جست‌وجوی استاد و پژوهشگر

Thom de Vriesمشاهده پروفایل

دانشیار

• Team Management
• Organizational Resilience
• Boundary Spanning
• +۴ مورد دیگر

Thom de Vries is an Associate Professor at the University of Groningen's Faculty of Economics and Business, based in the HRM & Organizational Behavior department. He also serves as Program Director for the Master of Science in Human Resource Management and holds a lecturer position in Industrial Engineering Management. His expertise focuses on organizational resilience, team boundary spanning, and infrastructure management. He holds a Ph.D. in Business Administration (cum laude) from the University of Groningen (2015), with prior experience as a Business Researcher at TNO and academic leadership roles in program committees. Education highlights include: Ph.D. in Business Administration (cum laude), University of Groningen (2015) Res. M.A. and M.A. in Business Administration (cum laude), University of Groningen (2011, 2009) B.A. in Public Administration, Thorbecke Academie (2007) Research interests emphasize team dynamics in complex environments, particularly: Resilience and adaptability in critical infrastructure organizations Boundary spanning across teams/organizations Design of multiteam systems for complex tasks Disruption management in supply chains Notable projects include the NGinfra-funded ConCorCom initiative studying coordination during infrastructure challenges, and NWO grants exploring resilience and decision-making under uncertainty. His work has earned awards like the Academy of Management Best Paper Award and multiple teaching recognitions. Grants include: NGINFRA/NWO: ConCorCom (Context, Coordination, Competencies) NWO VENI: Team Boundary Spanning in Disruptions NWO: Resilience-Efficiency Balance in Planning Teaching roles span disciplines including Organizational Behavior, Survey Research, and Experimental Design across faculties. He actively supervises research master students and contributes to executive education programs.

Iris F.A. Visمشاهده پروفایل

استاد

• Operations Management
• Supply Chain Design
• Sustainable Logistics
• +۵ مورد دیگر

Prof. Iris F.A. Vis is a Professor of Industrial Engineering at the University of Groningen's Faculty of Economics and Business. She specializes in logistics and operations management, focusing on optimizing processes through quantitative and qualitative methods. Her work intersects logistics with sectors like healthcare, education, and energy. She leads major projects such as SMiLES (sustainable mobility-logistics integration) and designs logistics solutions for personalized learning systems in schools. She has advised over a dozen PhD students and collaborates with industry partners globally. Awards include Fellowship in the Netherlands Academy of Engineering. Education: M.Sc. Mathematics (Leiden University), PhD in Operations Management (Erasmus University Rotterdam) Roles: Captain of Science for Topsector Logistics, Member of multiple national advisory boards Research interests span sustainable transportation networks, port optimization, healthcare logistics, and educational logistics. Key projects include LNG supply chain design, offshore wind farm maintenance planning, and synchromodal transport networks. Over 45 peer-reviewed publications and 18 media engagements highlight her impactful contributions. Teaching includes courses on supply chain network design, technology-enabled innovation, and operations management at all academic levels. She advises on industrial partnerships and digital transformation initiatives in the Northern Netherlands region.

Rob van Beersمشاهده پروفایل

استادیار

• Motor Control
• Motor Learning
• Spatial Perception
• +۳ مورد دیگر

Rob van Beers is an Assistant Professor at the Faculty of Behavioural and Movement Sciences at Vrije Universiteit Amsterdam, with affiliations to Neurocontrol, IBBA, and AMS - Sports. His research focuses on human motor control, spatial perception, and computational modeling using Bayesian approaches to understand sensory-motor integration under uncertainty. He holds ancillary roles as a Researcher at Radboud University (Nijmegen) since 2015 and serves on the Editorial Board of the Journal of Neurophysiology since 2015. His work contributes to UN Sustainable Development Goals related to health and well-being. Key research interests include motor learning dynamics, sensorimotor adaptation, and the neural basis of spatial orientation. Recent studies explore Alzheimer’s impacts on motor adaptation and Bayesian inference in vestibular path integration. Teaching responsibilities include courses on linear systems dynamics, physical measurement techniques, and motor systems regulation. His work spans 42 peer-reviewed articles, with datasets published on platforms like Dryad and Zenodo.

