جستجوی استاد راهنمای دانشگاهی

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

• Exploratory data mining
• Machine learning
• Data science
• +۴ مورد دیگر

Prof. A.D. Pimentel holds a full professorship at the Informatics Institute of the University of Amsterdam, leading the Parallel Computing Systems (PCS) group within the Systems and Networking Lab. His research focuses on multi-core and multi-processor systems, emphasizing performance, energy efficiency, dependability, and productivity in system design and runtime management. He earned his PhD and MSc in Computer Science from the University of Amsterdam in 1998 and 1993, respectively. Current roles: Chair of PCS group, Board member of Advanced School for Computing and Imaging (ASCI), and ICT Research Platform Nederland (IPN) Teaching: Courses on Multi-core Processor Systems, Embedded Software, and Architecture Research interests span edge AI, sustainable computing, and system-level modeling. Recent work includes innovations in energy-efficient scheduling, thermal management in 3D-stacked systems, and adaptive CNN inference at the edge. Over 25 years of contributions to embedded systems design space exploration and hardware/software co-design have been recognized through awards like the IEEE CEDA Outstanding Service Award (2025). Awards: IEEE DATE Fellow (2025), NWO Knowledge & Innovation Covenant grant lead Active in conference organization, serving as General Chair for Embedded Systems Week (2026) and Design Automation and Test in Europe (DATE 2024). Engages in cross-disciplinary projects like improved secure semiconductor evaluation (ISSE) and energy labeling for digital services.

• Parallel Computing Systems
• Multi-core Embedded Systems
• System-level Design
• +۵ مورد دیگر

Dr. Hamid Montazeri is an Assistant Professor in the Department of the Built Environment at Eindhoven University of Technology (TU/e) , Netherlands. He joined TU/e in 2020 and completed his PhD in Building Physics and Wind Engineering at the same institution in 2015. Prior to his current role, he held postdoctoral fellowships from the Research Foundation Flanders (FWO) in 2015 and 2018, focusing on convective heat transfer in turbulent boundary layers and building-integrated photovoltaics at KU Leuven (Belgium). Dr. Montazeri's research expertise includes aerodynamics and fluid dynamics applied to buildings , urban wind energy systems , and renewable energy solutions . He develops advanced multi-scale multi-physics models using computational fluid dynamics (CFD) and machine learning to enhance building energy efficiency and sustainable urban design. His work contributes to UN Sustainable Development Goals related to affordable and clean energy (SDG7) and sustainable cities (SDG11). He has received several accolades, including the Best PhD/Postdoc Award (2015) and Best PhD Supervision Team Award (2016) from TU/e's Department of the Built Environment. His 2017 paper was ranked among the top-cited in Building & Environment for 2012-2017. He serves on the editorial boards of Energy , Renewable Energy , and Journal of Wind Engineering and Industrial Aerodynamics , among others. His educational background includes a MSc in Mechanical Engineering (2006) and PhD in Building Physics and Wind Engineering (2015) , both from TU/e. He has supervised 16 research projects and actively collaborates on urban microclimate studies, wind energy harvesting, and building-integrated photovoltaic systems.

• Aerodynamics
• Fluid Dynamics
• Building-Integrated Photovoltaics
• +۳ مورد دیگر

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.

• Anomaly Detection
• Artificial Intelligence
• Smart City Surveillance
• +۴ مورد دیگر

