جستجوی استاد راهنمای دانشگاهی

Maria Pregnolatoمشاهده پروفایل

استادیار

• Infrastructure Resilience
• Flood Risk Management
• Bridge Engineering
• +۵ مورد دیگر

Dr. Maria Pregnolato is an Assistant Professor in the Hydraulic Structures and Flood Risk department within the Civil Engineering and Geosciences faculty at Delft University of Technology. She also holds the position of Honorary Associate Professor at the University of Bristol. Her research focuses on infrastructure resilience and risk management from natural hazards, with particular expertise in flood impacts on transportation networks and structural systems. Dr. Pregnolato earned her PhD in Civil Engineering from Newcastle University in 2017, an MSc in Building Engineering and Architecture from the University of Pavia and Tongji University in 2012, and an MSc in Strategy, Leadership and Change from the University of Bristol in 2022. Her educational background reflects a strong foundation in both technical engineering and strategic leadership. Her research interests span infrastructure resilience, flood risk management, bridge engineering, structural health monitoring, and Digital Twins for civil infrastructure. A significant portion of her work explores the integration of digital technologies with traditional civil engineering practices, particularly in the context of climate change adaptation and natural hazard risk management. She has pioneered approaches to bridge scour management and developed novel frameworks for assessing flood impacts on critical infrastructure networks. Analysis of her recent publications (2019-2025) reveals a clear evolution in her research focus, with increasing emphasis on digital solutions for infrastructure resilience. Her work has expanded from traditional flood risk assessment to incorporate Digital Twins, sensor technologies, and nature-based solutions. The publications demonstrate strong international collaboration, with research spanning multiple continents and addressing both developed and developing world contexts. Dr. Pregnolato has received several prestigious awards recognizing her contributions to the field: EGU Outstanding Early Career Award (2021) EPSRC Fellowship (2018) EPSRC RISE Award (2018) Fellow of the Women in Engineering Society (2019) She actively supervises MSc students and has secured competitive research funding, including an EPSRC Fellowship to investigate extreme flooding impacts on bridges. Her research program integrates hydrodynamic modeling, structural analysis, and digital monitoring technologies to develop comprehensive risk management frameworks for critical infrastructure. Dr. Pregnolato maintains strong international collaborations, with visiting researcher positions at Politecnico di Milano (2022) and the University of Washington (2019), demonstrating her global impact in the field of civil infrastructure resilience.

• Educational Technology
• Programming Tutoring Systems
• Game-Based Learning
• +۱ مورد دیگر

Johan Jeuring is Professor of Software Technology for Learning and Teaching at Utrecht University's Department of Information and Computing Sciences. His research focuses on intelligent tutoring systems, computational thinking education, and game-based learning environments. Research Interests: Prof. Jeuring's work explores computational thinking pedagogy, automated feedback systems, and educational game design. His research bridges artificial intelligence with educational psychology to develop effective learning tools. Publications: Recent publications focus on augmented reality learning environments, programming education methodologies, automated assessment systems, and AI applications in education. His work combines empirical studies of learning behaviors with technological innovations in educational tools.

Evert van Nieuwenburgمشاهده پروفایل

استادیار

• Quantum Physics
• Machine Learning
• Condensed Matter Physics
• +۶ مورد دیگر

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.

