Bas P. de Hon is an Assistant Professor in the Electromagnetics group at TU/e's Department of Electrical Engineering. His research focuses on analytical techniques and numerical modeling for electromagnetic, acoustic, and elastic field problems, ranging from exploration geophysics to THz and optical applications. His educational background includes an MSc from Delft University of Technology and a PhD from TU/e. His research group specializes in multi-scale modeling techniques including MD, DSMC, hybrid MD-DSMC, and CFD, validated through experimental methods like micro-PIV and 3D micro-PTV. Recent publications demonstrate a focus on electromagnetic scattering analysis, optical fiber connections, and computational methods. Research trends show advanced applications in wave propagation, numerical optimization, and photonic device modeling. Laboratory affiliations include the Electromagnetic and Multi-Physics Modeling and Computation Lab at TU/e and collaborations with industry partners including ASML and Philips.
Gijs L. Derks is a doctoral researcher in the Mechanical Engineering school at Eindhoven University of Technology (TU/e) , affiliated with the Dutch Institute for Fundamental Energy Research (DIFFER) . His work focuses on Fusion Energy and Plasma Physics , particularly Tokamak Device optimization and Control Systems . Key Research Areas : Tokamak power exhaust, scrape-off layer dynamics, real-time plasma control, and sensor placement in fusion reactors. Collaborations : Eurofusion Tokamak Exploitation Team, MAST Upgrade team, TCV team. Notable Contributions : Development of physics-based models for divertor plasma detachment and feedback control systems for fusion reactor stability. His recent publications highlight innovations in neutral baffling and density feedback mechanisms in tokamaks.
Peter A.J. Hilbers is a Full Professor of BioModeling and BioInformatics at Eindhoven University of Technology's Biomedical Engineering department, holding a part-time position supported by Nobian. His research develops large-scale computational models for biomedical and chemical systems, including metabolic networks, molecular systems, and polymer chemistry applications. Recent work focuses on spatiotemporal modeling of gut microbiota and ultrasound ablation techniques. Hilbers contributes to sustainable development through energy-efficient chemical process modeling and maintains collaborations via the Sino-Dutch Biomedical & Information Engineering School.
Sebastiaan J.A.M. van den Eijnden is an Assistant Professor at the Department of Control Systems Technology within the College of Mechanical Engineering at Eindhoven University of Technology. His research focuses on leveraging machine data to develop graphical and algorithmic methods for enhancing the performance of nonlinear control systems in high-tech machinery, emphasizing productivity, precision, and efficiency. He is a key member of the Projection-based Control (PROACTHIS) project (2022–2028), aiming to advance control methodologies for constrained dynamical systems. He teaches courses such as Control Engineering , Hybrid Systems and Control , and Performance of Nonlinear Control Systems . His research interests encompass nonlinear control systems, system stability analysis, motion control, and data-driven approaches. Recent work includes contributions to dissipativity frameworks, nonlinear system characterization, and phase-optimized integrators. He holds a Master’s degree in Mechanical Engineering (thesis: Cascade based tracking control of quadrotors ) and maintains active collaborations in systems engineering and control theory.
Mircea Lazar is an Associate Professor in the Control Systems group at Eindhoven University of Technology. His research spans constrained control, model predictive control (MPC), and stability analysis of hybrid systems, with applications in power systems, smart grids, and precision mechatronics. Research highlights include: Development of data-driven predictive control methods Stability guarantees for nonlinear and hybrid systems Applications in water networks, power converters, and mechatronics He has received the EECI PhD Award and NWO VENI grant. Recent publications focus on real-time MPC implementations, physics-guided neural networks, and optimization for large-scale systems. Collaborations include ASML, DAF, Philips, and Ford. He chairs the IEEE CSS Technical Committee on Hybrid Systems and has supervised 9 PhD students.
