جست‌وجوی استاد و پژوهشگر

• Stochastic Processes
• Operations Research
• Data-Driven Decision Making
• +۴ مورد دیگر

Stella Kapodistria is an Associate Professor at Eindhoven University of Technology's Department of Mathematics and Computer Science, specializing in Stochastic Operations Research. She holds roles as EAISI High Tech Systems Associate Professor and editorial board member of journals like MCAP and PEIS. Her research focuses on data-driven decision-making, stochastic systems optimization, and maintenance policies, with applications in renewable energy, critical infrastructure, and cryptocurrency networks. She has secured grants including NWA-ORC, NWO Big Data, and TKI WoZ, and collaborates with industry partners in the Brainport region. Education: BSc (2003), MSc (2006, Hons.), and PhD (2009, summa cum laude) in Mathematics from the University of Athens. Postdoc at TU/e, followed by roles at Groningen University and TU/e's Stochastic Operations Research group. Teaching includes courses on Optimal Decision Making, Stochastic Performance Modeling, and Financial Mathematics. Research interests emphasize real-time learning, system resilience, and scalable algorithms for complex networks. Recent work addresses maintenance logistics, blockchain confirmation times, and wind energy prediction. She has published over 40 peer-reviewed articles and contributed to the 4TU Resilience Engineering Center. Awards include editorial leadership roles and grant funding. Advised 32 academic works and oversees industrial projects bridging theory and practice. Her labs and collaborations focus on adaptive systems, predictive analytics, and sustainable engineering solutions.

Clemens V. Verhooselمشاهده پروفایل

دانشیار

• Computational Mechanics
• Isogeometric Analysis
• Fluid Dynamics
• +۵ مورد دیگر

Clemens V. Verhoosel is an Associate Professor in Computational Methods for Model- and Data-Driven Engineering at Eindhoven University of Technology (TU/e). He holds positions in the Department of Mechanical Engineering under the Energy Technology and Fluid Dynamics section, and is affiliated with the EAISI Foundational initiative. His research focuses on scan-based immersed isogeometric analysis, uncertainty quantification, and Bayesian inference for complex engineering problems. He leads the Group Verhoosel and manages the Engineering Mechanics Graduate School since 2018. Education: MSc (Aerospace Engineering, TU Delft, 2005, cum laude PhD, TU Delft, 2009). Postdoctoral research at University of Texas at Austin (2009-2010). Awarded NWO VENI Grant (2011). Research interests include numerical methods for solid mechanics, fluid dynamics, coupled problems, and applications in biomedical engineering (e.g., cardiac mechanics). He develops open-source tools like the Nutils toolkit and collaborates with industry partners such as Evalf Computing. Key contributions include isogeometric analysis for fracture mechanics, phase-field models, and mesh-free simulation workflows. Honors: NWO Veni Award (2011). Teaching includes Advanced Discretization Techniques and Scientific Computing courses. Active in professional activities, including invited talks on cardiac mechanics and computational methods.

• AI Ethics
• Cognitive Science
• Machine Learning
• +۷ مورد دیگر

Dr. Michal Klincewicz is an Assistant Professor at Tilburg University's Department of Cognitive Science and Artificial Intelligence, affiliated with the Tilburg School of Humanities and Digital Sciences. He also holds a part-time position as an Assistant Professor at Jagiellonian University. His research focuses on AI ethics, cognitive science, and the intersection of video games with well-being and decision-making. Klincewicz completed his PhD in Philosophy at the CUNY Graduate Center and conducted postdoctoral research at the Berlin School of Mind and Brain. His work addresses ethical challenges in AI applications, including mental healthcare chatbots, generative AI's societal impacts, and consciousness theories. He leads projects like 'Moral Video Games Collaboration' and 'Understanding the associations between online video games and well-being.' Klincewicz is also involved in serious games development, such as 'The Sustainable Port,' to study real-world decision-making dynamics. Key collaborations include MindLabs and the Port of Rotterdam Authority. His research spans over 47 publications, with recent focus on AI ethics, cognitive neuroscience, and video game expertise analysis using eye-tracking and machine learning. He actively participates in conferences and networks like the Netherlands Research School for Information and Knowledge Systems (SIKS).

