جستجوی استاد راهنمای دانشگاهی

برای مسیر پژوهشی‌تان، استاد راهنما، استاد دانشگاه و پژوهشگر مناسب را پیدا کنید و با آن‌ها ارتباط بگیرید.

نمایش ۳۱-۴۵ از ۳۱۹ استاد راهنما

اطلاعات پروفایل‌ها از صفحات عمومی دانشگاه‌ها گرفته شده و ممکن است به‌روز نباشد.

Rineke Verbruggeمشاهده پروفایل

استاد

University of GroningenNetherlands

Prof. Rineke Verbrugge is a Professor in Artificial Intelligence at the University of Groningen's Faculty of Science and Engineering, affiliated with the Bernoulli Institute. Her research focuses on computational theory of mind, multi-agent systems, hybrid intelligence, and logical frameworks applied to social networks and legal reasoning. She holds additional roles on the Institute Advisory Board of CWI (Dutch National Research Institute for Mathematics and Computer Science) and several ERC/NWO selection committees. Her work bridges cognitive science and AI, emphasizing human-agent collaboration, belief formation in groups, and ethical AI design. Recent projects include developing computational models for theory of mind in negotiations and scenario-based Bayesian networks for legal evidence analysis. She has authored over 220 publications and supervised multiple PhD candidates in AI and logic. Key research themes include higher-order theory of mind applications, zero-one laws in provability logic, and agent-based policy evaluation for sustainable technologies. Her contributions span conferences like AAMAS, ICAIL, and HHAI, addressing topics from lie detection mechanisms to privacy conflicts in multi-user systems.

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Arno Siebesمشاهده پروفایل

استاد

Utrecht UniversityNetherlands

Arno Siebes is Professor of Algorithmic Data Analysis in the Department of Information and Computing Sciences at Utrecht University's Faculty of Science. His research focuses on data mining methodologies, particularly pattern mining and Minimum Description Length (MDL) principles. Key research areas include: Developing efficient algorithms for pattern discovery Applying MDL to data characterization Creating interpretable models for complex datasets Addressing challenges in data science education Recent publications demonstrate applications in diverse domains including mobility analysis, genomic screening, and pandemic response. His work combines theoretical foundations with practical implementations for knowledge discovery.

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R. Arthur Bouwmanمشاهده پروفایل

استاد

Eindhoven University of TechnologyNetherlands

Prof.dr. R. Arthur Bouwman is a Full Professor at the Electrical Engineering department of the Eindhoven University of Technology and affiliated with the Eindhoven MedTech Innovation Center . His work bridges biomedical engineering and clinical medicine , focusing on physiological monitoring , medical imaging , and biomarker validation for real-time patient care. Education : Not explicitly detailed in the text His research emphasizes non-invasive diagnostics and AI-driven health monitoring , including video-based cardiac arrhythmia detection , sweat-based renal function analysis , and Doppler ultrasound optimization . Recent work explores causal inference in observational studies and automated early warning systems in surgical wards. Key article trends highlight biomedical signal processing , medical device innovation , and integration of wearables in perioperative care . Collaborations span institutions like Catharina Hospital and research centers across cardiovascular and renal domains.

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Hans Vernooijمشاهده پروفایل

مدرس

Utrecht UniversityNetherlands

Hans Vernooij is a Lecturer in Farm Animal Health at the Faculty of Veterinary Medicine, Utrecht University. He specializes in statistical methods and data science applications in veterinary epidemiology and animal health. His areas of expertise include: Statistical methods for veterinary research Applied Data Science in Life Sciences Epidemiological modeling Machine learning applications in animal health Vernooij has extensive experience in developing statistical models for animal health applications. His research focuses on applying advanced statistical techniques and data science methods to solve problems in veterinary epidemiology and farm animal health. He has particular expertise in Random Forest models, as demonstrated during his sabbatical at the Human Sciences Research Council in Pretoria where he developed a model for HIV status prediction based on demographic information and knowledge of HIV prevention from large-scale survey data. His publication record shows consistent contributions across veterinary epidemiology, with recent work emphasizing machine learning applications and big data analytics in animal health surveillance. The research demonstrates a clear trajectory from traditional statistical methods toward more advanced data science approaches. Vernooij is actively involved in teaching and mentoring: Teaches statistics to Bachelor students at the veterinary faculty Supports PhD candidates and Master students during data analysis phases of their research Provides statistics education for the Master of Epidemiology program at the Julius Centre of University Medical Centre

