جستجوی استاد راهنمای دانشگاهی

• Machine Learning
• Deep Learning
• Anomaly Detection
• +۵ مورد دیگر

Peter Karsmakers serves as Associate Professor at KU Leuven's Department of Computer Science within the Faculty of Engineering Technology, based at the Geel Campus. He coordinates the Declarative Languages and Artificial Intelligence (DTAI) research group and holds leadership roles including coordinator of Research and Education for Computer Science across Geel and Diepenbeek Campuses. Karsmakers earned his PhD in Engineering Science in May 2010, focusing on kernel-based learning algorithms for sparse modeling and efficient predictions from large datasets. His doctoral work established foundations for his current research trajectory in resource-constrained machine learning systems. His research integrates machine learning with signal processing for real-time sensor data interpretation, specializing in anomaly detection from acoustic, radar, and accelerometer signals on embedded devices. Current projects address industrial condition monitoring, elderly care systems, and livestock facility monitoring through three main tracks: acoustic monitoring (e.g., SINS, WATCHDOG), radar-based systems (e.g., FARADAY, NextPerception), and smart electronics for power converters. Recent publications demonstrate strong trends in constraint-guided deep learning architectures for industrial applications, cross-environment robustness in sensor systems, and domain-knowledge integration to reduce data requirements. His work consistently bridges theoretical machine learning with practical implementations in resource-constrained environments. No scientific awards or fellowships were mentioned in the provided materials. Karsmakers supervises over 10 master's theses annually and coordinates a research team of 10 PhD students and a post-doc within DTAI-ADVISE. He has secured approximately 2.3 million euros in funding through VLAIO, EU-ECSEL, and bilateral industry contracts, including 10 active projects such as AutoEdgeML (2024-2028) and Fault Tolerant Neural Networks for Space Applications (2024-2027). He leads the DTAI-ADVISE research group focused on developing software that attaches semantics to sensor data on resource-constrained devices. The team operates across multiple campuses with specialized labs for acoustic monitoring (Geel), radar-based systems (in collaboration with ESAT-TELEMIC), and smart electronics (with Electrical Engineering department), maintaining strong industry partnerships with companies in healthcare, manufacturing, and agriculture sectors.

• Additive Manufacturing
• Process Monitoring
• Control Systems
• +۶ مورد دیگر

Tegoeh Tjahjowidodo is a Senior Lecturer at the Faculty of Industrial Engineering Sciences , KU Leuven , affiliated with the Department of Mechanical Engineering and the Manufacturing Processes and Systems (MaPS) unit at Campus De Nayer. He serves as Head of Education for Electromechanics programs and leads Subdivision 17 at the campus. Research Areas: Additive Manufacturing (Wire-Arc Additive Manufacturing), Process Monitoring, Control Systems, Laser Micromanufacturing, Wear Analysis, Robotics, and Condition Monitoring. Publication Trends: Focus on in-situ monitoring of laser micromanufacturing, machine learning for abrasive belt grinding, WAAM parameter optimization , and multi-sensor fusion for process control. Scientific Contributions: Co-promotor for MultiTRIBO (tribology), Promotor for WAAM structural integrity and pedicle screw surgical simulators . Active in international collaborations (e.g., 25th International Symposium on Laser Precision Microfabrication, Spain 2024).

