جستجوی استاد راهنمای دانشگاهی

Mohammad Aliannejadi is an Assistant Professor at the IRLab (formerly ILPS) within the Informatics Institute at the University of Amsterdam. His research focuses on Information Retrieval (IR), machine learning, natural language processing (NLP), and conversational systems, particularly in modeling user information needs on mobile devices and conversational search systems. He holds a Ph.D. in Informatics from Università della Svizzera italiana (USI), Lugano, Switzerland, and a M.Sc. in Computer Engineering from Tehran Polytechnic. During his Ph.D., he visited the CIIR Lab at the University of Massachusetts Amherst, USA. His research interests include conversational search systems, recommender systems, unified search frameworks, and user-centric evaluation methodologies. Notable contributions include work on clarifying questions in open-domain dialogues, contextual suggestion systems, and cross-market recommendation. Aliannejadi has organized major shared tasks and workshops, including the IGLU Contest (NeurIPS 2021) and XMRec Workshop (RecSys 2021). He serves on program committees of top IR conferences like SIGIR, CIKM, and ECIR, and has authored over 50 peer-reviewed publications in these areas. His work has received recognition, including top performance in TREC Contextual Suggestion tracks (2015, 2016). He actively contributes to the IR community through teaching, including courses on Information Retrieval and Human-in-the-Loop Machine Learning at the University of Amsterdam.

• Information Retrieval
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Pascal Mettes is a tenured Assistant Professor at the University of Amsterdam within the Informatics Institute, specializing in Artificial Intelligence. He leads groundbreaking research in hyperbolic deep learning, a field he has significantly advanced through theoretical developments and practical applications in computer vision and multimodal learning. His research focuses on three primary domains: hyperbolic vision-language models that address the hierarchical nature of language-vision relationships; hierarchical deep learning using hyperbolic embeddings that naturally accommodate exponential growth patterns; and robust deep learning in hyperbolic space that improves out-of-distribution detection and network resilience. Mettes has established himself as a leading figure in this emerging field through numerous publications at top-tier conferences including CVPR, ICCV, ICML, NeurIPS, and ICLR. His recent work demonstrates how hyperbolic geometry provides natural solutions to fundamental limitations in modern deep learning, particularly regarding hierarchical data structures that cannot be adequately represented in Euclidean space. The publication trends show increasing impact and recognition in the computer vision and machine learning communities, with multiple papers receiving oral presentations and best paper nominations. Best paper nomination ESWC25 for 'Designing Hierarchies for Optimal Hyperbolic Embedding' Finalist MM 2023 Best Open-Source Software Competition (for HypLL) Multiple reviewer awards across major conferences including CVPR, ICLR, ECCV, ICML, and NeurIPS MM 2016 Best Doctoral Student Award TRECVID 2015 Winner Multimedia Event Detection Benchmark Mettes actively mentors eight PhD students working on hyperbolic learning and related topics, while also securing significant research funding including ELLIs PhD Award, NWO ClickNL, Google Perception Academic Funding, and Data Science Centre PhD Grants. He serves in prominent academic roles as Program Chair for International Conference on Multimedia Retrieval 2026 and has organized multiple workshops on hyperbolic learning at major conferences. His leadership in establishing hyperbolic deep learning as a recognized research direction is evident through his survey paper in IJCV 2024 and the development of the HypLL library for hyperbolic learning.

• Hyperbolic Deep Learning
• Computer Vision
• Vision-Language Models
• +۳ مورد دیگر

Khiet P. Truong is an Associate Professor affiliated with the Digital Society Institute and the Human Media Interaction group. Their research focuses on the intersection of artificial intelligence, robotics, and human-computer interaction, with particular emphasis on speech emotion recognition, conversational agents for children, and multimodal interaction analysis. Recent work includes exploring how robots can restore trust through apologies, benchmarking Dutch automatic speech recognition systems, and developing child-friendly interfaces for cultural heritage archives. Truong has also investigated physiological signals like laughter and stress markers in speech across diverse contexts. Scientific Awards : Best Functional Design Award (2019) Research Trends : Analysis of speech patterns, emotion recognition, robot-human dialogue, and multimodal behavioral cues dominate their recent publications. Key subfields include Dutch language processing, laughter classification, trust indicators in child-robot interactions, and healthcare applications of speech technology. Academic Activities : Chair of the 26th ACM International Conference on Multimodal Interaction (2024) Examiner roles for PhD defenses and research evaluations (2024–2023) Contributions to workshops on emotion representation and social signal processing

• Artificial Intelligence
• Natural Language Processing
• Human-AI Interaction
• +۴ مورد دیگر