Maryam Tavakol is an Assistant Professor at Eindhoven University of Technology, affiliated with the Uncertainty in AI group within the Mathematics and Computer Science department. Her expertise bridges artificial intelligence, machine learning, and computational modeling for sequential systems. PhD in Machine Learning, TU Darmstadt B.Sc. & M.Sc. in Computer Science, University of Tehran Her research focuses on uncertainty estimation , reinforcement learning , and transformer architectures applied to domains like drug-target interaction prediction, soccer analytics, and music generation. Recent work explores activation sparsity in LLMs and robust VAE frameworks. Key trends in her publications (2012–2024) span reinforcement learning , neural network architectures , and applications in healthcare and creative domains . Notable themes include contextual bandits, variational inference, and energy-efficient computing. Contributed to UN Sustainable Development Goals (Education/Academic Qualification) She has supervised multiple research projects and served as corresponding author in peer-reviewed conferences. Collaborations include TU Dortmund, Leuphana University, and industry internships at Criteo.
Martijn van Beurden is a Full Professor in the Electromagnetics group at Eindhoven University of Technology's Department of Electrical Engineering. His research focuses on computational electromagnetics for high-tech systems, specializing in inverse scattering and antenna design. He holds a cum laude MSc (1997) and PhD (2003) from TU/e, where he received the C.I.V.I. Prize and ASML Prize for his thesis work. His research interests center on developing advanced numerical methods for electromagnetic modeling, with applications in wave behavior analysis, inverse scattering problems, and antenna optimization. Current projects include electromagnetic field modeling in stochastic environments and periodic structures. Recent publications demonstrate strong focus on computational efficiency in electromagnetic simulations, with innovations in Green's function discretization and FFT-accelerated integral equations applied to biomedical and scattering problems. Awards: C.I.V.I. Prize for Electrical Engineering (MSc thesis) ASML Prize for Best PhD Thesis in Applied Research He leads multiple projects including INNOSTAR and MAX META-XT, focusing on meta-lens design and electromagnetic scattering analysis. Manages the Electromagnetics research group and Center for Wireless Technology Eindhoven.
Dr. Johan Morren is a part-time Assistant Professor in Electrical Energy Systems at Eindhoven University of Technology (TU/e), concurrently serving as Senior System Responsible at Enexis Netbeheer. His research focuses on smart grid technologies for distribution networks, including distributed generation integration and grid automation. Education: PhD and MSc in Electrical Power Engineering from Delft University of Technology. Research: Develops solutions for network planning, congestion management, and renewable integration using optimization methods and real-time control strategies. Current projects examine grid flexibility aggregation, power-to-gas coordination, and thermal rating of underground cables. Professional Experience: Combines academic research with practical grid operation expertise from Dutch distribution system operator Enexis, focusing on implementing smart grid solutions in real-world networks.
Rafael Bailo is an Assistant Professor of Mathematics at the Eindhoven University of Technology , affiliated with the Centre for Analysis, Scientific Computing and Applications . His primary research focuses on the numerical analysis of kinetic equations and partial differential equations (PDEs), with additional interests in collective dynamics, self-organization, and agent-based modeling. He teaches courses such as Advanced Calculus I and Continuous Optimization . Department: Mathematics and Computer Science His research explores computational methods for complex systems, including uncertainty quantification in kinetic models, pedestrian dynamics with congestion effects, and consensus-based particle methods. Recent work emphasizes the development of numerical schemes for fractional diffusion, plasma physics simulations, and aggregation-diffusion equations. He collaborates internationally on topics like particle-in-cell methods and software tools for interacting particle systems (e.g., the CBX package). Key trends in his publications include advancing finite-volume schemes for nonlinear PDEs, analyzing structural properties of kinetic equations, and bridging theoretical analysis with computational implementation. His work often addresses challenges in preserving physical properties (e.g., positivity, energy dissipation) in numerical solutions. Bailo advises students on topics related to PDEs and computational methods but specific supervisee names are not listed. His contributions span applied mathematics, computational physics, and interdisciplinary modeling, with applications in plasma dynamics, crowd movement, and materials science.
