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George PapafotiouView profile

Professor

• Power Electronics
• Model Predictive Control (MPC)
• Renewable Energy Systems
• +3 more

George Papafotiou is a Full Professor in the Department of Electrical Engineering at Eindhoven University of Technology (TU/e) and Head of the Power Electronics Lab. His research focuses on computational intelligence, particularly Model Predictive Control (MPC), applied to power electronics systems for applications in renewable energy, high-efficiency power conversion, and grid integration. He holds affiliations with the EIRES Research Institute and the Cyber-Physical Systems Center in Eindhoven. Academic Background: Papafotiou earned his PhD in Electrical Engineering from Aristotle University of Thessaloniki (2002) and completed a postdoc at ETH Zurich (Switzerland). He spent 15 years at ABB in roles including Scientist, Global R&D Manager for SW/Control HW, and Quality Manager, leading projects on MPC-based control systems for industrial drives and energy management. Research Interests: His work centers on advancing power electronics through MPC for microsecond-scale control, enabling reliable systems critical to a carbon-free future. Key areas include modular multilevel converters (MMCs), soft-switching techniques, grid-tied power electronics, and harmonics mitigation in high-voltage systems. Projects: Current projects include RelSST: Developing reliable solid-state transformers for smart grids. HiVECAF: Designing versatile, efficient active filters for power quality improvement. Teaching: He teaches Electric Circuits Analysis for Energy Conversion Electric Drive Systems Grants & Labs: Leads the Power Electronics Lab, focusing on applied research in sustainable energy systems and industrial automation.

Marc SchröderView profile

Assistant Professor

• Game Theory
• Mechanism Design
• Algorithms
• +3 more

Marc Schröder is an Assistant Professor at the School of Business and Economics, Maastricht University, Netherlands. His research focuses on game theory, mechanism design, algorithms, and operations research with applications in scheduling, network economics, and strategic decision-making. He holds a PhD from Maastricht University and has held postdoctoral positions at RWTH Aachen University and Universidad de Chile. His work bridges theoretical foundations and practical applications in algorithmic game theory and optimization. Education and Career: PhD and postdoc at Maastricht University (2011-2016), followed by postdocs at RWTH Aachen (2017-2020) and Universidad de Chile (2016-2017). Current role since 2020. Research Interests: Specializes in strategic interactions in networks, scheduling under constraints, congestion games, and algorithmic mechanisms. His work emphasizes equilibrium analysis, approximation algorithms, and optimization under uncertainty. Recent Publications Trends: Focus on bicriteria network flows, scheduling algorithms with deadlines, congestion game equilibria, and prophet inequalities. Active in venues like Operations Research, Mathematics of Operations Research, and Game Theory conferences. Awards: No awards explicitly listed in provided texts. Teaching: Covers Actuarial Mathematics, Dynamic Modelling, Game Theory applications in economics, and computational methods. Coordinates courses in quantitative economics and game-theoretic optimization.

Jim W. PortegiesView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Applied Mathematics
• +5 more

Jim W. Portegies is an Assistant Professor in the Applied Analysis group at the Eindhoven University of Technology (TU/e). He holds additional roles as EAISI Foundational Assistant Professor and EAISI Mobility Assistant Professor within the TU/e's interdisciplinary initiatives. His research focuses on applying mathematical techniques from analysis, geometry, and measure theory to problems in machine learning and artificial intelligence, with a particular interest in designing algorithms that emulate human learning mechanisms. He obtained his MSc in Industrial and Applied Mathematics and Applied Physics from TU/e before completing his PhD in Mathematics at the Courant Institute of Mathematical Sciences, New York. After postdoctoral work at the Max Planck Institute in Leipzig, he returned to TU/e in 2016. Education: MSc in Industrial and Applied Mathematics & Applied Physics (TU/e, 2009) PhD in Mathematics (Courant Institute, NYU, 2014) Research Interests: Jim's work bridges pure mathematics and applied AI. Key areas include: Geometric and spectral methods for machine learning Universal learning algorithms inspired by human cognition Analysis of dimensionality reduction techniques Stochastic processes and control systems His recent work explores neural stochastic differential equations and interpretable AI mechanisms. Awards: Member of TU/e Young Academy of Engineering (2016–present) Collaborations & Grants: Jim collaborates extensively in interdisciplinary projects, including EU-funded initiatives on AI mobility systems. His research has been supported by grants from NWO (Netherlands Organization for Scientific Research) and industry partnerships with semiconductor and control systems companies. Labs & Teams: He contributes to the TU/e's Center for Analysis, Scientific Computing and Applications (CASA) and leads the Applied Analysis & AI research group, focusing on theoretical foundations of AI and their practical implementations.