Yannis Velegrakis is a Professor in the Department of Information and Computing Sciences at Utrecht University, where he holds the chair of Very Large Data Management. He leads the Data Intensive Systems research group and is the program leader for the Master’s in Data Science . He is also a part-time faculty member at the University of Trento and a Principal Investigator at the Archimedes AI and Data Science hub, Athena Research Center . Education: PhD in Computer Science, University of Toronto MSc in Computer Science, University of Crete BSc in Computer Science, University of Crete His research focuses on Big Data Management & Analytics , Knowledge Discovery , Graph Management , Data Integration , and Data Quality . His work combines theoretical rigor with practical system development, emphasizing human-centered approaches to data exploration and analysis. He has pioneered methods in example-based search, entity resolution, and dynamic graph analysis. His recent publications reflect a strong trend in knowledge graph exploration , data quality assessment , and dynamic network mining . These works often integrate machine learning with database systems to support interactive and intelligent data discovery. His research bridges foundational data management with applied AI, particularly in the context of real-world data challenges. Scientific Leadership and Awards: PC Chair, ICDE 2024 General Chair, VLDB 2013 PC Chair, EDBT 2021 He has advised numerous researchers and contributed to major projects in data integration and semantic search. His secondary activities include visiting positions at IBM Almaden , AT&T Research Labs , UC Santa Cruz , and University of Paris-Saclay . He has secured funding through collaborative research initiatives and has served on program committees of top international conferences. He leads the Data Intensive Systems group, which develops innovative tools for managing and exploring large-scale, heterogeneous datasets. The group emphasizes systems that support intuitive, example-driven interaction with complex data, aligning with the principles of human-centered AI.

• Big Data Management & Analytics
• Knowledge Discovery
• Graph Management
• +۱۵ مورد دیگر

Yeni Plasencia Calaña is an Assistant Professor at the Faculty of Science and Engineering , Maastricht University, and affiliated with the Institute for Digital Smart Society and Brightlands Institute for Smart Society (BISS) . Her research focuses on sound recognition, deep learning, ontology development, and biometric identification systems. University: Maastricht University School: Faculty of Science and Engineering Rank: Assistant Professor Her research integrates semantic modeling with deep neural networks , particularly in auditory perception and face recognition. Recent work includes ontologies for everyday sounds and human-aware AI in educational assessment. Key publication areas include: 2023-2025: Automated essay scoring and sound recognition via semantic deep learning 2022: Taxonomies of everyday sound semantics 2018: Persistent homology for gait recognition and metric learning 2016-2017: Dissimilarity space methods for face recognition and chemical data Scientific recognition includes the 2024 NWO-SGW Open Competition grant for the project "Am AI right?", emphasizing ethical and legal AI integration. She actively contributes to academic events, co-organizing the 2021 Women in Data Science Maastricht Conference . Her work spans applied machine learning, from low-resolution imaging to semantic modeling.

• Sound Recognition
• Deep Neural Networks
• Ontology Development
• +۷ مورد دیگر

Koos Gardebroek is an Associate Professor at the Wageningen School of Social Sciences (WASS) , specializing in Agricultural Economics and Rural Policy . His work integrates machine learning and econometric methods to address critical issues in food security , policy evaluation , and environmental risk analysis . University: Wageningen University & Research School: Wageningen School of Social Sciences (WASS) Department: Agricultural Economics and Rural Policy Academic Rank: Associate Professor Gardebroek's research spans agricultural economics , machine learning applications , and policy analysis , with a focus on household resilience , commodity futures markets , and biofuel policy impacts . His recent work includes modeling food demand shocks in West Africa and analyzing tree failure risks in the Netherlands . Key trends in his publications include machine learning techniques for grains futures price prediction , policy evaluation using FADN data , and conflict-food price dynamics in low- and middle-income countries . These works highlight his interdisciplinary approach combining environmental risk , economic modeling , and data science . Gardebroek actively supervises PhD projects on topics like Ghanaian food systems , Ukrainian land policies , and Irish agricultural efficiency . He has contributed to policy debates via media engagements, particularly on food price volatility and its societal impacts.

• Agricultural Economics
• Machine Learning
• Econometrics
• +۶ مورد دیگر

Dr. Hamidreza Kasaei is an Associate Professor in the Department of Artificial Intelligence at the University of Groningen, Netherlands. He leads the Interactive Robot Learning Lab (IRL-Lab) , focusing on robotics, machine learning, and computer vision. Develops algorithms for lifelong interactive robot learning Specializes in 3D perception and multi-arm manipulation Actively involved in IEEE RAS as Associate Editor His research directions include: Perception systems for dynamic environments Dual-arm manipulation techniques Lifelong learning architectures Multimodal integration for object understanding Dynamic motion planning for reactive systems Recent work trends show emphasis on: Vision-language model integration for open-world grasping Dual-arm reinforcement learning frameworks Neural ODE applications for video generation Ensemble methods in continual learning Imitation learning for agricultural robotics Transformer-based view planning systems Scientific awards include: Google Research Scholar Award (2023) Outstanding Associate Editor - IEEE RAS (2023) His lab actively trains students through PhD and Master's projects in domains like 3D object perception, dual-arm coordination, and multimodal learning. The lab has successfully graduated multiple PhD candidates including Zhenxing Zhang and Hamed Ayoobi .