Heidi Ottevaereمشاهده پروفایل

استاد

• Photonics
• Biophotonics
• Optical fiber sensors
• +۷ مورد دیگر

Heidi Ottevaere is a Professor at the Faculty of Engineering of the Vrije Universiteit Brussel (VUB) since October 1, 2009. She serves as the head of the Instrumentation and Metrology platform at the Photonics Innovation Center and leads the 'biophotonics' research unit of the Brussels Photonics Team (B-PHOT), which is chaired by Prof. Hugo Thienpont. Her work focuses on the design, fabrication, and characterization of photonic components and systems for diverse applications in medical diagnostics, environmental monitoring, and industrial processes. Dr. Ottevaere earned her Electrotechnical Engineering degree with majors in Photonics from Vrije Universiteit Brussel in 1997 and completed her PhD in Applied Sciences at the same institution in 2003. Her doctoral research focused on 'Refractive microlenses and micro-optical structures for multi-parameter sensing: a touch of micro-photonics.' Professor Ottevaere's research spans multiple cutting-edge areas of photonics with particular emphasis on biophotonics, micro-optics, and optical metrology . Her work bridges fundamental science with practical applications, developing novel photonic components and systems that address real-world challenges. She has pioneered research in miniaturized optical systems for medical diagnostics, environmental monitoring, and industrial applications. Her current research focuses on advancing lab-on-a-chip technologies, microfluidic optical sensors, and novel optical fiber systems for biomedical applications. She has developed microminiaturized, integrated plastic detection units for absorbance and laser-induced fluorescence measurements in microfluidic channels, enabling portable, robust, and disposable diagnostic systems. Her recent publications demonstrate a strong trend toward integrated optical sensing systems with applications in medical diagnostics and environmental monitoring. There's a clear progression from fundamental optical component design to complete system integration, with increasing emphasis on artificial intelligence for data analysis and computational imaging techniques. Her work bridges photonics with biomedical engineering, materials science, and data science, reflecting the interdisciplinary nature of modern photonics research. Dr. Ottevaere has been recognized with several prestigious awards: Best Application award (2008) Educational award - Bronze (2019) MOC09 Contribution Award Winners (2009) As an educator and mentor, Professor Ottevaere has promoted 9 PhD students and supervised numerous master's theses. She has secured substantial research funding from diverse sources including the Fund for Scientific Research Flanders (FWO), the Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT), and multiple European Framework Programs. Her current portfolio includes projects on miniaturized biosensors for drinking water screening, precision manufacturing, and photonics education initiatives in Uzbekistan. She has coordinated multiple strategic research and networking projects with regional, national, and international funding bodies. Professor Ottevaere leads the biophotonics research unit within the Brussels Photonics Team (B-PHOT), one of Europe's leading photonics research groups. Her team includes researchers working on optical metrology, micro-optics fabrication, and biophotonic applications. She collaborates extensively with industry partners including Melexis, Umicore, and Anteryon, as well as academic institutions across Europe through various EU-funded projects. She has been instrumental in developing the interuniversity engineering curriculum 'Master in Photonics' which received the EC Erasmus Mundus quality label in 2006, and continues to be the driving force behind photonics education at VUB.

• Noise Abatement in Machines and Vehicles
• Numerical Modeling in Mechanics
• Automotive Structures and Dynamics
• +۱۲ مورد دیگر

Prof. Wim Desmet is a full professor at the Faculty of Engineering Science and head of the Department of Mechanical Engineering at KU Leuven . His research focuses on advanced modeling techniques for mechanical systems, including: noise and vibration control in automotive and industrial systems computational acoustics and interval field uncertainty modeling metamaterials for broadband vibroacoustic performance AI-driven diagnostic systems in renewable energy and manufacturing Current research projects address challenges in electric vehicle drivetrains, wind turbine monitoring, and multi-physical digital twin development. He actively contributes to academic governance as: Managing Director of KU Leuven Head of Subdivision HIST Chair of multiple executive committees Member of 15+ academic and administrative councils

• Materials Science
• Manufacturing Engineering
• Additive Manufacturing
• +۳ مورد دیگر

Robrecht Abts is a researcher at KU Leuven’s Manufacturing Processes and Systems (MaPS) group, affiliated with the De Nayer Campus. His work bridges electromechanics, materials science, and advanced manufacturing techniques. His research focuses on structure, behavior, and sustainability of materials , with particular emphasis on thermal analysis in additive manufacturing processes like fused filament fabrication (FFF). This includes leveraging deep learning for real-time thermal data interpretation and optimizing electrical discharge machining (EDM) through adaptive pulse classification. Recent publications highlight his expertise in Threshold-free machine learning pipelines Thermal modeling in 3D printing COMSOL simulations for manufacturing processes