Maarten SchoukensView profile

Associate Professor

• Nonlinear System Identification
• Machine Learning for Control Systems
• Data-Driven Modeling
• +5 more

Maarten Schoukens is an Associate Professor in the Control Systems group at the Department of Electrical Engineering, Eindhoven University of Technology. He holds a PhD from Vrije Universiteit Brussel (2015) and has held postdoctoral positions at both VUB and TU/e. His research focuses on nonlinear system identification, machine learning applications in control, and data-driven modeling of complex systems. He has received prestigious awards including an ERC Starting Grant (2022) and an EU Marie Skłodowska-Curie Fellowship (2018). He leads projects like DAMOCLES (2021–2025) and CELLSYSTEMICS (2022–2026), advancing constitutive law modeling and human measurement systems. Active in editorial roles for IEEE and ECC conferences, he also co-organizes the nonlinearbenchmark.org initiative. Education: MSc and PhD in Electrical Engineering from VUB (2010–2015). Postdoctoral Researcher at TU/e (2017–2018) before becoming Assistant Professor (2018) and later Associate Professor (2023). Research emphasizes nonlinear dynamical systems, with contributions to system identification, neural network architectures for control, and frequency-domain methods. Key projects include developing LPV control strategies and physics-guided learning frameworks. His work bridges classical control theory with modern machine learning, addressing challenges in autonomous systems and high-tech engineering. Awards include FWO PhD Fellowship (2011), Marie Skłodowska-Curie Fellowship (2018), and ERC Starting Grant (2022). Supervised over 15 graduate students and led multidisciplinary teams in EU-funded initiatives. Labs include the Autonomous Motion Control Lab and High Tech Systems Center, fostering collaborations in robotics, aerospace, and biomedical engineering.

Kees KoedijkView profile

Professor

• Finance
• Sustainable Finance
• ESG Integration
• +5 more

Prof. Kees Koedijk is a Professor in Finance at the Utrecht University School of Economics, specializing in sustainable finance, ESG integration, and international economics. His research focuses on climate risk, investor sentiment, and corporate social responsibility, with a particular emphasis on emerging markets like China. He serves on supervisory boards for institutions such as DNB Pension Fund and Pensioenfonds Zoetwaren. Koedijk’s work bridges theoretical finance with practical applications, addressing topics such as capital account liberalization, trade policy impacts, and long-term investment strategies for pension funds. His recent projects include scaling sustainable finance solutions and analyzing the role of social media in stock market dynamics. Education background not explicitly detailed in texts, but his academic career spans over two decades with significant contributions to finance literature. Key research areas include behavioral finance, market anomalies, and the intersection of environmental policies with financial markets. He has collaborated on EU-funded projects and co-authored influential papers on topics like institutional investor sentiment and FDI effects on export performance. Supervisory Board member at multiple pension funds and financial institutions Co-founder of Finance Ideas and partner at Fontys University of Applied Sciences Recipient of extensive international recognition for contributions to finance education and policy Leading expert in sustainable investment models and long-term financial strategies

