Find Academic Supervisors

• human-centered ai
• psychology of technology
• machine learning
• +5 more

Chao Zhang is an Assistant Professor in Human-Centered AI at the Human-Technology Interaction group, Eindhoven University of Technology. His research bridges psychology and technology through data-driven methods and psychological theories, focusing on behavior change, AI-human dynamics, and trustworthy systems. MSc in Human-Technology Interaction (TU/e) PhD in Intelligent Systems (TU/e, 2019) Postdoc at Utrecht University Research interests span Human-Centered AI , Explainable AI , and Behavioral Data Science . Key contributions include: Integrating psychological principles with machine learning Modeling longitudinal behavioral data Developing interpretable AI systems for lifestyle interventions Investigating social characteristics in AI-driven environments Current work includes the ENFIELD project on Trustworthy Green AI. Scientific contributions appear in CHI Conference , arXiv , and International Journal of Social Robotics . Recognized with a TU/e PhD Thesis Award nomination in 2021.

Hilde J.P. WeertsView profile

Assistant Professor

• Algorithmic Fairness
• Machine Learning
• Data Mining
• +5 more

Hilde J.P. Weerts is an Assistant Professor at Eindhoven University of Technology , affiliated with the Department of Data Mining within the School of Mathematics and Computer Science . Her research focuses on algorithmic fairness, machine learning ethics, and mitigating discrimination in AI systems. She obtained her MSc and PhD (2025 thesis) from TU/e, with a background in interpretable machine learning and fraud detection systems. Her research interests include fairness-aware AutoML, legal frameworks for algorithmic accountability, and interdisciplinary approaches to understanding bias in decision-making systems. She teaches the Responsible Data Science course and has supervised over 10 academic works, including her own Master's thesis on interpretable machine learning for fraud alerts. Key contributions include frameworks for subgroup harm assessment, unlawful proxy discrimination analysis, and guidelines for fairness in automated machine learning. Her work bridges computer science, law, and ethics, addressing real-world implications of AI systems.

Fons van der SommenView profile

Associate Professor

• Medical Image Analysis
• Computer-Aided Diagnosis
• Artificial Intelligence
• +2 more

Fons van der Sommen is an Associate Professor in Electrical Engineering at Eindhoven University of Technology, specializing in Video Coding & Architectures. He leads research on computer-aided detection systems for early cancer diagnosis, particularly focusing on esophageal and colorectal neoplasia through advanced AI and computer vision techniques. His research interests span medical image analysis, AI-assisted diagnostics, and developing robust systems for clinical deployment. Recent publications focus on overcoming real-world implementation challenges of AI in endoscopy and enhancing the trustworthiness of diagnostic systems. Recent research trends show strong emphasis on surgical AI applications (robot-assisted procedures), generative models for medical data augmentation, and quality assurance frameworks for clinical AI deployment. His work integrates deep learning with clinical validation across gastrointestinal and pulmonary oncology. TU/e Best PhD Thesis Award (2018) Best Poster Presentation (2017, 2013) He coordinates multiple research projects including TASTI-XECS221002 (Advanced AR for AI-based Servitization) and XL-ARGOS (extended reality solutions). Manages collaborations with medical centers on AI implementation for cancer screening.

• Biomedical Engineering
• Fetal Monitoring
• Signal Processing
• +4 more

SG Oei is a Professor at Eindhoven University of Technology in the Department of Electrical Engineering, affiliated with the Eindhoven MedTech Innovation Center and Biomedical Diagnostics Lab. His research focuses on developing novel technologies for maternal-fetal health monitoring, particularly in obstetric care settings. Research interests center on: Advanced fetal monitoring techniques including electrohysterography and non-invasive fetal electrocardiography Biomedical signal processing applications in perinatal care Development of AI-driven diagnostic tools for fetal distress detection Simulation-based obstetric training systems for low-resource settings Publication analysis reveals consistent focus on improving fetal monitoring through: Novel signal processing algorithms for cardiotocography interpretation Validation of non-invasive electrophysiological monitoring techniques Application of speckle tracking echocardiography in fetal assessment AI-powered diagnostic systems for high-risk pregnancies Extensive student supervision includes 28 doctoral candidates since 2010, with dissertations covering fetal cardiac function monitoring, electrohysterography applications, and obstetric simulation training. Research is conducted through the Biomedical Diagnostics Lab and Center for Care & Cure Technology Eindhoven.

