Find Academic Supervisors

• Obesity
• Diabetes
• Bariatric Surgery
• +5 more

Roman Vangoitsenhoven serves as an Assistant Professor at KU Leuven's Faculty of Medicine within the Department of Chronic Diseases and Metabolism. He leads the Subdivision Metabolic unit at the Clinical and Experimental Endocrinology research group (UZ Herestraat 49, box 7003, 3000 Leuven) and holds membership in both the Council of the Faculty of Medicine and the Council of the Department of Chronic Diseases and Metabolism. His research focuses on obesity and diabetes, with particular emphasis on GLP-1 based pharmacotherapy , bariatric surgery outcomes , and cardiometabolic health optimization . Current projects investigate neuropsychobiological approaches to patient selection for weight management therapies, de novo serine biosynthesis in obesity-related cardiac dysfunction, and data-driven interventions for type 2 diabetes management in diverse populations including low-income settings and urban Uganda. Analysis of his 2025 publications reveals a strong thematic concentration on translational obesity research , with recurring subfields spanning pharmacotherapy mechanisms, surgical outcomes, nutritional interventions, and metabolic monitoring technologies. His work demonstrates consistent collaboration across European institutions with significant contributions to clinical guidelines development. Member of Faculty of Medicine Council Member of Department of Chronic Diseases and Metabolism Council Dr. Vangoitsenhoven actively supervises multiple major research projects including a €2.3M ERC-funded study on GLP-1 pharmacotherapy optimization (2025-2029) and international collaborations on diabetes management in low-resource settings. His teaching portfolio includes advanced courses in clinical nutrition, disease mechanisms, and evidence-based medicine within KU Leuven's medical curriculum. He leads the Clinical and Experimental Endocrinology unit's metabolic research subdivision, directing a multidisciplinary team of 12 researchers focused on obesity-diabetes pathophysiology and therapeutic interventions. Current initiatives include the SABO cohort study on post-bariatric sarcopenia in elderly patients and development of digital health tools for personalized diabetes management.

• Riemannian Optimization
• Matrix Manifolds
• Low-Rank Matrix Completion
• +4 more

Pierre-Antoine Absil is a Full Professor at the Louvain Polytechnic School (EPL) , part of the Université catholique de Louvain (UCLouvain) , affiliated with the Mathematical Engineering Center (INMA) and the Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM) . His research spans Riemannian optimization , matrix manifolds , and low-rank approximation with applications in astronomical imaging , signal processing , and environmental data imputation . His work emphasizes Riemannian optimization algorithms matrix and tensor completion geometric data analysis applications in astrophysics and bioinformatics Recent publications focus on Stiefel and Grassmann manifolds , variable projection methods , and exoplanet detection via alternating minimization . While no explicit scientific awards are listed, his collaborations with institutions like IEEE and Springer highlight his impact in computational mathematics and engineering. He has supervised researchers such as Simon Vary , Guillaume Olikier , and Valentin Christiaens through projects in direct imaging , economic dispatch , and graph-based data processing . His laboratory, INMA , drives methodological advances in manifold-valued data analysis and nonlinear optimization .

Orphée De ClercqView profile

Assistant Professor

• Natural Language Processing
• Machine Learning
• Readability Prediction
• +7 more

Orphée De Clercq is an Assistant Professor at Ghent University, specializing in language technology for educational applications. Her research focuses on leveraging Natural Language Processing (NLP) and Machine Learning (ML) to enhance computer-assisted language learning , readability prediction , and automated writing evaluation . She also explores sentiment analysis , emotion detection , and event coreference resolution in Dutch and multilingual contexts. Education : PhD in 2015 with groundbreaking work in readability prediction and fine-grained sentiment analysis for Dutch. Research Trends in her recent publications emphasize: Readability across domains and languages Emotion Detection using transformers and affect lexica Event Coreference in cross-document news Automated Writing Evaluation through NLP Implicit Sentiment Analysis in user-generated content Cross-Lingual Transfer with multilingual datasets She co-supervises four PhD students and contributes to interdisciplinary projects like Steunpunt Toetsen , SentEMO , and NewsDNA . Her teaching includes courses on digital communication and Computer-Assisted Language Learning .