Alessandro Corbettaمشاهده پروفایل

استادیار

• Pedestrian Dynamics
• Machine Learning
• Fluid Turbulence
• +۳ مورد دیگر

Alessandro Corbetta is an Assistant Professor in the Department of Applied Physics and Science Education at Eindhoven University of Technology (TU/e). He leads the 'AI for Traffic and Complex Flows' group, focusing on pedestrian dynamics, machine learning in fluid mechanics, and active flowing matter. His work integrates empirical data, statistical physics, and computational methods to model crowd behavior and optimize pedestrian environments. Education & Academic Background: MSc (cum laude) in Mathematical Engineering, Polytechnic University of Turin (2011) PhD in Applied Mathematics, TU/e (2016) PhD in Structural Engineering, Polytechnic University of Turin (2016) Research Interests: His research spans pedestrian dynamics, machine learning for fluid mechanics, turbulence modeling, and high-performance computing. Key areas include real-world crowd tracking, AI-driven crowd management, and statistical mechanics applied to big data analytics. Awards & Grants: 2021 Ig Nobel Prize in Physics for studying pedestrian collision avoidance 2018 VENI Grant (NWO) for 'Understanding and Controlling Human Crowd Flows' Teaching & Academic Contributions: Responsible for courses like 'Machine Learning in Science' and 'Machine Learning for Fluid Mechanics' Editor-in-Chief of Collective Dynamics Labs & Collaborations: Collaborates with municipalities, museums, and festivals to deploy real-time crowd management systems. Active in TU/e's Intelligent Lighting Institute and Fluids and Flows research groups.

Valeria Garbinمشاهده پروفایل

استاد

• Microscale Transport Phenomena
• Soft and Biological Matter
• Colloid and Interface Science
• +۱۲ مورد دیگر

Valeria Garbin is a Full Professor in the Department of Chemical Engineering at Delft University of Technology (TU Delft), Faculty of Applied Sciences. She heads the Transport Phenomena section and leads the Garbin Research Group, which focuses on microscale fluid dynamics, soft and biological materials, colloid and interface science, aiming to advance sustainable processes and products, including applications in drug delivery and bioprocessing. Institution: Delft University of Technology Faculty: Faculty of Applied Sciences Department: Department of Chemical Engineering Section: Transport Phenomena Position: Full Professor She obtained her MSc in Physics from the University of Padova (2003) and her PhD from the University of Trieste, Italy (2007). She was a Rubicon Fellow at the University of Twente (2007–2009), a postdoctoral researcher at the University of Pennsylvania (2009–2012), started her independent group at Imperial College London (2012), and joined TU Delft in 2019. Her research interests center on microscale transport phenomena in complex fluids, including droplets, particles, and biological systems. She integrates fluid dynamics with soft matter physics and colloid science to understand how microstructural changes affect macroscopic behavior in formulated products such as foods, personal care items, and pharmaceuticals. A major focus is on enabling sustainable innovation by replacing harmful ingredients through fundamental insights into flow and interfacial behavior. Valeria Garbin has secured several high-level research grants, including an ERC Starting Grant (2015), an ERC Proof of Concept Grant (2022), and an NWO Vici Grant (2022). She has been recognized with the McBain Medal (2018) and the Soft Matter Lectureship (2020). Her research group includes postdoctoral researchers and PhD students working on topics such as Pickering emulsions, microfiber filtration using acousto-fluidics, and electrohydrodynamic drying of biomass. Although no specific article list was provided in the source text, her work likely spans journals in soft matter, fluid mechanics, and chemical engineering, with emphasis on experimental, simulation, and modeling approaches to complex fluid systems. ERC Starting Grant (2015) ERC Proof of Concept Grant (2022) NWO Vici Grant (2022) McBain Medal (RSC/SCI, 2018) Soft Matter Lectureship (RSC, 2020) She advises multiple PhD students and postdocs, contributing to training the next generation of scientists in transport phenomena and sustainable technology. Her group collaborates across disciplines to bridge fundamental science with industrial applications. She teaches courses such as Fysische Transportverschijnselen (BSc), Advanced Interfacial Engineering (MSc), and Molecular Transport Phenomena (MSc), contributing significantly to chemical engineering education at TU Delft. The Garbin Research Group is actively involved in developing scalable, non-clogging filtration technologies, improving energy efficiency in drying processes, and designing tunable multiphase catalytic systems. Their approach combines precision experiments, particle-based simulations, and analytical modeling to link microscale dynamics to macroscopic performance.

