Find Academic Supervisors

Anne van LoonView profile

Associate Professor

• Drought Risk Analysis
• Groundwater Drought
• Flood Risk Management
• +4 more

Dr. Anne van Loon is an Associate Professor in Drought Risk at the Institute for Environmental Studies (IVM) within the Faculty of Science at Vrije Universiteit Amsterdam. She specializes in interdisciplinary drought and flood risk science, focusing on hydrological processes, human-water interactions, and climate adaptation. Her work integrates quantitative and qualitative methods, including modeling, workshops, and participatory approaches. Education: PhD in Drought Propagation (Wageningen University, 2013) MSc in Hydrology and Water Quality (Wageningen University, 2006) BSc in Soil, Water and Atmosphere (Wageningen University, 2002) Research Interests: Propagation of drought and flood risks Groundwater drought dynamics Climate risk storylines and adaptation pathways Interdisciplinary methodologies for socio-hydrological systems Her current projects include the ERC-funded PerfectSTORM project on drought-to-flood disasters, the EU-funded RECEIPT project on climate risk storylines, and collaborations in southern Africa (Connect4WR) and Europe (GDI). Grants and Projects: ERC Starting Grant: PerfectSTORM (2021–2026) I-CISK Project: Climate Services Integration (2021–2025) DOWN2EARTH: Drought Risk Modeling in Eastern Africa Labs/Teams: Leads the Water & Climate Risk group at IVM, collaborating with global networks on socio-hydrological research and disaster risk reduction.

Robbert KleerebezemView profile

Associate Professor

• Microbial Ecosystems
• Environmental Biotechnology
• Bioprocess Engineering
• +3 more

Robbert Kleerebezem is an Associate Professor at Delft University of Technology's Faculty of Applied Sciences, Department of Biotechnology. His research focuses on microbial community engineering, particularly in anaerobic processes, bioremediation, and sustainable resource recovery. He leads projects like Unlock (syngas conversion) and FABULOUS (urban waste valorization). He teaches courses on environmental thermodynamics and microbial community engineering. His group develops bioprocesses for wastewater treatment and biopolymer production (e.g., PHA) from waste streams. Notable achievements include advancing understanding of microbial ecosystem dynamics and scaling-up PHA production. Collaborations with Wetsus and industry partners highlight applied research. Education: MSc Environmental Sciences from Wageningen University. Advised over 20 PhD students, with expertise in microbial ecology and process optimization. Active in teaching fermentation technology and environmental biotechnology at both BSc and MSc levels. His lab explores topics like anaerobic digestion, syntrophic associations, and biofilm engineering.

Victoria DegelerView profile

Assistant Professor

• Computer Science
• Artificial Intelligence
• Software Engineering
• +4 more

Victoria Degeler is an Assistant Professor at the University of Groningen’s Faculty of Science and Engineering, affiliated with the Bernoulli Institute. Her research focuses on AI-driven solutions for complex systems, combining expertise in artificial intelligence, software engineering, and service-oriented computing. She actively contributes to EU Horizon initiatives through her role in evaluating projects for the EC REA and EASME. Her work emphasizes real-world applications such as digital twins for infrastructure optimization, machine learning for IoT systems, and adaptive service architectures. Her research interests span digital twins in water distribution networks, quality-aware IoT processing, and machine learning methodologies. Notable projects include developing self-adaptive service selection frameworks and analyzing human activity recognition biases. She collaborates widely, evidenced by co-authored publications in top-tier conferences and journals.

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.

• Data Integration
• Machine Learning
• Schema Matching
• +5 more

C. Koutras is a researcher at the Data-Intensive Systems group within the School of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology. His work focuses on data integration, schema matching, and machine learning applications in modern data systems. Research Areas: Data Integration, Schema Matching, Machine Learning, Data Lakes, Graph Neural Networks, Biomedical Data Systems Collaborations: Active collaborations with researchers including R. Hai, A. Katsifodimos, and M. Jarke. Recent work includes developing tools like Amalur and Valentine , which address challenges in data lake integration and scalable schema matching. His research leverages large language models and graph-based techniques for biomedical and distributed data environments. Despite significant contributions to data integration and machine learning, no explicit scientific awards or part-time status are documented in the provided materials. His 2024 dissertation at TU Delft highlights expertise in modern data challenges.

