Gert Desmet is a Full Professor at Vrije Universiteit Brussel , leading research in the Department of Chemical Engineering and Separations Science within the Chemical Engineering and Applied Biological Sciences faculty. With an h-index of 49 and over 842 research outputs, his work focuses on advanced liquid chromatography systems and separation science innovations. 1995–2029: Active fundamental and applied research projects 2020–2025: Supervised 102 research works and 3 PhD theses His research explores stationary phase design , dispersion modeling , and 3D-printed chromatography media , with recent emphasis on AI-driven peak detection and theoretical plate height modeling. He leads international collaborations like the VUB-Universiteit Twente joint PhD program. Notable scientific awards include: First Prize - HPLC 2008 & 2020 Poster Awards First Prize - Analytical Science Advances Best Poster Second Prize - Udias Master Thesis Award 2012 As principal advisor, he has guided doctoral research on topics such as: Gradient Stationary Phase Selectivity (2015) 3D-Printed Sample Preparation (2025) Computational Flow in Porous Media (2019)
Nico Vandaele is a Professor at the Faculty of Business and Economics , KU Leuven, with appointments at both the Leuven and Kortrijk campuses. He contributes to research and teaching in operations management, systems thinking, and logistics optimization. His work spans interdisciplinary collaborations with institutes like Leuven One Health , Institute for Rare Diseases , and Institute for Mobility . Faculty of Economics and Business (FEB) Faculty of Business and Economics, Kulak Kortrijk Campus Research Focus: Vandaele specializes in applying operations research and systems thinking to complex logistical challenges, particularly in healthcare and industrial production. Recent projects explore vaccine manufacturing ecosystems , decentralized diagnostic systems for pandemics , and adaptive pandemic preparedness frameworks . His work integrates mathematical modeling, sustainability, and cross-sector collaboration. Projects (Selected): VaxMod (2025-2028): Vaccine manufacturing ecosystem modeling VacxSYS (2024-2027): Systems thinking for vaccination strategies DURABLE (2023-2027): Biomedical-public health lab alliances against epidemics Machine Intelligence-based Radio Access Infrastructure (2025-2028)
Koen De Gussemé is a visiting lecturer at KU Leuven , affiliated with the Faculty of Engineering Science . His academic work bridges power electronics and corrosion engineering, with a focus on converter design, harmonic distortion mitigation, and infrastructure corrosion analysis. Power Electronics Digital Control Systems Harmonic Distortion Corrosion Engineering Railway Infrastructure Pipeline Integrity Publication Trends His research has advanced digitally controlled power converters, including high-conversion-ratio topologies, programmable harmonic resistance in PFC converters, and zero-crossing distortion analysis. He has also contributed to computational models for railway-pipeline corrosion risk assessment and resonance damping in power distribution. No scientific awards, student advisement records, or current lab affiliations are documented in the provided information.
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.
Clara Mihaela Ionescu is a Full Professor at the Faculty of Engineering and Architecture , Ghent University, Belgium, since October 2016. She leads the Dynamical Systems and Control (DYSC) research unit and contributes to the Centre of Excellence in Sustainable Pharmaceutical Engineering . Her academic journey includes an M.Sc. in Industrial Informatics and Automation (2003, Dunarea de Jos University) and a PhD in Biomedical Engineering (2009, Ghent University). Education : M.Sc. Automation & Applied Informatics, 2003 PhD Biomedical Engineering, 2009 Dr. Ionescu’s research spans fractional order systems , predictive control algorithms , and AI-driven biomedical applications . Her work focuses on optimizing multi-drug administration during anesthesia , developing non-invasive medical devices , and modeling respiratory and hemodynamic systems . She integrates hybrid AI methods with control theory to address clinical challenges. Recent publications highlight advancements in fractional-order control for anesthesia , LSTM-based pain prognosis , and ECG electrode sustainability . Her ERC Consolidator Grant (AMICAS) supports AI-enhanced drug infusion systems. She organizes international conferences (e.g., IFAC Advances in PID Control ) and serves as Associate Editor for IEEE Control Systems Society. Scientific Awards : ERC Consolidator Grant (2022) FWO Postdoctoral Fellowship (2011-2017) International Innovation Prizes in Medical Devices She mentors PhD researchers in AI for pain assessment, fractional control strategies, and hemodynamic stabilization. Her lab collaborates with Flanders Make and Centre for Sustainable Pharmaceutical Engineering , emphasizing cross-disciplinary innovation and clinical translation .