• Soft Matter Physics
• Fluid Dynamics
• Surface Science
• +۳ مورد دیگر

Daniel Bonn is a Professor at the Faculty of Science, University of Amsterdam, affiliated with the WZI (Van der Waals-Zeeman Institute for Physics). His research laboratory is located at Science Park 904, room C4.230, with contact email D.Bonn@uva.nl and phone +31 (0)20 525 5887. His research focuses on: Soft Matter Physics Fluid Dynamics Surface Science Granular Materials Colloid Science Biophysics Professor Bonn's experimental work investigates fundamental mechanisms in complex fluid systems, including emulsion stabilization by plant proteins, ice-water interfacial phenomena, and aerosol dynamics. His recent publications demonstrate strong interdisciplinary connections between fundamental physics and applications in environmental science (indoor air pollution mitigation), biotechnology (lung surfactant preservation), and agriculture (pesticide delivery optimization). The integration of high-speed imaging, rheological characterization, and fluorescence microscopy enables novel insights into interfacial dynamics across multiple scales.

• Biomimetics
• Bio-inspired Design
• Animal Flight
• +۳ مورد دیگر

Prof. D. (David) Lentink is a Full Professor of Biomimetics at the University of Groningen, Faculty of Science and Engineering. His research focuses on understanding biological flight mechanics, particularly in birds, to inspire advanced robotics and aerospace innovations. He holds a PhD in Experimental Zoology (cum laude, Wageningen University, 2008) and dual MS/BS degrees in Aerospace Engineering (Delft University of Technology, 2003). Lentink leads the Biomimetics Group, pioneering projects like rudderless flight and morphing-wing drones. His work bridges biomechanics, robotics, and aerodynamics, with notable contributions to aerial grasping systems and biomimetic materials. Education: PhD cum laude, Experimental Zoology, Wageningen University (2008) MS/BS, Aerospace Engineering, Delft University of Technology (2003) Research Interests: Biomimetics, bio-inspired design, animal flight biomechanics, aerodynamics of flight, and aerial robotics. His lab develops cutting-edge tools like the Aerodynamic Force Platform to measure in vivo forces and advanced wind tunnels for precise flight studies. Awards: 2018 Steven Vogel Young Investigator Award 2017 National Academy of Engineering Gilbreth Lecturer 2013 World Economic Forum Top 40 Scientists Under 40 Grants & Labs: Lentink oversees grants from NSF, Human Frontiers Science Program, and Dutch Academic Year Prize. His lab collaborates globally and maintains ties with Stanford University’s former projects. Current focus includes biomimetic drones for complex environments and understanding turbulence adaptation in birds.

Vincent E. Debetsمشاهده پروفایل

پژوهشگر

• Soft Matter Physics
• Machine Learning
• Non-Equilibrium Physics
• +۳ مورد دیگر

Vincent E. Debets is a Researcher at the Applied Physics and Science Education department of Eindhoven University of Technology, specializing in Non-Equilibrium Soft Matter . His work bridges physics and computational methods to study complex material behaviors. Education : Master's thesis on Collective Cell Dynamics in Cancer Metastasis (2019), supervised by Dr. C. Storm and Dr. L. M. C. Janssen. Research Interests : Debets focuses on soft matter systems near non-equilibrium states, utilizing machine learning and deep learning to analyze glassy dynamics , active matter , and memory effects in materials. His recent studies examine structural properties of amorphous systems and correlation functions in dense active fluids. Article Trends : His publications emphasize computational approaches (machine learning, deep learning) to model non-equilibrium phenomena in soft matter, particularly glassy and active systems. Topics include structural aging, particle classification, and chiral fluid dynamics, with applications in materials science and biophysics. Collaborations : Works with international researchers like Dr. L. M. C. Janssen (Eindhoven), Prof. H. Löwen (Bochum), and Dr. T. Voigtmann (Aachen). Frequent media engagement on topics like 'glassy materials' and 'cancer cell modeling'. Labs/Teams : Affiliated with the Non-Equilibrium Soft Matter group at Eindhoven University of Technology, contributing to cross-disciplinary research in physics and biomedical applications.