Dim CoumouView profile

Professor

• Extreme weather
• Climate change
• Atmosphere dynamics
• +4 more

Prof. Dim Coumou is a Full Professor at the Faculty of Science, Vrije Universiteit Amsterdam (VU), and holds a second position at the Royal Netherlands Meteorological Institute (KNMI). He leads the Climate Data Science research group coordinating efforts between IVM and KNMI. His work focuses on extreme weather events linked to climate change, employing advanced statistical methods, climate models, and machine learning. He has authored over 60 peer-reviewed papers, contributed to IPCC reports, and is a Web of Science Highly Cited Scientist. Education: PhD in Natural Sciences (ETH Zurich, 2003-2008) MSc in Geophysics (University of Utrecht, 1995-2001) Research Interests: Coumou’s research emphasizes how global warming intensifies extreme weather events like heatwaves, heavy rainfall, and droughts. He investigates atmospheric dynamics, circulation patterns, and their societal impacts. His work bridges climate science with stakeholders like the World Bank to prioritize high-risk extremes. Grants & Projects: Coordinator of XAIDA (Horizon-2020, €700k) Co-PI in TiPES (Horizon-2020, €300k) and RECEIPT (Horizon-2020, €300k) NWO VIDI Award (€800k) for Persistent Summer Extremes research Teaching: Leads BSc Honours 'Climate Change' and MSc 'Climate Hydrological Processes' courses. Outreach & Awards: Recipient of the Web of Science Highly Cited Scientist distinction. Active in science communication through projects like 'Aufgeheizt' and 'Extreme Dialogues', fostering dialogue between scientists and the public.

Annette ten TeijeView profile

Professor

• Artificial Intelligence
• Knowledge Representation
• Medical Knowledge Representation
• +7 more

Annette ten Teije is a Full Professor at Vrije Universiteit Amsterdam (VU Amsterdam) with appointments in the Faculty of Science, Artificial Intelligence department, the Network Institute, and the Knowledge Representation and Reasoning research group. Her academic career spans several decades with a strong focus on the intersection of artificial intelligence and healthcare applications. Professor ten Teije's research interests center around Knowledge Representation, particularly in medical contexts. Her work bridges multiple domains including Semantic Web technologies, Ontology development, Neuro-Symbolic AI systems, and Clinical Decision Support. She has made significant contributions to the formalization of clinical guidelines, handling multimorbidity in healthcare systems, and developing design patterns for hybrid AI systems. Her research integrates machine learning with symbolic reasoning to create explainable and reliable AI systems for healthcare applications. Analysis of Professor ten Teije's recent publications reveals a strong trajectory toward neuro-symbolic AI approaches that combine the strengths of neural networks and symbolic reasoning. Her work increasingly focuses on explainability in medical AI systems, with numerous publications on feature selection, interaction detection, and narrative-based understanding. She has developed frameworks for shared understanding in multi-agent systems and created design patterns specifically for medical decision-making contexts. Her research consistently bridges theoretical AI advances with practical healthcare applications. Professor ten Teije has supervised 5 PhD theses as indicated in her academic profile and teaches courses including "AI in Health" and "Machine Learning and Reasoning for Health" for the 2024-2025 academic year. Her academic contributions extend to editorial work, including serving as editor for conference proceedings and special issues on Knowledge Representation for Healthcare Processes.