Rutger-Jan LangeView profile

Associate Professor

• Time-Series Econometrics
• Financial Econometrics
• Optimal Stopping Theory
• +3 more

Rutger-Jan Lange is an Associate Professor at the Econometric Institute of Erasmus School of Economics, Erasmus University Rotterdam. His research spans time-series econometrics, filtering, stochastic processes, real options, and optimal stopping. He completed his PhD at the University of Cambridge and has held positions at Boston Consulting Group and Vrije Universiteit Amsterdam. Education: PhD in Management Science & Operations Research (Cambridge), Master's in Theoretical Physics (Cambridge). Research Interests: Focus on developing advanced econometric models for financial forecasting, climate policy optimization, and high-dimensional data analysis. Recent work integrates machine learning with traditional econometrics to enhance predictive accuracy. Publication Trends: Articles emphasize methodological innovations in time-series analysis (e.g., Bellman filtering), applications in climate economics, and real-option valuation. Cross-disciplinary themes include quantum physics and financial risk modeling. Research Supervision: Mentors PhD students in projects on score-driven filters and economic modeling. Received a Starter Grant to fund new PhD positions. Affiliations: Tinbergen Institute, Econometric Institute, ERIM.

• Econometrics
• Financial Economics
• Machine Learning in Finance
• +3 more

Dr. Maria Grith is an Assistant Professor in the Department of Econometrics at the Erasmus School of Economics, Erasmus University Rotterdam. Her research focuses on econometric methodologies applied to financial markets, with particular emphasis on machine learning techniques in finance, risk management, and time series analysis. She actively contributes to academic supervision, as evidenced by her involvement in bachelor thesis projects in Econometrics and Economics. Her research interests span quantitative finance, including applications of neural networks to volatility forecasting, cryptocurrency risk analysis, and reinforcement learning in high-dimensional settings. She also explores traditional econometric challenges such as delta-hedged options pricing, sovereign default risk, and macroeconomic persistence heterogeneity. Her publication record includes over 20 peer-reviewed articles, with recent work addressing cutting-edge topics like neural tangent kernels in finance and graphical models for multivariate time series. While no scientific awards are explicitly mentioned, her prolific output reflects a strong research trajectory in financial econometrics and computational methods.

• Ultrasound Technology
• Biomedical Engineering
• Acoustic Sensors
• +4 more

Tim Joseph Segers holds the position of Associate Professor in three departments: Biomedical and Environmental Sensorsystems, MESA+ Institute, and TechMed Centre. His academic roles span multiple interdisciplinary fields within biomedical engineering and materials science. His research focuses on advancing ultrasound technology, microbubble dynamics, and biomedical imaging applications. Key interests include the behavior of microbubbles under varying pressures, development of contrast agents for medical imaging, and the application of acoustic principles in inkjet printing and fluid dynamics. His work bridges fundamental physics with clinical and engineering innovations. Dr. Segers has supervised seven students, contributing to their academic and research development. While no specific grants are detailed in this profile, his research has been published in high-impact journals, reflecting ongoing support for his projects. He is affiliated with the TechMed Centre and MESA+ Institute, which provide collaborative environments for his interdisciplinary research endeavors.

Xi YuView profile

Researcher

• Structural Equation Modeling
• Materials Science
• Nanotechnology
• +5 more

Xi Yu is a Researcher at the Digital Society Institute, University of Twente. Their work focuses on advancing statistical methodologies in structural equation modeling while also exploring materials science innovations in nanocomposites and phase change materials. Yu has made significant contributions to composite specification techniques and their applications in ecological and technological systems. Education & Research Expertise Completed a PhD thesis titled 'Incorporating Composites in Structural Equation Modeling: Exploiting the Henseler—Ogasawara Specification' at the University of Twente. Active research areas include composite model refinement, statistical data analysis, and material degradation studies. Collaborations involve interdisciplinary projects with institutions globally. Research Highlights Publications span methodological advancements in structural equation modeling and applied material science innovations such as thermoregulating textiles and nanocomposite synthesis. Recent work emphasizes eco-friendly materials and energy-efficient solutions. Awards : Best Conference Paper Award (2021), First Runner-Up Best Paper Award (2022), Top Cited Article (2022-2023) Invited Talks : Presented on composite specification methods at international conferences (2023-2024) Dataset Contributions : Open-access materials on composite modeling techniques via Zenodo Future Focus : Continues developing hybrid methodologies bridging statistical theory and material innovation, with applications in sustainability and digital societal challenges.

