جست‌وجوی استاد و پژوهشگر

Brent De Weerdtمشاهده پروفایل

پژوهشگر

• Interpretable Deep Learning
• Distributed Source Coding
• Generative AI
• +۳ مورد دیگر

Brent De Weerdt is a doctoral student and researcher at the Vrije Universiteit Brussel, affiliated with the Faculty of Engineering and the Electronics and Informatics department. His research focuses on interpretable deep learning, distributed source coding, generative AI, and AI explainability. He holds a Master of Science in Electrical Engineering from the same institution (2021). Current projects include the FWOSB140 DUST project, exploring interpretable and efficient deep unfolding sparse transformers for multimodal image processing and generation. He has published in top-tier venues like IEEE Transactions on Signal Processing and IEEE conferences, with contributions to sparse recovery, trajectory anomaly detection, and layered coding. De Weerdt actively engages in peer-review activities for journals such as IEEE Transactions on Multimedia and conferences like the European Signal Processing Conference. His work bridges theoretical signal processing with practical AI applications, emphasizing interpretability and efficiency in neural architectures.

• 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.

• Applied Mechanics
• AI and Robotics
• Mechatronics
• +۳ مورد دیگر

James William Flack is affiliated with the Faculty of Engineering at Vrije Universiteit Brussel (VUB), Belgium. His primary activities include editorial work for IEEE Transactions on Robotics and supervision of doctoral scholarships in Applied Mechanics and AI & Robotics PhD programs at Federated Labs. He holds a Researcher position within the Department of Applied Mechanics. Research interests focus on advanced engineering systems integration, with emphases on robotics control, mechatronics design, and material science applications in mechanical systems. His editorial involvement highlights expertise in cutting-edge robotics technologies and peer-review processes. No formal awards or grant details were explicitly mentioned in the provided text. His current responsibilities include overseeing doctoral candidates in Federated Labs' AI and Robotics initiatives, though specific student names or grant amounts are unreported.

• 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.

• Power Electronics
• Artificial Intelligence in Design Automation
• DC-DC Converters
• +۷ مورد دیگر

Diego Gerardo Bernal Cobaleda is a researcher at KU Leuven , affiliated with the Electrical Engineering Department (ELEKTRA) and the EnergyVille research institute. His work focuses on power electronics design automation, integrating artificial intelligence (AI) and machine learning (ML) techniques to optimize converter efficiency and component selection. Research Highlights : AI-driven semiconductor datasheet analysis, variable transformer design for wide voltage ranges, reinforcement learning for converter parameter optimization. Collaborators : Fanghao Tian, Wilmar Martinez, Camilo Suarez Buitrago, Miguel Vivert. His recent publications address: Automated extraction of dynamic characteristics from MOSFET datasheets using computer vision. Development of resonant multi-input multi-output (MIMO) converters for aerospace applications. Integration of variable transformers in LLC and dual active bridge (DAB) topologies for improved efficiency. Application of deep reinforcement learning (DDPG) to optimize converter parameters. He has contributed to journals including IEEE Transactions on Power Electronics , Energies , and IEEE Access , as well as conferences such as IEEE APEC, ECCE Asia, and ISIE.

Steven Van de Walleمشاهده پروفایل

استاد

• Public Administration
• Public Management
• Public Sector Reform
• +۴ مورد دیگر

Steven Van de Walle is a full Professor at the Public Governance Institute within the Faculty of Social Sciences at KU Leuven. He serves as a Promotor (principal investigator) for multiple research projects spanning from 2022 to 2029, focusing on public administration, governance, and trust in government. His institutional roles include membership in the Social and Societal Ethics Committee, the Council of the Faculty of Social Sciences, and the Faculty Evaluation Committee. His research focuses on: Public sector reform and modernization Public management and administration Political trust and socialization Policy implementation and bureaucratic behavior Governance innovations and democratic anchorage Professor Van de Walle's recent work demonstrates a strong emphasis on understanding how policymakers deal with uncertainty, the impact of administrative burdens, and the relationship between performance information and decision-making. His research employs various methodologies including survey experiments, eye-tracking studies, and systematic reviews to investigate complex public administration phenomena. He has particular expertise in studying trust dynamics between citizens and government institutions, as well as examining the effects of populist politics on bureaucratic systems. His current research projects address critical contemporary issues such as: Warning fatigue among policymakers Street-level bureaucracy during political transitions Digital democracy innovations Public procurement of broader social outcomes Waste management policy Professor Van de Walle has received significant research funding for his projects, including Horizon RADAR and INNOVADE, indicating the relevance and impact of his work in the field of public administration. His research has practical implications for improving public sector governance and addressing contemporary challenges facing democratic institutions.