Yvan Saeys is a Professor of Machine Learning at Ghent University and a Principal Investigator in Systems Immunology at VIB (Flanders Institute for Biotechnology). He obtained his PhD in Computer Science from Ghent University and VIB, with postdoctoral research at the University of the Basque Country and Université Claude Bernard. His work bridges machine learning and biomedical data analysis, particularly in single-cell transcriptomics and cytometry. Develops advanced data mining and machine learning techniques for biological and medical applications Focuses on regulatory network inference and biomarker discovery from high-throughput datasets Leads the DAMBI research group at the Inflammation Research Centre His methods have achieved top performance in international challenges like DREAM5 and FlowCAP-IV. With over 100 publications and 9,500 citations, he specializes in computational models for single-cell data analysis, interpretable AI, and large-scale data mining. Recent projects include tools like CytoNormPy , SPArrOW , and Spotless for spatial and single-cell omics workflows. Scientific Awards Best performing team at the DREAM5 challenge Best performing team at the FlowCAP-IV challenge Research Trends Recent 2024-2025 publications emphasize single-cell analysis , spatial omics , immune profiling , and machine learning explainability . Key subfields include cytometry normalization , cell-cell communication , biomarker discovery , network inference , adversarial robustness , and multiomics integration .
Sofie Van Hoecke is an Associate Professor at Ghent University, affiliated with the Internet Technology and Data Science Lab (IDLab) - imec. She leads the PreDiCT (Predictive Diagnostics through Contextual and Trustworthy AI) team, which develops innovative machine learning solutions for predictive maintenance and healthcare diagnostics. Her educational background includes: Engineering Degree, Ghent University (2003) PhD in Computer Science Engineering, Department of Information Technology, Ghent University (thesis: 'Efficient service management in healthcare') Van Hoecke's research centers on hybrid AI systems that fuse machine learning with semantic technologies and physical modeling. Her work targets predictive maintenance (e.g., condition monitoring via thermal imaging and vibration analysis) and predictive healthcare (e.g., migraine tracking, COPD management, and infection diagnostics). Key specialties include context-aware modeling, expert-driven machine learning, and dynamic semantic dashboards for real-time decision support. She has pioneered applications in industrial IoT, medical diagnostics, and environmental monitoring. Her 2025 publications reveal a strong interdisciplinary trajectory applying knowledge graphs, conformal prediction, and explainable AI to diverse domains including network security, digital pathology, building inspection, and neurology. A unifying theme is trustworthy AI with emphasis on uncertainty quantification, out-of-distribution robustness, and seamless integration of domain knowledge into ML pipelines. Her work bridges theoretical advances with industrial deployment through imec collaborations. Scientific Awards: No scientific awards mentioned in provided sources Van Hoecke directs the PreDiCT team across multiple EU and industry-funded projects including AI-SWEEP-2 (smart wound care), HEROI2C (ICU infection management), mBrain (migraine monitoring), and SmartWaterConnect 5.0 (water grid analytics). While specific student advisees aren't listed, her leadership of a 15+ member research team and graduate teaching duties indicate active mentorship. Current grants focus on deploying context-aware AI in healthcare (S.E.P.S.I.S. Connect, ADAM) and industrial settings (RE-ENNOVATE, PACSOI). The PreDiCT team operates within IDLab's Ghent University-imec ecosystem, maintaining strong industry partnerships with Siemens, UZ Ghent Hospital, and water management authorities. Their research infrastructure includes specialized testbeds for predictive maintenance validation and clinical trial frameworks for digital health applications. Ongoing work emphasizes real-world validation of hybrid AI models in resource-constrained environments, particularly for neglected tropical diseases and chronic condition management.
