جست‌وجوی استاد و پژوهشگر

Beat Signerمشاهده پروفایل

استاد

• Cross-Media Information Spaces and Architectures (CISA)
• Interactive Paper
• Dynamic Data Physicalisation
• +۷ مورد دیگر

Beat Signer is Professor of Computer Science at Vrije Universiteit Brussel (VUB) and Director of the Web & Information System Engineering (WISE) laboratory. His research focuses on cross-media information spaces and architectures (CISA) spanning interactive paper, dynamic data physicalisation, and tangible holograms. His educational background includes a Computer Science degree from ETH Zurich, where he completed his PhD thesis on fundamental concepts for interactive paper and cross-media information spaces in 2005. Key research areas include: Cross-media document formats and resource-selector-link (RSL) hypermedia metamodel Context-sensitive adaptation and cross-media transclusion Multimodal interfaces (Mudra, iGesture) and tangible computing Internet of Things interoperability and semantic middleware Recent publications (2023-2025) reveal strong trends in dynamic data physicalisation hardware, FAIR positioning systems (OpenHPS), end-user IoT development (eSPACE), and next-generation human-information interaction paradigms. His work increasingly integrates Solid protocols for decentralized data and explores thermal dimensions in tangible holograms. As Principal Investigator, he leads multiple EU-funded projects including OpenHPS, TangHo, and eSPACE. His team has developed influential frameworks like MindXpres for cross-media presentations and CMT for context modeling. He supervises 13 doctoral researchers including Dr. Maxim Van de Wynckel and Dr. Audrey Sanctorum, focusing on cross-media applications in education, IoT, and data visualization. His WISE laboratory develops innovative solutions bridging digital and physical information spaces through projects like ArtVis for collaborative data exploration and TangHo for physically augmented virtual objects.

• Computer Networks
• Software Defined Networking (SDN)
• Network Function Virtualization (NFV)
• +۵ مورد دیگر

Wouter Tavernier is an Associate Professor at Ghent University and a Postdoctoral researcher at IMEC, affiliated with the Faculty of Engineering and Architecture's Department of Information Technology (EA05). He leads research at the Internet Technology and Data Science Lab, focusing on networking innovations for future communication systems. His research explores: Network architectures for 5G/6G, HPC, and deterministic systems SDN/NFV orchestration for cloud/edge services Optical networking including programmable photonics and routing optimization Network resilience through fault-tolerant protocols and traffic engineering Publications (2020–2025) emphasize: Deterministic networking for real-time systems and industrial IoT Resource optimization in HPC/cloud networks Convergence of optical/wireless technologies in 6G Decentralized edge intelligence via programmable swarm solutions He advises doctoral researchers on projects including: QoS Optimization in 6G Networks (Jakob Miserez) Control Strategies for Network-Cloud Services (Abhinaba Chakraborty) Software-Based Networking for 6G (Mohammadreza Heydarian) and secured grants such as Network optimization for HPC workloads (Special Research Fund). At the Internet Technology and Data Science Lab, he collaborates on EU-US initiatives like the Next Generation Internet program, advancing open internet architectures.

• Algebraic Combinatorics
• Graph Theory
• Symmetric Functions
• +۳ مورد دیگر

Logan Crew is an Assistant Professor in the Department of Combinatorics & Optimization at the University of Waterloo. His research focuses on algebraic combinatorics, particularly applications of algebra to graph theory, including the chromatic symmetric function and its generalizations. He holds a PhD from the University of Pennsylvania (2020), with advisors Jim Haglund and Greta Panova, and has conducted postdoctoral work with Sophie Spirkl, also at Waterloo. Education: PhD in Mathematics, University of Pennsylvania (2020) Master's Thesis: 'The Shuffle Conjecture' (University of Pennsylvania, 2017) Bachelor's Thesis: 'On the characterization of group orders with specific properties' (University of Maryland, 2015) Research Interests: Dr. Crew explores algebraic structures in graph theory, including chromatic symmetric functions, Tutte symmetric functions, and their applications in graph polynomial construction and combinatorial topology. His work often involves symmetric function theory, algebraic graph invariants, and computational methods for graph analysis. Key themes include graph isomorphism detection, modular relations, and deletion-contraction operations. Teaching & Collaboration: Recent courses include MATH239 (Combinatorics), CO480 (History of Mathematics), and MATH119 (Calculus for Engineering). He co-supervises multiple graduate and undergraduate students, emphasizing collaborative research. His work frequently intersects with colleagues like Sophie Spirkl, Oliver Pechenik, and others in graph theory and algebraic combinatorics. Contributions: Dr. Crew's research has been supported by institutions like the Simons Foundation. He actively reviews for journals such as Mathematical Reviews and Advances in Mathematics, and regularly presents at conferences like FPSAC and CanaDAM. His preprints explore K-theoretic analogues (Kromatic functions) and vertex-weighted generalizations of symmetric functions.

