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Koenraad MuylaertView profile

Professor

• Microalgae Ecology
• Phytoplankton Physiology
• Eutrophication
• +7 more

Koenraad Muylaert is a Full Professor at the Faculty of Science, KU Leuven, and head of the Biology department at KU Leuven Kulak. His research focuses on microalgae ecology and phytoplankton physiology , with applications in eutrophication studies , wastewater treatment , and biofuel production . Based in Kortrijk, Belgium, he works with international teams in Ecuador, Qatar, and Belgium. Current projects on mountain lake eutrophication and urban aquatic systems Specializes in nano-material flocculation and omega-3 fatty acid production from microalgae Research Trends from his recent articles show emphasis on: Microalgae harvesting innovations (cellulose nanocrystals, PDMAEMA polymers) Comparative processing techniques (DAF vs sedimentation, drying methods) Biotechnological applications in flavor chemistry and microbiome interactions Laboratory operates at KU Leuven's Kortrijk campus, with strong collaborations in environmental engineering and food science . His work bridges fundamental ecological research with industrial biotechnology for sustainable solutions.

Ruben SnellingsView profile

Professor

• Sustainable Metals and Minerals
• Cementitious Materials
• Mineral Carbonation
• +7 more

Ruben Snellings is a Professor at the KU Leuven , affiliated with the Institute for Sustainable Metals and Minerals and the Division of Geology . His research focuses on sustainable cementitious materials, mineral carbonation, and valorization of industrial by-products in construction. He actively contributes to RILEM technical committees, including TC 309-MCP and TC 267-TRM, establishing terminology and testing protocols for carbonation-based construction products. Research Highlights Advancing low-carbon binders through co-calcination of waste materials Investigating hydration kinetics and reactivity of supplementary cementitious materials (SCMs) Developing CO2 mineralization techniques for sustainable construction Technical Contributions Co-developing standardized R3 reactivity tests for SCMs Leading interlaboratory validation studies for binder performance Environmental Focus Reducing environmental leaching via carbonation of metallurgical slags Optimizing circular economy approaches for concrete recycling

• Signal processing
• Fault detection and diagnosis
• Rotordynamics
• +7 more

Konstantinos Gryllias is a Professor in the Department of Mechanical Engineering at KU Leuven's Faculty of Engineering Sciences. He leads research in the Mechatronic System Dynamics (LMSD) unit at the Arenberg campus. His academic affiliations extend across multiple KU Leuven institutes including Leuven.AI, Leuven.AM (Additive Manufacturing), and the Gravitation Institute. He serves on important governance bodies as a member of the Faculty Council of Engineering Sciences, Faculty Doctoral Committee of Engineering Sciences, and Departmental Council of Mechanical Engineering. Dr. Gryllias specializes in signal processing, fault detection and diagnosis of rotating machinery, condition monitoring, and machine learning applications in structural health monitoring. His research spans linear and nonlinear vibrations, anomaly detection, rotordynamics, and pattern recognition. His work bridges theoretical signal processing with practical engineering applications in wind turbines, marine propulsion systems, and industrial machinery. His recent publications demonstrate strong focus on deep learning approaches for wind turbine anomaly detection, bearing diagnostics, stern bearing lubrication optimization, and structural health monitoring using advanced signal processing techniques. The research shows increasing integration of explainable AI methods with traditional vibration analysis. Dr. Gryllias teaches advanced courses including Monitoring & Prognostics, Structural Dynamics, Smart Sensing Technologies, and Applied AI perspectives. His teaching portfolio reflects the interdisciplinary nature of his research, connecting mechanical engineering fundamentals with cutting-edge AI methodologies. He currently leads multiple research projects through 2025-2029, primarily as Promotor, focusing on fault detection in gears using fiber optic sensors, multi-sensor monitoring of drivelines, physics-inspired machine learning for condition monitoring, and digital twin applications for wind turbine efficiency improvement.