• Self-regulated Learning
• Learning Analytics
• Online Education
• +۴ مورد دیگر

Dr. Jacqueline Wong is an Assistant Professor in the Department of Education and Pedagogy at the Faculty of Social and Behavioural Sciences, Utrecht University. Her academic work focuses on enhancing educational practices through evidence-based research in self-regulated learning and learning analytics, with particular applications in higher education contexts. Dr. Wong earned her Ph.D. (cum laude) from Erasmus University Rotterdam with a thesis entitled "Enhancing Self-Regulated Learning Through Instructional Supports and Learning Analytics in Online Higher Education," which received the Best Thesis Award 2021 from the Erasmus Graduate School of Social Sciences and the Humanities. She completed her Master's degree (cum laude) in Educational Psychology, where she examined the use of movements to support text comprehension. Prior to joining Utrecht University, she was a postdoctoral researcher at Delft University of Technology (2020-2022) working with the Program of Innovation in Mathematics Education (PRIME). Dr. Wong's research program spans several interconnected areas: the application of learning analytics to support self-regulated learning, the design of instructional interventions that promote metacognitive awareness, and the investigation of how emerging technologies like generative AI can enhance educational experiences. Her work frequently examines how technology can be leveraged to support students' metacognitive processes and goal-setting behaviors in both traditional and online educational settings. She has made significant contributions to understanding how conversational agents and learning analytics can provide personalized support for students' self-regulated learning processes. Analysis of her recent publications reveals a clear trajectory toward integrating emerging technologies with established educational theories. Her 2024-2025 work increasingly focuses on the intersection of artificial intelligence and educational psychology, particularly examining how generative AI tools can enhance academic text accessibility and provide adaptive support for learning. She also continues to investigate cognitive aspects of learning in educational games and augmented reality environments, with implications for both educational practice and learning theory development. Among her notable achievements, Dr. Wong received the Best Thesis Award 2021 from the Erasmus Graduate School of Social Sciences and the Humanities. This recognition highlights the significant contribution her doctoral work has made to the field of educational psychology and learning sciences. Her systematic reviews, including examinations of student engagement in mathematics education and gamification in MOOCs, have provided valuable syntheses of research in growing areas of educational technology. Dr. Wong has been an active research member of the Leiden-Delft-Erasmus Centre for Learning and Education (LDE-CEL) and the network for Self-Regulated Learning in Digitalised Schools (SeReLeDiS). Her collaborative approach to research is evident in her extensive co-authorship network across multiple institutions. Before joining Utrecht University, she worked with lecturers to promote student motivation and self-regulated learning in blended service mathematics courses and examined the effectiveness of technology tools for learning mathematics. Dr. Wong's work bridges theoretical frameworks with practical implementations, often involving close partnerships with educators to ensure research relevance to real-world classroom challenges. Her current research appears to be moving toward deeper integration of AI technologies with educational theory, particularly examining how generative AI can support metacognitive processes and self-regulated learning across diverse educational contexts.

• Pharmacoepidemiology
• Epidemiology
• Observational Research
• +۹ مورد دیگر

D.L. (Daniala) Weir is an Assistant Professor at Utrecht University's Science college, specifically within the Pharmacoepidemiology and Clinical Pharmacology department. Her research bridges clinical pharmacology , epidemiology , and applied data science to enhance medication safety for patients with multimorbidity. Education: B.Sc. in Biological Sciences (University of Alberta, 2012) M.Sc. in Epidemiology (University of Alberta, 2014) Ph.D. in Epidemiology (McGill University, 2019) Postdoctoral Fellowship (University of Toronto, 2021) Her work combines causal inference frameworks with machine learning to address clinical challenges using real-world data from Canada, the UK, and the Netherlands. She collaborates across disciplines with biostatisticians , data scientists , and healthcare professionals , emphasizing patient-centered approaches through engagement with patients and caregivers. Key research areas include: Dynamic treatment regimens Medication-related harm prevention Adverse drug event detection mHealth applications for adherence Her publications demonstrate expertise in applied data science methodologies for time-to-event analysis , predictive modeling , and multimorbidity treatment optimization .

• Computational Neuroscience
• Neurophysics
• Brain Oscillations
• +۲ مورد دیگر

Paul Tiesinga is Professor of Neuroinformatics at Radboud University's Faculty of Science, where he directs the Neuroinformatics group at the Donders Centre for Neuroscience. His research focuses on understanding information processing in the brain, particularly the role of oscillations ('brain waves') in cognitive functions and disease mechanisms. He combines computational modeling with experimental approaches including optogenetics to investigate cortical dynamics. As Director of Education for the Institute for Mathematics, Physics and Astronomy, he oversees academic programs while maintaining active research in: Computational modeling of cortical circuits Oscillation mechanisms in neural coding Data sharing policies for neuroscience Information transfer between brain areas His recent publications reflect advances in neurotechnology development, thalamocortical connectivity analysis, and novel methodologies for neural data interpretation.

Martin A. Vinckمشاهده پروفایل

استاد

• Computational Neuroscience
• Systems Neuroscience
• Neural Coding
• +۱۰ مورد دیگر

Prof. Dr. Martin Vinck is a Professor at the Donders Centre for Neuroscience in the Department of Neurophysics , Radboud University. He is also a Research Group Leader at the Ernst Strüngmann Institute for Neuroscience and a faculty member at the International Max Planck Research School for Neural Circuits . Current affiliations: Radboud University, ESI Frankfurt, Max Planck Research School Positions: Professor, Research Group Leader, Review Editor for eLife Research Interests span computational and systems neuroscience, focusing on predictive processing in biological and artificial neural networks. His lab investigates: How distinct excitatory/inhibitory neurons regulate plasticity and flexible information processing Neural ensemble coding via spatio-temporal patterns and spike sequences Self-supervised learning mechanisms using spatial/temporal predictions Gamma oscillations, synchronization, and their role in visual encoding Scientific Contributions include developing algorithms for high-dimensional neural data analysis, co-supervising key studies on interneuron dynamics, and pioneering work on predictive coding in V1. His grants include the ERC Starting Grant (2019) and VIDI Grant (2024) . Lab Members include PhD students Jinke Liu, Jahan Esfandiarii, Athanasia Tzanou and alumni Irene Onorato, Marius Schneider, Ana Clara Silveira Broggini . He actively promotes open science , develops the Spike Toolbox , and reviews for journals like Neuron, eLife, PLoS Computational Biology .