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Rianne de Heideمشاهده پروفایل

استادیار

University of TwenteNetherlands

Rianne de Heide is an Assistant Professor in the Statistics group (STAT) within the Department of Applied Mathematics at the University of Twente's Faculty of Electrical Engineering, Mathematics and Computer Science. She maintains collaborative arrangements with LUXs Data Science in Leiden, CWI, and VU Mathematics in Amsterdam as a guest researcher while working partly remotely during her family's relocation. Her academic journey includes a previous position as Assistant Professor at Vrije Universiteit Amsterdam. PhD Dissertation: 'Bayesian Learning: Challenges, Limitations and Pragmatics' (2020) MSc Thesis: 'The Safe-Bayesian Lasso' (2016) De Heide's research spans multiple interconnected domains within statistics and machine learning, with particular emphasis on developing mathematically rigorous frameworks that remain accessible to diverse audiences. Her work bridges theoretical foundations with practical applications, focusing on hypothesis testing with e-values, Bayesian learning methodologies, and best-arm identification problems in multi-armed bandit settings. She demonstrates exceptional interdisciplinary range, connecting statistical theory with philosophical inquiry and even theological discussions as evidenced by her publications on biblical authorship verification and mathematical beauty. Analysis of her publication trajectory reveals a clear evolution toward developing anytime-valid statistical methods, particularly through e-values and e-processes for multiple testing scenarios. Her recent work shows increasing focus on foundational questions in statistical inference while maintaining strong connections to practical machine learning applications. The 2024 'Safe Testing' paper in the Journal of the Royal Statistical Society represents a significant contribution that generated a formal discussion meeting. VENI project 'E-values for Multiple Testing' NWO M2 grant of €742,708 with Jelle Goeman (funding 2 PhD students and a scientific programmer) 2025 Bernoulli Society New Researcher Award De Heide actively supervises research through her VENI project and the NWO M2 grant, while also contributing to broader academic service through the 'Kindness and Excellence in Academia' initiative she co-founded. This initiative addresses critical cultural issues in academic environments through opinion pieces, resources, and community building around compassionate academic practices. She has organized specialized events like the E-Day meet-up for e-value researchers at CWI in Amsterdam, demonstrating leadership in her niche research community. Her research activities are centered around the Statistics group at the University of Twente, with significant external collaborations through the E-mailing list for e-value researchers and partnerships with institutions including CWI, VU Amsterdam, and Leiden's LUXs Data Science. The interdisciplinary nature of her work creates connections across mathematics, computer science, philosophy, and even religious studies.

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Arianna Bisazzaمشاهده پروفایل

دانشیار

University of GroningenNetherlands

Arianna Bisazza is an Associate Professor in the Computational Linguistics Group at the University of Groningen, where she leads the InClow research group focused on Interpretable, Cognitively inspired, Low-resource language models. Her work bridges computational linguistics, cognitive science, and language acquisition to develop more robust and interpretable language processing algorithms that can adapt to diverse linguistic phenomena worldwide. Dr. Bisazza's research interests span statistical modeling of human languages in multilingual contexts, with particular focus on improving language model performance for "challenging" or low-resource languages. Her work explores how insights from human language acquisition can inform better language modeling techniques, and she investigates methods to make state-of-the-art NLP systems more interpretable and transparent. As a cross-disciplinary researcher, she actively seeks to enhance our understanding of human language processing and evolution through computational modeling tools. Her recent publications reveal a strong emphasis on multilingual evaluation frameworks (like TurBLiMP and MultiBLiMP), interpretability of language models, and connections between human language acquisition and neural network learning. Her work consistently addresses the challenge of making language technology more robust across diverse linguistic structures and typological features. Outstanding Paper Award at the BabyLM Challenge (CoNLL'24 Shared Task) for "BabyLM Challenge: Exploring the Effect of Variation Sets on Language Model Training Efficiency" Dr. Bisazza currently leads a Vidi project funded by the Dutch Research Council (NWO) on improving low-resource language modeling through child language acquisition insights. She is also part of two national consortium projects funded by NWA-ORC initiatives: InDeep (Interpreting deep learning models for language, speech & music) and LESSEN (Low Resource Chat-based Conversational Intelligence). She supervises multiple PhD students, including two China Scholarship Council (CSC)-funded researchers working on simulating human patterns of language learning and change. Her earlier research was supported by a Veni grant (2017-2021) focused on understanding and improving the encoding of linguistic structure in Neural Machine Translation models. As head of the InClow research group, Dr. Bisazza oversees a team investigating interpretable, cognitively inspired approaches to low-resource language modeling. The group's work combines insights from cognitive science and linguistics with cutting-edge NLP techniques to develop language models that better reflect human language processing capabilities, particularly in resource-constrained settings.