• Polyhedral combinatorics
• Extended formulations
• Combinatorial optimization
• +۴ مورد دیگر

Samuel Fiorini is an Associate Professor in the Department of Mathematics at the Free University of Brussels (Université libre de Bruxelles). His office is located in the Algebra and Combinatorics building (CP 216) at Boulevard du Triomphe, B-1050 Brussels, Belgium. He has been actively contributing to theoretical computer science and discrete mathematics research for over two decades. Professor Fiorini's research focuses on polyhedral combinatorics, extended formulations, combinatorial optimization, approximation algorithms, and structural graph theory. His work bridges theoretical computer science and discrete mathematics, with significant contributions to understanding the limitations of linear programming approaches for combinatorial optimization problems. His research has demonstrated fundamental limits on how well certain NP-hard problems can be approximated using linear programming techniques. An analysis of his recent publications reveals a strong focus on polyhedral combinatorics and extended formulations, with particular emphasis on understanding the expressive power and limitations of linear and semidefinite programming for combinatorial optimization problems. His work often explores the connection between communication complexity and extended formulations, revealing deep theoretical insights about the inherent complexity of representing combinatorial polytopes. His notable scientific achievements include receiving the Best Paper Award at STOC 2012 for his groundbreaking work "Linear vs. Semidefinite Extended Formulations: Exponential Separation and Strong Lower Bounds," which demonstrated an exponential separation between the power of linear and semidefinite extended formulations for combinatorial optimization problems. Professor Fiorini has supervised numerous PhD students and hosted many postdoctoral researchers. His current and former PhD students include Carole Muller, Matthew Drescher, Marco Macchia, Selim Rexhep, Eglantine Camby, and Céline Engelbeen. Among his postdoctoral researchers are notable scholars such as Manuel Aprile, Krystal Guo, Tony Huynh, and Hans Raj Tiwary. He has also served on program committees for major conferences including APPROX 2019, IPCO 2017, WAOA 2016, and STACS 2016. His research group works on fundamental problems in combinatorial optimization, with connections to theoretical computer science and discrete geometry. He leads an ERC-funded project focusing on extended formulations and their applications to combinatorial optimization problems.

• 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.

• Process Mining
• Artificial Intelligence
• Predictive Analytics
• +۷ مورد دیگر

Johannes De Smedt is an Assistant Professor (tenure track) at the Faculty of Economics and Business (FEB) at KU Leuven, affiliated with the Information Systems Engineering Research Group (LIRIS). He leads the education commission for the OC Handelsingenieur in de beleidsinformatica program at the FEB's Leuven campus and is a member of Leuven.AI, the university's artificial intelligence institute. His work focuses on advancing process mining, AI-driven decision support systems, and data-aware business process optimization. Research interests include predictive process monitoring, adversarial machine learning for process analytics, object-centric event log analysis, and human resource analytics through process mining. He explores applications in employee mobility, bankruptcy prediction, and explainable AI for business processes. Notable contributions include developing methodologies for synthetic time series generation, adversarial robustness in process models, and integrating decision models with business process frameworks. His work bridges theoretical advancements with practical implementation in domains like healthcare, education, and finance. Recent publications emphasize trustworthy AI practices and scalable data preparation techniques for complex process data. No scientific awards are explicitly listed in the provided information. Advising and grants sections remain unpopulated in the available data. His research is anchored in the LIRIS group, collaborating on projects like MERODE for data-aware process systems and the iDOCEM framework for object-centric event logging standards.

