Find Academic Supervisors

• Water Resources Management
• Climate Change Adaptation
• Agricultural Productivity
• +3 more

Gerardo van Halsema is an Associate Professor in Water Resources Management at Wageningen University. His expertise focuses on sustainable water management, delta planning, and agricultural productivity. He has been actively involved in strategic delta planning initiatives in Bangladesh, Vietnam, and the Netherlands, emphasizing ecosystem services and adaptive governance. His research interests span climate change adaptation, irrigation systems, and water policy reform. Notable projects include analyzing water distribution networks in Spain and exploring transformative changes in Jordan’s water systems. He has supervised multiple PhD candidates, including Shahid R. and Urfels A., focusing on topics like rice-wheat systems and urban delta dynamics. Van Halsema frequently engages in public discourse through media contributions, addressing issues like Mekong Delta sustainability and food security challenges. His work bridges technical analysis with policy implementation, emphasizing contextual approaches to water management.

Hajo ReijersView profile

Professor

• Process Mining
• Data Science
• Robotic Process Automation
• +4 more

Prof. Hajo Reijers is a full professor at Utrecht University's Department of Information and Computing Sciences, leading the Process Science group. He also holds a part-time professorship at Eindhoven University of Technology's Department of Mathematics and Computer Science. His expertise spans Process Mining, Data Science, Robotic Process Automation (RPA), and Business Process Redesign. His research addresses challenges in healthcare, services, and collaboration with international scholars. Research Interests: Process Mining: Focusing on discovering, analyzing, and improving business processes through event log analysis. Data Science: Leveraging data-driven approaches for organizational decision-making and automation. Robotic Process Automation: Developing smart RPA systems and understanding human-robot collaboration. Future of Work: Investigating how technology impacts work practices and employee well-being. Key Contributions: Prof. Reijers' work bridges academic rigor and industry application, with notable contributions to process discovery algorithms, concept drift detection, and RPA frameworks. His recent studies explore the ethical and practical implications of AI in workflows. Awards: Best Paper Award (2021), Utrecht University Research Portal. Advising & Grants: Supervised 6 doctoral projects (details on Scopus). Active in securing grants for projects on process mining, RPA, and organizational change. Labs & Teams: Leads the Process Science group at Utrecht University, collaborating with industry partners to deploy process analytics solutions in real-world settings.

Wim P.M. NuijtenView profile

Professor

• Job Shop Scheduling
• Graph Neural Networks
• Assignment Problem
• +4 more

Wim P.M. Nuijten is a Full Professor at the Eindhoven University of Technology , affiliated with the Mathematics and Computer Science school and the Process Analytics department. His research focuses on Job Shop Scheduling Problems , Graph Neural Networks , and Deep Reinforcement Learning , with significant contributions to combinatorial optimization and dynamic task assignment. Recent publications include a comprehensive survey on graph neural networks for job shop scheduling (2025), addressing stochastic optimization challenges in manufacturing systems (2025), and novel frameworks for dynamic task assignment using Petri nets (2023). His work aligns with UN Sustainable Development Goals, particularly in transport optimization and system analysis. European Journal of Operational Research best review paper (2016)

Matthijs van LeeuwenView profile

Associate Professor

• Exploratory data mining
• Machine learning
• Data science
• +4 more

Matthijs van Leeuwen is an Associate Professor and Director of Education at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University. He leads the Explanatory Data Analysis group and is affiliated with the university-wide SAILS AI research program. Academic rank: Associate Professor Institution: Leiden University Research group: Explanatory Data Analysis University affiliation: SAILS AI research program His research focuses on exploratory data mining with emphasis on explainability for domain experts. Key areas include pattern discovery, anomaly detection, and human-in-the-loop systems. He applies information-theoretic concepts like Minimum Description Length (MDL) and develops interactive methods for real-world applications in life sciences, social sciences, manufacturing, and healthcare. Recent publications demonstrate his work in explainable AI through diverse subgroup discovery, graph anomaly detection, and medical data analysis. Notable contributions include synthetic health record generation, wearable sensor data interpretation, and hemoglobin deferral prediction models. Scientific Awards : Senior Teaching Qualification (SKO) certificate (2022) As Director of Education for LIACS' Master's programs, he plays a leadership role in academic training while maintaining active research partnerships across multiple domains including manufacturing, aviation, and medical informatics.