Karl J. Fristonمشاهده پروفایل

استاد

• Neuroscience
• Radiology and Medical Imaging
• Computational Neuroscience
• +۴ مورد دیگر

Karl J. Friston is Professor of Imaging Neuroscience and Wellcome Principal Research Fellow at the Faculty of Brain Science, University College London. He serves as Scientific Director of the Wellcome Trust Centre for Neuroimaging and has been a leading figure in computational neuroscience for decades. Friston previously served as President of the international Organization of Human Brain Mapping in 2000 and has received numerous prestigious awards for his groundbreaking work. Professor Friston's research centers on theoretical neuroscience with a focus on the free energy principle , active inference , and predictive coding frameworks. His work spans multiple disciplines including neuroimaging methodology , computational psychiatry , and cognitive neuroscience . Friston has developed statistical approaches like Statistical Parametric Mapping (SPM) that have become standard tools in brain imaging research worldwide. His theoretical contributions provide unifying frameworks for understanding perception, action, and consciousness from a computational perspective. Analysis of Friston's recent publications reveals continued innovation in applying mathematical frameworks to complex neuroscience questions. His work increasingly bridges theoretical neuroscience with clinical applications in psychiatry and expands into artificial intelligence, exploring how brain principles can inform machine learning. The articles demonstrate consistent development of the free energy principle across diverse domains from psychedelic drug action to climate change impacts on brain health. Minerva Golden Brain Award (2003) Fellow of the Royal Society (2006) Medal, Collège de France (2008) Honorary Doctorate from the University of York (2011) Fellow of the Society of Biology (2012) As Scientific Director of the Wellcome Trust Centre for Neuroimaging, Friston leads a major research program that has secured substantial funding from the Wellcome Trust and other major research bodies. His theoretical frameworks have generated numerous research projects worldwide, influencing both basic neuroscience and clinical applications. Friston's work has been particularly influential in precision psychiatry, where computational approaches are being applied to understand and treat mental disorders through frameworks like the 'Bayesian Dysconnections' model. Friston is based at the Wellcome Trust Centre for Neuroimaging at University College London, a leading institution for brain imaging research. His work involves extensive collaborations across multiple disciplines and institutions, bringing together neuroscientists, physicists, mathematicians, and clinicians to advance our understanding of brain function through advanced imaging and computational techniques.

• Soil Organic Carbon
• Digital Soil Mapping
• Soil Data Standardization
• +۷ مورد دیگر

Niels Batjes is a leading researcher at ISRIC - World Soil Information , specializing in soil data standardization, digital soil mapping, and carbon sequestration modeling. His work focuses on creating harmonized global soil databases like WoSIS and SoilGrids to support climate mitigation and environmental conservation. Key Research Areas : Soil Organic Carbon, Digital Soil Mapping, Pedotransfer Functions, Measurement Error Analysis Projects : Development of soil databases with quantified uncertainties, HoliSoils initiative for European forest soils Recent publications highlight his contributions to global soil water retention modeling, interoperable soil data exchange frameworks, and machine learning applications for carbon stock assessment. He plays a central role in international soil data infrastructure development.

• Philosophy of Artificial Intelligence
• Cognitive Science
• Neuroscience
• +۳ مورد دیگر

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.

• Distributed Systems
• Cloud Computing
• Edge Computing
• +۷ مورد دیگر

Nishant Saurabh is a tenured Assistant Professor at the Department of Information and Computing Sciences , Utrecht University , Netherlands. His research focuses on resource and data management , application scheduling , performance modeling , and optimization in large-scale hybrid distributed systems including Cloud, Edge, and Quantum computing. Recently, he has expanded into ML workload scheduling and causal performance attribution across the Cloud-Edge continuum. Research Expertise : Distributed Systems and Cloud Computing Quantum-HPC Middleware Systems Performance Modeling and Optimization ML/AI Workload Scheduling Cloud-Edge Continuum Architectures Observability Frameworks Scientific Leadership : Associate Editor, Springer's Journal of Cloud Computing Editorial Board, Frontiers of AI book series IBM HPC-Quantum Working Committee member Workshop Chair, IEEE IPDPS 2025 Supervision : He has supervised 16+ Master/Bachelor students on projects including container migration, reinforcement learning for serverless computing, and quantum middleware evaluation. Current PhD students include Diogo Landau (Performance Observability), Nathan Keyaerts (LLM Methods), and Negar Alizadeh (Energy Efficiency).