• Cognitive Neuroscience
• Decision Making
• Noradrenergic System
• +۳ مورد دیگر

Professor Sander Nieuwenhuis (Leiden University) specializes in Cognitive Neuroscience of Decision Making through behavioral, EEG, and pharmacological approaches. His work bridges prefrontal cortex function and noradrenergic system mechanisms in human cognition. Studied cognitive psychology (Groningen) and earned PhD (Amsterdam, 2001) International experience: Cambridge University (visiting research), Princeton University (postdoc) Research focuses on noradrenaline's role in attention, decision-making, and cognitive control via: Locus coeruleus-norepinephrine system dynamics Phasic vs. tonic alertness effects Cognitive task performance under neuromodulator deficiency Neuroprotective pathways in Alzheimer's models Recent publications analyze DBH deficiency (Jepma et al., 2011) and attentional blink mechanisms (Nieuwenhuis et al., 2005). His Temporal Attention Lab integrates fMRI, genetics, and computational modeling . Teaching leadership includes: Chair of Research Master Program Committee Coordinator of Scientific Writing courses

Ivana Nikoloskaمشاهده پروفایل

استادیار

• Machine Learning
• Quantum Computing
• Communication Engineering
• +۷ مورد دیگر

Ivana Nikoloska is an Assistant Professor at the Department of Electrical Engineering, Eindhoven University of Technology (TU/e). She is affiliated with the Center for Quantum Materials and Technology Eindhoven and BIASlab (Bayesian Intelligence and Stochastic Agents Lab). Her academic career includes prior roles as a Research Associate at King’s College London and a Visiting Researcher at Aalborg University. PhD: Monash University, Australia (2023) MSc & Dipl.-Ing.: University of Ss. Cyril and Methodius, North Macedonia Research Interests span foundational and applied machine learning, quantum computing, and information/communication engineering. Her work focuses on integrating Bayesian inference, variational methods, and quantum technologies for tasks like signal processing, channel estimation, and power control optimization. Quantum Machine Learning Bayesian Simulation-Based Inference Meta-learning for Wireless Systems Hybrid Quantum-Classical Architectures Stochastic Signal Processing Quantum Sensing & Metrology Notable Trends in Publications include quantum recurrent neural networks with adaptive gating, Bayesian frameworks for quantum sensing, and meta-learning applications in communication systems. She explores variational inference for planning and robust algorithms for channel estimation under non-ideal conditions.

Remco M. Dijkmanمشاهده پروفایل

استاد

• Business Process Management
• Data-Driven Process Optimization
• Supply Chain Management
• +۳ مورد دیگر

Remco M. Dijkman serves as Full Professor in Information Systems at Eindhoven University of Technology (TU/e), chairing the Information Systems group within the Industrial Engineering and Innovation Sciences school. He additionally holds a Full Professor position at EAISI High Tech Systems and acts as research director for high-tech supply chains at the European Supply Chain Forum—a network of over 50 multinational companies. His research centers on Business Process Management with emphasis on data-driven optimization of business processes. His academic background includes both PhD and Master's degrees in Computer Science from the University of Twente. Publications span Information Systems, Computers in Industry, and Transactions on Software Engineering and Methodology, with over 100 papers and service on the editorial board of Information Systems. He has held visiting positions at New York University, Hasso Plattner Institute, IBM Zurich Research Lab, Humboldt-University Berlin, and Queensland University of Technology. Dijkman's research interests focus on detecting, diagnosing, and predicting optimal execution scenarios in business processes, developing mathematical models for quantitative process analysis , and resource assignment optimization . These are primarily applied in transportation logistics and high-tech supply chains, where he investigates data-driven predictions for transport order assignment and supply chain planning. His work bridges artificial intelligence with practical business applications. Recent publications (2024-2025) reveal concentrated efforts in deep reinforcement learning for resource allocation, process pattern discovery, and software library development (GymPN, SimPN). Key trends include predictive process monitoring for healthcare applications, event data enrichment frameworks, and uncertainty handling in logistics planning—demonstrating strong interdisciplinary integration. Scientific recognition includes: Best Demo Award (2019) Best Reviewer Award (2016) Test of Time Award (2019) He has supervised 150 students, including Lotte Vugs who received the Dow Chemical Best OML Master Thesis Award in 2020. Grant leadership spans eight projects: NXTGEN Smart Industry (2023-2030), CollChain (2023-2029), CERTIF-AI (2020-2025), FENIX (2019-2023), and DynaPlex (2021-2024), focusing on digital twins, federated networks, and AI-driven supply chain solutions. Dijkman directs the Information Systems group at TU/e and leads the European Supply Chain Forum's high-tech supply chain research. His work integrates with semiconductor manufacturing and transportation logistics through collaborations with industry partners, while his 2023 invited talks at Technical University of Munich and Humboldt University Berlin highlight his international engagement.

