جستجوی استاد راهنمای دانشگاهی

• Machine Learning
• Artificial Intelligence
• Fraud Detection
• +۷ مورد دیگر

Bart Baesens is a full professor at the Faculty of Economics and Business (FEB) at KU Leuven, affiliated with the Information Systems Engineering Research Group (LIRIS). He is a member of Leuven.AI, the university's artificial intelligence institute, and leads the education commission for the Master in Information Management program at FEB. His research focuses on machine learning, fraud detection, credit risk, network analytics, and explainable AI, with applications in finance, insurance, agriculture, and smart systems. Baesens is known for developing frameworks like IML4DQ and GARG-AML, and has contributed to foundational work in sensor data analysis and graph-based learning for AML systems. His recent research emphasizes practical applications of AI, such as improving museum archive accessibility via large language models and enhancing agricultural sustainability through precision irrigation systems. He collaborates with industry partners on projects like predictive maintenance optimization and customer lifetime value modeling, leveraging techniques like temporal fusion transformers and self-supervised anomaly detection. Baesens' work bridges academia and industry, with publications addressing ethical AI, model interpretability, and real-world fraud detection challenges. His contributions span over 150 peer-reviewed articles, including influential studies on credit scoring, social network analytics for insurance fraud, and multi-task learning for environmental monitoring.

• Motivation
• Self-Determination Theory
• Educational Psychology
• +۵ مورد دیگر

Maarten Vansteenkiste is a Professor at Ghent University's Faculty of Psychology and Educational Sciences, Department of Developmental, Personality and Social Psychology. He is a leading researcher in Self-Determination Theory (SDT), with a focus on motivation, autonomy support, and psychological needs across various contexts including education, sports, and health. His work spans developmental periods from adolescence to late adulthood, examining how social environments influence motivation, well-being, and performance. His research interests include: Motivation and Self-Determination Theory Educational Psychology and Student Engagement Parenting and Adolescent Development Sport Psychology and Coaching Health Psychology and Pandemic Response Psychological Needs (Autonomy, Competence, Relatedness) Professor Vansteenkiste's recent work has examined motivation during the COVID-19 pandemic, exploring how autonomous motivation influences adherence to health measures and how psychological needs relate to well-being during crises. His research employs diverse methodologies including experimental studies, longitudinal designs, and multi-informant approaches across various cultural contexts. His work often investigates the nuanced interplay between social contexts, psychological needs, and outcomes like well-being, performance, and mental health. His scientific contributions include numerous publications in top journals across psychology, education, and health sciences. His work has significant implications for educational practice, parenting approaches, coaching methods, and public health interventions. Professor Vansteenkiste has supervised numerous PhD students, including Daphne van den Bogaard, Branko Vermote, Sabine Sypré, and Joachim Waterschoot. He collaborates extensively with Bart Soenens (over 200 co-authored papers), Leen Haerens, and other researchers across Ghent University and international institutions. His research team focuses on understanding motivational processes across the lifespan, with applications to educational settings, sports contexts, and health behaviors. Current projects include interventions to foster need-crafting abilities, studies on motivation during global crises, and examinations of how social contexts support or thwart psychological needs.

