Find Academic Supervisors

• Distributed Systems
• Cloud Computing
• Data-Intensive Workflow Management
• +5 more

Dr. Zhiming Zhao is an Associate Professor and Chair of the Multiscale Networked Systems (MNS) research group at the Informatics Institute (IvI), University of Amsterdam (UvA). He serves as the technical manager of the Virtual Lab and Innovation Center (VLIC) of LifeWatch ERIC, a European research infrastructure for ecology and biodiversity science. Zhao holds an IEEE Senior Member designation and is the Managing Editor of the Journal of Cloud Computing . He earned his Ph.D. in Computer Science from UvA in 2004. His research focuses on quality-critical distributed computing, data-intensive workflows, virtual research environments, and digital twins. He leads projects such as LTER-LIFE (Dutch research infrastructure for digital twins) and coordinates UvA contributions to EU initiatives like ENVRI-HUB Next , EVERSE , and BlueCloud-2026 . Zhao’s work spans technical development in EU projects (e.g., ENVRI-FAIR , ARTICONF , CLARIFY ) and leadership roles in international workshops and conferences. His team develops frameworks like NaaVRE (Jupyter-based collaborative environments) and CloudsStorm (dynamic infrastructure planning). Current research emphasizes trustworthy AI in cloud systems, federated learning, and edge-cloud resource optimization. Key achievements include over 150 peer-reviewed publications, supervision of numerous PhD students, and contributions to open science initiatives. His lab actively explores interdisciplinary applications in environmental science, medical imaging, and blockchain-based decentralized systems.

Alain HecqView profile

Professor

• Econometrics
• Time Series Analysis
• Financial Economics
• +5 more

Alain Hecq is a Full Professor in the department of QE Econometrics at the School of Business and Economics, Maastricht University. His research focuses on econometric methodologies, particularly in time series analysis, noncausal models, and financial econometrics. He has contributed significantly to the understanding of volatility dynamics, cryptocurrency markets, and inflation targeting regimes. His work often addresses policy-relevant questions in macroeconomics and financial markets. Key research interests include mixed causal-noncausal autoregressive models, volatility modeling with MARMA-GARCH frameworks, and the application of these techniques to real-world phenomena such as oil price bubbles and cryptocurrency volatility. He has also explored the credibility of central banking policies during crises, such as the Brazilian inflation-targeting regime during the pandemic. His recent work emphasizes methodological advancements in high-dimensional time series analysis, including spectral estimation, hierarchical regularizers for mixed-frequency data, and reduced-rank matrix autoregressive models. These contributions reflect a blend of theoretical rigor and practical applicability in addressing complex economic and financial problems. While no formal awards are listed, his extensive publication record and focus on cutting-edge econometric techniques underscore his scholarly impact. Advising and grant activities are not detailed in the provided information, but his research demonstrates sustained engagement with both academic and policy-oriented audiences.

Elmine MeyerView profile

Assistant Professor

• Antenna Design
• Millimeter Wave Technology
• RF Engineering
• +3 more

Elmine Meyer is an Assistant Professor in the Electrical Engineering department at Eindhoven University of Technology. Her research focuses on antenna design, millimeter wave technology, and RF engineering. She leads the EM Antenna Systems Lab and EM for Radio Science Lab, contributing to advancements in 5G/6G communication systems and space-based radio astronomy. She holds a PhD in Electrical Engineering from the University of Stellenbosch (2018) and a Bachelor of Engineering from Fairleigh Dickinson University (2013). Meyer's projects include leadership roles in the ANTERRA (6G Non-Terrestrial Networks), INNOSTAR (EM), and MyWave initiatives. She has organized events like the 28th European Microwave Week 2025 and contributed to high-impact research on antenna arrays, 3D-printed RF devices, and pedestal resonators. Her work bridges academic research with industrial applications, emphasizing practical implementations in wireless communication systems. Her research interests span antenna array design, millimeter wave systems, and electromagnetic compatibility. Over 38 citations highlight her contributions to fields like substrate integrated waveguide (SIW) technology and adaptive beamforming. Collaboration networks include institutions globally, particularly in antenna measurement systems and space-based communication technologies.