Erik Quaeghebeurمشاهده پروفایل

استادیار

• Probability Theory
• Statistics
• Uncertainty in Artificial Intelligence
• +۴ مورد دیگر

Erik Quaeghebeur is an Assistant Professor at Eindhoven University of Technology's School of Mathematics and Computer Science, focusing on uncertainty modeling in artificial intelligence. His work spans probabilistic circuits, imprecise probability theory, and wind energy applications. PhD in Applied Mathematics (Ghent University, 2002-2009) Master's in Applied Mathematics (Université catholique de Louvain, 2001-2002) Master's in Physics Engineering (Ghent University, 1998-2001) Research interests include probabilistic modeling under uncertainty, with applications in AI and wind energy systems. His recent work explores tensor factorizations, equivariant graph neural networks, and scalable probabilistic circuits. Scientific contributions include 60 research outputs and 2 datasets . Awards encompass the ERCIM Alain Bensoussan Fellowship (2013), BOF Postdoc (2010), and B.A.E.F. Francqui Fellowship (2009). He serves on committees for the Society for Imprecise Probability and acts as editorial board member for related conferences. Foundations of Artificial Intelligence course (since 2020) Uncertainty Representations and Reasoning course (since 2021)

Laura Toniمشاهده پروفایل

دانشیار

• Machine Learning
• Signal Processing
• Immersive Communications
• +۵ مورد دیگر

Laura Toni is an Associate Professor in the Department of Electronic & Electrical Engineering at University College London (UCL). She serves as Director of the MSc in Telecommunications and Internet Engineering and the MRes in Telecommunications. Additionally, she is a Turing Fellow at the Alan Turing Institute and a member of ELLIS (European Lab for Learning and Intelligent Systems). Her research focuses on coding, streaming technologies, machine learning for immersive communications, decision-making under uncertainty, and large-scale signal processing. She leads the LASP (Learning And Signal Processing) group at UCL. Education: MSc (2005) and PhD (2009) from the University of Bologna, followed by postdoctoral research at UC San Diego and EPFL under Professors L. Milstein, P. Cosman, and P. Frossard. Key roles include Technical Program Chair at ACM MM 2022, Keynote Co-Chair at ACM MMSys 2022, and leadership in organizing workshops on graph-based machine learning and emerging technologies in performing arts. She is a Senior IEEE Member and holds editorial roles in IEEE Multimedia Magazine and EURASIP Journal on Signal Processing. Her work bridges communication systems and machine learning, with contributions to adaptive streaming, network optimization, and graph signal processing. She actively promotes diversity and inclusion in technical conferences, including roles as Diversity Chair at MMSys 2021 and PIMRC 2020.

• Cavitation physics
• Vortex dynamics
• Microbubble technology
• +۳ مورد دیگر

T.J.C. van Terwisga is a Professor at the Ship Hydromechanics and Structures department of Delft University of Technology (Faculty of Mechanical, Maritime and Materials Engineering). His research focuses on cavitation phenomena, vortical flows, and microbubble dynamics, with applications in ship hydrodynamics and marine technology. PhD in Mechanical Engineering (specialization in cavitation physics) Editorial Board Member: The Journal of Ocean Technology (2006–present) Research Interests : Cavitation inception and erosion mechanisms Air lubrication systems for ship drag reduction Underwater shipping noise propagation Bubble dynamics in vortical flows Experimental fluid mechanics Hydrofoil performance optimization Scientific Contributions : Developed advanced calibration methods for microbubble measurement systems Investigated air lubrication regime transitions under varying flow conditions Studied cavitation onset in counter-rotating vortex flows Explored bubble capture mechanisms in vortical flows Contributed to underwater soundscape modeling for maritime operations Editorial Roles : Editor, The Journal of Ocean Technology (2006–present) Editor, The Journal of Ocean Technology (2009–present)