Sarah Bouragaمشاهده پروفایل

پژوهشگر

• Blockchain
• Business Intelligence
• Dashboards
• +۵ مورد دیگر

Sarah Bouraga is a researcher at the University of Namur , affiliated with the Namur Digital Institute . She holds a PhD in Economics and Management Sciences (2017) with a focus on Online Social Networks and Knowledge-Based Recommendation Systems. Her academic work spans Blockchain technology, Business Intelligence, Dashboards, and Social Network Analysis. Education: Bachelor in Business Engineering - Information Management Master in Business Engineering - Information Management PhD in Economics and Management Sciences (2017) Research Interests: Bouraga specializes in Blockchain applications for business processes, Business Intelligence dashboard design, and social network dynamics. Her work examines user adoption challenges, cognitive load effects, and decentralized governance systems. Research Trends: Recent publications (2021-2024) focus on Blockchain-based business processes, Ethereum network analysis, Fashion NFTs, and dashboard usability. Her work combines technical implementation with socio-technical impacts of digital transformation. Collaborations: Active in organizing academic events like the Blockchain for Information Systems workshops (2018-2024), where she served as organizer and committee member alongside colleagues from the University of Namur.

• Developmental Psychology
• Personality Development
• Psychopathology
• +۹ مورد دیگر

Peter Prinzie is a Professor at Ghent University in Belgium, specializing in Developmental Psychology , Personality Pathology , and Child Psychopathology . His research spans over two decades and focuses on: Parenting-child personality interactions Developmental trajectories in autism and genetic syndromes Executive functioning interventions Social media's impact on mental health Recent 2024-2025 studies include meta-analyses on brain injury interventions, personality profiles in autism, and social media self-control failures. Earlier work examined: Cross-lagged associations between identity and psychopathology Parental burnout during the pandemic Behavioral phenotypes in VCFS and neurofibromatosis Collaborations with Sarah De Pauw, Bart Soenens, and Maja Dekovic highlight his cross-disability comparative research. Methodologically, he employs: Latent Profile Analysis Network modeling Multilevel meta-analysis Cross-lagged longitudinal designs

Leticia Arco Garciaمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Complex Networks
• Deep Learning
• +۵ مورد دیگر

Leticia Arco Garcia is a postdoctoral researcher at Vrije Universiteit Brussel working in the Department of Informatics and Applied Informatics with affiliations at Federated Labs AI and Robotics. Her research spans computational linguistics, complex network analysis, and more recently, interdisciplinary applications in archaeobotany. Her research interests focus on Natural Language Processing , Complex Networks , and Deep Learning methodologies. Early work centered on community detection in complex networks and customer behavior analysis, while recent publications demonstrate expansion into archaeobotanical applications using morphometric analysis of phytoliths. She employs advanced computational techniques including LSTM networks, case-based reasoning, and multi-objective optimization across diverse domains from smart power grids to electronic money systems. Analysis of her 15 most recent publications reveals an interdisciplinary trajectory moving from pure computer science applications toward biological/archaeological domains. The earliest works focus on network construction and community detection, while more recent papers apply similar analytical frameworks to botanical specimens, indicating methodological transfer between fields. Her work consistently applies computational rigor to complex data structures across diverse application areas. Her scientific contributions include: Development of novel network construction methodologies for large purchasing collections Advancements in overlapping community detection using rough clustering Application of deep learning to sentiment analysis and customer behavior prediction Recent innovative work applying morphometric analysis to archaeobotanical specimens Dr. Garcia actively presents her research at international conferences including INTERSPEECH and specialized meetings on phytolith research. Her collaborative network spans multiple institutions, with co-authorship on interdisciplinary projects connecting computer science with archaeology and plant science. While specific grant information isn't detailed in the available text, her consistent publication output across multiple domains suggests successful research funding.

Bas Ketsmanمشاهده پروفایل

دانشیار

• Big Data Systems
• Database Theory
• Transaction Processing
• +۳ مورد دیگر

Bas Ketsman is an Associate Professor in Computer Science at the Vrije Universiteit Brussel , affiliated with the Software Languages Lab . His research focuses on modern, scalable big data systems, particularly on query processing with formal optimality guarantees, high-availability transaction processing, and querying linked data on the web. Education: PhD in Computer Science from Hasselt University (2017) under advisor Frank Neven , postdoctoral research at EPFL (2018–2019), and internships at University of Washington (Seattle, Madison). His recent work explores robustness in transaction systems, isolation level allocation, and distributed query processing. He has authored numerous publications in top-tier venues like ACM SIGMOD , PODS , and ICDT , with several receiving best paper and distinguished dissertation awards. Scientific Awards: ACM PODS Best Paper Award (2014, 2015) 2018 EACTS Distinguished Dissertation Award 2019 SIGMOD Jim Gray Doctoral Dissertation Award Honorable Mention ACM SIGMOD Research Highlight Award (2016, 2023) PODS 2023 Distinguished Paper Award Bas collaborates with researchers like Christoph Koch and Frank Neven and is involved in projects such as FWOTM1224: Formal Approaches to Join Queries and SRP92: Real-Time Cross-Media Data Exploration . He actively mentors PhD and postdoc candidates and welcomes new applicants with expertise in formal theory or engineering.