Lien Smeestersمشاهده پروفایل

پژوهشگر

• Optical Engineering
• Spectroscopy
• Climate Monitoring
• +۳ مورد دیگر

Lien Smeesters is a postdoctoral researcher at the Department of Applied Physics and Photonics, Vrije Universiteit Brussel (VUB). Her work focuses on advancing optical technologies for applications in climate monitoring, food safety, and industrial photonics. She is part of Brussels Photonics, a research group specializing in optical design and engineering. Her research interests include spectroscopy, LiDAR systems, space-based imaging, and the development of novel optical devices. Notably, she contributed to projects like the GRADΞD SBO initiative aimed at additive manufacturing process control. She has presented at international conferences such as SPIE Photonics Europe and organized events like the Belgian Photonics Online Meetup 2022. Education: Master's and PhD in Applied Physics and Photonics (VUB). Her awards include the Best Student Paper Award (2014), Photonics21 Student Innovation Award (2017), and EO Educational Award Finalist (2016). She has supervised master’s theses on topics like optical design for climate monitoring and tunable lenses in camera systems. Her lab work involves collaborations on projects such as wide-field-of-view cameras for Earth radiation monitoring and polymer lightguide fabrication.

• Signal processing
• Fault detection and diagnosis
• Rotordynamics
• +۷ مورد دیگر

Konstantinos Gryllias is a Professor in the Department of Mechanical Engineering at KU Leuven's Faculty of Engineering Sciences. He leads research in the Mechatronic System Dynamics (LMSD) unit at the Arenberg campus. His academic affiliations extend across multiple KU Leuven institutes including Leuven.AI, Leuven.AM (Additive Manufacturing), and the Gravitation Institute. He serves on important governance bodies as a member of the Faculty Council of Engineering Sciences, Faculty Doctoral Committee of Engineering Sciences, and Departmental Council of Mechanical Engineering. Dr. Gryllias specializes in signal processing, fault detection and diagnosis of rotating machinery, condition monitoring, and machine learning applications in structural health monitoring. His research spans linear and nonlinear vibrations, anomaly detection, rotordynamics, and pattern recognition. His work bridges theoretical signal processing with practical engineering applications in wind turbines, marine propulsion systems, and industrial machinery. His recent publications demonstrate strong focus on deep learning approaches for wind turbine anomaly detection, bearing diagnostics, stern bearing lubrication optimization, and structural health monitoring using advanced signal processing techniques. The research shows increasing integration of explainable AI methods with traditional vibration analysis. Dr. Gryllias teaches advanced courses including Monitoring & Prognostics, Structural Dynamics, Smart Sensing Technologies, and Applied AI perspectives. His teaching portfolio reflects the interdisciplinary nature of his research, connecting mechanical engineering fundamentals with cutting-edge AI methodologies. He currently leads multiple research projects through 2025-2029, primarily as Promotor, focusing on fault detection in gears using fiber optic sensors, multi-sensor monitoring of drivelines, physics-inspired machine learning for condition monitoring, and digital twin applications for wind turbine efficiency improvement.

Herman Terrynمشاهده پروفایل

استاد

• Materials Science
• Corrosion Engineering
• Surface Chemistry
• +۳ مورد دیگر

Herman Terryn is a Professor at Vrije Universiteit Brussel, specializing in Materials and Chemistry. His primary research focuses on surface analytical techniques, electrochemical reactor design, and corrosion studies of aluminum and magnesium surfaces. He has led over 100 projects and authored 842 publications, demonstrating expertise in coatings, corrosion mechanisms, and material degradation. Expertise: Surface treatments, electrochemical analysis, and corrosion prevention Key Projects: Includes predictive machine learning for corrosion damage and sustainable additive manufacturing His research integrates experimental and computational approaches, such as FEM modeling and sensor-based methodologies. Notable contributions include studies on lithium carbonate inhibitor leaching and hydrogen evolution in EV coolants. Awards: European Corrosion Medal (2014), Fellow CSCP (2019), Franqcui Chair (2016) He supervises PhD students and actively participates in international conferences, showcasing innovations in 3D printing for biomedical applications and corrosion-resistant materials. His work bridges fundamental science with practical engineering solutions, addressing industrial challenges in sustainable materials.