Pieter SimoensView profile

Assistant Professor

• Distributed Artificial Intelligence
• Edge Computing
• Algorithms for Neuromorphic Computing
• +2 more

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

• Knowledge Representation
• Machine Learning
• Ubiquitous Computing
• +7 more

Femke De Backere is a part-time Associate Professor at Ghent University's IDLab research group (Faculty of Engineering and Architecture, Department of Information Technology) and a full-time Senior Scientist at imec. She holds a Bachelor's in Informatics and Master's in Computer Science Engineering from Ghent University, with doctoral research focused on semantic technologies and personalized healthcare systems through collaborations with intensive care units and industry partners. Her research integrates computer science, health psychology, and movement science across three primary domains: (1) developing explainable knowledge models, (2) creating personalized context-aware systems, and (3) designing engagement mechanisms like serious games and gamification. Current projects include digital interventions for cancer survivors, VR-based ecological validity platforms, and physical activity promotion in older adults. Recent publications (2020-2025) demonstrate strong focus areas: 60% address personalized health interventions using machine learning and ontologies; 30% explore virtual reality applications in clinical and behavioral contexts; and 10% examine academic systems development . Longitudinal trends show increasing emphasis on sensor-driven behavioral analytics and adaptive eHealth platforms. She leads significant research initiatives including: GRAY – Ghent University Research for Aging Young (2020-2030) FWO projects on eHealth self-management for cancer survivors and sedentary behavior interruption Interdisciplinary grants for VR-enabled consumer preference studies and physical activity interventions Her lab coordinates doctoral training for 9+ students and collaborates with the eBehaviourChange research unit, focusing on technology-mediated behavior modification across clinical and wellness domains.

• Reproductive Medicine
• Andrology
• Spermatogenesis
• +3 more

Herman Tournaye is a Physician Scientist at the Center for Reproductive Medicine of the Vrije Universiteit Brussel (VUB). With over 760 research outputs and an h-index of 86 , his work spans Reproductive Medicine , Andrology , and Stem Cell Research , focusing on fertility preservation and pregnancy complications . Education : Limited details provided, but inferred MD/PhD in Clinical Sciences. Research Trends : Recent articles emphasize AI in reproductive clinics , testicular stem cell transplantation , and pre-eclampsia biomarkers . His work bridges basic science (spermatogenesis, stem cells) with clinical applications (IVF, embryo transfer, surgical interventions). Scientific Awards : 2008 Fellowship from BSRM Central Office - Orga-med Advisory Role : Supervised 10+ theses, including studies on genetic infertility , IVF outcomes , and testicular fibrosis models . Grants & Projects : Led initiatives like SRP89 (spermatogonial stem cells) and FWOAL1141 (testicular tissue cryopreservation), funded by VUB and FWO. Media Engagement : Frequent expert commentator on fertility topics, appearing in De Morgen , Radio 1 , and Le VIF .

Van Anh Huynh-ThuView profile

Assistant Professor

• Machine Learning
• Bioinformatics
• Gene Regulatory Network Inference
• +3 more

Vân Anh Huynh-Thu is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Liège (Belgium). Her research focuses on improving machine learning techniques with an emphasis on model interpretability. She is based at B28: Systems and Modeling in Quartier Polytech, Allée de la Découverte 10, 4000 Liège, Belgium. Her primary research interests span Machine Learning , Bioinformatics , and Gene Regulatory Network Inference . Dr. Huynh-Thu has developed several influential methods including GENIE3, dynGENIE3, and Jump3 for inferring gene regulatory networks from expression data. Her work bridges the gap between machine learning theory and biological applications, particularly in understanding complex disease mechanisms through computational approaches. She has made significant contributions to interpretable machine learning models that maintain high predictive accuracy while providing insights into feature importance and model behavior. Her research demonstrates a progression from purely computational methods toward translational applications in medical research. Analysis of her recent publications reveals a clear trajectory from foundational work on gene regulatory network inference toward broader applications in medical research, particularly in Crohn's disease. Her research increasingly integrates machine learning with clinical applications, demonstrating a shift from purely computational methods to translational research with direct medical implications. The consistent emphasis across her work is on interpretability, rigorous validation, and the application of tree-based methods to complex biological systems, with a growing focus on proteomics and biomarker discovery for inflammatory bowel diseases. Dr. Huynh-Thu maintains an active GitHub presence with implementations of her methods, demonstrating her commitment to open science and reproducibility. Her software repositories have garnered significant attention from the research community, with GENIE3 alone having 88 stars and 37 forks on GitHub. She has developed multiple implementations of her algorithms in Python, MATLAB, and R, making them accessible to researchers across different computational environments. Her work has been influential in the DREAM challenges, where GENIE3 was the best performer in two network inference competitions.