Marta Vanin is an Assistant Professor at the Department of Electrical Engineering (ESAT) within the Faculty of Engineering Technology at KU Leuven . She is affiliated with the Electrical Energy Systems and Applications (ELECTA) unit and serves as head of the Subdivisie EnergyVille Electa - Vanin . Her work focuses on distribution network optimization, state estimation, and grid modeling. Research Highlights : Distribution system state estimation, unbalanced AC/DC networks, phase identification, smart grid technologies, reactive power analysis, data quality challenges. Projects : Promotor for Decision support methods (2025-2029), DeciMAD (2024-2026), Co-promotor for FlexIQ (2024-2028), and Distribution System State Estimation (2023-2027). Her recent publications emphasize advancements in state estimation techniques, unbalanced network modeling, and integration of renewable energy systems. She collaborates extensively on topics like optimization algorithms, digital twins, and data-driven grid monitoring. Teaching : Delivers courses on energy management and sustainable engineering (JPI335 Energiemanagement, JPI0VN Ingenieur en duurzaamheid).
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.
Mehrdad Asadi is a Research Fellow at the Vrije Universiteit Brussel (VUB), affiliated with the Department of AI and Robotics within the School of Informatics and Applied Informatics. His research focuses on advancing AI and robotics with an emphasis on safety, explainability, and real-world applications in transportation systems. He holds post-doctoral scholarships at Federated Labs AI and Robotics and the Informatics and Applied Informatics unit. Research Interests: - Development of trustworthy AI systems resistant to adversarial attacks - Optimization algorithms for multi-agent robotics and transportation networks - Explainable AI methodologies for ethical and transparent decision-making - Heuristic approaches for real-time system coordination - Integration of human preferences in automated systems Collaborations: - Active participation in international conferences like GECCO and eXplainable AI workshops - Co-organized events including 'Interactive eXplainable AI - Theory and Practice' (2025) - Cross-disciplinary projects with institutions like IEEE and academic consortia Key Contributions: - Developed the SPATIAL architecture for monitoring AI inference capabilities - Pioneered heuristic methods for intelligent transportation systems - Published 9 peer-reviewed works between 2021-2025 with a focus on safety-critical applications
Laurence Leherte is a researcher at the Unit of theoretical and structural physico-chemistry within the University of Namur , affiliated with multiple research institutes including NAmur MEdicine & Drug Innovation Center and Namur Research Institute for Life Sciences. Their work spans computational chemistry, structural biology, and supramolecular systems. Research Interests : Electron density analysis, molecular dynamics simulations, non-covalent interactions in proteins, theoretical modeling of supramolecular assemblies, and computational drug design. Their projects include analyzing metal complexes in resorcinarene cages and studying biomimetic cavity complexes. Scientific Awards : Recipient of prestigious fellowships such as the NATO research grant (1993), FSR funding (2001), FNRS subvention (2015), and the Classe des Sciences annual chemistry prize (2009). Collaborations : Active in interdisciplinary projects with institutions like the University of Namur, contributing to journals including Inorganic Chemistry Frontiers and Theoretical Chemistry Accounts .
Prof. Bertrand Cornélusse is a faculty member at the University of Liège, affiliated with the Montefiore Institute (Faculty of Applied Sciences). His work bridges optimization, machine learning, and energy systems, focusing on microgrids, control algorithms for distribution networks, and smart energy solutions. Current research emphasizes multi-energy local energy communities and hardware-in-the-loop distributed control. Pioneered the Euphemia algorithm for European Power Exchanges and contributed to operational planning of EDF's power plants. Teaching Portfolio : ELEC0053 Circuits électriques (with open-source materials on GitHub) INFO2059-1 Laboratoire de programmation mathématique et physique ELEN0445-1 Microgrids ELEC0447-1 Analysis of Electric Power and Energy Systems ELEC0448-1 Planning and Operation of Electric Power Systems ELEC0449-1 Practices and Evolution of the Electric Power Industry He mentors seven active PhD students and maintains open-source projects like Microgrid-bench , openDAM , and DSIMA for energy system analysis and optimization.