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Thijs van Edeمشاهده پروفایل

استادیار

University of TwenteNetherlands

Thijs van Ede serves as an Assistant Professor in the Semantics, Cybersecurity and Services research group at the University of Twente's Faculty of Electrical Engineering, Mathematics and Computer Science. His academic work centers on security automation through machine learning and AI integration, with specialized expertise in large language models for intrusion detection systems and anomaly analysis in rapidly evolving network environments. His research program bridges theoretical AI advancements with practical cybersecurity applications, focusing on contextual security analysis using deep neural networks and Natural Language Processing techniques for cyber threat intelligence sharing. This work manifests in open-source tool development emphasizing collaborative science, including frameworks for encrypted traffic analysis and security event log interpretation. Recent publications demonstrate consistent innovation in applying deep learning to network security challenges, particularly through the DeepCASE (2022) and FlowPrint (2020) frameworks that address anomaly detection in security logs and mobile application fingerprinting respectively. These works establish patterns in leveraging sequential data analysis for real-time threat identification. Through active mentorship of the Twente Hacking Squad student team, he cultivates next-generation cybersecurity talent via Capture The Flag competitions while maintaining research infrastructure through open-source security tools. His academic service includes developing accessible research implementations that bridge theoretical concepts and operational security solutions. As a core member of the Semantics, Cybersecurity and Services research group, he contributes to the University of Twente's Digital Society Institute initiatives, focusing on deployable AI security systems that operate effectively in dynamic enterprise environments. His work maintains strong connections between academic research and practical security operations through toolchain development and student engagement.

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Reinder Haakmaمشاهده پروفایل

پژوهشگر

Eindhoven University of TechnologyNetherlands

Reinder Haakma is a University Researcher at Eindhoven University of Technology in the Signal Processing Systems department. His work bridges biomedical engineering and machine learning , focusing on advanced signal processing techniques for healthcare applications. Research interests include: Photoplethysmography (PPG) signal decomposition Cardiovascular monitoring via wearable sensors Anomaly detection in physiological signals Improving pulse arrival time estimation Smartwatch-based cardiac arrest detection systems His contributions to rapid eye movement sleep analysis and heart rate variability align with UN Sustainable Development Goals for health innovation. Notable recognition includes the Best paper full paper award at IE 2018 for his work with Tax, van der Aalst, and Sidorova. Recent publications (2024-2025) demonstrate expertise in predictive coding algorithms, Gaussian modeling, and real-time physiological monitoring. Collaborations span cardiology (Dekker, Vullings) and human-computer interaction (Markopoulos) domains.