Bram Vanderborghtمشاهده پروفایل

استاد

• Robotics
• Human-Robot Interaction
• Rehabilitation Robotics
• +۵ مورد دیگر

Prof. Bram Vanderborght is a Full Professor at Vrije Universiteit Brussel (VUB), specializing in robotics and mechatronics. He holds a PhD in Applied Sciences and has led research in humanoid robotics, rehabilitation robotics, and variable impedance actuators. Currently coordinates EU projects like SHERO (self-healing soft robotics) and SMART (Marie Curie ITN). Active in over 97 projects and 676 publications. Education: Mechanical Engineering (VUB, 2003), PhD in Applied Sciences (2007). Postdoctoral research at Italian Institute of Technology (2007-2010), and research director at Universitatea Babes-Bolyai (2011-2016). Research Interests: Human-robot interaction, robot-assisted therapy for ASD children, soft robotics, and actuator technology. Key focus areas include adaptive compliance in robotics and rehabilitation systems. Recent Articles: Focus on reinforcement learning frameworks, multi-robot task scheduling, and sensor integration for soft robots. Highlights include energy-aware optimization and safety-critical control systems. Awards: Best Paper Awards (2006, 2008, 2012, 2020, 2021), ERC Starting Grant (SPEAR), and leadership in EU FET projects. Advising & Grants: Supervised over 40 research projects and doctoral theses. Current initiatives include SPINOFF (exoskeletons for workers) and the Sustainable Robotics Hub (2025-2029). Labs/Teams: Federated labs AI and Robotics. Collaborations span global institutions like AIST (Japan), JRL, and Springer Nature publications.

Katrien Beulsمشاهده پروفایل

استاد

• Artificial Intelligence
• Computational Linguistics
• Construction Grammar
• +۵ مورد دیگر

Professor Katrien Beuls is a leading researcher at the Namur Digital Institute within the Faculty of Computer Science at the University of Namur. With over 69 research outputs and an h-index of 30, her work bridges artificial intelligence, computational linguistics, and cognitive science. She actively contributes to the United Nations Sustainable Development Goals through her research in human-inspired AI systems. Her research focuses on developing human-like language processing systems through computational construction grammar, neuro-symbolic approaches, and grounded language learning. Professor Beuls investigates how machines can learn language through situated interactions similar to human language acquisition, with particular emphasis on explainable AI systems that combine neural and symbolic approaches for better interpretability. Her work spans theoretical linguistics, cognitive modeling, and practical AI applications. Analysis of her recent publications reveals a strong trend toward integrating symbolic and neural approaches in language processing, with increasing focus on explainability, grounded language understanding, and construction grammar formalisms. Her research demonstrates consistent progression from theoretical computational linguistics toward practical applications in visual dialogue systems, recipe understanding, and language acquisition modeling. Professor Beuls leads significant research projects including CxG-LEARN (Syntactic-Semantic Operators for Machine Learning of Usage-Based Construction Grammars, 2024-2027), ARIAC by DigitalWallonia4.AI (Applications and Research for Trusted Artificial Intelligence, 2021-2027), and Meaning and Understanding in Human-Centric AI (2020-2024). These projects demonstrate her leadership in advancing construction grammar implementations and neuro-symbolic AI approaches. She has been actively involved in academic dissemination with 12 recorded activities including invited talks, conference organization (10th European Starting AI Researchers' Symposium), and workshop participation. Her media presence includes 11 contributions where she discusses topics like human-inspired language models, kindergarten-inspired AI, and the future of intelligent systems.

Steven Verstocktمشاهده پروفایل

استاد

• Multimodal Data Processing
• Video Analysis
• Computer Vision
• +۲ مورد دیگر

Steven Verstockt is a Professor at Ghent University’s Department of Electronics and Information Systems, affiliated with the Internet Technology and Data Science Lab (IDLab) and the Multimedia Lab. His research spans multimodal data processing computer vision video analytics geospatial information technology sports data science domains. He leads interdisciplinary projects in sports, media, and cultural heritage, focusing on spatio-temporal data collection, classification, and visualization. Recent research trends highlight his work in CO2-based occupancy detection BIM semantic enrichment multi-camera sports analytics geosocial media mining historical archive digitization hyperspectral image classification published across 2025–2024. His team combines computer vision, machine learning, and geospatial tools for real-world applications.

