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.
Louis Wehenkel is a full Professor of Stochastic Methods at the Department of Electrical Engineering and Computer Science, University of Liège, Belgium. He joined the faculty in 1998 and has held leadership roles, including Chair of the Department (2011–2014) and Director of the Montefiore Institute’s Research Unit (2016–2022). His work bridges machine learning, stochastic simulation, and optimization, with applications in electric power systems, bioinformatics, and industrial control. Education: Master's (1986), Ph.D. (1990), and Thèse d'agrégation (1994) from the University of Liège. Research Interests focus on: Machine learning (tree-based models, graphical models, reinforcement learning) Electric power systems reliability and decision-making under uncertainty Bioinformatics and computational systems biology Stochastic optimization for complex systems Publications emphasize probabilistic approaches to power systems, tree ensemble methods, and interdisciplinary applications in energy, biology, and industrial control. Scientific Awards : 2024 Elected Member, Academia Europaea 2019 SIMONS Foundation Fellowship 2007 Emeritus Member, SEE 1996 SIEMENS Belgium Prize 1987 IBM Belgium Prize Advising and Grants : He served as Scientific Advisor for the GARPUR European FP7 project (2013–2017) and spent sabbatical periods at the Isaac Newton Institute (Cambridge, UK) and RTE-France’s R&D division (2019). Labs and Teams : He leads research at the Montefiore Institute, University of Liège, focusing on data-driven methods for power systems and bioinformatics.
Johan Segers is a Full Professor at the Department of Mathematics, KU Leuven , and a Visiting Professor at the Institut de statistique, biostatistique et sciences actuarielles (UCLouvain). His research bridges extreme value theory , copulas , statistical learning , and optimal transport , with applications in finance, environmental risk analysis, and multivariate statistics. Research Trends : Focus on extremal dependence, vine copulas, and Monte Carlo methods. Recent Article Trends : Combines extreme value theory with graphical models and optimal transport for high-dimensional data analysis. Awards & Honors : Prix Adolphe Wetrems (2012–2013) from the Académie Royale des Sciences, des Lettres et des Beaux-arts de Belgique Fellow of the Institute for Mathematical Statistics (IMS) Elected Member of the International Statistical Institute (ISI) Editorial Roles : Associate Editor for Advances in Applied Probability , Bernoulli , Electronic Journal of Statistics , and others. He also contributes to software development, including the R-package spatialTailDep .
Sam Leroux is a Tenure Track Assistant Professor and IMEC Postdoctoral Researcher at Ghent University's Faculty of Engineering and Architecture, Department of Information Technology. His research spans multiple interdisciplinary domains where artificial intelligence meets real-world applications, with particular emphasis on machine learning, deep neural networks, and distributed systems. He maintains active collaborations with IMEC and contributes to several EU and national research initiatives focused on practical AI deployment. Dr. Leroux's research interests center on developing efficient and privacy-aware machine learning systems that can operate effectively on resource-constrained devices. His work bridges theoretical advances in neural network architectures with practical applications in agriculture, healthcare monitoring, industrial IoT, and sustainable computing. He has pioneered approaches in adaptive neural networks that dynamically adjust computation based on available resources, enabling AI deployment at the network edge without compromising privacy. His publication record reveals a clear trajectory from foundational work in neural network architectures toward increasingly applied research. Recent publications demonstrate strong focus on privacy-preserving AI techniques, hardware-efficient machine learning, and computer vision applications in agriculture and industrial settings. His work consistently addresses the tension between model performance and resource constraints, with growing emphasis on ethical considerations in AI deployment. As an academic supervisor, Leroux currently guides numerous doctoral researchers across diverse projects including broiler welfare monitoring, hardware-efficient continuous learning, UAV-based agricultural sensing, and privacy-aware ergonomic analysis. He serves as Promotor for the 'Hardware-efficient continuous learning' project funded by the Special Research Fund, demonstrating his leadership in securing competitive research funding. His work environment features strong connections between Ghent University's academic research and IMEC's technological expertise, creating a fertile ground for translating theoretical advances into practical solutions. This positioning enables his research group to tackle challenges spanning from algorithm development to hardware implementation, with particular focus on real-world validation of proposed techniques.
Eugen Pircalabelu is a Lecturer at UCLouvain (Université catholique de Louvain) working at the Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA) within LIDAM at the Faculty of Science. Prior to his current position, he held a Visiting Professor position at Ghent University and a Postdoctoral position at KU Leuven. Dr. Pircalabelu's research focuses on high-dimensional statistics, with particular emphasis on probabilistic graphical models, social network analysis, copula models, and information criteria. His work bridges theoretical statistics with practical applications in fields such as neuroscience (fMRI data analysis) and epidemiology (COVID-19 modeling). His recent publications reveal a strong trend toward distributed and federated learning approaches for high-dimensional graphical models, with applications spanning from financial time series to brain connectivity networks. His methodological contributions include innovations in model selection criteria, sparse estimation techniques, and time-varying network models that have advanced the field of high-dimensional statistical inference. Dr. Pircalabelu actively supervises multiple PhD students including Mengxue Li, Lise Léonard, and Lara Wautier, and has recently guided Ensiyeh Nezakati and Alexandre Jacquemain to completion of their doctoral studies. He teaches courses in nonparametric statistics, numerical methods for statistics, and statistical learning. He serves the statistical community through organizing regular Statistical/Econometrics seminars at ISBA and contributing to the RShiny@UCLouvain platform for open educational resources. Notably, during the COVID-19 pandemic, he developed a SHINY app for predicting hospitalizations and ICU admissions in Belgium.