Bettina SpeckmannView profile

Professor

• Computational Geometry
• Geometric Algorithms
• Spatial Data Analysis
• +12 more

Bettina Speckmann is a full Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU Eindhoven), where she leads the Applied Geometric Algorithms group. She holds additional appointments as EAISI Health Professor and EAISI Foundational Professor, and is affiliated with the Data Science Center Eindhoven. Her research bridges theoretical algorithm design with practical applications in spatial computing. Her research interests lie primarily in computational geometry and geometric algorithms, with strong applications in GIScience, Smart Mobility (including moving object analysis and automated cartography), geo-visualization, visual analytics, and e-Humanities. She focuses on developing efficient algorithms and data structures for spatial data, combining rigorous theoretical methods with practical engineering for real-world impact. Her recent publications (2025) show a strong trend in geometric data processing, particularly in polycube segmentations, dual loop algorithms, density estimation for moving groups, and topological analysis using merge trees and Fréchet distances. These works reflect her interdisciplinary focus on computational geometry, visualization, and data structures. Scientific awards received include: Netherlands Prize for ICT Research (2011) NWO Vici Award (2012) PEriTiA Prize (2020) Bettina Speckmann has advised numerous students and researchers through her group and has secured major grants, including the NWO Vici. She has served in leadership roles such as PC co-chair for Graph Drawing (GD 2011), PC chair for ICALP Track A (2015), and PC co-chair for SoCG (2018). She teaches courses such as Data Structures and Heuristic Algorithms. She leads the Applied Geometric Algorithms group, which actively collaborates with industry partners like HERE Global B.V., Fugro NL Land B.V., and OCLC B.V., and contributes to UN Sustainable Development Goals in areas related to data and mobility.

• 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.

• Emotion
• Interpersonal Relationships
• Psychophysiology
• +5 more

Dr. Monika Donker serves as Assistant Professor in the Department of Youth and Family within Utrecht University's Faculty of Social and Behavioural Sciences. Her academic base is the Martinus J. Langeveld Building (Heidelberglaan 1, Room E2.28) in Utrecht, Netherlands. As an active researcher, she contributes to the Dynamics of Youth (DoY) initiative and leads investigations within the InTransition project focusing on parent-adolescent interactions and adolescent autonomy development. Her research expertise spans emotion dynamics, interpersonal relationships, and psychophysiological measurement techniques. Specializing in longitudinal and dynamical systems approaches, she examines teacher-student relationships, parent-child interactions, and adolescent development through multimodal methodologies including heart rate monitoring and behavioral coding. Her work integrates personality theory with developmental frameworks to understand stress responses in educational and family contexts. Analysis of her recent publications reveals strong thematic continuity in educational and developmental psychology, with increasing emphasis on pandemic-related family dynamics and physiological assessment methods. Her scholarly output demonstrates methodological sophistication through integration of self-report, observational, and physiological data streams, particularly in classroom and family settings. Professional activities include presentations at major international conferences (EARLI, SAA, ISPA), service on the PhD council of the Faculty of Social Sciences, and membership in the Educational Committee of the Interuniversity Centre of Educational Sciences (ICO). She completed a visiting research period at the University of Konstanz (Germany) under Prof. Thomas Goetz in 2018. Her technical proficiency encompasses advanced statistical software (MLwiN, Mplus, HLM, AMOS, SPSS, R) for multilevel and structural equation modeling. Current media engagements include expert commentary on teacher emotional experiences and research dissemination regarding parent-adolescent relationships during the COVID-19 pandemic.

• 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.