• Robotics
• Machine Learning
• Computer Vision
• +۱۱ مورد دیگر

R.J. de Almeida e Santos Nogueira is an Assistant Professor in the Department of Quantitative Economics at the School of Business and Economics. His research focuses on econometrics, operations research, and machine learning applications in finance, healthcare, and policy analysis. He is affiliated with the Data Analytics and Digitalisation unit, emphasizing modern computational methods for complex decision-making. His work spans causal inference, optimization algorithms, and text analytics, with notable contributions to portfolio rebalancing, order batching in logistics, and healthcare analytics for inflammatory bowel disease prediction. Recent studies explore risk perception of AI technologies and causal modeling with discretized data. No scientific awards or grants are explicitly listed in the provided texts. He has advised no students, and his lab/team affiliations are unspecified.

• Econometrics
• Operations Research
• Data Analytics
• +۵ مورد دیگر

Annika Betken is an Assistant Professor in Mathematics of Operations Research. Her research focuses on advanced statistical methodologies including long-range dependence analysis, stochastic volatility modeling, and machine learning applications. Notable contributions include work on two-sample tests using Wilcoxon rank sums and label shift adaptation in neural networks. She actively engages in academic discourse through conference presentations and publications. Her recent research explores classification scenarios using kernel logistic regression and depth pattern analysis in time series. Betken has delivered invited talks on topics like class probability matching and change-point detection in time series analysis. Her work has been featured in peer-reviewed conferences and journals, contributing to 65 citations and an h-index of 5 as of 2025. Betken has collaborated internationally and contributed to the NAW article in February 2022. Her research spans theoretical advancements in statistical testing and practical implementations in adaptive machine learning systems.

• Long Range Mathematics
• Stochastic Volatility
• Simulation Study
• +۳ مورد دیگر

Congcong Sun is an Assistant Professor in Agricultural Biosystems Engineering, specializing in control systems for agricultural environments and water distribution networks. Her research spans predictive modeling, reinforcement learning, and sensor technology integration. Affiliation: Agricultural Biosystems Engineering Academic Rank: Assistant Professor Research Focus: Congcong Sun's work emphasizes: Reinforcement Learning for climate control in greenhouses Model-based Predictive Control (MPC) for water distribution systems Handling Parametric Uncertainty in urban drainage networks Multisensor Fusion for precision agriculture Food Security through advanced agricultural technology Collaborative Projects: Current projects include: Soft and Biodegradable Sensing Technologies for Precision Soil and Crop Management Zero-emission Indoor Horticulture with Renewable Energy Autonomous Plant Factory Light Control using Phenomics SMARTFARMING: Vertical Farming with Self-learning Control Manure Removal Robotics in Dairy Barns Scientific Contributions: Her publications focus on: Deep Learning for Temperature Prediction in Solar Greenhouses Stochastic MPC for Urban Drainage Systems Sliding Mode Control for Nonlinear Systems Safe Stabilization Techniques for Robotic Systems Data-Driven Pipe Life Prognosis in Water Networks Advising & Collaboration: Active advisor for multiple PhD and EngD candidates, collaborating with researchers like E. van Henten, P. Groot Koerkamp, and G. Kootstra on agricultural automation and water system controls.