• Mathematical Analysis
• Functional Analysis
• Partial Differential Equations
• +4 more

Andreas Debrouwere is affiliated with the Department of Mathematics at the Vrije Universiteit Brussel . His research focuses on mathematical analysis, particularly in the areas of ultradifferentiable functions, ultradistribution theory, and surjectivity of differential operators. Projects: Applied harmonic analysis and partial differential equations (2023-2027), Bilateral cooperation for joint PhD (2023-2027), Francqui Chair Fellowship (2023-2024) Debrouwere's work explores functional analysis , operator theory , and weighted spaces . His recent publications analyze surjectivity conditions for differential operators and topological invariants in function spaces. Collaborations with researchers like Thomas Kalmes and Jasson Vindas highlight his contributions to partial differential equations and harmonic analysis. Scientific Awards: Francqui Chair Fellowship (2023-2024) As an advisor, he guided the master's thesis on representation theory of classical Type I groups and quantizations. His research also intersects with applications in image processing and mathematical physics.

• Mental Fatigue
• Human Physiology
• Sports Physiotherapy
• +6 more

Kevin De Pauw is a postdoctoral researcher at the Department of Physiotherapy, Human Physiology and Anatomy at Vrije Universiteit Brussel (VUB). He actively contributes to 12 research projects with a focus on robotics, mental fatigue, brain physiology, and sports physiotherapy. Current projects include Brubotics, APEX, and TBrainBoost Collaboration network spans Belgium, Germany, and Netherlands Key research themes: Mental Fatigue (100%), Robotics (100%), and Prosthetics (52%) Research Interests: His work explores the intersection of brain physiology, fatigue mechanisms, and robotics applications in rehabilitation. He develops predictive musculoskeletal simulations and investigates inter-limb asymmetry in athletes. Article Trends: Recent publications show increasing focus on robot-assisted rehabilitation , brain neuroplasticity , and mental fatigue quantification . Research combines AI-driven wearable robotics with neurophysiological monitoring . Student Supervision: He mentors Master's students in topics related to Lower limb asymmetry analysis Adolescent cognition-fitness relationships Exoskeleton interface design Laboratory Affiliation: Member of Brubotics and TBrainBoost teams at VUB, working on sustainable human-centered robotics and neurocirculation enhancement technologies.

Ben JeurissenView profile

Research Professor

• Medical Image Computing
• Quantitative Magnetic Resonance Imaging
• Diffusion MRI
• +4 more

Prof. Dr. Ben Jeurissen is an Associate Research Professor at the Department of Physics, University of Antwerp , Belgium, specializing in Medical Image Computing , Quantitative MRI , and Diffusion MRI . He received an ERC Consolidator Grant (2023) and FWO Senior Postdoctoral Fellowship (2018–2021) , with a focus on data-driven approaches to study brain microstructure. Education : MSc in Computer Science (2004), Biomedical Imaging (2006), PhD in Science (2012) from University of Antwerp. Research : His work bridges Neuroscience , Medical Imaging , and Computational Methods , particularly in Diffusion MRI and Quantitative MRI for applications in Alzheimer’s disease , spaceflight effects on the brain, and knee imaging . Awards : ERC Consolidator Grant (2023) Australian Museum Eureka Prize Finalist (2024) Multiple Magna/Summa Cum Laude Merit Awards at ISMRM conferences Prize Robert Oppenheimer (2015) Publications span NeuroImage , Human Brain Mapping , Journal of Alzheimer's Disease , and Investigative Radiology , with key contributions to super-resolution MRI , fiber tracking , and brain microstructure analysis . He serves as a contributor to MRtrix and advisor for PhD theses in Computational Anatomy and Medical Imaging .