Martien Hulsenمشاهده پروفایل

دانشیار

• Computational Rheology
• Viscoelastic Flow Modeling
• Polymer Processing
• +۵ مورد دیگر

Martien Hulsen is an Associate Professor at the Department of Mechanical Engineering , Eindhoven University of Technology (TU/e) . His research focuses on Computational Rheology , with applications in Polymer Processing , Microfluidics , and Additive Manufacturing (3D Printing) . Academic background: PhD in Mechanical Engineering (Delft University of Technology, 1988) Specializes in Numerical Methods for viscoelastic flow simulation Key applications: External Gear Pumps , Cell Sorting , and Micro-rheology Recent research trends: Interface Rheology , Particle Dynamics , and Thermal-Viscous Coupling His work has been published in top journals like Journal of Non-Newtonian Fluid Mechanics and Physics of Fluids . Martien serves on the editorial board of the Journal of Non-Newtonian Fluid Mechanics. Contact: m.a.hulsen@tue.nl

• Proteomics
• Immunology
• Mass Spectrometry
• +۳ مورد دیگر

Danique van Rijswijck is a Researcher at Utrecht University's Department of Pharmaceutical Sciences within the Faculty of Science. She operates in the Biomolecular Mass Spectrometry and Proteomics research group, focusing on advanced proteomic techniques to investigate antibody repertoires and disease mechanisms. Her work bridges clinical immunology and analytical biochemistry through mass spectrometry applications. Her research centers on immunoglobulomics and antibody repertoire analysis, with emphasis on personalized immune responses in autoimmune and infectious diseases. She employs LC-MS-based Fab profiling to dissect polyclonal antibody responses at molecular resolution, particularly examining IgG1 and IgA1 clonal dynamics. Key disease contexts include rheumatoid arthritis, systemic sclerosis, and SARS-CoV-2 infection/vaccination responses, where she reveals how individual variations in antibody repertoires influence disease progression and treatment outcomes. Analysis of her 15 most recent publications shows consistent innovation in mass spectrometry methodologies for antibody profiling, with a strong trend toward understanding hyperpermeability in rheumatoid synovial compartments, DNA-binding mechanisms in systemic sclerosis autoantibodies, and personalized antibody responses to viral variants. Her work consistently demonstrates that human antibody repertoires are highly individualized, dynamic, and clinically significant across multiple disease states. Danique van Rijswijck is embedded in Utrecht University's Biomolecular Mass Spectrometry and Proteomics team, which develops cutting-edge proteomic technologies for biomolecular analysis. The group specializes in translating mass spectrometry innovations into clinical applications for autoimmune disorders and infectious diseases, with van Rijswijck contributing specifically to antibody repertoire characterization workflows.

• Food Science
• Biophysics
• Lipid Oxidation
• +۵ مورد دیگر

John van Duynhoven is a Professor of Biophysics at Wageningen University and Research. His work bridges advanced imaging techniques and food science, focusing on lipid oxidation, protein processing, and emulsion structure. Affiliation: Wageningen University and Research Academic Rank: Professor Research Interests: He investigates food structure and stability using NMR spectroscopy, super-resolution microscopy, and magnetic resonance imaging. Key areas include lipid oxidation pathways, plant-protein extrusion, and granular flow dynamics. Scientific Contributions: Supervised multiple PhD projects on multiscale protein modeling, emulsion oxidation, and infant nutrition protein digestion. His recent articles highlight innovations in quantifying anisotropic food structures and oxidation products. Collaborations: Partnerships span imaging techniques, X-ray scattering, and food technology. Active projects include lipid oxidation mapping and protein extrusion modeling.