• Air Quality
• Environmental Monitoring
• Sensor Networks
• +۴ مورد دیگر

Esther Rodrigo Bonet is a postdoctoral researcher at the Vrije Universiteit Brussel , affiliated with the Department of Electronics and Informatics in the College of Engineering. Her work focuses on air pollution modeling, sensor networks, and explainable AI, combining deep learning with physics-guided frameworks to analyze environmental data. Research Interests: Esther specializes in air quality monitoring, leveraging graph neural networks and variational autoencoders to model spatiotemporal pollution patterns. Her research integrates explainable AI principles to enhance transparency in environmental inference systems, particularly for low-cost sensor networks and urban exposure assessments. Scientific Awards: Best Student Paper Award (2024) FWO PhD Strategic Basic Research Fellowship (Nov 2020) Research Output Trends: Over the past five years, her work has emphasized AI-driven environmental monitoring, with a focus on graph-based deep learning architectures. Key applications include urban air quality mapping, electromagnetic field exposure analysis, and collaborative filtering systems adapted for environmental data. Collaborations: She collaborates extensively with Professor Nikolaos Deligiannis and multidisciplinary teams across Belgium, contributing to projects like Explainable Physics-guided Deep Learning for Air Pollution Inference and the development of frameworks for IoT cities.

• Information Theory
• Medical Imaging
• Deep Learning
• +۳ مورد دیگر

Abel Diaz Berenguer is a Researcher at the Department of Electronics and Informatics at Vrije Universiteit Brussel. He holds two postdoctoral researcher positions within the Research-Development-Innovation group. His work focuses on applying artificial intelligence techniques to healthcare, medical imaging, and transportation systems. He has been actively involved in projects such as the VLIR409 initiative addressing treatment response prediction for COVID-19 in Cuba and the BRGRD81 project on precision medicine for COPD. Education: Postdoctoral Researcher in Artificial Intelligence (Vrije Universiteit Brussel) Research Interests: Abel's research spans information theory, medical imaging analysis, deep learning, and causal inference applications. He develops algorithms for facial expression recognition, disease prognosis models, and financial time series prediction. His work emphasizes practical applications in healthcare diagnostics, driver safety systems, and personalized medicine. Grants & Projects (2022-2027): BRGRD81: Applied AI for COPD precision medicine ($1.5M funding) VLIR409: Multicentric validation of COVID-19 prognostic models ($1.2M funding) Awards: BENELEARN2019 Best Paper Award (2019) Academic Engagement: Abel has reviewed for IEEE Transactions on Medical Imaging, participated in the 27th Iberoamerican Congress on Pattern Recognition, and delivered lectures on item response theory modeling in 2024. Labs/Teams: Part of the Medical Imaging and Artificial Intelligence Lab at VUB, collaborating with global institutions like Mbarara University and MUST in Uganda.

• Decision science
• Data science
• Causal machine learning
• +۶ مورد دیگر

Wouter Verbeke is Professor of Decision Science at KU Leuven's Faculty of Economics and Business (FEB), where he directs the POC Doctoraatsprogramma Economie en Bedrijfswetenschappen and chairs the Doctoral Committee. He is a core member of Leuven.AI and leads the Information Systems Engineering Research Group (LIRIS), focusing on data-driven business decision-making. His research spans causal machine learning, uplift modeling, and cost-sensitive learning with applications in fraud detection, credit risk, operations management, and pricing. Verbeke pioneers decision-centric frameworks that optimize business outcomes rather than purely predictive accuracy, emphasizing causal inference for resource allocation and fairness in algorithmic decision systems. Analysis of his recent publications reveals a strong trend toward graph-based anti-fraud systems (particularly for AML/smurfing detection), interpretable causal machine learning in finance, and HR analytics using process mining. His work increasingly integrates fairness considerations into operational decision systems while advancing theoretical foundations of treatment effect estimation under network interference. Scientific awards: EURO award for best article in European Journal of Operational Research (Innovative Applications of O.R.) (2014) Verbeke actively supervises doctoral candidates including S. De Vos and M. Reusens, with research funded through major grants like ANUBIS (Aligned oNline and multilevel User and entity Behavior) and prescriptive analytics projects for intelligent organizations. He serves on editorial boards of Decision Support Systems, Business Information Systems Engineering, and INFORMS Journal on Data Science. As leader of the LIRIS research group within FEB, he drives interdisciplinary collaborations between data science and business domains, maintaining strong industry partnerships for real-world validation of fraud analytics, credit risk, and operational decision systems.