David Wozabal is a Full Professor of Management Science and Machine Learning at the Department of Operations Analytics, School of Business and Economics, Vrije Universiteit Amsterdam, appointed in 2023. Previously, he served as a professor at the Technical University of Munich. His academic foundation includes studies in business information systems and mathematics, culminating in a doctoral degree in statistics from the University of Vienna in 2008. Wozabal's educational background includes business information systems and mathematics, with a PhD in statistics from the University of Vienna (2008). His academic journey has led him through positions at Technical University of Munich before his current professorship at Vrije Universiteit Amsterdam. Professor Wozabal's research centers on optimal data-driven decision making under uncertainty, with emphasis on numerical algorithms, quantitative stability, and model uncertainty. His work provides decision support for practical problems in individual and group decision contexts. He applies theoretical research to energy, finance, supply chain management, logistics, and risk management domains. Additionally, he investigates the energy transition using machine learning, operations research, and statistical methods to analyze energy market efficiency, optimal design, and energy price properties. His research output demonstrates strong integration of theoretical foundations with practical applications, particularly in electricity markets and renewable energy systems. Analysis of Wozabal's recent publications reveals a consistent focus on energy markets, particularly electricity trading mechanisms and renewable energy integration. His work combines sophisticated stochastic optimization techniques with practical market applications, showing particular strength in intraday electricity markets, battery storage optimization, and power purchase agreements. The research demonstrates a clear trajectory from theoretical stochastic programming methods to concrete energy market applications. OeGOR (Austrian Society of Operations Research) PhD Prize (2009) As a department editor for OR Spectrum: Quantitative Approaches in Management since 2021, Wozabal contributes to academic discourse in operations research. His teaching portfolio includes Business Mathematics, Fair Transparent and Interpretable Machine Learning, and Operations Research II. While specific grant details are limited in the provided information, his research output suggests active engagement with energy market challenges. His professional activities extend to advisory work with Entrix Energy in Munich since 2021, demonstrating strong industry-academia connections in the energy sector.
Reinout de Vries is a Full Professor at the Faculty of Behavioural and Movement Sciences and IBBA (Institute for Behavioural and Brain Science) at Vrije Universiteit Amsterdam, specializing in Organizational Psychology. His research focuses on personality traits (e.g., HEXACO, TNT), their applications in work and organizational settings, and the use of AI for personality assessment. He has authored over 176 publications and actively contributes to academic discourse through editorial roles and media commentary. Research Interests: Personality Psychology (HEXACO Model) Workplace Dynamics and Leadership Psychometrics and Assessment Technologies AI-driven Personality Evaluation Social and Organizational Behavior Recent Articles: Highlighting advancements in personality assessment via large language models, the impact of nonnormative traits on social preferences, and meta-analyses of HEXACO traits. These studies bridge theoretical models with practical applications in hiring and leadership. Awards/Grants: Secured the NWO Open Science Fund (2023) to support transparent research practices. Media contributions include expert commentary on leadership, assessments, and ethical hiring. Advising & Grants: Supervised 8 PhD theses and collaborates on grants promoting open science. Teaching includes courses like Personality at Work and Personality Theory and Assessment. Labs/Teams: Active in interdisciplinary teams at IBBA and VU, focusing on behavioral and brain sciences research.
Dr. Colleen Caldwell is a Researcher at Vrije Universiteit Amsterdam, affiliated with both the Faculty of Science and the LaserLaB - Molecular Biophysics group. She holds a dual role as a Research Associate in the Physics of Living Systems and Molecular Biophysics. Her work focuses on molecular biophysics, particularly the dynamics of DNA repair proteins like RPA and helicases, as well as structural biology of DNA-protein interactions. Key research areas include single-molecule analysis, DNA replication, and the mechanics of mitosis. Her recent studies explore topics such as cohesin dynamics in mitosis, nucleotide excision repair mechanisms, and the development of computational tools for analyzing single-molecular data. She has received notable funding through the 2024 grant on CCCTC-binding factor (CTCF) research. Key Research Tools: Optical tweezers, fluorescence microscopy, single-molecule TIRFM, and stochastic modeling. Key Collaborations: Global collaborations in biophysics and molecular biology, with recent work involving teams in Europe and North America. Grants: CCCTC-binding Factor (CTCF) grant (2024) focuses on genome organization and transcription factor dynamics. Dr. Caldwell’s articles highlight interdisciplinary approaches, integrating experimental biophysics with computational analysis to unravel molecular mechanisms in DNA repair and replication. Her work bridges structural biology and cellular processes, contributing to understanding genetic stability and disease-related mechanisms.