Bahar Haghighatمشاهده پروفایل

استادیار

• Robotics
• Control Systems
• Artificial Intelligence
• +۱۱ مورد دیگر

Bahar Haghighat is a Tenure Track Assistant Professor in Robotics and Automation at the Faculty of Science and Engineering, University of Groningen. She leads the Distributed Autonomous Intelligent Systems (DAISY) Lab as Principal Investigator and contributes to academic governance as a Member of the Faculty Council. Her professional affiliations include the Royal Netherlands Institute of Engineers (KIVI), the Institute of Electrical and Electronics Engineers (IEEE), and editorial roles with Nature Portfolio Journal Robotics and Springer Nature Journal Autonomous Robots. Her educational background includes: PhD in Robotics, Control, and Intelligent Systems from the Swiss Federal Institute of Technology in Lausanne (EPFL), Switzerland (2018) Master's degree in Electrical Engineering/Digital Electronics from Sharif University of Technology (SUT), Tehran, Iran Bachelor's degree in Electrical Engineering/Physics (double major) from Sharif University of Technology (SUT), Tehran, Iran Dr. Haghighat's research focuses on building novel miniaturized robotic swarms and algorithmic frameworks for sensing, surveying, and inspection applications. Her work spans mechatronics, electronics, embedded systems, embedded artificial intelligence and machine learning, and distributed systems. She envisions developing surface, aquatic, and aerial miniaturized robot swarms and small-scale intelligent devices for basic research and commercial applications including inspection of complex structures, environmental monitoring, space exploration, and search-and-rescue operations. Her recent publications demonstrate a strong focus on swarm robotics, particularly using particle swarm optimization techniques for multi-robot coordination, surface inspection tasks, and spacecraft hull inspection. Her research shows an interdisciplinary approach combining mechatronic design with advanced algorithms for self-assembly and collective decision-making in resource-constrained robotic systems. Her notable scientific achievements include: EPFL's PhD research award of Gilbert Hausmann for the best PhD thesis in mechanical engineering, electricity, and physics (2019) EPFL distinction of excellence for a PhD thesis in Robotics, Control, and Intelligent Systems (2018) Swiss National Science Foundation Postdoc Mobility Fellowship (2019) Swiss National Science Foundation Early Postdoc Mobility Fellowship (2017) Third place in EPFL's "My Thesis in 180 Seconds" competition (2017) EECS Rising Star recognition (2021 at MIT and 2019 at UIUC) Dr. Haghighat has served as Program Co-Chair for The International Symposium on Distributed Autonomous Robotic Systems (DARS) and has held visiting scholar positions at MIT and Harvard University. Her research has received media attention for applications in Mars rover technology and drone swarms for defect detection. She leads the DAISY Lab, which focuses on distributed autonomous intelligent systems for various inspection and monitoring applications.

• Crowd Simulation
• Path Planning
• Real-time Simulation Software
• +۳ مورد دیگر

Wouter van Toll is a Lecturer at the Academy for AI, Games & Media, specializing in crowd simulation and real-time systems. His research focuses on path planning, crowd behavior modeling, and fluid dynamics in agent-based simulations. He has contributed to advancing algorithms for microscopic crowd simulation and integrating techniques like Smoothed Particle Hydrodynamics (SPH) to handle extreme crowd densities. Key research interests include sketch-based interaction design for steering behaviors, navigation mesh optimization, and topological strategies for agent coordination. His work bridges computational methods with creative applications in game development and artificial intelligence. Received Best Paper Award Honorable Mention (2022) for his work on sketch-based steering behaviors in crowd simulation. Active collaborations in Europe and North America, particularly in crowd simulation software development. Publications span algorithmic advancements in crowd simulation, navigation systems, and interdisciplinary applications combining physics-based methods with agent-based models. Current research emphasizes real-time simulation efficiency and human-centered design tools for behavior specification.