• Computer Science
• Artificial Intelligence
• Explainable AI
• +۱۶ مورد دیگر

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.

Steven L. Salzbergمشاهده پروفایل

استاد

• Computational Biology
• Genomics
• Bioinformatics
• +۴ مورد دیگر

Steven L. Salzberg is the Bloomberg Distinguished Professor of Biomedical Engineering, Computer Science, and Biostatistics at Johns Hopkins University, where he also serves as Director of the Center for Computational Biology. His research focuses on developing computational methods for DNA analysis from next-generation sequencing technologies, with applications across gene finding, genome assembly, comparative genomics, and evolutionary genomics. Dr. Salzberg's research interests center on computational biology and genomics, with particular emphasis on developing innovative software tools for analyzing DNA and RNA sequences. His work has significantly advanced genome assembly techniques, annotation systems, and the understanding of genomic variation. He maintains a strong commitment to data accuracy, as demonstrated by his recent work debunking flawed cancer microbiome research that made headlines in 2023 and 2025 publications. Analysis of his recent publications reveals a consistent trajectory toward increasingly sophisticated genomic analysis tools. His lab has pioneered methods for splice site prediction (Splam), genome annotation (LiftOn), protein-coding region detection (ORFanage), and assembly polishing (JASPER). The research spans computational methodology development while maintaining strong connections to biological discovery and medical applications. Scientific Recognition: His former postdoc Martin Steinegger awarded the 2024 Overton Prize from ISCB Contributed to the landmark publication of the first complete human genome in Science (2022) Dr. Salzberg actively mentors numerous PhD students and postdoctoral fellows, with recent thesis defenses by Kuan-Hao Chao, Markus Sommer, and Ales Varabyou. His lab has secured substantial research funding supporting multiple graduate students and postdocs working on cutting-edge genomic research. The lab maintains extensive collaborations, particularly with Dr. Mihaela Pertea's group, as evidenced by joint publications and shared lab members working on genome assembly and annotation projects. The Salzberg Lab at Johns Hopkins represents a dynamic research environment with continuous growth and evolution. Recent lab news shows consistent expansion with new members joining across multiple years, thesis defenses by key researchers, and high-impact publications spanning Nature, Science, and specialized genomics journals. The lab's collaborative approach with other research groups has resulted in significant contributions to the field of computational genomics.

Jean Vroomenمشاهده پروفایل

استاد

• Multisensory Perception
• Audiovisual Speech Perception
• Auditory Processing
• +۵ مورد دیگر

Prof. Jean Vroomen is a Full Professor in Cognitive Neuropsychology at Tilburg University, Netherlands. His research focuses on multisensory perception, audiovisual speech processing, and clinical populations like autism and schizophrenia. He holds a PhD from Tilburg University (1992) and has authored over 160 peer-reviewed publications. His work spans neuroimaging, behavioral studies, and computational modeling. Key appointments include Head of the Department of Cognitive Neuropsychology (2013-2022) and Director of the Bachelor Psychology program (2006-2012). He has supervised 14 PhD students and serves on editorial boards of journals like Frontiers in Human Neuroscience . Awards include NWO Vidi Grant (2002-2007) and KNAW fellowship (1992-1997). Research interests include: multisensory integration, predictive coding, speech adaptation, and developmental disorders. Recent studies explore neural mechanisms of trust perception and maternal-infant interactions using EEG hyperscanning. Grants include leadership in the NWO-Gravity 'Language in Interaction' program (€27.6M). His lab investigates auditory processing deficits in autism and schizophrenia, with a focus on electrophysiological markers.

• Wind Turbine Engineering
• Wind Farm Flow Control
• Wake Dynamics
• +۴ مورد دیگر

J.W. van Wingerden is a Professor at the Wind Energy Institute (DUWIND) within the Mechanical Engineering school at Delft University of Technology (TU Delft) . His work focuses on wind turbine engineering, flow control strategies, and wake dynamics optimization in wind farms. Active research in model-predictive control systems Leadership in multidisciplinary wind energy projects Development of open-source simulation tools His research explores wake dynamics , atmospheric boundary layer interactions , and data-driven modeling to enhance wind farm efficiency. Recent publications address open-source frameworks for flow control and validation studies on turbine arrays. He received the O. Hugo Schuck Prize (2023) for contributions to automatic control applications. Current projects include Active Wind Farm Cluster Wake Mixing (2024–2028), aiming to improve wake mixing and energy capture in large wind farms.