• Deep Learning
• Neural Networks
• Artificial Intelligence
• +9 more

Elena Mocanu is an Assistant Professor in the Department of Datamanagement & Biometrics and a faculty member of the Digital Society Institute. Her research focuses on advancing neural network architectures, particularly through dynamic sparse training techniques to enhance computational efficiency and model performance. Key areas include deep reinforcement learning applications in building energy optimization, federated learning for collaborative data environments, and sparse connectivity models that reduce resource usage without sacrificing accuracy. She has contributed to frameworks like the Digital Twin for autonomous driving and energy systems, emphasizing sustainability and scalability. Her work bridges theoretical advancements and practical implementations, addressing challenges in energy-efficient AI, robust noise filtering in reinforcement learning, and feature selection for medical imaging tasks. She actively organizes conferences such as ICLR workshops on sparsity in neural networks and IJCAI events, fostering interdisciplinary collaboration in artificial intelligence. Awards : Best Paper Award at AAMAS 2022 Workshop, ICML 2022 Outstanding Reviewer Award Conference Leadership : Organized ICLR 2023 Sparsity Workshop, EPIA 2023/2022 Conferences Research highlights include scalable training methods inspired by network science, energy optimization in smart buildings, and sparse ensembling techniques that achieve efficiency gains without overhead. Her contributions span foundational machine learning theory to applied domains like smart grids and autonomous systems.

Clemens V. VerhooselView profile

Associate Professor

• Computational Mechanics
• Isogeometric Analysis
• Fluid Dynamics
• +5 more

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.

Bert de VriesView profile

Professor

• Bayesian Machine Learning
• Signal Processing
• Biomedical Applications
• +3 more

Bert de Vries is a Professor at the Signal Processing Systems Group at Eindhoven University of Technology (TU/e), where he has been employed since January 2012. He maintains a dual career, also working at GN Hearing in the hearing aids industry since April 1999, where he holds both research and managerial roles. His academic journey began at TU/e, where he earned his MSc in Electrical Engineering in 1986, followed by a PhD from the University of Florida in 1991. Between 1992 and 1999, he worked at Sarnoff Research Center in Princeton, NJ, contributing to diverse signal and image processing projects. Professor de Vries's research centers on Bayesian Machine Learning, with particular focus on the Free Energy Principle and its applications to engineering problems. His work bridges theoretical neuroscience with practical signal processing systems, especially in biomedical applications. He directs the BIASlab research team at TU/e, which develops probabilistic programming tools including RxInfer.jl, ForneyLab.jl, GraphPPL.jl, ReactiveMP.jl, and Rocket.jl. His research spans active inference, variational message passing, probabilistic programming, and Bayesian neural networks, with applications ranging from hearing aids to multi-agent systems. Analysis of his recent publications reveals a strong trend toward practical implementations of Bayesian inference frameworks, particularly through Julia-based probabilistic programming tools. His work shows increasing focus on active inference applications, message passing algorithms, and the intersection of Riemannian geometry with probabilistic modeling. The research demonstrates consistent progression from theoretical foundations toward real-world engineering applications, particularly in biomedical signal processing and autonomous systems. Professor de Vries teaches a graduate-level course on Bayesian Machine Learning at TU/e and actively contributes to open-source software development through his GitHub profile (bertdv), with recent activity as recent as August 2025. His research team has developed several influential probabilistic programming libraries that have gained significant attention in the machine learning community. The BIASlab research group continues to advance the state of the art in Bayesian inference methods with applications in hearing technology, robotics, and signal processing.