• Banking Competition
• Financial Regulation
• Corporate Finance
• +۴ مورد دیگر

Hans Degryse is a full professor at the Faculty of Economics and Business (FEB) at KU Leuven, affiliated with the Finance Research Group. His research focuses on banking competition, financial regulation, corporate finance, and the intersection of financial markets with environmental and technological trends. He explores topics such as cryptocurrency investing, green transition financing, and the impact of fiscal policies during crises. Key research areas include banking competition dynamics, the role of technology in supervision (SupTech), and the effects of regulatory frameworks on bank behavior. His work addresses issues like zombie lending, sustainable investments, and the implications of foreign bank penetration on global trade patterns. Recent studies highlight his analysis of crypto market behaviors influenced by social preferences and hype, fiscal support mechanisms during the pandemic, and the green transition's challenges related to stranded assets. Degryse’s contributions span empirical research on banking practices, risk management, and the evolution of financial systems in response to technological and regulatory changes.

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

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

• Antenna Engineering
• Electromagnetics
• Microwave Engineering
• +۳ مورد دیگر

Stijn Gies is affiliated with the Faculty of Engineering at Vrije Universiteit Brussel. He holds a PhD in Electricity and is actively involved in research and academic committees. Education: Doctoral scholarship recipient in Electricity (PhD completed). Research focuses on antenna engineering, including phased array antennas, beamforming, anechoic chamber testing, and microwave device fabrication. His work bridges theoretical electromagnetic analysis with practical applications in antenna design and materials science. Recent publications emphasize advanced antenna characterization techniques and novel manufacturing methods for microwave devices. No scientific awards explicitly mentioned. Actively participates in academic governance as a member of the Faculty of Engineering committee (2022–present) and peer review for journals like IEEE Transactions on Instrumentation and Measurement. Labs/Teams: Involved in antenna testing facilities utilizing anechoic chambers, collaborating with groups specializing in microwave materials and additive manufacturing.

Nikolaos Deligiannisمشاهده پروفایل

دانشیار

• Interpretable Machine Learning
• Explainable AI
• Federated AI
• +۵ مورد دیگر

Nikolaos Deligiannis is an Associate Professor at the Department of Electronics and Informatics (ETRO) of Vrije Universiteit Brussel (VUB). He holds a PhD in Engineering Sciences from VUB (2012) and a Diploma in Electrical & Computer Engineering from the University of Patras (2006). Previously, he was a Senior Researcher at University College London (2013-2015). His research focuses on interpretable machine learning, federated AI, and applications in image/video processing, computer vision, and natural language processing. He is an IEEE member and serves as an Associate Editor for the IEEE Transactions on Image Processing. He leads major projects such as the EU-funded 'Reinventing Multiterminal Coding for Intelligent Machines' (2025-2030) and 'ENACT: Environmental Effect on Health Care' (2025-2028). His work spans 202+ publications, including seminal papers on explainable AI and federated learning. Key awards include the EURASIP Best PhD Award (2017) and the Francqui Research Professorship (2024). Deligiannis has supervised 39 student theses, including master's works on distributed query systems, algorithmic trading, and water level forecasting. His research also addresses healthcare applications, such as developing a prognostic model for COVID-19 severity using thoracic CT scans. He actively participates in conferences and workshops, delivering talks on topics like graph-based explanations and deep learning optimization.

• RF Engineering
• Medical Imaging (PET/CT)
• Prosthetics Analysis
• +۳ مورد دیگر

Jeroen De Geeter is a Researcher at the Faculty of Engineering Sciences, Vrije Universiteit Brussel, specializing in Electronics and Informatics. His roles include Scientific Employee and Electronics and Computer Science Doctorate. He is affiliated with the VUB's tauCAMTM research group and holds an ORCID identifier: 0000-0001-5373-3505 . Education: Doctorate in Electronics and Computer Science Research Interests: RF engineering and instrumentation Medical imaging innovations in PET/CT and radiation correction Prosthetics analysis and metal artifact reduction in diagnostics Signal processing for modulated systems Environmental health impacts on reproductive biology Recent Research Trends: Focus on interdisciplinary applications combining electrical engineering with medical diagnostics, including advancements in automatic ranging for RF experiments and artifact reduction in clinical imaging. His work bridges signal processing techniques with clinical challenges like prosthetic-induced imaging artifacts. Awards: No specific awards listed, but maintains an h-index of 29 with impactful publications in IEEE Transactions and EJNMMI Phys. Grants & Labs: Collaborates on projects involving VUB's tauCAMTM imaging systems and participates in conferences on multivariate function decoupling. Research teams include collaborations on PET/CT attenuation correction and cigarette smoking's reproductive effects.