جستجوی استاد راهنمای دانشگاهی

Tijl De Bieمشاهده پروفایل

استاد

• Foundations of Data Science
• Human-Centric Data Science
• Exploratory Data Analysis
• +۲ مورد دیگر

Tijl De Bie is a Senior Full Professor at the University of Ghent, specializing in machine learning, data science, and their applications in bioinformatics, computational social sciences, and HR analytics. He leads the AI and Data Analytics (AIDA) research group within IDLab-ELIS. PhD in Machine Learning (KU Leuven, 2005) Worked at U.C. Berkeley, U.C. Davis, University of Southampton, and University of Bristol His research focuses on foundational aspects of data science, including fairness in AI, network embeddings, and human-centric methodologies. Recent work explores temporal network simulation, bias mitigation, and large-scale career trajectory datasets. Notable awards include an FWO Odysseus Group I grant and three ERC grants (Consolidator, Proof of Concept, Advanced). Current projects involve ethical AI frameworks and dynamic network analysis. Scientific Awards : FWO Odysseus Group I, ERC Consolidator, ERC Proof of Concept, ERC Advanced Grant He collaborates extensively in interdisciplinary research, applying machine learning to social media analysis and financial domains. His team develops open-source tools like EvalNE and Fondue for network embedding evaluation.

Gerda Claeskensمشاهده پروفایل

استاد

• Model Selection
• Model Averaging
• Post-Selection Inference
• +۴ مورد دیگر

Gerda Claeskens is a full professor of Statistics at the Faculty of Economics and Business (FEB) at KU Leuven, Belgium. She holds positions in the Operations Research and Statistics Research Group (ORSTAT) and is affiliated with the Leuven Statistics Research Center. Her academic journey includes a Licentiate in Mathematics (Summa Cum Laude) from the University of Antwerp and a Ph.D. in Statistics from Limburgs Universitair Centrum (now Hasselt University). Research Interests: Her work focuses on model selection, post-selection inference, nonparametric methods, and high-dimensional statistics. She has contributed extensively to methodologies like focused information criteria, penalized splines, and quantile regression. Awards and Honors: Notable accolades include Fellow of the American Statistical Association (2019), Goodeve Medal (2017), and Medallion Lecturer (2016). She has also held editorial roles in top journals such as Biometrika and Journal of the American Statistical Association . Teaching and Mentoring: She has advised over 20 PhD students and mentored postdoctoral researchers. Her teaching spans advanced statistical methods, probability theory, and business statistics. She has delivered invited lectures globally, including at the European Meeting of Statisticians and the International Society for NonParametric Statistics. Key Contributions: Her book Model Selection and Model Averaging (2008) is a seminal work in statistical methodology. Her research addresses challenges in high-dimensional data, survival analysis, and model averaging, with applications in finance, insurance, and biostatistics.

• Artificial Intelligence
• Health Informatics
• Data Analytics
• +۹ مورد دیگر

Femke Ongenae is an Associate Professor at Ghent University's Faculty of Engineering and Architecture within the Department of Information Technology . She leads research at the IMEC postdoctoral level in areas bridging eHealth, predictive healthcare, and knowledge graph technologies . Her work focuses on context-aware systems, stream reasoning, and hybrid AI for healthcare and smart infrastructure applications. Key research domains: Artificial Intelligence , Health Informatics , Knowledge Graphs Leadership roles: Digital Innovation for Man and Society research unit, eBehaviourChange group Her recent publications (2023-2025) highlight advancements in: Semantic rule mining for decision support systems Anomaly detection in healthcare and water networks Context-aware machine learning for COPD and migraine monitoring Knowledge graph embeddings for industrial process monitoring Collaborative projects involve: Developing INK framework for knowledge graph rule mining Building DIVIDE system for adaptive IoT querying Creating MASSIF platform for semantic IoT services Advancing stream reasoning for real-time healthcare applications

Herman Bruyninckxمشاهده پروفایل

استاد

• Robotics
• Mechatronics
• Bayesian Sensor Processing
• +۱۰ مورد دیگر

Herman Bruyninckx is a Professor at the Faculty of Engineering Sciences , KU Leuven , where he also serves as Vice-Chair of the Department of Mechanical Engineering and head of the Robotics, Automation and Mechatronics (RAM) subdivision. His research focuses on integrating formally represented domain knowledge into robotic systems for real-time, self-explanatory, and certifiable control. He advocates for open standards and software engineering practices in robotics, with a career-long emphasis on knowledge-driven robotic systems over data-driven approaches.