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Alraune Zechمشاهده پروفایل

استادیار

Utrecht UniversityNetherlands

Dr. Alraune Zech is an Assistant Professor for Computational Environmental Hydrogeology at Utrecht University, Faculty of Geosciences, Department of Earth Sciences, Hydrogeology group. Her research focuses on groundwater flow and transport processes in heterogeneous environments, with emphasis on practical applications in contaminated aquifers, PFAS remediation, and construction-related groundwater issues. She is affiliated with the Helmholtz-Centre for Environmental Research and serves as convener for EGU sessions on contaminant transport. Dr. Zech's educational background includes: PhD in Computational Hydrosystems from Friedrich-Schiller-University Jena and Helmholtz-Centre for Environmental Research (2010-2013) Prediploma Degree in Business Mathematics from University Leipzig (2011) Diploma in Mathematics with minor in Chemistry from University Leipzig (2003-2009) Her research interests span hydrogeology, groundwater modeling, and environmental remediation. She specializes in stochastic and computational modeling of subsurface processes, with focus on contaminated aquifers, PFAS removal strategies, biodegradation, bioremediation, and heat transport in the subsurface. Her work bridges theoretical approaches with practical applications in construction engineering and environmental protection. Analysis of Dr. Zech's recent publications (2021-2025) reveals strong focus on advancing hydrogeological modeling techniques, particularly in aquifer heterogeneity characterization, machine learning applications in hydrogeology, and practical remediation strategies. Her work spans fundamental research on pore-scale processes to field-scale applications, with increasing integration of artificial intelligence methods for solving complex groundwater problems. Dr. Zech leads several significant research projects: Living Lab PFAS Remediation (2024-2028): Developing strategies for PFAS contamination SilPit (2024-2028): Studying erosion of silicate grouting in construction pits MIBIREM (2022-2027): Creating innovative technological toolbox for bioremediation She actively supervises multiple PhD students including Alexandra Hockin, Kim Bartsch, Mahammad Valibeknejad, Sona Aseyednezad, Hannah Gebhardt, and Martijn van Leer. Her research is supported by funding from the Ministry of Infrastructure and Water Management, NWO, and EU Horizon programs.

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Maurice van Keulenمشاهده پروفایل

دانشیار

University of TwenteNetherlands

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

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Sanne Frequinمشاهده پروفایل

استادیار

Utrecht UniversityNetherlands

Dr. Sanne Frequin is an Assistant Professor in Art History at Utrecht University within the Faculty of Humanities. Her work bridges traditional art historical research with digital methodologies. Academic coordinator of the Master’s program in Art History Board member of the Utrecht University Teaching Academy (T@UU) Coordinator of the Digital ArtLab Her research focuses on digital and 3D reconstruction of medieval and early modern art , particularly lost or damaged artefacts. Key projects include: The 3D reconstruction of Guy of Avesnes’ tomb Digital analysis of the Lindau Master’s panel painting Development of the #MedievalMe serious game She actively engages in science communication through: Radio appearances (NPO Radio 1’s De Wild in de Middag , OVT ) Collaborations with heritage institutions (Museum Catharijneconvent, KB National Library) Public projects like the Medieval Meme Machine

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Krista H. Lagusمشاهده پروفایل

استاد

National Research Institute for Mathematics and Computer ScienceNetherlands

Krista H. Lagus serves as Professor of Digital Social Science at the University of Helsinki's Faculty of Social Science, where she co-founded and directed the Center for Social Data Science (CSDS) starting in 2019. Her interdisciplinary work bridges computational methods with social science inquiry to analyze complex societal behaviors through digital footprints. Her educational foundation includes an M.Sc. in Computer Science (1996) and Ph.D. in Computer and Information Sciences (2000), both earned at Helsinki University of Technology (now Aalto University). This technical background enabled her transition into pioneering computational social science methodologies. Lagus's research integrates artificial intelligence and social science through signature projects: WEBSOM for visualizing large text corpora via self-organizing maps, Citizen Mindscapes for gauging public opinion through digital communication analysis, and Morfessor , a groundbreaking unsupervised morphological segmentation algorithm widely adopted in natural language processing. Her work demonstrates how machine learning can decode societal patterns from digital traces. Her scientific recognition includes: Academy Research Fellow, Finnish Academy of Sciences (2006-2012) Lagus actively supervises graduate students in AI and digital social science while contributing to academic governance as an Editorial Board Member for PeerJ Computer Science. Her research leadership is anchored in the Center for Social Data Science, which she established to foster cross-disciplinary collaboration between computational and social scientists. The Center for Social Data Science (CSDS) operates as her primary research ecosystem, driving initiatives that apply artificial intelligence to contemporary societal challenges. Through CSDS, Lagus cultivates methodological innovations that transform how social phenomena are observed and interpreted in the digital age.