• Chromatography
• Separation Science
• Analytical Chemistry
• +3 more

Ken Broeckhoven holds a prominent academic position at Vrije Universiteit Brussel (VUB) as a Professor in the Department of Bio-engineering Sciences, within the Chemical Engineering and Industrial Chemistry school. His roles include leadership in research groups and administrative positions such as Dean and Department Chair. Specializing in chromatography and separation science, his work focuses on advancing techniques like liquid chromatography, supercritical fluid chromatography, and hydrophobic interaction chromatography for applications in protein analysis and biotherapeutics. He has authored over 236 research outputs and secured 13 major projects, including ones on column performance and sustainable chemical technologies. His contributions have earned 20 scientific awards, including top poster prizes and innovation recognitions. Broeckhoven actively participates in PhD committees and international conferences, emphasizing collaborative research and cutting-edge instrumentation development. Education: Not explicitly detailed in the provided text. Research Interests: Chromatography systems optimization, diffusion dynamics, column design, and analytical method development for complex biological samples. His work bridges theoretical modeling with practical applications in separation science. Recent Research Trends: Recent articles emphasize overcoming extracolumn band broadening, advancing HIC methods, and leveraging computational fluid dynamics (CFD) to enhance column efficiency. His studies often integrate experimental and numerical approaches to dissect separation mechanics. Awards: Includes 1st Prize Poster Award (HPLC 2008), Analytical Science Advances Best Poster (2020), and recognition for master’s thesis supervision (2012). Grants & Activities: Leads projects like the OZR Basisfinanciering voor Grote Onderzoeksgroepen (2024–2029) and participates in over 105 professional activities, including talks at global conferences. Supervises 12 students, including Timothy Januarius (2024) and T. Swier (2015).

Chris CornelisView profile

Professor

• Machine Learning
• Fuzzy Sets
• Rough Sets
• +3 more

Chris Cornelis is a full-time Professor in fuzziness and uncertainty modelling at Ghent University's Department of Applied Mathematics, Computer Science and Statistics. His research integrates fuzzy logic and rough set theory to advance machine learning methodologies for complex data analysis. Education: M.Sc. in Computer Science, Ghent University (2000) Ph.D. in Computer Science, Ghent University (2004) Research Focus: Cornelis pioneers fuzzy-rough hybrid systems for uncertainty handling in machine learning. His work spans theoretical foundations (e.g., implication operators, granular approximations) and practical applications including emotion detection, medical diagnosis, and imbalanced data classification. Key innovations include FRNN-OWA classifiers and polar encoding for missing values, demonstrating exceptional versatility in bridging abstract mathematics with real-world AI challenges. Publication Trends: Recent work (2023-2025) reveals intensified exploration of topological data analysis (Mapper-based rough sets), advanced granular computing (disjoint/adjacent fuzzy granules), and ethical AI ("No Imputation Without Representation"). His research shows consistent progression from foundational fuzzy-rough theory toward multi-disciplinary applications while maintaining mathematical rigor, particularly in Choquet integration and quantifier-based frameworks. Scientific Awards: No specific awards were documented in the provided sources. Research Support: Cornelis has secured competitive funding including FWO postdoctoral mandates, a Ramón y Cajal contract at the University of Granada, and an FWO Odysseus Type II project at Ghent University. These grants enabled foundational work in fuzzy-rough set theory and its applications to complex data problems. Research Unit: He leads research within Ghent University's Computational Web Intelligence (CWI) unit, focusing on intelligent data analysis systems that leverage fuzzy-rough methodologies for web-scale information processing.

• Nanotechnology
• Materials Science
• Biocatalysis
• +3 more

Sophie Demoustier-Champagne is a Full Professor at the University of Louvain (UCLouvain) in the School of Engineering (EPL), affiliated with the Institute of Condensed Matter and Nanosciences (IMCN) and the Bio and Soft Matter (BSMA) research group. Her work focuses on nanotechnology, biomaterials, and polymer science, particularly on layer-by-layer assembly techniques and nanostructured materials for biomedical and catalytic applications. Research Interests: She specializes in developing bioactive nanotubes, stimuli-responsive materials, and hybrid polymer-metal structures. Her research spans biocatalysis, drug delivery, tissue engineering, and plasmonic catalysis, with a strong emphasis on environmental and biomedical applications. Publications: Recent studies highlight her work on laccase-coated membranes for biocatalytic flow reactors, 3D-printed porous materials, and gold/silica nanotubes for catalysis, demonstrating her interdisciplinary approach. Labs & Collaborations: She collaborates with teams like the Jonas Group and Dupont-Gillain Group, utilizing advanced templating methods and nanofabrication techniques at BSMA and UCLouvain.