• Cardiac biophysics
• Cardiac arrhythmia
• Directed graph mapping
• +۵ مورد دیگر

Nele Vandersickel is an Associate Professor in the Department of Physics and Astronomy at Ghent University's Faculty of Sciences. Her research bridges physics and cardiology, focusing on the application of computational methods and network theory to understand cardiac arrhythmias. She leads multiple research projects funded by the Research Foundation - Flanders (FWO) and European funding programs, with current work extending through 2025-2029 including a BOF-ZAP professorship in biophysics. Dr. Vandersickel's research interests center on cardiac biophysics, particularly the development and application of Directed Graph Mapping (DGM) as a novel approach for analyzing cardiac arrhythmias. Her work integrates topology , network theory , and computational modeling to investigate mechanisms of atrial and ventricular arrhythmias. She has pioneered methods to identify critical boundaries in atrial tachycardia, analyze fibrotic tissue effects on arrhythmia drivers, and distinguish between different types of cardiac reentry patterns. Her research has significant clinical implications for improving cardiac mapping and ablation procedures. Analysis of her recent publications reveals a clear trajectory from fundamental physics research toward increasingly clinically relevant cardiac electrophysiology applications. Her work now focuses on translating topological approaches into clinical tools that can automatically detect critical arrhythmia boundaries and improve understanding of complex reentrant circuits. The research spans from basic computational modeling to validation in animal models and clinical data analysis. Principal Investigator for 'Directed networks as a novel approach for improving the management of cardiac arrhythmias' (2021-2027, European funding) Promotor for 'Networks and topology to tackle cardiac arrhythmia' (2025-2026, Special Research Fund) Administrative supervisor for multiple doctoral projects on directed graph mapping applications Recipient of BOF-ZAP professorship in biophysics (2019-2029) Dr. Vandersickel actively mentors the next generation of researchers, currently supervising multiple doctoral students including Arthur Santos Bezerra, Robin Van Den Abeele, and Bjorn Verstraeten. Her laboratory develops computational tools like the DG-Mapping software package that enable analysis of reentry and focal activation patterns in cardiac arrhythmias. The research group collaborates extensively with clinical electrophysiologists to ensure their computational approaches address real clinical challenges in arrhythmia diagnosis and treatment.

• Reinforcement Learning
• Artificial Intelligence
• Robotics
• +۳ مورد دیگر

Ann Nowe is a Professor at the Department of Electronics and Informatics (ETRO) at Vrije Universiteit Brussel. Her research focuses on reinforcement learning, multi-agent systems, robotics, and energy management. She leads the Federated labs AI and Robotics, emphasizing interdisciplinary collaboration in artificial intelligence and sustainable robotics. With a prolific publication record (555+ outputs) and 152 active projects, her work spans theoretical advancements and applied solutions in AI ethics, explainability, and energy systems. Her research interests include AI security, neuro-symbolic integration, and federated robotics. Notable contributions include frameworks for explainable AI in multi-agent environments and predictive maintenance algorithms for wind turbines. She has received multiple awards, including the ALA Best Paper Award (2018) and Best Demonstration Award (2019). Dr. Nowe actively participates in conferences and workshops on trustworthy AI, co-organizing events like the World Conference on eXplainable AI (2025). Her work integrates ethical considerations and human-centric design principles across robotics, energy systems, and collective decision-making.

• Complex Systems
• Network Analysis
• Mobility Data
• +۳ مورد دیگر

Vsevolod Salnikov is a Researcher affiliated with the Namur Institute for Complex Systems at the University of Namur. His work focuses on complex systems, network analysis, and mobility data applications. He has led research projects such as OpenNext (2017-2019) and MyOpenNext (2020-2021), which developed predictive models and mobile applications for transportation analysis. Salnikov holds a Doctor of Sciences degree in Human Mobility from the University of Namur (2015), focusing on data acquisition and algorithmic solutions for mobility challenges. His research interests span topology, dynamics of networks, transportation economics, and higher-order network analysis. Notable contributions include studies on emergent grammar dynamics, simplicial complex applications in mathematics, and taxi price comparison mobile apps. Salnikov actively participates in conferences, presenting work on topics like non-Markovian dynamics and community detection in temporal networks. Key projects include leveraging mobility data for public interest and developing tools for urban transport optimization. His datasets include contributions to studies on emergent grammar dynamics, reflecting interdisciplinary collaborations in linguistics and network science.