John Ray BergadoView profile

Lecturer

• Machine Learning
• Deep Learning
• Remote Sensing
• +3 more

John Ray Bergado is a Lecturer and Researcher at the Department of Earth Observation Science (EOS), Faculty of Geoinformation Science and Earth Observation (ITC), University of Twente, The Netherlands. He holds a PhD in End-to-end predictive models for remote sensing applications (2020), an MSc in Geoinformation Science and Earth Observation (2016, with distinction), and a BSc in Geodetic Engineering (2013, magna cum laude). His research focuses on integrating machine learning and deep learning with geospatial technologies like remote sensing, photogrammetry, and UAVs to address environmental and urban challenges. Education: PhD, University of Twente, 2016–2020 MSc (Distinction), University of Twente, 2013–2016 BSc (Magna Cum Laude), University of the Philippines, 2009–2013 His work emphasizes applications such as wildfire prediction, UAV-based point cloud extraction, and land use classification. He has contributed to projects with institutions like NEO BV, RMIT (Melbourne), and SRDP Consulting Inc. His research aligns with UN SDGs, particularly in sustainable development through geospatial innovation. Key trends in his articles include deep learning for geospatial data analysis, UAV applications in disaster response, and multispectral image fusion. His methodologies often combine neural networks and attention mechanisms to enhance remote sensing outcomes. He has no listed awards but has received academic distinctions for his degrees. His advisory roles and grants are not explicitly mentioned, though his research collaborations span global institutions. He is affiliated with ITC’s EOS department, contributing to its mission in geoinformation science and earth observation.

Shreyasi PathakView profile

Researcher

• Explainable Artificial Intelligence
• Deep Learning
• Machine Learning
• +3 more

Shreyasi Pathak is a Researcher and Internal PhD Candidate at the TechMed Centre, University of Twente, specializing in Biomedical Signals and Systems. Her work bridges artificial intelligence and clinical medicine through interpretable machine learning models for healthcare decision support systems. Education: Master of Science, University of Twente (2016-2018) Her research focuses on Explainable Artificial Intelligence (XAI) with emphasis on multimodal prediction systems for medical applications. She develops transparent deep learning models to address clinical challenges including breast cancer diagnosis and hip fracture risk assessment, prioritizing model interpretability for clinician trust and adoption. Her methodology integrates preoperative data with Long Short-Term Memory networks and prototype-based evaluation frameworks. Recent publications demonstrate a clear trend toward clinically deployable AI systems where explainability is non-negotiable. Her work on feature importance mapping and prototype-based validation establishes new standards for trustworthy healthcare AI, moving beyond 'black box' solutions to reality-centric implementations that respect clinical workflows and domain knowledge constraints. Research Environment: She operates within the Biomedical Signals and Systems group at TechMed Centre, an interdisciplinary hub fostering collaboration between engineers, computer scientists, and medical professionals to translate technological innovations into tangible healthcare improvements.

Ilker BirbilView profile

Professor

• Optimization Methods
• Data Science
• Decision Making
• +3 more

Prof. Dr. Ilker Birbil is a Professor of AI & Optimization Techniques for Business & Society at the University of Amsterdam (UvA), affiliated with the Amsterdam Business School's Business Analytics section. He previously held professorships at Erasmus University and Sabancı University, focusing on optimization and data science. His research interests include interpretable machine learning, data privacy, and optimization methods in decision-making. Education: PhD in Operations Research from North Carolina State University Postdoc at Erasmus Research Institute of Management (ERIM), Netherlands Research Interests: Optimization in data science, interpretable AI, privacy-preserving algorithms, and decision-making systems. Recent work focuses on differentially private optimization and meta-learning techniques like LESS. Key Achievements: Recipient of multiple teaching awards at Sabancı University Affiliated researcher at OPTIMAL (Optimization for and with Machine Learning) Organized workshops on ML for optimization and mathematics of ML Grants & Teams: Leads the UvA group on Optimization and Machine Learning at LNMB. Collaborates on projects blending OR and ML, including privacy-aware algorithms and revenue management systems. Labs/Teams: OPTIMAL, UvA Optimization Group, and interdisciplinary teams in data analytics.