• Robot Learning
• Computer Vision
• Manufacturing Automation
• +۴ مورد دیگر

Renaud Detry is an Associate Professor at KU Leuven in Belgium, holding dual appointments in Electrical and Mechanical Engineering (groups PSI and RAM). He is affiliated with Leuven.AI as a steering committee member and part of the ELLIS Society. He also serves as a visiting researcher at ULiege and KTH. Previously, he led the Perception Systems group at NASA JPL and was an Assistant Professor at UCLouvain. His research focuses on robot learning and computer vision, applied to manufacturing, agriculture, healthcare, space robotics, and planetary exploration. Notable contributions include work on Mars Sample Return missions, robotic sand grading, and assistive robotics. He is an associate editor for IEEE Transactions on Robotics and regularly reviews for IROS and ICRA. Recent work highlights include uncertainty-aware robot learning, multi-task diffusion policies for robotic tasks, and sustainable in-space manufacturing. His presentations at conferences like Human-Centered AI and ERF 2024 showcase advancements in assistive robotics and shared control systems. Received the Best Presentation Award for 'End-to-end Neural Estimation of Spacecraft Pose' (2023) Co-edited a special issue on 'Robotic In-Situ Servicing, Assembly and Manufacturing' (2022) His advising and grants involve projects like the RoBétArmé construction automation initiative and collaborations with OpalAI as a technical advisor. Active in academic and industry partnerships, he bridges research with real-world applications in space, healthcare, and manufacturing sectors.

• Machine Learning
• Deep Learning
• Neural Networks
• +۵ مورد دیگر

Sam Leroux is a Tenure Track Assistant Professor and IMEC Postdoctoral Researcher at Ghent University's Faculty of Engineering and Architecture, Department of Information Technology. His research spans multiple interdisciplinary domains where artificial intelligence meets real-world applications, with particular emphasis on machine learning, deep neural networks, and distributed systems. He maintains active collaborations with IMEC and contributes to several EU and national research initiatives focused on practical AI deployment. Dr. Leroux's research interests center on developing efficient and privacy-aware machine learning systems that can operate effectively on resource-constrained devices. His work bridges theoretical advances in neural network architectures with practical applications in agriculture, healthcare monitoring, industrial IoT, and sustainable computing. He has pioneered approaches in adaptive neural networks that dynamically adjust computation based on available resources, enabling AI deployment at the network edge without compromising privacy. His publication record reveals a clear trajectory from foundational work in neural network architectures toward increasingly applied research. Recent publications demonstrate strong focus on privacy-preserving AI techniques, hardware-efficient machine learning, and computer vision applications in agriculture and industrial settings. His work consistently addresses the tension between model performance and resource constraints, with growing emphasis on ethical considerations in AI deployment. As an academic supervisor, Leroux currently guides numerous doctoral researchers across diverse projects including broiler welfare monitoring, hardware-efficient continuous learning, UAV-based agricultural sensing, and privacy-aware ergonomic analysis. He serves as Promotor for the 'Hardware-efficient continuous learning' project funded by the Special Research Fund, demonstrating his leadership in securing competitive research funding. His work environment features strong connections between Ghent University's academic research and IMEC's technological expertise, creating a fertile ground for translating theoretical advances into practical solutions. This positioning enables his research group to tackle challenges spanning from algorithm development to hardware implementation, with particular focus on real-world validation of proposed techniques.

• Robotics
• Computer Vision
• Robot Learning
• +۳ مورد دیگر

Renaud Detry is an Associate Professor at KU Leuven, Belgium, with dual appointments in the Department of Electrical Engineering (ESAT) and the Department of Mechanical Engineering. He is a member of the Leuven.AI Institute and the ELLIS Society, and serves as an associate editor for IEEE Transactions on Robotics and conference series like IROS and ICRA. His research focuses on robot learning, computer vision, and their applications in manufacturing, agriculture, healthcare, space robotics, and planetary exploration. He holds a Ph.D. and has prior roles as an Assistant Professor at UCLouvain and Group Lead at NASA JPL. His work includes projects like the robotic construction system for shotcrete digitization (RoBétArmé) and space robotics for Mars Sample Return. He advises students such as Louis Hanut, Yurui Du, and Pinhao Song, and collaborates with institutions like ULiege and KTH. His recent publications emphasize adaptive robotic frameworks, uncertainty-aware learning, and multi-task diffusion policies. Notable awards include a Best Presentation Award at the ECCV 2022 Workshops. His grants include leadership in projects like Neurobotics (2022–2026) and Learning Partially-factorized Manipulation Tasks (2021–2023). He maintains active roles in academic networks, including the Council of the Faculty of Engineering Science and departmental councils. His lab focuses on bridging perception, learning, and control for real-world robotic applications in challenging environments.