Prof. Julia Schaumburg is a Full Professor of Econometric Methods and Applications at Vrije Universiteit Amsterdam's School of Business and Economics, serving as Head of the Department of Econometrics and Data Science. She holds concurrent roles as a research professor at Halle Institute for Economic Research and a research fellow at Tinbergen Institute. Her research focuses on time series econometrics, financial stability, systemic risk, and climate economics, integrating machine learning and panel data methods. She has secured significant grants including NWO-Veni (2015), NWO-Vidi (2020), and the ECB Lamfalussy Fellowship (2018). Educated at Humboldt-Universität zu Berlin (PhD 2013), she teaches advanced econometric courses and supervises PhD students. Her work addresses macro-financial interdependencies, climate policy implications, and financial system resilience. She contributes to peer-reviewed journals like Journal of Econometrics and serves on editorial boards including International Journal of Forecasting and Oxford Open Economics . Recent publications explore dynamic factor models, clustering algorithms for financial data, and policy responses to climate change. Award-winning scholar with the Engle Prize (2019), she advocates for ethical academic-industry collaborations and participates in public debates about fossil fuel ties in academia. Her work aligns with UN Sustainable Development Goals on climate action and sustainable finance.
Dr. Keiichi Ito is a Research Associate affiliated with the Faculty of Science and Network Institute at Vrije Universiteit Amsterdam. His primary roles include Artificial Intelligence Research Associate and Research Associate in the Faculty of Science. He holds a doctoral degree and has been actively contributing to interdisciplinary research since at least 2016. His research focuses on machine learning applications, optimization algorithms, and design space exploration. Notable areas include transfer learning in neural networks, cloud-based machine learning frameworks, stochastic approximation methods, and self-organizing map-based sampling techniques. His work bridges computational methods with engineering challenges such as seaplane stability, heteroscedasticity analysis, and structural optimization. Recent publications (2016-2021) demonstrate a strong trend in developing innovative optimization frameworks and applying deep learning solutions to interdisciplinary problems. His contributions span computational biology, aerospace engineering, and distributed computing systems. No formal scientific awards or grants are explicitly listed in the provided texts. His affiliations suggest involvement with collaborative research groups within the Network Institute and Faculty of Science.
Bernadette van Wijk is an Associate Professor at the Vrije Universiteit Amsterdam, affiliated with the Faculty of Behavioural and Movement Sciences. She holds additional appointments as an Associate Professor in Neurocontrol, the Institute of Biomaterials, Biotechnology and Diagnostics (IBBA), and the Amsterdam Movement Sciences (AMS) - Rehabilitation & Development. Her work contributes to UN Sustainable Development Goals related to health and well-being. Her research focuses on neural mechanisms underlying motor control and Parkinson's disease, leveraging techniques such as local field potential (LFP) recordings, EEG/MEG, and computational modeling. Key interests include beta oscillations in basal ganglia circuits, adaptive deep brain stimulation (DBS), and sensorimotor integration during movement. She leads courses in Neuroscience and Neurowetenschappen, emphasizing translational research and clinical applications. Recent articles highlight her contributions to understanding reward-based motor learning, LFP-based physiomarkers in Parkinson’s, and the neural correlates of gait and arm swing. Her work emphasizes methodological rigor, such as artifact suppression in neurophysiological data and optimizing DBS electrode localization. Dr. van Wijk has supervised one PhD thesis and actively collaborates internationally on projects involving neurophysiology and clinical neuroscience. Her research bridges fundamental science and clinical practice, aiming to improve therapies for movement disorders.