• Structural integrity of materials
• Advanced manufacturing techniques
• Fracture mechanics
• +۷ مورد دیگر

Reza Talemi is a Professor and Head of the Elooi Research Laboratory within the SCALINT Division at the Department of Materials Engineering (MTM), Faculty of Engineering Technology, KU Leuven, Belgium. His research focuses on structural integrity of materials fabricated through advanced manufacturing techniques, with particular expertise in impact dynamics, fracture mechanics, and fatigue analysis of metallic materials. His educational background shows extensive specialization in materials science and engineering, with research focusing on structural integrity assessment of advanced materials. His work bridges experimental and numerical approaches to understand material behavior under various loading conditions. Talemi's research interests span structural integrity of advanced materials, tribo-mechanical fracture, innovative testing methods for material behavior assessment, and advanced numerical modeling of material failure. His work has significant applications in aerospace, energy, and manufacturing sectors where material integrity is critical for safety and performance. His recent publications demonstrate a strong focus on additive manufacturing technologies, particularly examining fretting fatigue behavior of additively manufactured components, residual stress characterization, and microstructural analysis. The research shows consistent advancement in understanding how advanced manufacturing processes affect material properties and structural performance. Among his notable contributions are development of specialized testing apparatus for evaluating material performance in dovetail joint configurations, advanced numerical modeling techniques for predicting material behavior, and innovative approaches to characterize material integrity under complex loading conditions. Professor Talemi actively supervises numerous PhD students and leads multiple research projects, including 'Precise advanced material processing via controlled fatigue fracture' and 'Hybrid Experimental and Numerical Framework for Monitoring Structural Integrity in Aerospace Structures,' demonstrating his leadership in both academic and applied research domains.

• Machine Learning
• Data Mining
• Big Data
• +۴ مورد دیگر

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

• Industrial Artificial Intelligence
• Digital Twin Technology
• Condition Monitoring
• +۷ مورد دیگر

Pradeep Kundu serves as an Assistant Professor in the Department of Mechanical Engineering at KU Leuven's Faculty of Engineering Technology (Bruges Campus), where he leads the M-Group Asset Performance Management subdivision. He is an active member of Leuven.AI Institute for Artificial Intelligence and holds governance roles in the Mechanical Engineering Department Council and Faculty Council. His research centers on Industrial Artificial Intelligence integrated with Digital Twin Technology, focusing on three core domains: rotating machinery condition monitoring (fault diagnosis/prognosis), manufacturing quality control (tool wear/surface monitoring), and production process optimization. Key methodological contributions address data scarcity through synthetic data generation (Digital Twin/Generative AI) and enhance model robustness via Physics-Informed Machine Learning, Hybrid Modeling, and advanced Statistical Regression. Analysis of his 15 most recent publications (2024-2025) reveals a dominant focus on predictive maintenance for mechanical systems, utilizing diffusion models for damage imaging, entropy-based domain adaptation for bearing failure prediction, and sensor fusion techniques. His work consistently bridges physics-based modeling with deep learning across rotating machinery, structural health monitoring, and smart manufacturing applications. Dr. Kundu currently leads multiple funded projects including 'Intelligent Prognosis of Rotating Machines in Industry 4.0 using Generative AI' (2024-2025) and 'Digital Twin Framework for Fleet-Level Feed Drive Systems Health Assessment' (2023-2027), addressing critical challenges in data-limited industrial AI deployment. His research directly supports Industry 4.0 transformation through maintenance optimization and quality control innovations. He actively contributes to academic governance as member of the OC Smart Operations and Maintenance in Industry committee and the Mechanical Engineering Department Council, while his research group within the Mecha(tro)nic System Dynamics unit develops practical AI solutions for industrial asset performance management.