• Metabolomics
• Mass Spectrometry
• Food Science
• +5 more

Dr. Jos Hageman is an Assistant Professor affiliated with the Mathematical and Statistical Methods group (Biometris) at Wageningen University & Research. His research focuses on integrating advanced analytical techniques (e.g., mass spectrometry, metabolomics) with statistical modeling to address challenges in food science, plant biology, and environmental systems. He specializes in developing predictive models for food product design, microbial activity analysis, and environmental impact assessment. Education: Not explicitly stated in provided texts. Key research interests include metabolomics-driven food innovation, lipid and protein interaction modeling, and the environmental effects of microplastics. His work combines proteomics, metabolomics, and statistical methods to investigate plant-microbe interactions, crop diversity, and sustainable agriculture. He has collaborated on projects involving statistical analysis of fermentation processes, QSAR modeling for antibacterial compounds, and machine learning for ingredient characterization. Recent publications highlight advancements in QSAR models for food systems, microplastic toxicity assessment, and lipid species analysis via mass spectrometry. His datasets include metabolite profiles of grassland plants and breast milk protein dynamics. As a co-promotor, he oversees PhD projects on kwashiorkor etiology, plant-based protein optimization, and bioreactor transcriptomics. Current active projects focus on improving plant-based food proteins and understanding kwashiorkor pathophysiology through causal learning. His work bridges analytical chemistry, computational methods, and applied biology, contributing to food security and environmental sustainability initiatives.

L.M. (Michelle) BruijnView profile

Associate Professor

• Administrative Law
• Drug Policy Regulation
• Housing Rights
• +8 more

Dr. L.M. Michelle Bruijn is an Associate Professor at the Department of Legal Methods, Faculty of Law, University of Groningen. Her research focuses on drug-related crime, housing rights, eviction cases, and cannabis regulation, employing quantitative legal research and data science. She holds a Veni Grant (NWO) for the PROHOUSE project (2024–present), investigating housing rights in eviction cases. She is a member of the Young Academy Groningen (YAG), network coordinator of the Scientific Network on Evictions and Housing Research (SNEHR), and a board member of the Dutch Lawyers Association (NJV). Education: PhD in Law (cum laude, 2021) Research Interests: Her work bridges legal theory and societal impact, addressing issues like the interaction between private, criminal, and administrative law. Recent projects include platforms for mayors to tackle drug crime ethically and an interactive case law database. She also collaborates with NGOs like the Centrum voor Criminaliteitspreventie en Veiligheid (CCV) and health institutions like the Trimbos Institute. Awards: Ben Feringa Impact Award (2022) for societal research impact Veni Grant (NWO, 2024) for PROHOUSE project Teaching and Skills: Founder and chair of the Groningen Centre for Legal Skills, focusing on legal education and training. Coordinates master’s theses and the Minor Law program. Teaches courses such as 'Law and Legal Skills: The Dutch Example' and 'Juridische Onderzoeksvaardigheden.' Labs/Networks: Leads the PROHOUSE project and contributes to the Groningen Centre for Legal Skills, offering workshops and teaching materials for legal education.

A.M.G. Zuiderwijk-van EijkView profile

Associate Professor

• Open Data
• Digital Inclusion
• Social Inclusion
• +4 more

A.M.G. Zuiderwijk-van Eijk serves as Associate Professor at Delft University of Technology's Faculty of Technology, Policy and Management, where she also directs the Bachelor Systems Engineering Policy Analysis and Management program (Technische Bestuurskunde). Her academic home resides within the Information and Communication Technology department, focusing on critical digital society issues. Her research spans Open Data, Digital Inclusion, and Social Inclusion , examining technological and institutional barriers affecting vulnerable groups' digital technology adoption. Current work investigates data sovereignty frameworks, AI ethics in public governance, and climate policy support systems through interdisciplinary lenses combining information science, public administration, and social contract theory. Recent publications reveal strong emphasis on ethical AI implementation and equitable data governance. Scientific Recognition: Outstanding Paper Award at 36th Bled eConference (2023) She leads significant research initiatives including the active ODECO: Towards a sustainable Open Data ECOsystem project (2021-2025) and previously directed TRUSTS: Trusted Secure Data Sharing Space (2020-2022). Her scholarly impact is evidenced by 119 research outputs including 37 articles, 56 conference contributions, and 23 curated datasets. She actively engages in academic service through conference organization and policy workshops.