• Combinatorial Optimization
• Algorithm Design and Analysis
• Game Theory and Social Choice
• +4 more

Jannik Matuschke serves as Associate Professor in the Department of Decision Sciences and Information Management at KU Leuven's Faculty of Economics and Business (FEB). He is an active member of both the KU Leuven Institute for Artificial Intelligence (Leuven.AI) and the KU Leuven Institute for Mobility (LIM), holding office at Hogenheuvelcollege (HOGC) 04.123 in Leuven, Belgium. His academic career spans multiple prestigious institutions across Europe, reflecting his international recognition in operations research and theoretical computer science. Dr. Matuschke completed his PhD in Mathematics in 2013 at Technische Universität Berlin under Martin Skutella and Britta Peis. His academic journey included a postdoctoral position at Universidad de Chile (2014), a DAAD P.R.I.M.E. fellowship at the University of Rome 'Tor Vergata' (2015), and a junior group leader/lecturer position at Technische Universität München (2016-2018) before joining KU Leuven in 2019. His research focuses on the intersection of discrete mathematics, theoretical computer science, and operations research, with specific expertise in combinatorial optimization, algorithm design and analysis, game theory and social choice, robustness and uncertainty, and logistics applications. Dr. Matuschke has made significant contributions to network flow theory, security games, and robust optimization frameworks, developing both theoretical foundations and practical applications. Analysis of his recent publications reveals a strong emphasis on robustness under uncertainty, with increasing focus on security applications and theoretical aspects of scheduling problems. His work demonstrates consistent innovation in decomposing complex optimization problems and developing approximation algorithms for challenging combinatorial settings. 2024 Meritorious Service Reward of the journal Operations Research Dr. Matuschke actively mentors several PhD students and postdocs, including Yanfei Chen, Léonie Gallois, Phablo Moura, Felix Rauh, and Fei Wu. He leads significant research projects such as 'Rigidity and Flexibility: Structures and Algorithms' (starting 2025), 'Designing resilient and recoverable infrastructures via multi-level and multi-stage optimization' (2022), and 'Optimization and analytics for stochastic and robust project scheduling' (2020), with funding from diverse sources including KU Leuven start-up grants, FWO research projects, and DAAD fellowships. As an integral member of the academic community, Dr. Matuschke serves as Associate Editor for Omega (since 2023), Operations Research Letters (since 2024), and OR Spectrum (since 2019). He co-chairs the program committee for WAOA 2025 and has participated in numerous prestigious conference committees. At KU Leuven, he coordinates the 'Master's Thesis in Production and Logistics' program and serves on the Teaching Portfolio Peer Review Committee.

Peter LambertView profile

Associate Professor

• multimedia signal processing
• visual communication
• computer graphics
• +2 more

Peter Lambert is a full-time Associate Professor at Ghent University – imec (Belgium), affiliated with the Internet Technology and Data Science Lab (IDLab) where he coordinates the MEDIA research team since 2013. His academic background includes a Master's degree in Science (Mathematics) and Applied Informatics from Ghent University, followed by a Ph.D. in Computer Science from the same institution in 2007. Prior to his current role, he served as a Technology Developer at Ghent University (2010-2013). Lambert's research focuses on: Multimedia signal processing and visual communication systems Computer graphics and computational geometry Augmented and virtual reality (AR/VR) technologies Video compression and perceptual quality assessment Multimedia security and digital watermarking His recent publications (2024-2025) demonstrate strong emphasis on real-time multimedia systems, VR/AR applications, perceptual quality metrics, and multimedia security. Research trends include deep learning-based video forensics, light field rendering optimizations, perceptual hashing techniques, and adaptive video streaming solutions. As leader of the IDLab-MEDIA team, Lambert oversees research on emerging visual media formats with applications in immersive experiences. The team develops technologies like OpenDIBR (real-time light field renderer) and maintains datasets such as SILVR (Synthetic Immersive Large-Volume Plenoptic Dataset).