Maurice van Keulenمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Data Mining
• Process Mining
• +۷ مورد دیگر

Maurice van Keulen is an Associate Professor affiliated with the University of Twente's research institutes including Datamanagement & Biometrics, Digital Society Institute, and TechMed Centre. His multidisciplinary work bridges computer science, healthcare, and social systems. Research Focus: Van Keulen specializes in artificial intelligence applications with emphasis on: Data management (quality, integration, probabilistic databases) Explainable AI and interpretable machine learning models Healthcare informatics (cancer prediction, medical imaging, outcome analysis) Natural language processing and social media analytics His recent work explores dynamic sparse training, meta-learning for data imputation, and ethical AI frameworks. Publication Trends: Recent articles (2023-2025) show strong focus on: Interpretable AI methods in healthcare diagnostics Robust machine learning under data corruption Meta-learning approaches for data preprocessing 3D medical imaging and reconstruction techniques Awards: Beste paper award (2018) for work on probabilistic data conditioning Supervision & Activities: Has supervised 14 research projects and serves on executive boards including EDBT (Extending DataBase Technology) and IFIP WG 2.6. Leads research on ethical dimensions of AI systems.

• Decision Making under Risk and Uncertainty
• Ambiguity and Ambiguity Attitudes
• Experimental Methods
• +۳ مورد دیگر

Prof. C (Chen) Li is a Professor of Behavioural Economics at the Erasmus School of Economics, Erasmus University Rotterdam. Her research focuses on decision making under uncertainty and over time, with particular emphasis on ambiguity attitudes, experimental methods, and gender stereotypes. She has collaborated extensively with scholars like Han Bleichrodt and Peter Wakker, contributing to top-tier journals such as Econometrica and Management Science . Her work bridges theoretical frameworks and empirical evidence, addressing real-world questions like how ambiguity aversion influences social interactions or gender disparities in competitive fields. Key research themes include the dynamics of ambiguity learning, the role of gender stereotypes in risk-taking, and the application of axiomatic models to behavioral economics. Her recent work explores bifurcation questions in multi-component decision optimization and the impact of ambiguity on gender inequality in STEM fields. Li’s experimental designs often incorporate behavioral experiments to measure uncertainty preferences, such as belief hedges and Bayesian updating deviations. Prof. Li’s academic contributions span theoretical advancements (e.g., Source Theory) and applied insights into societal issues. She maintains an active research agenda with ongoing projects on the interplay between ambiguity attitudes and gender representation, as well as the foundational aspects of multi-dimensional decision models. Her work underscores the importance of integrating psychological insights into economic theory to better understand human behavior under uncertainty.

• Supply Chain Management
• Healthcare Operations
• Operations Management
• +۴ مورد دیگر

Prof. Taco van der Vaart is a Professor and Department Chair at the Department of Operations within the Faculty of Economics and Business at the University of Groningen. He holds roles including President of the European Operations Management Association (2019-2022), Director of Graduate Studies at the SOM Research School (2010-2018), and research leadership in the OPERA program. His education includes a PhD in Business Administration (2000) and MSc in Mathematics (both from the University of Groningen). Professor of Supply Chain Management since 2017 Department Chair since 2019 Past roles include Associate Professor (2006-2017) and Assistant Professor (1997-2005) Research focuses on healthcare operations, supply chain sustainability, and institutional dynamics in healthcare purchasing. Notable projects include studies on pandemic patient flow coordination (2023), sustainable supplier governance (2023), and physician employment contract impacts (2022). His work bridges operational challenges in healthcare systems and global supply chains. Key awards: None explicitly listed, but leadership roles in prominent academic associations indicate recognition. Teaching spans MSc courses in Supply Chain Management, Healthcare Operations, and Global Supply Chain Sustainability. Engaged in advisory roles at Politecnico di Milano and ZonMw research initiatives. Labs/Teams: Active in the SOM Research School and leads the OPERA research program. Collaborates with international institutions on healthcare logistics and sustainability projects.

