جست‌وجوی استاد و پژوهشگر

Karoline Faustمشاهده پروفایل

دانشیار

• Microbial community dynamics
• Microbial systems biology
• Bioinformatics
• +۶ مورد دیگر

Karoline Faust is an Associate Professor at KU Leuven, affiliated with the Laboratory of Molecular Bacteriology (Rega Institute) and the Faculty of Medicine . She contributes to the iSi Health and Leuven One Health institutes, and serves on senior academic councils. Her research spans microbial systems biology, focusing on community dynamics and network analysis. Education: PhD in bioinformatics (2010, KU Leuven) Affiliations: KU Leuven, ISME Journal editorial board, Belgian Society for Microbiology Her research investigates microbial community dynamics , systems biology approaches to microbiomes, and bioinformatics tool development . She specializes in modeling human gut microbiota , synthetic microbial communities , and environmental microbiomes (e.g., microplastic impacts on Daphnia microbiomes). Her work integrates metabolic modeling , network analysis , and experimental systems to understand microbial interactions. Recent publications highlight her contributions to microbial network inference , 16S rRNA sequencing protocols , microfluidics , and ecological modeling of microbiomes. She develops tools like manta , miaSim , and CoNet to analyze community structures. Teaching: Karoline co-teaches courses in microbiology, bioinformatics, and network analysis at KU Leuven, and has contributed to international workshops on microbial network inference. Scientific Engagement: She serves as Senior Editor at ISME Journal and Secretary of the Belgian Society for Microbiology .

• Structural Equation Modeling
• Psychometrics
• Statistics
• +۵ مورد دیگر

Yves Rosseel is a Professor at Ghent University (UGent) specializing in Structural Equation Modeling (SEM) , Psychometrics , and Statistical Methodology . With over 15 recent publications (2024-2025), he focuses on small-sample SEM solutions, factor score regression, measurement error, and Bayesian extensions. His work bridges Statistics with applications in Psychology , Education , and Neuroimaging . Research Trends Developed Mixture Multigroup SEM for cross-group comparisons Proposed Information-Theoretic Hypergraphs in psychometrics Advanced Two-Stage Estimation for round-robin data Created blavaan R package for Bayesian SEM Investigated Measurement Error in hypothesis testing Scientific Contributions Published 84 Social Sciences papers, 37 Statistics works, and 11 Neuroimaging studies Promoted 8 PhDs including Sara Dhaene and Julie De Jonckere Co-authored 12+ works with Marijke Welvaert and 10+ with Stijn Vanheule

Van Anh Huynh-Thuمشاهده پروفایل

استادیار

• Machine Learning
• Bioinformatics
• Gene Regulatory Network Inference
• +۳ مورد دیگر

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.

Axel Cleeremansمشاهده پروفایل

استاد

• Consciousness
• Implicit Learning
• Statistical Learning
• +۸ مورد دیگر

Axel Cleeremans is a Professor of Cognitive Psychology at the Université Libre de Bruxelles and a Research Director at the Fonds de la Recherche Scientifique (F.R.S.-FNRS). He leads the Consciousness, Cognition and Computation Group (CO3) and the Center for Research in Cognition & Neurosciences . His work focuses on understanding how consciousness arises from unconscious learning mechanisms, encapsulated in the Radical Plasticity Thesis . Roles: Professor, Research Director Labs: CO3, CRCN Research Interests His research explores the interplay between conscious and unconscious processes, particularly in learning, memory, perception, decision-making, and social cognition . He investigates how the brain's internal states become conscious through continuous learning and how these mechanisms apply to cognitive control and motor skill acquisition. Scientific Awards Ernest-John Solvay F.R.S.-FNRS prize (2015) for human and social sciences Publications Trends The 15 most recent articles span topics from unconscious inference limits , metacognitive architectures , and neural correlates of agency to methodological critiques in consciousness science . They emphasize temporal vision acuity , collective decision-making , and computational modeling of cognitive processes.

Pedro Pintado Jorge Gonçalvesمشاهده پروفایل

مدرس ارشد

• Computational neuroscience
• Machine Learning
• Simulation-based inference
• +۴ مورد دیگر

Pedro Pintado Jorge Gonçalves is a Senior Lecturer at the Faculty of Engineering Sciences at KU Leuven, with primary affiliation in the Department of Electrical Engineering and strong ties to the Department of Computer Science. He leads the Gonçalves lab at the VIB-KU Leuven Center for Neuro Electronics Research Flanders (NERF), and is also a member of the KU Leuven Brain Institute and Leuven.AI Institute for Artificial Intelligence. His educational background includes training as a physicist with a focus on neuroscience applications. His research integrates machine learning with computational neuroscience to develop methods for extracting mechanistic insights from complex neural data. His work specializes in simulation-based inference techniques that bridge data-driven and theory-driven approaches in neuroscience. The publication trends reveal a strong focus on developing and applying machine learning methods to neuroscience problems, with particular emphasis on neural circuit modeling, biophysical simulations, and the development of computational tools like the sbi toolkit. His research spans multiple scales from single neurons to population dynamics and behavior. As an educator, he teaches courses including Calculus and Mathematical Engineering, and mentors numerous students through his lab. His collaborative approach is evident in his extensive co-authorship network across computational neuroscience and machine learning fields. Dr. Gonçalves actively supervises multiple PhD and master's students in his lab, fostering the next generation of computational neuroscientists. His research is supported by multiple ongoing projects including 'Simulation-based inference for mechanistic models of neural dynamics' (2025-2029) and 'Computational research into the principles of robustness in neural systems' (2023-2027). His laboratory focuses on combining theoretical models with advanced machine learning methods to design more accurate neural models, quantitatively test mechanistic hypotheses, and derive experimentally-testable predictions about neural systems in health and disease.