Alessio Franci is Lecturer in Electrical Engineering and Computer Science at the University of Liege, where he co-founded the ULiege Neuroengineering Lab. His research bridges mathematics, neuroscience and engineering through brain-inspired computing paradigms. Funded by a WEL-T Starting Grant, his work develops neuromorphic controllers and biomorphic systems for robotics and intelligent sensing. Research explores: neurocomputational principles of decision-making; neuromodulation in adaptive systems; and degenerate coding in neural circuits. Publications apply nonlinear dynamics to opinion formation, neural coding, and neuromorphic hardware design, demonstrating consistent innovation in theoretical frameworks. Honors include the WEL-T Starting Grant supporting his investigations into neuronal degeneracy and physiological mechanisms. Current work emphasizes real-world applications in robotics through spiking neural controllers and embodied neuromodulation strategies.
Gert Van der Auwera is a leading researcher in Molecular Parasitology at the Institute of Tropical Medicine (ITM) in Antwerp, Belgium. His work focuses on Leishmania parasites, molecular epidemiology, and tropical medicine. He earned his PhD in Molecular Biology from the University of Antwerp (1997) and has held postdoctoral positions at ITM, VIB/Ghent University, and international collaborations. Education: University of Antwerp (Biochemistry, 1992; Molecular Biology, 1997), with additional training in science journalism (2014). His research spans HIV genetics, plant biotechnology, and trypanosomatid parasites, with a focus on diagnostic tools and epidemiological studies in global health contexts. Research interests include phylogenetic analysis of pathogens, molecular diagnostics, and control strategies for neglected tropical diseases like visceral leishmaniasis. He leads projects in Nepal, Peru, and Ethiopia, and coordinates international workshops on molecular epidemiology and diagnostics. Key projects include the LeishMan consortium for harmonized treatments/diagnosis and collaborative efforts to combat vector-borne diseases. His recent articles address Leishmania genomics, epidemiological trends, and clinical diagnostics across Europe and endemic regions. Labs/Teams: Active in Prof. Jean-Claude Dujardin’s group at ITM, leading global collaborations through networks like LeishMan (www.leishman.eu).
Barbara Barbé is a leading researcher in Tropical Bacteriology at the Institute of Tropical Medicine Antwerp, focusing on diagnostic solutions for infectious diseases in resource-limited settings across sub-Saharan Africa. Her work spans the Democratic Republic of Congo, Benin, and Mozambique, addressing critical gaps in rural healthcare infrastructure through innovative laboratory and diagnostic approaches. Her primary research interests include diagnostic microbiology for neglected tropical diseases , antimicrobial resistance surveillance , and field-deployable diagnostic technologies . She specializes in developing practical solutions for rural hospitals, with particular emphasis on bloodstream infections, febrile illnesses, and pathogens like Salmonella Typhimurium. Her fingerprint analysis reveals strong expertise in routine diagnostic tests (36%), rural hospital systems (25%), African trypanosomiasis (23%), and nervous system complications of infections (17%). Analysis of her 49 research outputs shows consistent focus on translational diagnostics and health system integration . Recent publications (2023-2025) demonstrate growing emphasis on genomic approaches to antimicrobial resistance and mobile laboratory solutions, with 87% of her work directly applicable to primary healthcare settings in low-income countries. Key trends include the shift from single-pathogen diagnostics toward syndromic approaches and increasing use of genomic epidemiology. Her major funded projects include: NIDIAG (2010-2016): European Commission-funded research on integrated diagnosis-treatment platforms for neglected infectious diseases at primary healthcare level FA5 BENIN (2022-2026): Directorate-General for Development Cooperation project to increase institutional capacity of LRM and CERRHUD for improved healthcare quality in Benin Barbé leads the development of innovative solutions like the Laboratory-on-a-ship – a containerized microbiology culture media production facility for local use in remote areas. Her team maintains active collaborations across 12 similar research profiles focused on African infectious disease diagnostics, with particular strength in Democratic Republic of Congo research (100% fingerprint match).