• Chromatography
• Separation Science
• Computational Chemistry
• +3 more

Leon Ewoud Niezen is a postdoctoral researcher in the Department of Chemical Engineering at Vrije Universiteit Brussel, specializing in chromatographic separation science. His work bridges analytical chemistry and computational methods with a focus on liquid chromatography optimization. His research interests center on automated method development in liquid chromatography , particularly through reinforcement learning applications. Key areas include: Chromatographic response function optimization Retention time modeling in hybrid RPLC-HILIC systems Protein diffusion analysis under high-pressure conditions Integration of machine learning for experimental design His recent publications demonstrate a clear trend toward AI-driven chromatography, with 7 of 9 outputs since 2023 focusing on computational approaches to separation science. Niezen maintains an active research profile with an h-index of 11 (Scopus), primarily collaborating with Gert Desmet and Deirdre Cabooter. His experimental work frequently examines protein behavior in advanced column technologies, particularly using wide-pore core-shell particles under elevated pressure conditions.

• Analytical Chemistry
• Biochemistry
• Protein Analysis
• +3 more

Dániel Papp is a Researcher at the Vrije Universiteit Brussel (VUB), affiliated with the Department of Chemical Engineering and Industrial Chemistry. His work focuses on advanced separation techniques, particularly hydrophobic interaction chromatography (HIC) and their applications in protein characterization and biotherapeutics. He is a key member of the EU-funded project EU700: Next-gen Liquid Chromatography - Mass Spectrometry Technology for Unravelling the Plasma Proteome , collaborating with experts like Sven Eeltink and Daniele Guillarme. His research interests bridge analytical chemistry and biochemistry, emphasizing innovative methods for biomedical and pharmaceutical analyses. Recent contributions include a 2025 peer-reviewed article in Journal of Chromatography A , highlighting advancements in HIC methodologies. Papp’s projects often involve interdisciplinary collaborations, reflecting his commitment to advancing proteomics and separation science. While no formal awards or student advisement records are noted, his involvement in high-impact projects underscores his active role in cutting-edge research. His work is conducted at VUB’s Brussels campus, contributing to the broader scientific community through open-access publications.

• Theoretical and Structural Physico-Chemistry
• Biophysics
• Computational Chemistry
• +4 more

Tanguy Leyder is a researcher at the Unit of Theoretical and Structural Physico-Chemistry within the University of Namur. His work focuses on computational and biophysical approaches to understanding protein dynamics, particularly amyloid formation and zinc finger protein behavior. Key Research Areas: Intrinsically disordered proteins, molecular dynamics simulations, and environmental impacts on protein conformation. Collaborations: Active partnerships in studying DPF3 isoforms and their roles in amyloidogenesis. Recent Trends: Combines quantum mechanics/molecular mechanics (QM/MM) simulations with experimental data to resolve spectral properties of complex systems. Investigations span pH effects on autofluorescence and non-canonical roles of chromatin-related proteins. Scientific Output: Published peer-reviewed articles in journals like Chemistry and International Journal of Biological Macromolecules , with emerging topics including fuzzy protein complexes and phase-separated systems.

• Astrochemistry
• Cosmology and Extragalactic Astronomy
• Infrared and Optical Astronomy
• +7 more

Kosei Matsumoto is an FWO Junior Postdoctoral Fellow at Ghent University's Faculty of Sciences , Department of Physics and Astronomy . His research focuses on dust evolution in galaxies through hydrodynamic simulations and radiative transfer modeling . Key research areas: Dust grain size distribution dynamics Polycyclic Aromatic Hydrocarbon (PAH) abundance indicators Galaxy evolution simulations (Milky Way-like, NGC 628) Observational validation via JWST , Spitzer , and ALMA data Multi-phase interstellar medium analysis Active Galactic Nuclei (AGN) obscuration structures Recent publications demonstrate his expertise in dust radiative transfer modeling using SKIRT code, with notable work on: Calibrating dust accretion timescales in low-metallicity environments Validating sub-grid models for interstellar medium phases Characterizing buried AGN through submillimeter/near-infrared absorption Comparing simulation-observation frameworks for dust evolution

• Radiation protection
• Radiation dosimetry
• Nuclear physics
• +5 more

Peter Covens is a researcher at Vrije Universiteit Brussel (Brussels, Belgium) specializing in nuclear medicine, radiation protection, and dosimetry. His work focuses on optimizing radiopharmaceutical production (e.g., Astatine-211), advancing computational frameworks for nuclear dose assessment, and analyzing radiation effects in preclinical models. He leads projects like Exploit-PeptIT (peptide-based imaging/therapy) and collaborates with EURADOS-EANM on radiation safety standards. Key research areas: Nuclear medicine, radiation dosimetry, and radiochemistry Supervises Master's theses on topics like extremity dosimetry and radiopharmaceutical optimization Active in conferences/workshops (e.g., 2024 presentation on Astatine-211 accessibility) Contributes to European radiation safety policies and clinical dosimetry protocols