Dries Benoitمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Marketing Modeling
• Customer Relationship Management
• +۵ مورد دیگر

Dries Benoit is an Associate Professor in Data Analytics at Ghent University, specializing in Bayesian Statistics and its applications in business engineering. He teaches advanced courses in Bayesian Statistics, Statistical Modeling & Datamining, and Pricing & Revenue Management. Additionally, he serves as a visiting professor at Université de Namur and IESEG School of Management, where he contributes cross-institutional expertise in statistical modeling. Primary Affiliation : Ghent University Secondary Affiliations : Université de Namur, IESEG School of Management Research Focus : Dries Benoit's work bridges methodological advancements in Bayesian statistics with real-world applications across business domains. Core areas include marketing modeling, customer relationship management (CRM), learning analytics, and operations research. His interdisciplinary collaborations extend to medicine, energy, and education sectors, leveraging data science to solve complex problems in churn prediction, demand forecasting, and multimodal analysis. Publications & Research Trends : His recent publications highlight: Machine learning techniques for question difficulty estimation Uncertainty quantification in industrial AI Transformer-based models for ordinal regression Churn prediction in business-to-business contexts Deep learning applications in cultural analytics Ensemble methods for robust classification These works emphasize interpretable modeling, cross-domain adaptability, and balancing technical precision with business strategy applications.

• Reinforcement Learning
• Simulated Annealing
• Computational Genomics
• +۵ مورد دیگر

Bernard Manderick serves as Professor in the Department of Informatics and Applied Informatics at Vrije Universiteit Brussel (VUB), Belgium, with research activity spanning from 1988 to 2023. His work encompasses 116 research outputs across 40 projects, including fundamental AI research, international collaborations with institutions in Kenya and France, and interdisciplinary ethics initiatives. Manderick's research fingerprint reveals dominant expertise in Reinforcement Learning (100% match), Simulated Annealing (99%), and Computational Genomics (95%), with significant contributions to Support Vector Machines, Machine Learning, Sampling Methods, and Combinatorial Optimization. His methodology integrates engineering principles with materials science applications, particularly in optimization algorithms and bioinformatics. Recent publication trends (2016-2023) demonstrate sustained focus on adaptive sampling techniques, evolutionary algorithms, and AI ethics. His 2023 MCMC study builds upon 2017 work on CMA-based samplers, while 2016 publications bridge genomic variant classification and game-theoretic analysis of signaling dynamics in adaptive populations. No scientific awards were explicitly documented in the source material. Manderick has supervised three PhD candidates: David Catteeuw (2015, signaling evolution), Feng Liu (2010, gene normalization), and Yifei Chen (2010, protein interaction extraction). His grant portfolio includes the €2.7M SRP66 project on AI ethics (2019-2026), bilateral PhD programs with INSA Rouen, and Kenya-focused development initiatives through Moi University partnerships. Research leadership spans the Existential Ethics of AI platform and international networks including the PPSN conference series (active since 1991), with ongoing collaborations establishing VUB as a hub for adaptive systems research and global AI capacity building.

• Computational Neuroscience
• Machine Learning
• Simulation-Based Inference
• +۳ مورد دیگر

Pedro J. Goncalves is a computational neuroscientist and machine learning researcher serving as Group Leader at VIB-NERF and Associate Professor at KU Leuven . His work bridges theoretical neuroscience and advanced machine learning methodologies to study neural computation. Organizational Affiliations VIB-NERF (NERF.be/en#/) KU Leuven (www.kuleuven.be/english/kuleuven) Co-developer of the sbi Python package His research focuses on simulation-based inference (SBI) for modeling neural circuits, with applications in neurodegeneration studies (e.g., Parkinson's disease), uncertainty quantification in brain imaging, and development of open-source tools for computational neuroscience. Key projects involve: Collaborative work with Patrik Verstreken's lab on ML methods for brain-on-chip data analysis Leadership in the SBI Hackathon for improving open-source inference workflows PhD recruitment via VIB.AI International PhD Call 2025 He actively contributes to community initiatives like the SBI Discord Server and interdisciplinary collaborations across Europe. Publications include work on masked variational autoencoders for neural data modeling in Cell Reports and foundational SBI methodologies.