Tom Lenaertsمشاهده پروفایل

استاد

• Artificial Intelligence
• Artificial Life
• Machine Learning
• +۶ مورد دیگر

Tom Lenaerts is a Professor at Université libre de Bruxelles (since 2018) and Director of the Interuniversity Institute of Bioinformatics in Brussels (since 2017). He holds additional roles such as Visiting Scholar at the Center for Human-Compatible AI (CHAI) and Board member of AI4Belgium. His expertise spans Artificial Intelligence, Machine Learning, Evolutionary Game Theory, and Bioinformatics. Research Interests: Lenaerts focuses on cooperative intelligence, collective decision-making, and the intersection of AI with biological systems. His work includes theoretical models for multi-agent systems and applications in computational biology. Awards: He has received multiple recognitions, including the Best poster award (2010), Best regular paper award (2020), and BNVKI Best Thesis Abstract Award (2018). Labs/Teams: Active in the Interuniversity Institute of Bioinformatics and the Federated labs AI and Robotics at ULB.

Willem Waegemanمشاهده پروفایل

دانشیار

• Machine Learning
• Data Science
• Multi-target Prediction
• +۷ مورد دیگر

Willem Waegeman is an Associate Professor at the Department of Data Analysis and Mathematical Modelling within the Faculty of Bioscience Engineering at Ghent University. He is affiliated with the Knowledge-based Systems research unit KERMIT. Research Focus: Machine Learning, Data Science, Multi-target Prediction, Deep Learning, Sequence Models, Time Series Analysis His research spans theoretical developments and practical applications in life sciences, addressing challenges in uncertainty quantification, hierarchical classification, and multi-target learning. Recent work emphasizes epistemic uncertainty , conformal prediction , and transformer architectures for biomedical data. Publications from 2025 highlight applications in drug discovery, microbial analysis, and climate modeling, with methodological innovations in uncertainty-aware learning and multi-modal data integration.

Esha Mendirattaمشاهده پروفایل

دانشیار

• Corporate Governance
• International Strategy
• Diversity and Inclusion
• +۳ مورد دیگر

Esha Mendiratta is an Associate Professor of International Business at Vlerick Business School. Previously, she served as an Assistant Professor at the University of Groningen, Netherlands. Her research focuses on corporate governance, international strategy, diversity, and sustainability, particularly examining board composition, top management teams, and their impact on firm performance. She emphasizes multi-dimensional diversity analysis and inclusion of non-Western contexts like India in her studies. Esha holds a PhD in International Business from the University of New South Wales, Australia, a Master’s in Financial Sector Management from SOAS University of London, and a Bachelor’s in Economics from Delhi University, India. She has been recognized with teaching awards at Groningen and for her cross-cultural management project. Her work bridges academic research with practical insights, advocating for policies that address sustainability challenges through corporate governance and institutional investor collaboration. At Vlerick, she teaches international business on full-time, online, and executive MBA programs. She prioritizes practice-based learning, fostering critical thinking and real-world application among students. Her interests beyond academia include yoga, tennis, and giving back to her native India through education and research contributions.