Artem KaznatcheevView profile

Assistant Professor

• Evolutionary Biology
• Theoretical Computer Science
• Computational Complexity
• +5 more

Artem Kaznatcheev is an Assistant Professor at Utrecht University in the Department of Mathematics and Department of Information and Computing Sciences within the Science faculty. His research bridges theoretical computer science and evolutionary biology to analyze biological and social systems through an algorithmic lens. Current role since January 2023 Recruiting PhD students and postdocs Previously: James S. McDonnell postdoctoral fellow at University of Pennsylvania His work focuses on: Computational complexity of evolution Evolutionary game theory Algorithmic biology Mathematical modeling of cancer dynamics Cultural evolution of science Selected article trends show: Interdisciplinary integration of computer science and cancer biology Key themes: fitness landscapes, evolutionary games, treatment optimization 2021-2020 publications dominate Mathematical formalisms applied to biological and social phenomena Scientific contributions include: James S. McDonnell Foundation Independent Postdoctoral Fellowship 2019 Genetics paper on computational complexity as evolutionary constraint 2017 Nature Ecology & Evolution study on fibroblast-drug interactions in cancer Collaborative environments: Worked with Theory, Evolution and Games Group Previous affiliations: Oxford University, University of Pennsylvania, Moffitt Cancer Center, McGill University Developed teaching roles at Oriel College (Oxford)

Laurens BliekView profile

Assistant Professor

• Artificial Intelligence
• Optimization
• Sustainable Logistics
• +5 more

Laurens Bliek is an Assistant Professor at the Department of Industrial Engineering & Innovation Sciences, Eindhoven University of Technology (TU/e). He specializes in combining artificial intelligence (AI) with optimization techniques for computationally intensive problems, focusing on sustainable applications such as public transport, electric vehicles, and CO 2 reduction. Education: MSc in Applied Mathematics (2014), PhD in Systems & Control (2019) from Delft University of Technology. Prior Role: Postdoctoral researcher at the Algorithmics group, Delft University of Technology. Research Interests: His work addresses AI-driven optimization of expensive cost functions, particularly in logistics, communications, and healthcare. He develops methods to handle computationally intensive simulators and digital twins, emphasizing real-time decision-making and sustainability. Recent Publications: His research spans predictive maintenance using Fourier graph neural networks, real-time container yard allocation, and 5G network optimization. Articles appear in journals like IEEE Transactions on Neural Networks and Learning Systems and Computer Networks . Collaborations: Laurens collaborates with industry partners (LioniX, Dutch Railways) and organizations (European Supply Chain Forum, Logistics Community Brabant). He co-leads the 12-PhD program AI Planner of the Future and participates in AI sustainability working groups. Supervision: Co-promotor of PhD students Ya Song and Abdo Abouelrous, focusing on AI applications in routing and maintenance logistics.

Kerstin BunteView profile

Professor

• Machine Learning
• Artificial Intelligence
• XAI (Explainable Artificial Intelligence)
• +7 more

Kerstin Bunte is a Professor of Machine Learning for interdisciplinary data analysis at the University of Groningen, affiliated with the Faculty of Science and Engineering and the Bernoulli Institute's Intelligent Systems Group. She holds an Honorary Fellowship at the University of Birmingham and leads the Intelligent Systems Group. Her research focuses on interpretable machine learning, interdisciplinary applications (e.g., astrophysics and biomedical data), and visualization techniques. Research Interests: - Machine Learning - Artificial Intelligence - Explainable AI (XAI) - Interpretable Models - Dimensionality Reduction - Data Visualization - Astrophysical Data Analysis - Medical Imaging Awards & Grants: - DSSC XS funding (2023) - NWO VIDI grant (2020) - Rosalind Franklin Fellowship (2016–present) Advising & Students: Supervised PhD students include Elisa Oostwal, Janis Norden, Matteo Marcantoni, and Petra Awad. Research spans topics like tumor segmentation in medical imaging, astrophysical structure detection, and autonomous navigation systems. Labs & Collaborations: Leads the Intelligent Systems Group, collaborating with institutions like the University of Birmingham and the University of Warwick. Work involves interdisciplinary projects combining machine learning with astronomy, biomedical sciences, and robotics.