Antoon Bronselaerمشاهده پروفایل

استادیار

• Data Quality
• Data Fusion
• Uncertainty Management
• +۲ مورد دیگر

Antoon Bronselaer is an Assistant Professor at Ghent University's Department of Telecommunications and Information Processing within the Faculty of Engineering and Architecture. He is an active member of the Database, Document and Content Management (DDCM) research group, focusing on advancing database theory and applications. His research centers on data quality (particularly measurement models and improvement techniques), temporal databases , data fusion , and uncertainty management in databases . Key contributions include measure-theoretic foundations for data quality assessment, dynamical order construction in data fusion, and predicate enrichment techniques for wrapper induction. Analysis of his recent publications reveals consistent innovation in data cleaning algorithms (e.g., Swipe framework), temporal data modeling, and orthographic similarity measures for graph-based text representations. His work increasingly bridges database theory with healthcare applications (e.g., statin-gait relationship studies) and digital humanities (Byzantine epigrams project). No scientific awards are mentioned in the source materials. Dr. Bronselaer's academic guidance includes formal advisees at Ghent University, though specific student names aren't documented in the provided texts. His research is supported through university positions and likely Belgian/Flemish research grants given his publication venues. Current projects involve automated news update analysis, persistent identifier validation, and syntactic interoperability frameworks. He leads research within the DDCM group, specializing in data quality measurement systems and temporal data management solutions. Recent work demonstrates expansion into healthcare data analytics and cultural heritage informatics while maintaining core expertise in database theory.

Marc Van Hulleمشاهده پروفایل

استاد

• Computational Neuroscience
• Brain-Computer Interfacing
• Artificial Intelligence
• +۴ مورد دیگر

Marc Van Hulle is a full Professor at the Laboratory for Neuro- and Psychophysiology , KU Leuven Faculty of Medicine, Department of Neurosciences. He serves as promotor for multiple long-term projects (2022-2028) at the intersection of biomedical engineering and neuroscience, including Alzheimer's early detection, Parkinson's brainstem-basal ganglia interactions, and advanced brain-computer interfaces (BCIs). His affiliations include the KU Leuven Brain Institute (LBI) and Leuven.AI artificial intelligence institute. Research Focus: Specializing in computational neuroscience, Marc Van Hulle pioneers innovative BCI systems, neurophysiological modeling, and biomedical signal processing. His work spans cognitive modeling, neural network analysis, and translational applications for neurological disorders through advanced signal decoding techniques. Publication Trends: Recent articles emphasize decoding speech and motor activities from brain signals (ECoG/EEG), Alzheimer's detection via graph network analysis, and VR-integrated SSVEP systems. Key methodologies include tensor regression, multiway modeling, and phase synchrony analysis, with consistent application of AI to biomedical challenges. Academic Contributions: He supervises multiple researchers including Aurelie de Borman, Axel Faes, and Qiang Sun. His projects as promotor include European Doctoral Network for Neural Prostheses , Adaptive multistate decoding , and Decoding imagined speech , demonstrating sustained leadership in neurotechnology research. Laboratory Context: The Laboratory for Neuro- and Psychophysiology includes PhD members working on neural computing and BCI systems, with former members having completed significant contributions in similar domains.

• Bioinformatics
• Computational Biology
• Machine Learning
• +۳ مورد دیگر

Aranka Steyaert is a Postdoctoral researcher at the Department of Information Technology (EA05) within the Faculty of Engineering and Architecture at Ghent University. She is affiliated with IMEC and focuses on bioinformatics and computational biology, particularly in modeling de Bruijn graphs and correcting sequencing errors using probabilistic graphical models. Her research spans genome assembly, variant calling, and metagenomics applications. Her work integrates graph theory with machine learning to address challenges in next-generation sequencing data analysis. She has published extensively on methodologies for improving node and arc multiplicity estimation in de Bruijn graphs using conditional random fields. She also contributed to the development of the UMGAP metagenomics analysis pipeline. Steyaert was supported by the Research Foundation - Flanders (FWO) as a Fellow from 2018 to 2022.

• Deep Learning
• Image Processing
• Computer Vision
• +۳ مورد دیگر

Evangelia Tsiligianni is a postdoctoral researcher in the Department of Electronics and Informatics at Vrije Universiteit Brussel , Belgium. Her work focuses on deep learning, image processing, and computer vision, particularly for multimodal image restoration and super-resolution tasks. Active in deep learning and neural networks . Specializes in image resolution , compressed sensing , and medical imaging . Her recent research explores graph regularization for depth map super-resolution and interpretable deep learning in financial and healthcare domains. She has presented at conferences like ICASSP, MICCAI, and EUSIPCO.

Jérémie Rolandمشاهده پروفایل

استاد

• Quantum Algorithms
• Quantum Walks
• Adiabatic Quantum Computation
• +۴ مورد دیگر

Jérémie Roland is a Professor at the School of Engineering, Université Libre de Bruxelles (ULB), since October 2011. He is affiliated with the Quantum Information and Communication (QuIC) research group and has held positions at NEC Laboratories America, University of California, Berkeley, and FNRS (Belgian National Fund for Scientific Research). Education: Engineering in Physics (ULB & Ecole Centrale de Lille, 1999), Advanced Studies in Theoretical Physics (ULB, 2000) Research interests focus on quantum algorithms, particularly continuous-time quantum walks, adiabatic quantum computation, and quantum communication complexity. His work explores optimal spatial search algorithms, Hamiltonian simulation, and cryptographic protocols. Scientific awards include the Best of 2016 award for contributions to information theory and a BELSPO Return Grant in 2011. He has collaborated extensively with institutions in France, Canada, and the USA. Teaching roles include Quantum Information and Computation (INFO-H-514), Information and Complexity Theory (INFO-H-422), and mathematics courses like Analyse II (MATH-H-2000). His research is supported by grants including the BELSPO Return Grant.