• Vibration Isolation
• Control Systems
• Feedforward Control
• +3 more

Michiel Beijen is an Assistant Professor in the Applied Mechanics department, specializing in advanced control systems and vibration isolation. His research focuses on hybrid control strategies, feedforward control mechanisms, and adaptive control systems applied to vibration isolation in industrial and precision engineering contexts. Research Interests include vibration isolation techniques, feedforward and feedback control integration, model reference adaptive control, and parameter estimation in dynamic systems. His work emphasizes practical implementation in multi-axis systems and industrial applications. Recent work explores filtered-error recursive least squares optimization and self-tuning disturbance feedforward control , with applications to peristaltic pumps and hard-mounted vibration isolators. His research outputs (20 articles from 2014–2024) highlight contributions to both theoretical control frameworks and experimental validation. No awards or grants are explicitly mentioned in the provided texts. He has not listed advisees or students in the given data. His collaborations span academic and industrial partners, focusing on systems with periodic disturbances and multi-variable control challenges.

Kailai LiView profile

Assistant Professor

• Robotic Perception
• Sensor Fusion
• State Estimation
• +2 more

Kailai Li is a tenure-track Assistant Professor at the University of Groningen's Bernoulli Institute, where he leads the Agile Sensing and Intelligence Group (ASIG). His research develops novel methods for robotic perception, including continuous-time state estimation, sensor fusion, and visual navigation. Recent publications focus on Gaussian process representations for motion estimation and multi-robot collaboration using vision-language models. Dr. Li's lab maintains open-source projects like LiLi-OM (LiDAR-inertial odometry) and SFUISE (UWB-inertial fusion). Collaborations include Linköping University and industry partners. Current projects investigate trustworthy perception for autonomous systems under uncertainty and efficient representations for high-dimensional state estimation.

Saeed AhmedView profile

Assistant Professor

• Feedback Optimization
• Nonlinear Control Systems
• Robust Control
• +2 more

Saeed Ahmed is a Tenure Track Assistant Professor of Systems and Control at the University of Groningen, affiliated with the Engineering and Technology Institute Groningen (ENTEG) and the Jan C. Willems Center for Systems and Control. Research Interests: His work develops intelligent control algorithms for autonomous vehicles, energy systems, and medical devices. Core theoretical interests include: Feedback optimization strategies Nonlinear and hybrid system stability Robust control under uncertainty Time-delay system analysis His publications reveal strong focus on predictive control for energy networks (especially district heating), stability analysis of hybrid systems, and observer design for complex nonlinear systems. Recent work emphasizes distributed control solutions with practical constraints. Professional Activities: Associate Editor for Systems and Control Letters and member of IFAC Technical Committees on Nonlinear Control Systems and Networked Systems. Teaches courses in Mechatronics, Complex Networks, and Control Theory.

Wided MedjroubiView profile

Assistant Professor

• Energy System Modeling
• Computational Fluid Dynamics
• Wind Energy
• +4 more

Dr. Wided Medjroubi is an Assistant Professor at the Energy and Sustainability Research Institute Groningen, Faculty of Science and Engineering, University of Groningen. Her research examines energy system modeling with a focus on computational approaches to climate change adaptation, renewable integration, and sustainable urban energy systems. Her work investigates: How collaborative models can address complex future energy systems; How energy models enhance resilience against geopolitical conflicts and extreme weather; How GIS and Open Science accelerate energy transitions; and Climate change impacts on energy production/consumption. Her methodological expertise includes computational fluid dynamics, spatial analysis, and uncertainty quantification. Recent publications demonstrate strong focus on energy transition technologies (hydrogen systems, geothermal integration) and climate resilience planning. Over 75% of her last 15 articles address renewable integration challenges using advanced modeling techniques, particularly through GIS applications and machine learning approaches.