• Cognitive Psychology
• Experimental Psychology
• Applied Data Science
• +4 more

Baptist Liefooghe is an Associate Professor in the Social, Health and Organisational Psychology department at Utrecht University's Faculty of Social and Behavioural Sciences. His research bridges cognitive psychology with human-centered artificial intelligence, focusing on how humans interact with and trust AI systems while examining fundamental cognitive processes like task switching and instruction-based learning. His primary research interests include Cognitive Psychology , Experimental Psychology , Applied Data Science , and Human-centered Artificial Intelligence . Dr. Liefooghe investigates how humans process instructions, switch between tasks, and develop trust in artificial intelligence systems. His work demonstrates how cognitive processes influence human-AI interactions, particularly examining how people judge AI morality and competence, how social presence affects trust in algorithms, and how cognitive control mechanisms operate during task switching. Analysis of his recent publications reveals a strong trend toward interdisciplinary research combining cognitive psychology with AI applications. His work spans from fundamental cognitive processes (task switching, instruction implementation) to applied human-AI interaction research (trust in AI, facial perception of artificial agents, health behavior change through AI). This integration of basic cognitive research with practical AI applications represents a distinctive feature of his scholarly contributions. Dr. Liefooghe has published extensively in top-tier journals including Journal of Cognition, Cognitive Research, Cortex, and Frontiers in Artificial Intelligence. His research has garnered significant attention in the field, with multiple highly cited publications examining the cognitive mechanisms underlying human interaction with technology. His methodological expertise includes experimental design, statistical analysis using R programming language, and cognitive modeling. This technical proficiency enables him to conduct rigorous investigations into complex cognitive phenomena while applying these insights to real-world human-AI interaction scenarios.

Anna SalomonsView profile

Professor

• Labor Economics
• Technological Change
• Economic Inequality
• +4 more

Prof. Anna Salomons is an endowed professor at Utrecht University's School of Economics and holds a professorship at Tilburg University's Department of Economics. She is also a Research Fellow at IZA, CEPR, and multiple universities including MIT and Carnegie Mellon. Her research focuses on labor market impacts of technological change, including earnings inequality, employment structures, and skill demand. She received the 2024 European Economic Association's Role Models in Economics Award. Her work spans automation, minimum wage policies, and global trade effects on labor markets. She contributes to initiatives like the Future of Work at MIT and advises the Brookings Institution. Her affiliations include unpaid roles at Maastricht University, Boston University, and IZA, alongside paid advisory roles in technology and policy research.

Patrick J.F. GroenenView profile

Professor

• Data Science
• Multidimensional Scaling
• Optimization Algorithms
• +5 more

Patrick J.F. Groenen is a full Professor of Statistics at the Erasmus School of Economics (ESE), Erasmus University Rotterdam , and currently serves as its Dean. His academic career spans leadership roles, including Director of the Econometric Institute (2014-2020) and President of the International Association for Statistical Computing (2015-2017). He has held visiting positions at Stanford University and contributed extensively to multidimensional scaling (MDS), data science , and statistical genetics . His research focuses on numerical algorithms for MDS, support vector machines , and optimization techniques applied to diverse fields. He is a founding editor of the open-access journal Journal of Data Science, Statistics, and Visualisation and has authored major textbooks including Modern Multidimensional Scaling and Applied Multidimensional Scaling and Unfolding . His work appears in top journals like Nature Genetics , Psychometrika , and Journal of Machine Learning Research . Recent publications highlight advancements in convex clustering , genomic prediction , and robust statistical methods . He has developed influential R packages such as GenSVM , SVMMaj , and smacof for multiclass classification and MDS software. His supervisory record includes 14 PhD students across economic psychology, genetics, and data science. Academic activities include editorial roles at Advances in Data Analysis and Classification and Psychometrika , alongside organizing international conferences like CARME 2007 on correspondence analysis. His contributions to nonlinear biplots and response style modeling have advanced visual data interpretation in social sciences.