Daniel StrüberView profile

Associate Professor

• Model-Driven Engineering
• AI Engineering
• Software Quality Assurance
• +12 more

Daniel Strüber is an Associate Professor in Software Engineering at the joint Interaction Design and Software Engineering Division of Chalmers University of Technology and the University of Gothenburg, Sweden. He is also affiliated with the Department of Software Science at Radboud University Nijmegen, Netherlands. His research focuses on model-driven engineering, AI engineering, and variability management to enhance software quality, security, and collaborative development across domains like robotics and web systems. Education : Diplom (M.Sc. equivalent) in Computer Science from Philipps University Marburg (2011, with distinction), Ph.D. (summa cum laude) from Philipps University Marburg (2016). His research bridges empirical, formal, and engineering approaches to address developer challenges in ML-enabled systems. Key trends in his work include model-driven methods for ML deployment, variability management in robotics, and empirical studies of tool usability. He has co-edited a Journal of Systems and Software special issue on software product lines. Scientific Awards : Best Reviewer Awards (SoSyM 2025, GPCE 2024), Transformation Tool Contest Awards (2017, 2020), Best Paper Awards (VAMOS 2019, ICMT 2016), and multiple conference nominations. As a supervisor, he guides current Ph.D. students like Vladislav Indykov and Weixing Zhang, while his former advisees include Shayan Ahmadian and Samuel Idowu. Grants include a VR Open Call Grant (2022-2024) for SEMLA and a DFG Individual Fellowship (2019) for EUphORia. His teaching spans courses on Software Engineering for AI Systems and Software Product Lines , often integrated with real-world projects via GiPHouse.

• Animal Genomics
• Livestock Conservation
• Dairy Cattle Genetics
• +4 more

Ina Hulsegge is a Researcher in the Animal Breeding & Genomics group at Wageningen University & Research, specializing in livestock genomics and conservation. Her work focuses on genomic diversity analysis, inbreeding estimation, and precision livestock farming technologies, particularly for dairy cattle conservation. Her research spans Genomic profiling of livestock diversity Development of real-time animal identification systems using computer vision Conservation genomics for Dutch cattle breeds Deep learning applications in genotype data analysis with strong emphasis on practical implementation in breeding programs and gene banks. Recent publications highlight trends in Automated phenotyping through ear-tag recognition (2024) SNP array-based genomic diversity assessment (2024) Aneuploidy detection via deep learning (2023) Sub-population analysis in red-pied cattle (2023) demonstrating her dual focus on computational innovation and conservation applications. Scientific contributions include: Management systems for genomic information in gene banks (2024) EUGENA database updates for multi-species conservation (2024) Analysis of genetic changes in Dutch black-and-white cattle (2022) She actively engages in knowledge transfer through professional presentations at precision livestock farming conferences and collaborates with institutions like CGN (Centre for Genetic Resources, the Netherlands) on conservation initiatives. Her PhD thesis 'Genomics applied to conservation of genetic diversity in Dutch livestock' (2023) established foundational work for national conservation strategies.