Sam Verbovenمشاهده پروفایل

استادیار

• Causal Machine Learning
• Multi-task Learning
• Data Science
• +۴ مورد دیگر

Sam Verboven serves as Assistant Professor within the Department of Business Technology and Operations (BUTO) at the Vrije Universiteit Brussel (VUB), based at the Brussels Humanities, Sciences & Engineering Campus (PL5.4.30, Pleinlaan 2). His teaching portfolio includes core courses such as Fundamentals of Data Science, Information Systems Strategy and Management, IT Modeling, and Introduction to Informatics and SCM, reflecting his integration of technical and business perspectives. Verboven's research program centers on context-aware analytical methodologies that harmonize machine learning innovations with operational realities across finance, healthcare, HR, and marketing domains. His primary expertise lies in causal machine learning and multi-task learning frameworks, specifically designed to incorporate domain expertise and business requirements into algorithmic development. This approach enables the creation of decision-support systems that maintain technical rigor while addressing organizational constraints. Analysis of his recent publications (2023-2025) reveals consistent emphasis on causal inference methodologies, fairness-aware decision optimization, and legally aligned machine learning architectures. His work spans high-impact applications including mortgage loan pricing, organ transplantation logistics, financial compliance systems, and sustainable energy forecasting, demonstrating how machine learning can be adapted to meet regulatory and operational demands across diverse sectors. Scientific awards: No awards or fellowships documented in available materials. Advising and grants: Source information does not specify doctoral students, master's advisees, or externally funded research projects. Laboratory affiliations: No dedicated research labs or specialized teams mentioned in provided content.

• Semantic Web
• Knowledge Graphs
• Data Integration
• +۳ مورد دیگر

Anastasia Dimou is an Assistant Professor in the Department of Computer Science at KU Leuven's Faculty of Engineering Technology, affiliated with the Declarative Languages and Artificial Intelligence (DTAI) research group at the De Nayer Campus in Sint-Katelijne-Waver. Her research centers on semantic web technologies and knowledge graphs, specializing in RDF mapping frameworks (RML), SHACL validation, and knowledge graph generation for machine learning applications. She develops methods for heterogeneous data integration, stream processing, and ensuring data quality through rule-based validation and inconsistency resolution. Recent publications (2019-2023) demonstrate expertise in RML ontology design, SHACL validation techniques, and systematic reviews of RDF graph generation. Key projects include 'Knowledge Graphs for Machine Learning Data Management' (2021-2023) and 'Knowledge Graphs for Data Integration' (2022-2026), focusing on scalable knowledge graph applications. Dr. Dimou serves on the Council of the Faculty of Engineering Technology, the Computer Science Department Council, and the POC Elektronica-ICT for the Faculty of Industrial Engineering Sciences, contributing to institutional governance and academic strategy. As part of DTAI, she collaborates on declarative language applications in artificial intelligence, with teaching responsibilities spanning Web AI, Data Engineering, and Knowledge Graph courses that bridge theoretical research with practical implementation.

• Computational Geometry
• Combinatorial Algorithms
• Geometric Combinatorics
• +۲ مورد دیگر

Jean Cardinal is a full professor of computer science at the Free University of Brussels (ULB) , where he co-directs the Algorithms Research Group . His work bridges theoretical computer science and discrete mathematics. Education PhD in Computer Science, Université libre de Bruxelles (ULB), 2001 FNRS Fellowship during PhD studies Visiting professorships at ETH Zurich and Université Sorbonne Paris Nord His research focuses on computational geometry , combinatorial algorithms , and geometric combinatorics , with applications to data compression and information theory. Recent work explores algorithm design for geometric data, polytopes, and graph associahedra. The 15 most recent articles span topics in computational geometry, algorithmic complexity, and discrete structures, including publications in Combinatorial Theory , SODA , European Journal of Combinatorics , and Mathematical Programming . Key themes: polytopes, rectangulations, degeneracy testing, and dynamic graph algorithms. Jean actively participates in collaborative research and organizes workshops like the Annual workshop on geometry and graphs and Gremo's workshop on open problems . His publications are accessible via DBLP , ArXiv , and the ULB repository DI-fusion .