• Philosophy of Artificial Intelligence
• Cognitive Science
• Neuroscience
• +3 more

Carlos Zednik is an Assistant Professor for Philosophy of Artificial Intelligence at Eindhoven University of Technology , affiliated with the Industrial Engineering and Innovation Sciences department. He leads the Eindhoven Center for Philosophy of AI and participates in the alignAI (ERC) and ROBUST AI (NWO) consortia. His work bridges philosophy with AI and neuroscience, focusing on explainable AI (XAI), mechanistic explanation, and cognitive modeling. Education: BSc in Computer Science and Philosophy, Cornell University MSc in Philosophy of Mind, University of Warwick PhD in Cognitive Science, Indiana University Bloomington Zednik’s research investigates philosophical questions about biological and artificial intelligence, emphasizing: Methodological principles in cognitive psychology and neuroscience Norms and best practices for XAI in machine learning Knowledge representation in transformer models and large neural networks His recent publications explore the integration of cognitive models into XAI, the role of Bayesian reverse-engineering in cognitive science, and the mechanistic explanation of network neuroscience. Zednik also contributes to international standardization efforts through ISO/IEC TS 6254 and DIN SPEC 92001 . Scientific Awards include fellowships from: DAAD Alexander-von-Humboldt Foundation StandICT Fellowship Zednik supervises PhD students Zeynep Kabadere , Michela Ghezzi , Céline Budding , Miriam Gorr , and Hannes Boelsen , while mentoring postdocs Manuel Barbosa de Oliveira and Philippe Verreault-Julien . His teaching spans philosophy of AI, ethics of machine learning, and decision theory, with innovation projects on generative AI in higher education.

• Computer Science
• Artificial Intelligence
• Explainable AI
• +16 more

Faizan Ahmed is a Lecturer in the Formal Methods and Tools department at the University of Twente, part of the Faculty of Electrical Engineering, Mathematics and Computer Science. His work spans artificial intelligence, machine learning, and education technology. Research Focus: Explainable AI, software design, railway infrastructure analysis, and nonlinear equation optimization. Collaborations: Active in international conferences like CSEDU and FTC, with co-authors from diverse institutions. Key Contributions: Development of C-SHAP for temporal explanations, IterSHAP for feature selection, and studies on generative AI in programming education.

Elisabetta PeriView profile

Assistant Professor

• Biomedical Signal Processing
• Electrophysiological Signals
• Non-obtrusive Patient Monitoring
• +3 more

Elisabetta Peri is an Assistant Professor in the Signal Processing Systems group at the Eindhoven University of Technology (TU/e), affiliated with the Biomedical Diagnostics Lab and EAISI Health. Her research focuses on biomedical signal processing for non-obtrusive patient monitoring through electrophysiological signals and sweat sensing. M.Sc. in Biomedical Engineering (2013), Ph.D. in Bioengineering (2017) from Politecnico di Milano Her work spans biomedical signal processing , sweat sensing , sleep staging , and cardiovascular monitoring . She develops algorithms for non-invasive diagnostics in hemodialysis, diabetes, and sleep disorders. Key article trends include: Wearable device integration (2025) Sweat-based biomarker estimation (2024-2025) Transfer learning for sleep disorder detection (2025) Projects include: PISANO (Perioperative Innovations, Sleep Apnea, Newborn Monitoring) SEDAS (Sweat Sensing for Sepsis) PICASSO (Perinatal/Cardiovascular/Sleep Medtech)