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.
Jan Lemeire is an active researcher at Vrije Universiteit Brussel (VUB), affiliated with the Department of Electronics and Informatics within the Faculty of Engineering. Based in Brussels, Belgium at Pleinlaan 2, he maintains an active research profile with an h-index of 10 and 581 citations according to Scopus data. His research interests span multiple domains including GPU computing, machine learning, embedded systems, and their applications in diverse fields from biomedical engineering to forensic science. His work demonstrates strong interdisciplinary connections, bridging computer science with practical applications in health technology, crime analysis, and industrial systems. Analysis of his recent publications reveals a consistent focus on computational efficiency, with particular emphasis on GPU acceleration, embedded AI deployment, and machine learning applications. His work shows evolution from hardware-focused research toward more applied domains including healthcare technology and crime pattern analysis. Dr. Lemeire actively participates in multiple research projects including NSIS2: PRISMA network (2024-2029), IOF3016: GEAR (2021-2025), and Tech4Health (2024-2025), demonstrating sustained research funding and collaborative work across disciplines. His academic activities include supervision of graduate students, as evidenced by his role as advisor for the 2017 Master's thesis on GPU-accelerated holography, and regular participation in major conferences including the Conference on Uncertainty in Artificial Intelligence and the International Symposium on Embedded Multicore Systems. Dr. Lemeire maintains an active research laboratory focused on computational methods, with particular strengths in parallel processing techniques and their application to real-world problems across multiple domains including healthcare, industrial systems, and forensic science.
Aleksandra Pizurica is a Professor in statistical image modelling at Ghent University, Belgium, affiliated with the Group for Artificial Intelligence and Sparse Modelling (GAIM). She serves as Senior Area Editor for IEEE Transactions on Image Processing (2016–) and Associate Editor for IEEE Transactions on Circuits and Systems for Video Technology (2016–), having previously held editorial roles at IEEE Transactions on Image Processing (2012–2016). Her educational background includes: Dipl. Ing. in Electrical Engineering, University of Novi Sad (1994) MSc in Telecommunications, University of Belgrade (1997) PhD in Engineering, Ghent University (2002) Prof. Pizurica's research centers on statistical modelling , probabilistic graphical models , and Bayesian inference , with significant contributions to sparse coding , signal/image processing , and machine learning . Her work bridges theoretical advances with applications in medical imaging, remote sensing, and cultural heritage preservation, particularly in image denoising, inpainting, and hyperspectral analysis. Analysis of her 15 most recent publications (2023–2025) reveals dominant themes in hyperspectral image processing (clustering/classification via model-aware deep learning), medical imaging (3D foot/ankle alignment, musculoskeletal segmentation), and cultural heritage (crack detection in paintings). Emerging trends include fairness in AI (skin color bias mitigation), scalable seabed mapping, and generative models for point cloud processing. Her notable recognition includes: Scientific Prize “de Boelpaepe” for 2013-2014 from the Royal Academy of Science, Letters and Fine Arts of Belgium While specific grant details and student advisement records aren't provided in available sources, her editorial leadership and research output indicate active supervision of graduate researchers. She leads initiatives in the Group for Artificial Intelligence and Sparse Modelling (GAIM), focusing on statistical image modeling and sparse representations for real-world applications. The GAIM research unit under her affiliation drives innovation in probabilistic modeling and machine learning, with projects spanning medical diagnostics, remote sensing, and digital art restoration, evidenced by recent publications in IEEE Transactions and high-impact journals.
Glenn Van Wallendael is an Associate Professor at Ghent University's Faculty of Engineering and Architecture , affiliated with the Department of Electronics and Information Systems . He leads research in video coding, digital watermarking, and immersive media technologies. Academic Focus: Video compression standards (HEVC, H.266), AI for multimedia, virtual reality Key Collaborations: iMinds, imec, European research consortia Research Interests include: Video compression algorithms (HEVC, SVC, MV-HEVC) Digital watermarking for copyright protection Machine learning applications in image/video analysis Quality of Experience (QoE) in immersive environments Recent Publications (2024-2025) show expertise in: Deepfake detection using vision transformers Medical image landmarking tools Lightweight geometric approximation methods AI-driven video quality assessment Doctoral Mentorship includes supervising: 2021: Hannes Mareen (video forensics) 2020: Vasileios Avramelos (light field coding) 2017: Johan De Praeter (adaptive video encoding)