• Ecohydrology
• Groundwater Systems
• Integrated Water Management
• +7 more

Dr. Paul Schot serves as an Associate Professor in Environmental Sciences at the Copernicus Institute of Sustainable Development , Faculty of Geosciences , Utrecht University. With over 30 years of academic leadership, he specializes in ecohydrology and integrated water management, focusing on groundwater systems, climate impacts, and water quality patterns. His work bridges hydrological science with policy through decision support systems and spatial modeling applications. Education : Ph.D. in Geohydrology/Environmental Sciences (1991), M.Sc. in Geohydrology (1984), B.Sc. in Physical Geography (1980) Leadership : Program leader for Sustainable Development and Water Science & Management, Director of Education roles, and research coordinator His research investigates hydrological processes in natural and managed systems, including groundwater-surface water interactions, floodplain dynamics, and managed aquifer recharge. He applies optimization methods like Genetic Algorithms to spatial environmental planning, with fieldwork spanning tropical river systems (Songkhram River, Thailand), European peatlands, and deltas (Ganges-Brahmaputra-Meghna). Recent studies focus on climate adaptation strategies like Tidal River Management and chemical risk assessment for unconventional hydrocarbon extraction. Key trends in his 15 most recent articles (2017-2024) reveal: Delta hydrology and sea level rise adaptation (Bangladesh) Isotope-based groundwater analysis (Latvia, Bangladesh) Floodplain nutrient dynamics and vegetation interactions Hydrochemical modeling for policy support Managed aquifer recharge feasibility studies Cross-climatic zone river-floodplain comparisons PhD Supervision : Mentored 15 doctoral candidates with research spanning topics from hydrological modeling in Indian river basins to tiger conservation in Nepal , emphasizing ecohydrological approaches. His work has influenced 25+ consultancy projects with IWACO and Utrecht University, particularly in Southwestern Bangladesh and Dutch peatland policies.

• Machine Learning
• Neural Networks
• Neuromorphic Computing
• +3 more

Marcel van Gerven serves as Professor of Artificial Intelligence at Radboud University, leading the Artificial Cognitive Systems laboratory within the Donders Institute for Brain, Cognition and Behaviour. He holds dual Principal Investigator roles at both the Donders Centre for Cognition and the Donders Institute, while directing the ELLIS Unit Nijmegen as an ELLIS Fellow. His research program bridges artificial and natural intelligence through machine learning and neuromorphic computing, with core expertise in neural networks, brain-computer interfaces, and neuroprosthetics for vision restoration. Current work develops brain-inspired AI systems that enhance computational efficiency while modeling biological neural processes, particularly focusing on cortical stimulation safety and real-time adaptive systems. Analysis of his 2025 publications reveals dominant themes in reinforcement learning for neuroprosthetics, anomaly detection frameworks, and spiking neural network applications. His work consistently integrates medical applications including epilepsy regulation, immunotherapy diagnostics, and prosthetic vision enhancement, demonstrating strong translational impact from fundamental AI research to clinical solutions. Scientific recognition includes: Vidi laureate from the Dutch Research Council ELLIS Fellowship for European AI leadership Professor van Gerven directs significant research funding through national and international grants, including the Vidi award. His laboratory develops specialized frameworks like Abmax and Kozax for agent-based modeling while mentoring students in cognitive AI systems, though specific advisees aren't documented in available sources. The Artificial Cognitive Systems lab operates within the Donders Institute ecosystem, collaborating closely with the ELLIS Unit Nijmegen to advance European neuromorphic computing research. Current initiatives focus on biologically plausible learning rules, efficient neural network architectures for embedded systems, and closed-loop neuroprosthetic control systems.

Dragan BosnackiView profile

Assistant Professor

• Machine Learning
• Bioinformatics
• Computational Biology
• +6 more

Dragan Bosnacki is an Assistant Professor at Eindhoven University of Technology (TU/e), holding joint appointments in the departments of Biomedical Engineering and Mathematics and Computer Science. He specializes in interdisciplinary research at the intersection of computer science and biomedicine, with a focus on machine learning, bioinformatics, and computational modeling of medical therapies. His work combines formal verification techniques, GPU-accelerated algorithms, and biomedical applications such as High Intensity Focused Ultrasound (HIFU) therapy modeling. Dr. Bosnacki earned his BSc in Electrical Engineering and MSc in Computer Science from Sts. Cyril and Methodius University (Macedonia), followed by a PhD in Mathematics and Computer Science at TU/e under Professors Jos Baeten and Dennis Dams. He has pioneered advancements in parallel model checking and contributed significantly to the SPIN model checking tool. His research also spans separation logic, biomedical data analytics, and the application of mathematical methods to biological systems. Education: BSc in Electrical Engineering, Sts. Cyril and Methodius University (Macedonia) MSc in Computer Science, Sts. Cyril and Methodius University (Macedonia) PhD in Mathematics and Computer Science, TU/e (2001) Research Interests: Mechanisms of machine learning for biomedical data Formal verification of hardware/software systems GPU-optimized algorithms for computational biology Mathematical modeling of HIFU cancer therapies Reconstruction of biological networks and drug response modeling Teaching: Advanced programming and biomedical data analysis Simulation of biochemical systems Programming for data analytics Capita selecta in bioinformatics Thermodynamics & chemical kinetics Key Contributions: Co-developed GPU-based model checking tools (e.g., GPUexplore) Improved partial-order and symmetry reduction techniques Contributions to the SPIN model checker and VeriFast tool Advising: Supervised 37 academic works, focusing on interdisciplinary projects in computational biology and formal methods.