• Public Health
• Dietetics
• Healthy Aging
• +4 more

A. M. Werkman is an Associate Professor at Hanze University of Applied Sciences within the School of Health and Life Sciences, specifically affiliated with the Department of Allied Health Care and Nursing. As a core member of the Centre of Expertise Healthy Ageing, Werkman focuses on Healthy Lifestyle, Sports and Physical Activity research streams. Holding a 'dr ir' (Dutch engineering doctorate equivalent), they lead multiple research initiatives focused on public health interventions. Werkman's research centers on process-driven dietetic care , behavioral change interventions , and plant-based nutrition promotion . Their work spans European collaborations including the EU-funded IMPECD project developing standardized dietetic care processes. Key focus areas include process improvement in healthcare delivery, nutrition assessment methodologies, and community-based citizen science approaches for healthy lifestyle adoption. The research strongly aligns with UN Sustainable Development Goals related to public health and well-being. Analysis of recent publications reveals a consistent focus on process optimization in dietetics (82% cluster with Process of Care keyphrases), with significant emphasis on European healthcare frameworks (68% Europe keyphrases) and behavioral components in nutrition interventions . The work demonstrates strong translational impact through media engagement and public outreach initiatives. Werkman actively leads research projects including the Lecturer Platform Food, Nutrition and Health (2023-2026), Active and Healthy Lifestyle Minor (2017-present), Samen naar meer plantaardige voeding (2022), and the INPRO project on interprofessional education (2021-2023). These projects receive funding from both institutional and external sources, demonstrating sustained research productivity with outputs spanning 2006-2024. As an active science communicator, Werkman participates in European Researchers' Night events (2023, 2024) and media engagements including Floriade installations and expert commentary on plant-based nutrition. Their work bridges academic research with practical public health applications through community-based interventions and knowledge dissemination.

Eric VerbeekView profile

Researcher

• Process Mining
• Process Models
• Process Discovery
• +3 more

H.M.W. (Eric) Verbeek is an ICT Developer in the Department of Mathematics and Computer Science at Eindhoven University of Technology, specializing in Process Analytics. His work focuses on advancing process mining techniques, standardization of event data (e.g., IEEE XES), and the development of tools like ProM and CPN IDE. He has contributed to over 160 publications since 1986, with recent research emphasizing process discovery algorithms, workflow net synthesis, and context-rich process modeling. Research Interests include Process Mining, Process Models, Process Discovery, Petri Net Analysis, and Workflow Management. He has actively participated in projects like the GDPR compliance toolkit and has been involved in defining standards for event data interoperability. Notable contributions include the IEEE XES standard for process mining, tools for event log generation and analysis, and methods for decomposed conformance checking. His work bridges theoretical advancements in process modeling with practical tool development, supporting both academia and industry applications. Collaborations span international academia, focusing on process mining workflows and compliance monitoring. His research trends highlight a balance between algorithmic innovation and real-world applicability in areas like healthcare, logistics, and service management.

• Edge Computing
• Non-Orthogonal Multiple Access
• Energy Efficiency
• +7 more

Suzan Bayhan is an Associate Professor in the Design and Analysis of Communication Systems group and a member of the Digital Society Institute . Her research focuses on energy-efficient wireless networks, edge computing, and sustainable computing infrastructures. She has contributed to advancing technologies like Non-Orthogonal Multiple Access (NOMA), cellular network optimization, and federated learning in smart homes. Her work bridges theoretical advancements with practical applications, addressing challenges such as energy consumption in edge servers, security of critical infrastructure networks, and cross-technology communication between LTE-U and WiFi. Bayhan has received prestigious awards including the Best Demo Award at IEEE INFOCOM 2020 and Joint Best Paper Award at IEEE WoWMoM 2020 . Her research spans topics like network resilience, spectrum sensing, and distributed computing systems. She actively participates in academic activities, including organizing workshops like the 8th Networking Networking Women Workshop (N2Women 2019) and delivering invited talks on wireless network resilience. Key Research Themes: Edge Computing, Energy Efficiency, 5G/6G Networks, IoT Analytics Notable Contributions: LESS-ON energy-saving framework, MintEDGE simulator, and sustainable computing continuum proposals Collaborations: Cross-continental research partnerships in wireless systems and environmental computing