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Wessel Reijersمشاهده پروفایل

پژوهشگر ارشد

National Research Institute for Mathematics and Computer ScienceNetherlands

Wessel Reijers is a Postdoctoral Researcher at the Department of Media Studies, Paderborn University, Germany, with visiting scholarships at the Technion and the Robert Schuman Centre, European University Institute. Previously, he was a Max Weber Fellow at the European University Institute, a Research Associate in the ERC project "BlockchainGov" led by Dr. Primavera de Filippi, and a postdoctoral researcher at the University of Vienna. Reijers received his PhD in technology ethics from Dublin City University. His academic journey has spanned multiple European institutions, focusing consistently on the ethical implications of emerging technologies for society and citizenship. His research primarily explores how emerging technologies impact citizenship, with particular attention to social credit systems and their effects on civic virtue. He also investigates distributed governance models, analyzing both their potential to enhance democratic processes and their inherent risks. His work bridges philosophy, political theory, and technology studies, offering critical perspectives on how technological developments reshape societal structures. Recent publications demonstrate a growing focus on AI ethics, interpretability, and what he terms "Positive Luddism"—a critical yet constructive approach to technological development that emphasizes human flourishing while acknowledging technology's dangers. Reijers is the author of "Narrative and Technology Ethics" and co-editor of "Interpreting Technology." His upcoming book "Introduction to the Ethics of Emerging Technologies" (2025) further establishes him as a leading voice in technology ethics. His work has gained recognition in both academic and public spheres, with media appearances including a Deutschlandfunk interview on "Positive Luddism" (2024). Beyond academia, Reijers is a creative individual who writes fiction, composes and produces music (available on Soundcloud), and creates paintings. He is part of an international music collaboration called "Conditio Humana," demonstrating his commitment to interdisciplinary engagement between technology, ethics, and the arts.

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Gwenn Englebienneمشاهده پروفایل

استادیار

University of TwenteNetherlands

Gwenn Englebienne is an Assistant Professor at the Digital Society Institute and Human Media Interaction group of Utrecht University. Their research focuses on Artificial Intelligence, Computer Vision, and Human-AI Interaction, with applications in robotics, health, and social computing. They have contributed to over 80 research outputs since 2007, emphasizing embodied AI, social robotics, and explainable machine learning. Research interests span activity recognition, teleoperation systems, and ethical AI design. Notable work includes developing GNN-based group detection algorithms and evaluating chatbot reliability through automated question-answering frameworks. Their studies often bridge technical innovation with human-centered design, such as measuring embodiment via pupil dilation or addressing asymmetry in video-conferencing interactions. Key collaborations include work on social robotics, telepresence systems, and health monitoring using ambient sensors. Publications span conferences like IDA, CogMI, and LREC-COLING, reflecting interdisciplinary impact. A dataset on robot social positioning behavior is publicly accessible via 4TU.Centre for Research Data. Current work explores semi-supervised domain adaptation, spiking neural networks, and the psychological dimensions of AI trustworthiness. They lead initiatives in the Digital Society Institute to align technological advancements with societal needs.

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Evert van Nieuwenburgمشاهده پروفایل

استادیار

Leiden UniversityNetherlands

Dr. Evert van Nieuwenburg is an Assistant Professor at Leiden University, affiliated with both the Leiden Institute of Advanced Computer Science (LIACS) and the Leiden Institute of Physics (LION). His research bridges the fields of Quantum Physics , Machine Learning , and Condensed Matter Physics , with a focus on quantum algorithms, reinforcement learning, and quantum game development (e.g., Quantum TiqTaqToe ). He actively contributes to the Applied Quantum Algorithms (aQa) initiative and leads the QuantumPlayed subgroup for quantum games and education. Research Interests: AI-driven quantum experiment control, quantum machine learning, variational quantum circuits, and quantum games for education and intuition-building. Publications: 15+ peer-reviewed works spanning quantum error correction, phase transitions, reinforcement learning in quantum systems, and quantum dot array simulations. Community Engagement: Developer of educational quantum games, open science advocate, and active participant in interdisciplinary initiatives. Selected Trends: His work demonstrates AI's transformative role in quantum physics, from decoding error-correcting codes with graph neural networks to merging reinforcement learning with quantum control systems. Labs & Initiatives: Affiliated with the Applied Quantum Algorithms (aQa) initiative and co-founder of QuantumPlayed , where quantum mechanics meets game theory to engage diverse audiences.

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