Christian Colotمشاهده پروفایل

پژوهشگر

• Data Mining
• Social Network Analysis
• Marketing
• +۴ مورد دیگر

Christian Colot is a researcher at the Namur Institute for Complex Systems (University of Namur) with a focus on data mining, social network analysis, and decision support systems. His work bridges telecom data analytics, marketing, and credit risk, emphasizing data monetization and churn detection. Key Research Areas : Data Mining, Telecom Data Monetization, Churn Detection, Decision Support Systems. Notable Projects : Leveraging Mobile Data for Churn Detection (2015–2020), Network-Based Metrics for Decision Support (2022). Scientific Recognition : Nominated for the Dauby Award for his master's thesis. Conference Contributions : Presented at international events including ICDSST 2017 and SEKE 2022. His recent publications highlight innovative approaches to mobile data analysis, social influence disentanglement, and metaphor computational modeling. Collaborations with IBM and telecom stakeholders underscore his industry impact.

Marvyn Gulinaمشاهده پروفایل

پژوهشگر

• Dynamical Systems
• Artificial Neural Networks
• Reservoir Computing
• +۵ مورد دیگر

Marvyn Gulina is a researcher at the University of Namur , affiliated with the Faculty of Computer Science and the Namur Institute for Complex Systems . His work focuses on dynamical systems, reservoir computing, and spectral network identification, particularly through the approximation of the Koopman operator. Education: Bachelor in Physical Sciences (UNamur, 2014) Bachelor in Mathematical Sciences (UNamur, 2015) Master in Physical Sciences (ULB, 2017) Aggregation of Upper Secondary Education (UNamur, 2021) PhD in Sciences (UNamur, 2023) Marvyn's research explores the intersection of chaos theory , neural networks , and network analysis , with applications in cryptography and synchronization. His projects often involve dynamic mode decomposition and eigenvalue analysis for spectral identification. Recent projects include: Conception d'une Application Numérique en Ligne d'Aide à l'Apprentissage (PHD Project, 2024–) Cartographie et Mutualisation des Ressources Numériques en Mathématiques (Research Project, 2023–2025) Approximations of the Koopman Operator for Spectral Identification of Networks (PHD Project, 2017–2023) He has presented at international conferences like the Benelux Meeting on Systems and Control and the International Symposium on Mathematical Theory of Networks and Systems . His affiliation with the Namur Institute for Complex Systems underscores his interdisciplinary approach.

Andres Algabaمشاهده پروفایل

استاد

• Fairness in Machine Learning
• Large Language Models
• Science of Science
• +۷ مورد دیگر

Andres Algaba is a Part-time Professor in econometrics and mathematics for business and economics at Vrije Universiteit Brussel (VUB), where he also serves as an FWO Postdoctoral Researcher in Artificial Intelligence and Coordinator in Generative AI. He holds a joint PhD in Business Economics from VUB and Ghent University (2020), along with MSc (2015) and BSc (2014) degrees in Business Sciences from KU Leuven. His research focuses on: Fairness in machine learning and algorithmic decision-making Large language models and their scientific applications Generative AI and interpretability methods Science of science and bibliometric analysis Econometric modeling and sentiment analysis His publications demonstrate a strong evolution from econometric modeling (2020) toward cutting-edge AI fairness research and LLM analysis (2023-2025), with increasing focus on generative models and scientific disruption patterns. Awards: FWO Postdoctoral Fellowship Election to Young Academy Belgium (2024) Research Leadership: Principal investigator for Francqui Collen Start-Up Grants (2025-2028) focusing on generative AI and interpretability methods. Supervises multiple master's students in AI fairness and algorithmic bias. Affiliations: Member of Data Analytics Lab and Integrated Intelligence Lab at VUB, with frequent collaborations in national and international research networks focused on algorithmic transparency and AI ethics.