Marwan HassaniView profile

Assistant Professor

• Process Mining
• Data Mining
• Unsupervised Learning
• +7 more

Marwan Hassani is an Assistant Professor at Eindhoven University of Technology's Department of Mathematics and Computer Science, affiliated with the Process Analytics and EAISI Foundational groups. He leads the streaming process mining research group and the Customer Journey track at the Data Science Center Eindhoven (DSC/e). His academic background includes a PhD from RWTH Aachen University (2015) and postdoctoral research there until 2016. Research focuses on unsupervised learning methods for streaming event data, including clustering, outlier detection, and sequential pattern mining, with applications in customer journey optimization and real-time process analytics. Over 65 publications span data mining, process mining, and related areas. He serves on program committees for ECML/PKDD, SDM, and journals like KAIS and DAMI, co-chairing multiple events. Key technical contributions include PrefixCDD for concept drift detection, BFSPMiner for sequential pattern mining, and autoencoder-based anomaly detection systems. His work addresses GDPR compliance in business processes and traffic flow prediction using contrastive learning. Awards include a 2025 Best Paper Award at ACM SIGAPP. Education: PhD in Computer Science (RWTH Aachen, 2015) Projects: Led GDPR compliance project (2018-2021), developed BPR4GDPR framework Teaching: Advanced Process Mining, Foundations of Data Analytics Editorial roles: Guest editor for Data & Knowledge Engineering , editorial board member for Process Science Research aligns with UN SDGs through contributions to efficient resource management and privacy-preserving analytics.

Konstantina KatsanouView profile

Researcher

• Karst Hydrogeology
• Water Resource Conservation
• Water Quality Monitoring
• +3 more

Konstantina Katsanou is a researcher specializing in hydrogeology and environmental policy, with a focus on karst systems, water resource management, and groundwater vulnerability assessment. Her work integrates field studies, laboratory analysis, and policy implementation at both national and European levels. She has contributed to the revision of River Basin Management Plans (RBMPs) and Flood Risk Management Plans (FRMPs) in Greece, while also engaging in interdisciplinary projects across Europe, North/Central America, and East Africa. Her research employs chemical tracers (e.g., stable isotopes, rare earth elements) and hydrological modeling to understand groundwater circulation and karst aquifer dynamics. She has extensive experience in environmental monitoring, including studies on hyporheic zones, groundwater-surface water exchange, and lake restoration. Teaching responsibilities include modules on groundwater quality monitoring within an MSc program. Key research themes include karst hydrogeology, geochemical processes in aquifers, and the application of GIS-based methods for vulnerability assessment. Her publications span over 20 years, addressing topics such as thermal water origins, wildfire impacts on groundwater quality, and policy-driven water management initiatives. She actively collaborates on global water quality initiatives like the World Water Quality Alliance and has pioneered methodologies for karst system analysis using tools like MODKARST modeling and distributed temperature sensing. Her work bridges scientific research with practical policy solutions for sustainable water resource management.

Erkut AkdagView profile

Researcher

• Anomaly Detection
• Artificial Intelligence
• Smart City Surveillance
• +4 more

Erkut Akdag is a postdoctoral researcher at the Department of Electrical Engineering, Eindhoven University of Technology, specializing in artificial intelligence for smart city surveillance and transportation systems. He contributes to projects focused on anomaly detection, urban mobility optimization, and multi-modal sensing. Academic Rank: Researcher Contact: e.akdag@tue.nl His research addresses UN Sustainable Development Goals related to sustainable cities (SDG 11) and infrastructure (SDG 9). Key interests include Anomaly Detection , Smart City Surveillance , and Intelligent Transportation Systems . Recent publications emphasize anomaly detection in surveillance videos, distracted driver action recognition, and geo-spatial traffic analysis. His work leverages deep learning , temporal attention mechanisms , and spatio-temporal embeddings . Scientific recognition includes the Award for Exceptional Excellence (ITEA 2024) for collaborative research projects. He actively presents at international conferences, including IEEE VCIP and ICMV.