Chris CornelisView profile

Professor

• Machine Learning
• Fuzzy Sets
• Rough Sets
• +3 more

Chris Cornelis is a full-time Professor in fuzziness and uncertainty modelling at Ghent University's Department of Applied Mathematics, Computer Science and Statistics. His research integrates fuzzy logic and rough set theory to advance machine learning methodologies for complex data analysis. Education: M.Sc. in Computer Science, Ghent University (2000) Ph.D. in Computer Science, Ghent University (2004) Research Focus: Cornelis pioneers fuzzy-rough hybrid systems for uncertainty handling in machine learning. His work spans theoretical foundations (e.g., implication operators, granular approximations) and practical applications including emotion detection, medical diagnosis, and imbalanced data classification. Key innovations include FRNN-OWA classifiers and polar encoding for missing values, demonstrating exceptional versatility in bridging abstract mathematics with real-world AI challenges. Publication Trends: Recent work (2023-2025) reveals intensified exploration of topological data analysis (Mapper-based rough sets), advanced granular computing (disjoint/adjacent fuzzy granules), and ethical AI ("No Imputation Without Representation"). His research shows consistent progression from foundational fuzzy-rough theory toward multi-disciplinary applications while maintaining mathematical rigor, particularly in Choquet integration and quantifier-based frameworks. Scientific Awards: No specific awards were documented in the provided sources. Research Support: Cornelis has secured competitive funding including FWO postdoctoral mandates, a Ramón y Cajal contract at the University of Granada, and an FWO Odysseus Type II project at Ghent University. These grants enabled foundational work in fuzzy-rough set theory and its applications to complex data problems. Research Unit: He leads research within Ghent University's Computational Web Intelligence (CWI) unit, focusing on intelligent data analysis systems that leverage fuzzy-rough methodologies for web-scale information processing.

Moharram ChallengerView profile

Assistant Professor

• Cyber-physical Systems
• Multi-agent Systems
• Domain-specific Modeling Languages
• +5 more

Moharram Challenger is a tenure-track Assistant Professor in the Department of Computer Science at the University of Antwerp's Faculty of Sciences. Previously, he served as an assistant professor at Ege University (2017-2018) and as a post-doctoral researcher at the University of Antwerp (2019-2020) working on Flanders Make projects PACo and DTDesign. His academic journey includes R&D leadership roles at UNIT IT Ltd. (2012-2016), post-doctoral research at Wageningen University (2016-2017), and tenure-track faculty positions at IAU-Shabestar University (2005-2009). His research spans Cyber-physical Systems , Multi-agent Systems , and Domain-specific Modeling Languages , with recent publications focusing on quantum machine learning, digital twinning, and IoT optimization. Key projects include ITEA ModelWriter, ITEA Assume, and Flanders Make initiatives. His work demonstrates strong integration of model-driven engineering with emerging technologies like quantum computing and reinforcement learning. Challenger actively contributes to the academic community as a member of IEEE and ACM . His publication record shows consistent output across top venues, with 2025 featuring significant work in quantum-enhanced learning and CPS security. Current research emphasizes practical applications in drone energy modeling, medical diagnostics, and industrial IoT systems. His advising activities focus on cyber-physical systems and agent-based modeling, supported by grants from TUBITAK and Flanders Innovation & Entrepreneurship. Key collaborations include European ITEA projects and partnerships with industrial entities through UNIT IT Ltd. Challenger maintains active development through GitHub repositories related to code refactoring, model-driven engineering, and legacy system modernization, reflecting his commitment to practical software engineering solutions.

Philippe BaeckeView profile

Professor

• Big Data Analytics
• Digital Marketing
• Customer Relationship Management
• +5 more

Philippe Baecke is a Professor of Business Analytics and Artificial Intelligence at Vlerick Business School, where he also serves as Programme Director for the Master in Business Analytics & AI and two executive programmes. He holds adjunct roles as Lecturer at Trinity Business School, Ireland, and Professor at the University of Namur. He earned his Doctorate in Applied Economics from Ghent University, complemented by an Advanced Master and Master in Applied Economics, both from Ghent University. His research focuses on leveraging big data analytics across marketing, operations, and finance, with specialties in digital marketing, customer relationship management, and network analysis. Notable areas include ad targeting, spatial modeling, and the application of AI in call center optimization and emotion recognition. He advises companies like KBC, ING, and Telenet on data strategy, blending strategic and operational insights. His recent work explores digital transformation trends, cost-effective AI adoption for SMEs, and the integration of human judgment in forecasting systems. He has published extensively in peer-reviewed journals, addressing topics like telecom data monetization, churn detection, and consumer privacy in addressable TV advertising. Baecke’s expertise also extends to vehicle telematics in insurance risk selection and spatial econometrics in customer acquisition modeling. He has collaborated on projects involving Bluetooth tracking in retail environments and the analysis of urbanization effects on customer behavior.