• Reinforcement Learning
• Model-based Predictive Control
• Parametric Uncertainty
• +۶ مورد دیگر

Tianjin Huang is a University Researcher in the Mathematics and Computer Science school at Eindhoven University of Technology, focusing on deep learning, adversarial training, and sparsity. Their work spans traffic prediction, fairness in AI, and robustness evaluation. Specializes in Adversarial Machine Learning and Spatiotemporal Graph Modeling Develops novel frameworks for Traffic Prediction under real-world data challenges Explores Sparse Neural Networks for efficiency and performance Recent research trends include 4-bit training stability for LLMs, confusional spectral regularization for fairness, and principal eigenvalue methods for certified robustness. Their 2025 publications demonstrate cutting-edge work in adversarial training, graph neural networks, and low-bit optimization. Scientific recognition includes the Best Paper Award of LoG 2022 for collaborative work on sparse neural network training. They collaborate with institutions across Europe and Asia, focusing on trustworthy AI systems.

• Adversarial Machine Learning
• Anomaly Detection
• Graph Convolutional Network
• +۷ مورد دیگر

Mike J. Holenderski is an Assistant Professor at the Eindhoven University of Technology, affiliated with the Mathematics and Computer Science department. His work focuses on Reliable Machine Learning, particularly in industrial systems for manufacturing and maintenance optimization. University: Eindhoven University of Technology Department: Mathematics and Computer Science Academic Rank: Assistant Professor His research addresses challenges in dynamic, noisy environments by developing robust models that adapt to changing processes and handle missing/corrupt data. Key applications include semiconductor manufacturing and predictive maintenance. Recent publications highlight applications of Deep Learning in optical systems (2025), multivariate analysis for semiconductor data (2024), and equivariant representation learning (2023). Collaborations with industry through European/national projects dominate his applied research. He contributes to education via courses like Machine Learning for Industry and has supervised student theses on topics such as distributed systems programming.

• Machine Learning
• Computer Science
• Industrial Systems
• +۳ مورد دیگر

Dr. Michael Behrisch is an Associate Professor for Visual Analytics in the Visualization and Graphics Group at Utrecht University's Department of Information and Computing Sciences. With a PhD from University of Konstanz, his career includes postdoctoral work at Harvard and Tufts Universities, and over six years as a research associate at Konstanz. Specializes in matrix-based representations for relational data Focuses on cognitive load reduction in visual analytics Develops interactive systems for pattern discovery Research Highlights: Combines algorithmic approaches with user-centric visualization techniques to address challenges in large-scale, multivariate, and dynamic datasets. Research themes include: Automated pattern quantification Matrix reordering algorithms Explainable AI integration Game research applications Scientific Contributions: Recognized through 61 publications and multiple awards, including the EuroVA 2022 Best Paper and IEEE VAST 2018 Honorable Mention. His work bridges theoretical research with practical applications across life sciences, network analysis, and big data domains.

• Visual Analytics
• Big Data Analytics
• Information Visualization
• +۳ مورد دیگر

Dr. U. Güçlü is a Principal Investigator at the Donders Institute for Brain, Cognition and Behaviour at Radboud University, Netherlands. He leads the Neural Coding Lab and Donders AI for Neurotech Lab , focusing on bridging neuroscience and AI to simulate in vivo neural computation using in silico connectionism for "brain reading" and "brain writing." He ranks among the top 10 most-cited researchers in Neural Decoding, Neural Encoding, and NeuroAI on Google Scholar (H-index: 29). University: Radboud University Institution: Donders Institute Rank: Researcher His research interests include NeuroAI , Neural Coding , and Neurotechnology , particularly for restoring sensory and cognitive functions (e.g., vision in the blind, audition in the deaf). His articles highlight advancements in neural decoding, encoding models, and AI-driven prosthetic vision. He is an ELLIS Scholar and Computable Laureate . Scientific Awards : Computable Laureate Grants : 2023: ≈€250k internal research grant (co-applicant) 2022: ≈€20M NWO Gravitation grant (consortium member) 2020: ≈€500k internal grant (€250k co-applicant) 2019: ≈€250k internal grant 2016: ≈€1.5M NWO Perspective grant (consortium member)

• NeuroAI
• Neural Coding
• Deep Learning
• +۳ مورد دیگر