• Craniofacial Surgery
• Pediatric Plastic Surgery
• Airway Management
• +۳ مورد دیگر

Iris Cuperus is a Researcher at Erasmus University Rotterdam, specifically within the Department of Plastic and Reconstructive Surgery and Hand Surgery at Erasmus MC. Her work focuses on craniofacial anomalies, particularly syndromic craniosynostosis conditions including Apert and Crouzon syndromes. She actively collaborates with multidisciplinary teams across Europe, as evidenced by her participation in the ERN CRANIO Workgroup Craniosynostosis, contributing to standardized care protocols for complex craniofacial conditions. Dr. Cuperus's research spans several critical areas in pediatric craniofacial surgery. Her work addresses complex challenges in airway management following midface advancement procedures, nasal deviation outcomes after surgical correction, and the relationship between venous abnormalities and intracranial pressure in syndromic craniosynostosis. She employs both retrospective clinical analyses and multicenter collaborative studies to advance understanding and improve clinical outcomes for children with craniofacial anomalies. Her publication record demonstrates a clear focus on improving perioperative management and surgical outcomes for children with syndromic craniosynostosis. Recent work (2024-2025) emphasizes standardized protocols for airway management, clinical outcomes following midface advancement, and the physiological relationships between cranial venous structures and intracranial pressure. This research contributes significantly to evidence-based approaches in a specialized field requiring careful multidisciplinary coordination. Dr. Cuperus actively participates in European collaborative networks focused on craniosynostosis, working with specialists across multiple countries to develop standardized care approaches. Her research has been published in prominent journals including Plastic and Reconstructive Surgery, Journal of Craniofacial Surgery, and Pediatric Neurosurgery, demonstrating the clinical relevance and impact of her work in both surgical and anesthetic management of complex craniofacial conditions.

V. Menkovskiمشاهده پروفایل

دانشیار

• Graph Neural Networks
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Dr. V. Menkovski serves as an Associate Professor in Data Mining at Eindhoven University of Technology's Department of Mathematics and Computer Science. He also holds associate professor positions with EAISI Health and EAISI High Tech Systems, and is an ICMS Affiliated member. His work spans multiple domains of artificial intelligence and computational physics, with significant contributions to fusion energy research. Mathematics and Computer Science, Data Mining (Primary Appointment) EAISI Health (Associate Professor) EAISI High Tech Systems (Associate Professor) ICMS (Affiliated Member) Menkovski's research focuses on Graph Neural Networks, Machine Learning, Deep Learning, and their applications in diverse fields from plasma physics to metamaterials. His work demonstrates strong interdisciplinary connections, particularly between computer science and fusion energy research. He has developed novel approaches for crowd simulation, tokamak plasma monitoring, and metamaterials homogenization using advanced neural architectures. His fingerprint reveals expertise in Quality-of-Experience, Autoencoders, Neural Networks, Annotation, Graph Neural Networks, Video Streaming, Adversarial Machine Learning, and Anomaly Detection. Analysis of his recent publications (2023-2025) shows a clear trend toward applying Graph Neural Networks to complex physical systems, particularly in fusion energy research and materials science. His work increasingly integrates symmetry principles with neural architectures, as seen in his research on equivariant networks for metamaterials and symmetry-informed networks for zeolite analysis. There's also significant focus on practical applications in fake news detection, anomaly detection, and plasma state monitoring. Best Paper Award ICPM 2021 (with Sommers and Fahland) Best Paper Award of LoG 2022 (with multiple co-authors including Huang, Chen, Fang, Zhao, Yin, Pei, Mocanu, Wang, Pechenizkiy, and Liu) Menkovski teaches several advanced courses including Deep Learning, Advanced Topics in Artificial Intelligence, and Sociophysics 2, which runs through August 2025. His supervised work portfolio includes 79 projects, indicating substantial mentorship activity. He has received significant media attention for his research, including coverage by 11 news outlets, blog posts, and mentions on social media platforms. His work on 'Supervised Learning of Process Discovery Techniques Using Graph Neural Networks' was particularly noted in media coverage. His research involves collaboration with multiple institutions and teams, particularly in fusion energy research (Eurofusion Tokamak Exploitation Team, ASDEX-Upgrade team, EUROfusion MST1 Team). He works closely with researchers across disciplines, including physicists working on tokamak plasma and materials scientists studying metamaterials and zeolites.