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +۶ مورد دیگر

Matthew Blaschko is a Senior Lecturer BOF at KU Leuven, affiliated with the Department of Electrical Engineering (ESAT) within the Faculty of Engineering Sciences. He directs the KU Leuven ELLIS unit and serves as a fellow in the ELLIS Health program, part of the European Laboratory for Learning and Intelligent Systems. As a Core PI in the Flanders AI Research Program, he leads work packages for Decision Support Systems and Medical Imaging. He is also a member of the KU Leuven Institute for Artificial Intelligence and co-leads the working group on Machine Learning and Data Science. Habilitation (HDR) from École Normale Supérieure de Cachan Newton International Fellow at University of Oxford Dr. rer. nat. from Max Planck Institutes Tübingen (awarded by Technische Universität Berlin) M.S. from University of Massachusetts Amherst B.S. from Columbia University Blaschko's research focuses on machine learning, computer vision, and medical image analysis, with particular expertise in uncertainty quantification in deep neural networks and trustworthy AI for healthcare applications. His work bridges theoretical foundations with practical implementations, developing methods for calibration, uncertainty estimation, and efficient model deployment. He has made significant contributions to neural network architectures, loss functions, and evaluation metrics for medical imaging tasks, with applications spanning Alzheimer's disease research, surgical phase recognition, and ophthalmic image analysis. His recent publications reveal a strong emphasis on calibration methods, uncertainty quantification, and medical applications of AI. The research spans diverse areas including Alzheimer's disease analysis, Bayesian optimization, novel view synthesis, knowledge extraction from text, and surgical phase recognition. Many papers focus on improving model reliability and safety for healthcare applications, reflecting his commitment to developing trustworthy AI systems that can be deployed in clinical settings. Best Student Paper Award, ECCV 2008 Université Paris-Saclay STIC Doctoral School Best Scientific Contribution Award Best paper award, CVPR 2008 Best paper award, Benelearn 2014 Blaschko actively mentors numerous PhD and MSc students, with current advisees working on topics ranging from uncertainty in deep neural networks to medical image analysis and AI for healthcare. He leads multiple significant research projects including 'onzekerheid in diepe neurale netwerken' (2025-2029), 'Trustworthy AI for Medical Image Analysis and Computer Vision' (2025), and 'Van metingen naar biomarkers in medische beeldanalyse' (2024-2028). His research is supported by the Flanders AI Research Program and other substantial funding sources. As director of the KU Leuven ELLIS unit and a key member of the KU Leuven Institute for Artificial Intelligence, Blaschko leads a vibrant research team focused on machine learning and data science. His laboratory develops cutting-edge AI technologies with practical applications, particularly in healthcare. Technology from his research has been incorporated into MONA, software for ophthalmic image analysis, demonstrating the real-world impact of his work.

• Quantum Computing
• Neural Networks
• Genetic Algorithms
• +۳ مورد دیگر

Delphine Nicolay is a researcher at the Faculty of Science, University of Namur , affiliated with the Namur Institute for Complex Systems . Her work spans quantum computing, neural networks, and optimization techniques. Doctorate in Science (2018) Master in Mathematical Sciences (2012) Research Focus: She specializes in quantum neural networks , genetic algorithms , and their applications to robotics and renewable energy optimization . Her publications highlight advancements in quantum algorithm design, modular network training, and solar thermal collector efficiency. Scientific Contributions: Key conference participations include WIVACE 2017 and Complex Networks workshops. Her work on multi-objective optimization (2014) demonstrated significant improvements in solar technology.

• Motor Control
• Neuroplasticity
• Cerebellar Function
• +۵ مورد دیگر

Koen Cuypers serves as a special guest lecturer at KU Leuven, affiliated with the Movement Control & Neuroplasticity Research Group and the KU Leuven Brain Institute . His work focuses on the intersection of motor control, neuroplasticity, and aging, with particular emphasis on cerebellar function and neurometabolite dynamics. His research spans several critical domains in neuroscience: Investigating GABA dynamics in motor learning across age groups Exploring cerebellar brain inhibition mechanisms in aging populations Developing neuromodulation techniques like HD-tACS for motor control enhancement Validating novel neuro-mobinavigation assessment tools Recent publications (2024-2025) reveal a strong focus on age-related neural changes, with 70% of his work examining aging neuroscience through multimodal approaches including MRS, TMS, and systematic reviews. Cuypers actively contributes to major collaborative projects, notably as Co-promoter for the ongoing study The relationship between motor skill learning and GABA dynamics, synaptic density, and connectivity in motor brain regions (2020-2024). His methodological expertise spans neuroimaging, electrophysiology, and rehabilitation science, with consistent corresponding authorship on high-impact publications in journals like Cerebellum and NeuroImage .