• Optimization Models
• Systems and Control
• Cyber-Socio-Technical Systems
• +3 more

Mauro R.U. Salazar Villalon is an Assistant Professor at Eindhoven University of Technology (TU/e) in the Department of Mechanical Engineering and co-affiliated with the Eindhoven AI Systems Institute (EAISI). He obtained his B.Sc. and M.Sc. from ETH Zurich, followed by a Ph.D. from ETH Zurich in collaboration with Ferrari Formula 1. After postdoctoral work at Stanford's Autonomous Systems Lab, he joined TU/e in 2020. Educational Background: B.Sc. & M.Sc. in Mechanical Engineering, ETH Zurich Ph.D. in Mechanical Engineering, ETH Zurich (with Ferrari F1) His research focuses on optimization models for cyber-socio-technical systems, addressing challenges in mobility, pandemics, and material design. He develops methods for sustainable transportation systems, including incentive schemes for user behavior alignment. Recent trends in his publications span hydrogen-based hybrid aero engines, ride-pooling network flows, battery-electric truck infrastructure, and electric vehicle powertrain optimization. His work integrates advanced mathematical modeling with real-world applications. Scientific Awards: ETH Medal (Master & PhD theses) Best Student Paper Awards (2018, 2022) Best Teacher Award (2021) Nominated for TU/e Young Researcher Award (2022) IEEE VPPC 2024 Best Paper Award He contributes to projects like Model Predictive Pandemic Control and National Growth Fund initiatives, while teaching courses on powertrain design, engineering optimization, and optimal control. Collaborations extend to Stanford University and the Ferrari Formula 1 team.

Vinh DangView profile

Assistant Professor

• Manufacturing Automation
• Autonomous Mobile Robots
• Smart Factory Systems
• +4 more

Dr. Vinh Dang is an Assistant Professor at the Operations, Planning, Accounting & Control (OPAC) group within the Department of Industrial Engineering and Innovation Sciences at Eindhoven University of Technology (TU/e). His research focuses on automating manufacturing processes, material handling, and the integration of autonomous mobile robots (AMRs) and automated guided vehicles (AGVs) in smart factories, warehouses, and healthcare environments. He has contributed to projects such as Advanced Manufacturing Logistics and Green Transportation Delta, emphasizing sustainable and efficient industrial systems. Education: PhD in Operations Research, Aalborg University (2014) M.Sc. in Industrial Engineering, Pusan National University (South Korea) B.Eng. in Industrial Systems Engineering, Ho Chi Minh City University of Technology (Vietnam) Research Interests: Dr. Dang’s work centers on algorithmic methodologies for planning/scheduling automated systems, particularly in smart industries. He explores robotics integration, energy-efficient logistics, and real-time decision-making frameworks. His research combines operations research techniques with multi-agent systems and reinforcement learning to enhance manufacturing and service sector efficiency. Key Projects: DigiTwop: Digital Twin-based warehouse optimization (2025–2028) Green Transport Delta – Electrification: Sustainable transportation solutions (2021–2024) Decentralized Distribution: Logistics sector disruption analysis (2023–2025) Teaching: Courses include Manufacturing Integration , Modeling and Analysis of Manufacturing Systems , and Engineering Design Expertise , aligning with UN Sustainable Development Goals such as Industry, Innovation & Infrastructure (SDG 9). Labs/Teams: Collaborates on digital twin frameworks for AGV communication and energy management systems, emphasizing practical industry applications like smart hospitals and high-tech logistics.