• Mechanical Engineering
• Manufacturing
• Incremental Forming
• +۳ مورد دیگر

Marthe Vanhulst is a researcher affiliated with the Department of Mechanical Engineering at KU Leuven, specializing in incremental sheet metal forming and its applications in biomedical engineering. She works in the MaPS division (Manufacturing and Process Systems) and contributes to collaborative research initiatives like Flanders Make, the strategic research center for the manufacturing industry in Flanders. Research Interests Development of advanced manufacturing techniques for complex geometries Application of machine learning in optimizing sheet metal forming processes Biomedical applications of incremental forming, particularly cranial implants Knowledge management systems for industrial engineering workflows Integration of Digital Image Correlation for strain and thickness analysis Publication Trends Her research focuses on improving the geometric accuracy and thickness distribution in multi-stage incremental forming. Key themes include toolpath optimization, intermediate shape design, and digital compensation methods. Recent work integrates collaborative platforms for knowledge sharing in manufacturing, reflecting interdisciplinary efforts between mechanical engineering and computer science. Laboratory Affiliation Marthe is part of the Manufacturing and Process Systems (MaPS) research group at KU Leuven, which collaborates closely with Flanders Make. Her projects emphasize industrial partnerships and translating theoretical advancements into practical solutions for precision manufacturing.

• Artificial Intelligence
• Reinforcement Learning
• Bioinformatics
• +۴ مورد دیگر

Leander Schietgat is a postdoctoral researcher at Vrije Universiteit Brussel (VUB) working within the Informatics and Applied Informatics department. He is affiliated with the Federated Labs AI and Robotics research group and holds an ORCID identifier (0000-0002-2097-0974). His research spans multiple interdisciplinary domains where artificial intelligence intersects with practical applications. Dr. Schietgat's research interests focus on applying machine learning techniques to diverse domains. His work in reinforcement learning addresses real-world manufacturing challenges through job shop scheduling algorithms. In bioinformatics, he investigates metagenomics and haplotype analysis for microbial communities. His research also extends to computational toxicology, developing methods for toxicophore detection and mutagenicity prediction. This interdisciplinary approach demonstrates his ability to adapt AI methodologies across different scientific domains. His publication record shows consistent output with four research articles between 2021-2023, demonstrating expertise in both theoretical machine learning frameworks and practical implementations. The research trends indicate a strong focus on applying explainable AI to trustworthy control systems, with particular emphasis on reinforcement learning applications in manufacturing and biological systems. Dr. Schietgat is actively involved in multiple research projects including: CTRLxAI=T(H)RUST (2022-2026): CTRL schemes merged with eXplainable AI for t(h)rustworthy control of physical dynamic systems IOF GEAR: Artificial Intelligence Lab (2022-2026) TAILOR: Foundations of Trustworthy AI (2020-2024) ESF project: A people-oriented AI training through serious game experiences (2019-2021) RAINboW: Responsible AI for a smart connected world (2019-2022) He is scheduled to participate in the event 'Europe's Vision on Responsible AI: Research, Practice, Policy' as an organizer in April 2025, indicating his active engagement with the broader AI policy community.

• 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.

• Plasmonics
• Doped Metal Oxides
• Optical Materials
• +۳ مورد دیگر

Nicolas De Moor is an active researcher specializing in materials science with dual expertise in plasmonic optical properties and machine learning applications for industrial process monitoring. His recent publications in high-impact journals demonstrate significant contributions to understanding doped metal oxides and optimizing thin film deposition techniques. His core research focuses on: Fundamental plasmonic behavior of doped metal oxides using Kubelka-Munk optical modeling Integration of machine learning with magnetron sputtering processes Real-time anomaly detection in thin film manufacturing Visible-infrared spectral analysis of plasmonic materials De Moor's publication trend reveals a strategic shift from theoretical optical characterization (2025) toward applied industrial solutions (2024), demonstrating how computational methods solve real-world manufacturing challenges in materials engineering. This interdisciplinary approach bridges nanoscale material properties with production-line process control systems.