Guy De Tréمشاهده پروفایل

دانشیار

• Computational Intelligence
• Data Quality
• Decision Support Systems
• +۷ مورد دیگر

Guy De Tré is an Associate Professor at the Department of Telecommunications and Information Processing within Ghent University's Faculty of Engineering and Architecture. He leads the Database, Document and Content Management (DDCM) research group and focuses on computational intelligence in information systems, with expertise in bi-polarity handling, uncertainty modeling, and multi-valued logic systems. Primary Affiliation: Ghent University Research Focus: Data quality, fuzzy querying, spatio-temporal modeling Key Contributions: Foundational work in possibilistic databases and explainable AI His research combines theoretical and applied approaches to information management systems. Theoretical work includes: Bipolarity and uncertainty handling in databases Multi-valued logic frameworks Interval B-tree indexing for possibilistic data Applied research spans: NoSQL database optimization Decision support systems Contextualized machine learning 3D/4D modeling for geological resources Recent publications show increasing focus on explainable AI, with multiple works on contextualized support vector machine classification and orthographic similarity measures for graph-based data representations. His work bridges database theory with practical applications in data quality assessment, medical informatics, and cultural heritage projects like the Byzantine Book Epigrams database. Research Group: Leads the DDCM group at Ghent University, specializing in: Database management innovation Content modeling techniques Fuzzy logic implementations Temporal data indexing Intelligent information systems

Tim Seynnaeveمشاهده پروفایل

پژوهشگر ارشد

• Algebraic Geometry
• Applied Algebra
• Combinatorics
• +۴ مورد دیگر

Tim Seynnaeve is a postdoctoral researcher at KU Leuven, holding a dual affiliation with the Department of Computer Science and Mathematics. He serves as an FWO special research associate within the Numerical Analysis and Applied Mathematics (NUMA) research unit at the Arenberg campus. Previously, he completed his PhD at the Max Planck Institute for Mathematics in the Sciences in Leipzig under the supervision of Mateusz Michałek and held a postdoctoral position at the Mathematical Institute of the University of Bern. His research focuses on the intersection of algebraic geometry with computational mathematics, particularly applied and combinatorial algebraic geometry. Seynnaeve's work bridges theoretical mathematics with practical applications in quantum information theory, tensor networks, and computational complexity. His approach combines deep theoretical insights with computational techniques to solve problems in matrix analysis, representation theory, and combinatorial structures. Seynnaeve's recent publications reveal a strong trend toward interdisciplinary work connecting algebraic geometry with quantum information science, statistical models, and computational complexity. His research on matrix product states, tensor spaces, and algebraic structures demonstrates how geometric methods can advance our understanding of quantum systems and computational algorithms. The recurring themes in his work include the application of Schubert calculus to statistical models, the geometry of tensor networks, and the algebraic foundations of matrix multiplication complexity. Seynnaeve actively contributes to the academic community through organizing seminars, including the NUMA seminar at KU Leuven since Fall 2022. He has mentored students such as Jonas Adams on projects related to Group-Invariant Tree Tensor Networks. His teaching experience spans multiple institutions, including courses on Algebra II, Representation Theory, and specialized workshops on nonlinear algebra and tensor applications. His academic work is organized around collaborative research projects with institutions including the Max Planck Institute for Mathematics in the Sciences and participation in numerous international conferences on algebraic geometry and its applications. Seynnaeve maintains active collaborations with researchers across Europe, particularly in the Netherlands, Germany, and Switzerland, contributing to a vibrant network focused on the applications of algebraic geometry to real-world problems.

• Signal Processing
• Audio and Speech Processing
• Pattern Recognition
• +۳ مورد دیگر

Kris Demuynck is an Associate Professor at Ghent University's Faculty of Engineering and Architecture, Department of Electronics and Information Systems (EA06), and leads the Internet Technology and Data Science Lab. With expertise in audio/speech processing, pattern recognition, and biomedical NLP, he supervises 14 doctoral researchers and contributes to speech recognition for healthcare and low-resource languages. Current projects: Core speech technology, NeLF (Flemish speech recognition), BioLORD-2023 (biomedical semantic models) Past grants: Special Research Fund, FWO, bilateral agreements Research Interests : Speech recognition architectures combining linguistic features with neural networks Biomedical semantic representation learning using LLMs and knowledge graphs Robust speaker verification and diarization in multi-speaker environments Selected Publications (2024-2025) demonstrate advancements in audio hashing, clinical NLP, and stress biomarker detection. His 15 most recent works span speech enhancement, speaker separation, and multilingual model adaptation. Supervision : Mentored doctoral projects on time series analytics, speech intelligibility, and pathological voice analysis. Active collaborations include FWO-funded precision health initiatives and industrial research fund projects on child linguistic analysis.