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.

• Applied Mathematics
• Computational Science
• Plasma Physics
• +۷ مورد دیگر

Giovanni Samaey is a Professor of Applied Mathematics and Mathematical Engineering at KU Leuven's Faculty of Engineering Science. He leads research in computational and multiscale methods, focusing on plasma edge modeling for nuclear fusion reactors, Bayesian inversion, and Monte Carlo algorithms. Appointed in 2011, he currently supervises ten PhD students and has held a five-year membership in the Young Academy. His work bridges academic research with societal impact, co-founding Platform Wiskunde Vlaanderen to strengthen mathematics in Flanders. Education: Graduated in Computer Science (specializing in applied mathematics) from KU Leuven (1996), completed a PhD in 2001 under Prof. Dirk Roose, supported by an NFWO fellowship. He transitioned from engineering studies due to a passion for mathematics' societal impact, initially avoiding academia but ultimately embracing teaching and research. Research Interests: Development of numerical methods for multiscale phenomena, including micro-macro acceleration algorithms, multilevel Monte Carlo techniques, and hybrid fluid-kinetic models. His work addresses challenges in plasma physics, fusion energy systems, and inverse problems. Key contributions include the X-Factor book (with Joos Vandewalle) promoting mathematics outreach and advancing computational tools for plasma edge simulations. Awards/Honors: Member of the Young Academy (2016-2021), NFWO Aspirant Fellowship (2001-2002). His efforts in STEM advocacy and mathematics promotion through Platform Wiskunde Vlaanderen highlight his dedication to education and public engagement. Advising & Leadership: Supervises a team of PhD students in applied mathematics and computational science. Active in curriculum development and interdisciplinary collaborations, particularly in fusion energy modeling. His research group contributes to codes like EMC3-EIRENE for plasma edge simulations. Labs/Teams: Leads projects in multiscale numerical methods and plasma simulation within KU Leuven's engineering faculty. Collaborates internationally on fusion reactor modeling and Monte Carlo algorithm design, emphasizing computational efficiency and scalability.

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.

• Programming Language Theory
• Functional Programming
• Logic Programming
• +۳ مورد دیگر

Tom Schrijvers is a Professor at the Department of Computer Science, Faculty of Engineering Science, KU Leuven. His research focuses on programming language theory, functional programming, logic programming, and computational effects. Advancing multi-stage programming with computational effects (2024–2028) Generating Educational Feedback with Program Analysis (2024–2028) Programino: An Educational Programming Platform (2023–2027) eTeacher: Interactive web-based programming education platform (2023–2025) AmPERSand: Programming Education Runtime System (2023–2026) His recent publications emphasize bidirectional transformations, effect handlers, and programming education tools. He collaborates extensively with British institutions like Imperial College London and works on the book Language Engineering in Haskell with Dr. Nicolas Wu.

• Computer Science
• Mathematics
• Bioinformatics
• +۴ مورد دیگر

Pieter Audenaert is an Associate Professor at Ghent University's Faculty of Engineering and Architecture, affiliated with the Department of Information Technology and the Internet Technology and Data Science Lab. He simultaneously holds a postdoctoral researcher position at IMEC. His interdisciplinary research bridges computer science, mathematics, and engineering. Primary research domains include: Algorithm design for network optimization (Steiner trees, fiber networks) Computational biology (de Bruijn graphs, genome assembly) Transportation logistics (container drayage, port operations) Telecommunication systems (VLC, network flows) Discrete mathematics applied to network science Recent publications (2019-2025) demonstrate strong focus on optimization algorithms for both biological networks and transportation systems, with increasing applications of probabilistic modeling and simulation techniques. Bioinformatics work frequently employs graph-theoretic approaches to genome analysis. Awards and honors: Knuth Reward Check (2003) Laureaat Vlaamse Wiskunde Olympiade (1996) Leads research at the Internet Technology and Data Science Lab, collaborating extensively with industry partners like Port of Antwerp and Belgian retailers for transportation optimization projects.

• Machine Learning
• Surrogate Modeling
• Multi-Objective Optimization
• +۷ مورد دیگر

Ivo Couckuyt is an Assistant Professor at the Faculty of Engineering and Architecture , Ghent University, affiliated with the Department of Information Technology and the Internet Technology and Data Science Lab . He actively contributes to research projects funded by the Research Foundation - Flanders (FWO) and regional grants. Bayesian multi-objective optimization Surrogate modeling for engineering Uncertainty quantification Radar-based healthcare systems His research spans machine learning , computational engineering , and multi-fidelity simulations , with recent work focusing on physics-informed optimization, efficient data transmission for radar systems, and nuclear fuel analysis. Publications emphasize Bayesian frameworks and gradient-enhanced kriging for complex systems. As a PhD supervisor and promotor , he has guided 16 doctoral researchers across diverse topics including microwave filters , metamaterials , and radar healthcare applications . Current projects target additive manufacturing for electric machines and data-efficient generative models , combining uncertainty quantification with multi-physics simulations .