Damian TrillingView profile

Professor

• Computational Social Science
• Political Communication
• Digital Journalism
• +8 more

Prof. Damian Trilling is a Full Professor at the Vrije Universiteit Amsterdam's Faculty of Social Sciences and Humanities in the Department of Communication, and concurrently holds the same rank at the Network Institute. He also serves as a Medewerker (employee) at the Universiteit van Amsterdam and Universitetet i Bergen since February 2024, reflecting his active interdisciplinary and international collaborations. His research focuses on computational methods applied to communication science, particularly exploring how algorithms and digital platforms shape media exposure, political behavior, and information dissemination. Key areas include analyzing selective exposure in recommender systems, measuring ethnic biases in news content, and studying the agenda-setting role of dark platforms. He emphasizes transparency in news algorithms and the ethical implications of data donation frameworks, often employing machine learning techniques like word embeddings and agent-based modeling to address these topics. Trilling's recent articles (2024-2025) reveal a strong focus on understanding algorithmic impacts across media ecosystems. They highlight concerns about filter bubbles, dark platform dynamics, and the interplay between user behavior and platform design. His work bridges communication theory with computer science, offering practical tools such as the INCA infrastructure for automated content analysis and the 3bij3 framework to study recommender systems' effects on news diversity. He teaches courses like Impact van journalistieke producten , Inleiding Media en Journalistiek , and collaborates internationally, as seen in the Introduction to Machine Learning for Text Analysis with Python at GESIS (Mannheim, Germany). His pedagogical contributions complement his research agenda, aiming to equip students with computational skills for modern communication analysis.

Ling ChangView profile

Associate Professor

• Remote Sensing
• Synthetic Aperture Radar (SAR)
• InSAR Techniques
• +5 more

Ling Chang is an Associate Professor at the University of Twente, affiliated with the Faculty of Geo-Information Science and Earth Observation (ITC) and the Department of Earth Observation Science. She holds a Ph.D. from Delft University of Technology (2015) and an M.S.E. from Tongji University (2010). Her research focuses on statistical hypothesis testing, time series modeling, and change detection using satellite remote sensing, particularly InSAR techniques. Key projects include AlignSAR (ESA Open SAR library) and RailRadar (TUDelft-ProRail collaboration). Education: M.S.E. in Geodesy and Survey Engineering, Tongji University, China (2010) Ph.D. in Geodesy, Delft University of Technology, Netherlands (2015) Research interests emphasize InSAR applications in infrastructure monitoring, environmental geology, and disaster risk assessment. Notable achievements include the Prof. J.M. Tienstra Research Prize (2022) and leadership in the NCG talent program (2020). Teaching responsibilities include coordinating the Radar Remote Sensing course. Her work contributes to UN SDGs related to sustainable infrastructure and climate action.

Marloes MulView profile

Researcher

• Water Resources Management
• Remote Sensing
• Environmental Flows
• +4 more

Marloes Mul is a leading researcher in water resources management and remote sensing applications at IHE Delft Institute for Water Education. She specializes in integrating remote sensing technologies to assess water productivity, manage river basins, and address environmental flows. Her work spans transboundary water systems, drought impact analysis, and sustainable infrastructure development in regions like West Africa, Southeast Asia, and the Middle East. She has led major projects funded by FAO, World Bank, and the Dutch Ministry of Foreign Affairs, including contributions to the WaPOR remote sensing initiative and the EUROFLOW project on reservoir policies. Education: PhD in Hydrology (University of Zimbabwe, 2013) Research Interests: Her research focuses on climate change impacts on water resources, remote sensing-driven water management, equitable water allocation, and resolving transboundary water conflicts. She has pioneered methods for quantifying trade-offs in dam operations and improving water accounting frameworks to enhance sustainability. Projects & Grants: Key projects include WaPOR’s land and water productivity monitoring, the EUROFLOW dam re-operation study, and initiatives in the Volta and Pangani basins. She has secured funding from international organizations to address water scarcity and agricultural productivity challenges. Advising: Supervises a PhD student on reservoir operating policies for environmental flows. Labs/Teams: Active in IHE Delft’s water accounting team and collaborates globally on remote sensing applications for water governance.