• Digital Soil Mapping
• Soil Organic Carbon
• Machine Learning Applications
• +۳ مورد دیگر

Laura Poggio serves as a Research Associate at ISRIC - World Soil Information, which operates under Wageningen University & Research. Her work focuses on advancing digital soil mapping methodologies and global soil information systems through interdisciplinary research combining remote sensing, machine learning, and soil science. Her primary research interests include: Digital Soil Mapping at multiple scales (local to global) Soil Organic Carbon monitoring using satellite imagery Machine learning applications for soil property prediction Global soil information systems development (notably SoilGrids) Integration of remote sensing data with soil databases Analysis of her 15 most recent publications reveals strong emphasis on continental-scale soil monitoring systems, particularly using Sentinel-2 satellite data for soil organic carbon assessment across Europe. Her work consistently addresses methodological challenges in handling spatial uncertainty, integrating multi-source data, and developing scalable models applicable from local to global contexts. Key technological approaches include machine learning algorithms (particularly Random Forest), survival probability models for censored data, and multi-sensor remote sensing integration. Her research output demonstrates significant collaboration across European institutions through projects like EJP SOIL and contributions to the Global Soil Partnership. While no specific awards are documented in the available materials, her work shows substantial scholarly impact through high citation counts and dataset adoption. Dr. Poggio actively contributes to soil science through conference presentations and supervised research work, particularly focusing on operational implementation of digital soil mapping for environmental monitoring and policy support.

Hendrik Baierمشاهده پروفایل

استادیار

• Artificial Intelligence
• Planning
• Search Algorithms
• +۵ مورد دیگر

Hendrik Baier is an Assistant Professor in the Information Systems group at Eindhoven University of Technology (TU/e), where he joined in 2022. His research focuses on creating agents capable of succeeding in complex decision-making tasks to help human users solve real-world problems. His work spans planning for long-term goals, learning in unknown environments, and explainability of AI systems for effective human-AI interaction. Dr. Baier's research interests center on planning and search algorithms, reinforcement learning, and explainable AI systems. His work investigates how AI can think ahead and explain its reasoning process, particularly in sequential decision-making contexts. He applies these techniques to practical domains including logistics and transportation, smart manufacturing, and sustainable energy systems. His research bridges theoretical foundations with real-world applications through collaborative projects with industry partners. Analysis of his recent publications reveals a strong focus on explainability in sequential decision-making, with increasing integration of large language models to enhance traditional planning algorithms. His work spans theoretical foundations of Monte Carlo Tree Search, programmatic policy generation, multi-agent reinforcement learning, and practical applications of these techniques. A notable trend is the growing emphasis on human-AI collaboration, where AI systems must not only perform well but also effectively communicate their reasoning to human users. Dr. Baier actively collaborates with researchers across multiple institutions, including CWI Amsterdam where he maintains an affiliation, and has participated in significant interdisciplinary efforts such as the Dagstuhl Seminar on Explainable AI for Sequential Decision Making. His research group at TU/e works closely with industry partners to translate fundamental research into practical applications. He is affiliated with EAISI (Eindhoven Artificial Intelligence Systems Institute) and contributes to the Decision Making with Artificial Intelligence educational program at TU/e. His laboratory work focuses on developing benchmark environments and frameworks that enable rigorous evaluation of decision-making algorithms, with recent contributions including MOMAland for multi-objective multi-agent reinforcement learning.

• Geotechnical Engineering
• Soil Mechanics
• Material Point Method (MPM)
• +۷ مورد دیگر

Michael Hicks is a Professor at Delft University of Technology (TU Delft) in the Civil Engineering & Geosciences school, specializing in Geo-engineering . His research focuses on geotechnical risk assessment, soil variability, and advanced numerical methods like the Material Point Method (MPM) to model slope failures, earthquake simulations, and soil-structure interactions. Editorial board member for Géotechnique , Georisk , and Computers and Geotechnics (2019) Keynote speaker at workshops on slope failure modeling (2018) Active in public engagement about flood risk management (2017–2018) His work addresses challenges in soil heterogeneity, dynamic boundary conditions, and reliability-based design for infrastructure. Notable contributions include developing probabilistic MPM frameworks and machine learning tools for CPT data interpretation, with applications in earthquake resilience and radioactive waste repository safety. Recent publications highlight innovations in 3D slope stability analysis, nonlocal soil deformation modeling, and thermomechanical interface behavior under cyclic loading. Collaborations span academia and industry, focusing on improving numerical accuracy and understanding failure mechanisms in geotechnical systems.