belgium at Pleinlaan 2, he maintains an active research profile with an h-index of 10 and 581 citations according to Scopus data. His research interests span multiple domains including GPU computing, machine learning, embedded systems, and their applications in diverse fields from biomedical engineering to forensic science. His work demonstrates strong interdisciplinary connections, bridging computer science with practical applications in health technology, crime analysis, and industrial systems. Analysis of his recent publications reveals a consistent focus on computational efficiency, with particular emphasis on GPU acceleration, embedded AI deployment, and machine learning applications. His work shows evolution from hardware-focused research toward more applied domains including healthcare technology and crime pattern analysis. Dr. Lemeire actively participates in multiple research projects including NSIS2: PRISMA network (2024-2029), IOF3016: GEAR (2021-2025), and Tech4Health (2024-2025), demonstrating sustained research funding and collaborative work across disciplines. His academic activities include supervision of graduate students, as evidenced by his role as advisor for the 2017 Master's thesis on GPU-accelerated holography, and regular participation in major conferences including the Conference on Uncertainty in Artificial Intelligence and the International Symposium on Embedded Multicore Systems. Dr. Lemeire maintains an active research laboratory focused on computational methods, with particular strengths in parallel processing techniques and their application to real-world problems across multiple domains including healthcare, industrial systems, and forensic science.

• Structural Mechanics
• Building Acoustics
• Architectural Acoustics
• +۸ مورد دیگر

Edwin Reynders is a Professor at the Department of Civil Engineering , KU Leuven , specializing in Structural Mechanics and Building Acoustics . His research focuses on numerical prediction and optimization of airborne/impact sound insulation, signal processing, and structural health monitoring (SHM) of bridges and concrete structures. He leads a research team with three postdoctoral researchers and five PhD candidates , and has supervised five PhD dissertations and one postdoctoral researcher. His recent projects include an ERC Starting Grant on metamaterial noise reduction and collaborations with institutions like the University of Cambridge . 2024: IOMAC Leadership Award 2009: Best PhD Thesis Award (Royal Academies of Belgium) His work integrates advanced modeling techniques such as Bayesian updating , topology optimization , and cross-laminated timber analysis , with applications in fire safety, corrosion monitoring, and vibroacoustic metamaterials. He contributes to journals like Mechanical Systems and Signal Processing and the Belgian Acoustical Society .

Bart Dhoedtمشاهده پروفایل

استاد

• Distributed Machine Learning
• Edge Computing
• Neuromorphic Computing
• +۲ مورد دیگر

Bart Dhoedt is a Professor at Ghent University's Department of Information Technology, affiliated with the Internet Technology and Data Science Lab (IDLab). He teaches courses on algorithms, advanced programming, software development, and distributed systems. His research bridges distributed machine learning, edge computing, and hardware-efficient AI. Research interests focus on Distributed Machine Learning (parallel processing across systems), Edge Computing (decentralized data processing), Neuromorphic Computing (brain-inspired hardware), Sensor Fusion (multimodal data integration), and Representation Learning (efficient feature extraction). Recent publications emphasize active inference, robotic navigation, and object-centric AI. Publications (2017-present) show strong trends in robotic autonomy (navigation, manipulation), active inference (Bayesian modeling), and multimodal world models , with applications in industrial automation and cognitive systems. No awards or grants are documented. He leads research at IDLab, collaborating on embedded AI and distributed systems. No advised students or external affiliations are mentioned.

Guillaume Crevecoeurمشاهده پروفایل

دانشیار

• Dynamical Systems
• Nonlinear Optimal Control
• Model-Based Optimization Algorithms
• +۴ مورد دیگر

Guillaume Crevecoeur (born 1981) serves as Associate Professor at Ghent University's Faculty of Engineering and Architecture within the Department of Electrical Energy, Metals, Mechanical Constructions, and Systems. He leads research at the Electrical Energy Laboratory (EELAB) and is an affiliate member of Flanders Make, the strategic research center for manufacturing industry. His international collaborations include membership in scientific steering committees and prior visiting researcher positions at Technical University Ilmenau and Physikalische Technische Bundesanstalt in Berlin. Crevecoeur earned his MSc (2004) and PhD (2009) in Engineering Physics from Ghent University, followed by a postdoctoral fellowship with Research Foundation Flanders (FWO-Flanders). His research spans dynamical systems modeling, optimization, and control with foundational work in model-based algorithms, inverse problems, and nonlinear optimal control. Current strategic focus areas include machines and motion systems, particularly airborne wind energy applications and robotics. Analysis of his 2022-2025 publications reveals dominant trends in physics-informed machine learning for control systems , with significant contributions to reinforcement learning for underactuated systems, tactile sensing in robotics, and computational fluid dynamics for renewable energy. His work consistently integrates model-based optimization with data-driven approaches, especially in wind turbine control and aerial robotics co-design. Best Paper Award at 22nd International Conference on System Theory, Control and Computing (2018) As Principal Investigator, Crevecoeur directs research funded through FWO-Flanders and industry partnerships with Flanders Make. His lab focuses on mechatronic systems development, particularly in robotic manipulation and renewable energy conversion. Current projects include airborne wind energy system optimization, collision-tolerant robotics, and digital twin implementations for industrial machinery. He actively contributes to international scientific steering committees and serves as academic advisor for graduate students in electrical energy systems. Crevecoeur's research group operates within EELAB's infrastructure, utilizing high-performance computing resources for computational fluid dynamics simulations and experimental facilities for mechatronic system validation. The team collaborates with industry partners through Flanders Make on industrial automation challenges, with recent work focusing on real-time control for high-speed manufacturing processes and adaptive energy management systems.

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.