Doğa CeylanView profile

Assistant Professor

• Electromechanical Systems Design
• Electric Mobility
• Power Electronics
• +3 more

Doğa Ceylan is an Assistant Professor at the Electromechanics and Power Electronics department of Eindhoven University of Technology. His research focuses on sustainable electromechanical systems, particularly reluctance machines for heavy-duty electric vehicles and agricultural applications. He specializes in multi-physical design, rare-earth-free structures, and novel control strategies. Education: B.Sc. and M.Sc. in Electrical and Electronics Engineering from Middle East Technical University (2016–2018), followed by a Ph.D. at TU/e (2019–2024) culminating in a cum laude distinction. Postdoc work involved developing high-torque variable flux reluctance motors for electric tractors. Research Interests : Electromechanical systems design, electric mobility, finite element analysis, vector hysteresis modeling, and sustainable energy solutions. His work aligns with UN SDGs, emphasizing clean energy and innovation. Articles Trends : Recent publications emphasize variable flux reluctance machines, flux-weakening control, and cross-coupling effects in motor control. He explores heavy-duty applications, thermal characteristics, and optimization techniques for high-torque operations. Awards : PhD with distinction cum laude (2024). Grants & Projects : Participated in the Smaragd-Farmtronics project (2017–2021) focusing on solar energy and agricultural technologies. Current work includes EU-funded initiatives on sustainable drive systems. Labs & Teams : Leads research in the Electromechanics Lab and collaborates with the Electromechanics and Power Electronics group. Active in developing demonstrators for agricultural electric tractors and heavy-duty vehicle systems.

Alejandro Morales SierraView profile

Lecturer

• Computational Modeling
• Crop Production
• Plant Physiology
• +7 more

Dr. Alejandro Morales Sierra is a Lecturer and Researcher at the Centre for Crop Systems Analysis (CSA) at Wageningen University. He holds a double MEng in Forestry and Agronomy from the University of Córdoba, Spain, an MSc in Plant Sciences, and a PhD from Wageningen University, focusing on plant photosynthesis modeling funded by IPOP Systems Biology and Biosolar Cells programs. His postdoctoral work involved NWO-funded research on abiotic stress impacts on plant development. His research integrates computational tools with crop science, emphasizing functional-structural plant modeling, climate change effects on crops (e.g., cocoa), and invasive species risk modeling. He advocates open science and programming literacy in research. Key contributions include developing the Virtual Plant Laboratory in Julia and advancing methodologies linking data with scientific models. His expertise spans crop production, plant physiology, machine learning, and mathematical modeling. Current projects explore cereal-legume intercropping microclimate interactions and nitrogen-optical-photosynthesis relationships in crops. He teaches advanced data analysis and modeling courses, contributing to MSc programs in crop science.

Wesley Da Silva CostaView profile

Lecturer

• Visible Light Communication
• Internet of Things (IoT)
• Genetic Algorithm Optimization
• +4 more

Wesley Da Silva Costa is a Lecturer-Researcher at the Research Centre Biobased Economy, University of Groningen. His research focuses on Visible Light Communication (VLC), IoT systems, and optimization techniques such as genetic algorithms. He holds a PhD in Electrical and Electronic Engineering from the Federal University of Espirito Santo (2023). Key research areas include improving spectral and power efficiencies in VLC systems, optimizing mesh networks, and applying AI to communication frameworks. His work addresses challenges in low-power wireless networks, handover protocols, and hybrid IoT resource allocation. Recent publications (2021–2025) emphasize VLC system enhancements, IoT integration, and AI-driven solutions for industrial and healthcare applications. Collaborations span topics like chronobiological monitoring in extreme environments and sustainable communication technologies aligned with UN SDGs. Notable contributions include low-cost IoT diagnostic tools for tropical/Antarctic environments and optimized OFDM-VLC systems using multi-objective algorithms. His research bridges theoretical advancements with practical implementation in smart grids and medical monitoring.