• Chemical Engineering
• Chromatography
• Fluid Dynamics
• +3 more

Bram Huygens is a postdoctoral researcher at the Department of Bio-engineering Sciences , Vrije Universiteit Brussel, Belgium. His work focuses on axial dispersion in porous media , with applications in chromatography , chemical reactor design , and fluid dynamics . He is associated with the Chemical Engineering and Industrial Chemistry group. Research Interests : Understanding and modeling axial dispersion in porous media Computational Fluid Dynamics (CFD) simulations Microfluidic flow visualization Chromatographic column design and optimization Hybrid reversed-phase and HILIC chromatography Scientific Awards : Csaba Horváth Young Scientist Award (2025) Rising Stars of Separation Science Award (2024) Ilya Prigogine Prijs (2019) Multiple conference prizes (Slam contests at ISC 2024 and HPLC 2025) Collaborations and Activities : Huygens has collaborated with researchers like Gert Desmet and Claudia Venditti. He presented at international conferences (e.g., HPLC 2025, ISC 2024) on topics related to van Deemter’s equation and flow modeling.

Emile HayeView profile

Researcher

• Plasma Technology
• Materials Science
• Thin Films
• +3 more

Emile Haye is a researcher at the Namur Institute of Structured Matter (Université de Namur), specializing in advanced materials science and plasma-based thin film technologies. His work focuses on plasma synthesis, coating development, and surface engineering, with applications in nuclear materials, energy systems, and sensor technologies. He holds a PhD in Materials Science from Université de Lorraine (2016) and has pioneered research in magnetron sputtering, nanomaterials, and functional coatings. Key areas of expertise include thin film deposition (TiO₂, SiC, WSe₂), plasma-assisted synthesis of catalysts, and tribological/thermal stability of coatings in extreme environments like Lead-Bismuth Eutectic (LBE). He leads projects such as IITP (Innovative industrial target preparation) and TINALTA, addressing challenges in coating durability and process optimization using machine learning. Recipient of the PSE Early Career Award (2022), Haye has published extensively on topics including nanocomposite membranes, photocatalytic foams, and wear-resistant coatings. His research integrates experimental methods with computational modeling, contributing to sustainable energy solutions and advanced manufacturing processes. Active in international collaborations, he bridges fundamental materials science with industrial applications. Recent work explores 3D-printed foams for photocurrent generation, oxidation-resistant TiAlN coatings, and anomaly detection in magnetron sputtering processes. His lab develops novel materials for nuclear reactors, biomedical implants, and sensor platforms, emphasizing interdisciplinary innovation.

Laurent PoirrierView profile

Adjunct Professor

• Mathematical Optimization
• Combinatorics
• Operations Research
• +5 more

Laurent Poirrier is an adjunct professor in Computer Science at Bocconi University, Italy, and previously worked in the Combinatorics and Optimization department at the University of Waterloo, Canada. His research focuses on mathematical optimization, cutting plane theory, and algorithm design, with applications in integer programming and vehicle routing. He has developed open-source tools like hperf (Linux perf trace visualizer) and ben (batch job scheduler). His publications span topics from polyhedral theory for integer programs to computational methods for stable matching. Notably, he co-created Gemvid, an open-source rodent motion detection system. Current teaching includes Software Engineering courses (2024, 2023) and Computer Science (2023). Recent work involves fuzzing optimization software with tools like AFL++. The trend of his research highlights intersections between computational mathematics and practical algorithm development, emphasizing numerical stability and efficient problem-solving frameworks. His contributions to optimization theory and software reflect a dual focus on theoretical rigor and real-world implementation challenges.

• Pharmaceutical Analysis
• Liquid Chromatography
• Machine Learning Applications
• +3 more

Deirdre Cabooter is a full professor at the Faculty of Pharmaceutical Sciences, KU Leuven, specializing in Pharmaceutical Analysis. She leads the Department of Pharmaceutical and Pharmacological Sciences, focusing on advanced chromatographic techniques and their integration with AI for pharmaceutical and environmental applications. Rational design of stationary phase carriers for oligonucleotide separations Machine learning platforms for micropollutant removal High-resolution LC-MS methods for water treatment monitoring Ecotoxicity profiling of micropollutant degradation products Her recent publications highlight interdisciplinary work at the intersection of pharmaceutical analysis, machine learning, and environmental remediation, with a strong emphasis on liquid chromatography innovations and micropollutant management. Supervision of PhD candidates in chromatography-AI hybrid methodologies Development of molecular diffusion databases for biomolecules