• Statistical Genetics
• Graphical Models
• Genomics
• +5 more

Pariya Behrouzi is an Assistant Professor in the Department of Mathematical and Statistical Methods - Biometris at Wageningen University & Research. She specializes in developing statistical methodologies with applications in genetics, genomics, and nutrition science. Her research focuses on graphical models, copula theory, and Bayesian approaches to analyze complex biological systems. Behrouzi leads or collaborates on projects such as SignAphy (plant signaling dynamics) and Sensing Age (epigenetic aging studies). She co-developed the netgwas R package for network-based genome-wide association studies. Her work bridges statistical theory with practical applications in agriculture, nutrition, and computational biology. Education: PhD in Statistics (as indicated by title) Key Research Tools: Copula graphical models, spatial statistics, and Bayesian networks Current Projects: Multi-omics network reconstruction, resilience in lettuce crops, and microbiota systems analysis Her recent publications explore topics like meat analogue texture optimization, telomere length dietary correlations, and plant disease resistance mechanisms. Behrouzi advises four PhD candidates and collaborates with international teams on interdisciplinary research.

Pieter van der ZaagView profile

Professor

• Water Resources Management
• Transboundary River Basin Management
• Agricultural Water Management
• +7 more

Pieter van der Zaag is a Professor of Water Resources Management at IHE Delft Institute for Water Education and Delft University of Technology. His work focuses on transboundary water governance, agricultural water management, and institutional dynamics in water resources. He has held leadership roles including Head of the Department of Integrated Water Systems and Governance (2012–2015) and Chair of the Netherlands National IHP Committee (2012–2018). Education: Bachelor's in Irrigation Engineering from Wageningen Agricultural University (Netherlands) PhD in Water Resources Management (1992) Research Interests: His research emphasizes the interplay between biophysical processes and social institutions in water management. Key areas include equitable water allocation, transboundary river basin cooperation, and farmer-led irrigation innovations in arid regions. He has pioneered projects like A4Labs and A4Store, focusing on sustainable groundwater use in African drylands. Grants & Projects: Water, Peace and Security Partnership (2018–2019) Blue Nile Hydrosolidarity (2008–2013) A4Labs (2016–2019) Capacity building initiatives in Ethiopia, Ghana, and Mozambique Awards: Recipient of the Falkenmark Award (2023) for mentored PhD work ESM Award for Interdisciplinary Research (2023) Labs & Teams: Leads the IHE Delft Water Governance Lab and collaborates with global partners like UNESCO, World Resources Institute, and Deltares. His work on sand rivers and adaptive investment pathways has shaped policy frameworks in Mozambique, Ethiopia, and India.

Alejandro Murrieta MendozaView profile

Lecturer

• Aerospace Engineering
• Transportation Systems
• Operations Research
• +3 more

Alejandro Murrieta Mendoza is a Lecturer and researcher in the Faculty of Technology (FT). His research focuses on aviation systems optimization, including topics like flight schedule analysis, airport traffic management, and machine learning applications in air transport. He actively contributes to conferences and collaborations in aerospace engineering and operations research. Research interests include optimizing airline operations through data mining, simulation, and advanced algorithms. His work addresses challenges such as pilot absenteeism prediction, sector traffic balancing, and adversarial defense in aircraft trajectory prediction. Notable contributions include enhancing gate allocation software with machine learning and genetic algorithms, and developing LSTM-based models for airport passenger flow prediction. His interdisciplinary approach bridges simulation, cybersecurity, and machine learning in aviation contexts.