Herman Bruyninckx is a Part-Time Full Professor at Eindhoven University of Technology (TU/e) in the Mechanical Engineering department, specifically within the Control Systems Technology group and EAISI High Tech Systems initiative. He also serves as a professor (Hoogleraar) at KU Leuven in Belgium. Academic focus on robotics, control systems, and multi-agent coordination Active research in model predictive control , semantic mapping , and dynamic constraint algorithms Recent publications address industrial automation , agro-food robotics , and haptic technology Research Highlights : Developed hybrid decision-making frameworks for multi-agent navigation Innovated swing-free control methods for robotic pick-and-place operations Formulated constrained dynamics algorithms with LQR-Gauss principle integration Created ExoTen-Glove for haptic feedback in virtual environments Collaborative Projects : Coordinated with researchers like René van de Molengraft , Elena Torta , and Koen de Vos Contributed to NWO/TTW FlexCRAFT project for cognitive robotics in agro-food technology

• Robotics
• Control Systems
• Multi-Agent Systems
• +۶ مورد دیگر

Maarten de Rijke is a Professor at the University of Amsterdam's Informatics Institute, leading the Information Retrieval Lab (IRLab). He specializes in information retrieval, machine learning, and recommendation systems, focusing on neural ranking models, fairness, and conversational search. His work bridges theory and practice, addressing challenges in reproducibility, robustness, and ethical AI. He supervises numerous PhD students and postdocs, including recent defenses by Barrie Kersbergen, Antonis Krasakis, and Vera Provatorova. His lab collaborates internationally, organizing events like SIGIR workshops and the Search Engines Amsterdam (SEA) meetup. Key awards include the Best Reproducibility Paper Award (2025) and Best Paper at WSDM 2021. Research interests span generative retrieval, adversarial robustness, and fairness in ranking. Notable projects include the FULTR dataset, FairDiverse toolkit, and studies on empathetic conversational systems. He actively promotes open science through reproducible methodologies and community-driven benchmarks.

• Information Retrieval
• Machine Learning
• Neural Ranking Models
• +۴ مورد دیگر

Dr. Stevan Rudinac is a Researcher at the University of Amsterdam's Faculty of Economics and Business , Section Business Analytics . His work focuses on interactive learning systems and multimodal data analysis, particularly in urban contexts and multimedia modeling. Education: PhD in Multimedia and Information Retrieval from Delft University of Technology (2013). Research Interests: Stevan specializes in multimedia modeling , hypergraph learning , and interactive video search . He develops frameworks for scalable analysis of social networks, urban imagery, and large multimodal datasets, bridging machine learning with practical applications in city planning and financial social media. Recent Trends: His 2024-2025 publications highlight large language model optimization , diffusion model evaluation , and dynamic graph embedding for meme stocks. Collaborative projects include the CASTLE 2024 dataset and Exquisitor , a system for 100 million image exploration. Labs & Teams: He contributes to the Business Analytics group at UvA, collaborating with Prof. Marcel Worring and Dr. Björn Þór Jónsson. He co-organized the UrbanMM'21 workshop and participates in ACM Multimedia and MMM conferences.

• Multimedia Modeling
• Machine Learning
• Computer Vision
• +۷ مورد دیگر

Prof. Peter Boncz is a Visiting Professor at the Faculty of Science, Department of Computer Systems at Vrije Universiteit Amsterdam. He also holds visiting fellowships at the Network Institute and High Performance Distributed Computing group. His primary affiliation is with CWI (Centrum Wiskunde & Informatica) as a staff member since 2001. He serves in leadership roles at multiple organizations, including Director of LDBC (London), Advisor to Data.world (Austin), and Board Member of DuckDB Foundation (Amsterdam). Education: Not explicitly stated in provided texts, but his roles indicate advanced academic qualifications in computer science. Research focuses on graph databases, benchmarking, query optimization, and big data management. He co-founded the Linked Data Benchmark Council (LDBC) and contributed to projects like DuckDB. His work addresses challenges in scalable data systems, cloud computing, and knowledge extraction from structured data. Publications emphasize advancing graph data management standards and evaluating performance in distributed environments. He has co-authored influential papers on topics like TAKCO for table-based knowledge extraction and cloud-era benchmarking. Awards: No specific prizes listed, but his leadership roles and contributions to standards reflect industry recognition. Advising and grants: No formal advisees listed, but he contributes datasets (e.g., Public BI benchmarks on Zenodo) and collaborates globally. Active in editorial roles, such as the IEEE Data Engineering Bulletin. Labs/Teams: Core member of CWI's database group, involved in LDBC initiatives, and DuckDB Foundation development.

• Database Systems
• Big Data
• Graph Databases
• +۳ مورد دیگر

Simon Scheider is an Associate Professor of GIS in the Department of Human Geography and Spatial Planning at Utrecht University, Netherlands. His research focuses on conceptual modeling of geographic data, semantic web technologies, and geospatial analysis. He holds a Ph.D. in Geoinformatics from the University of Münster (2012) and has held postdoctoral roles at UC Santa Barbara, ETH Zurich, and other institutions before joining Utrecht in 2016. He received tenure in 2018 and became Associate Professor in 2024. Key achievements include an ERC Starting Grant (2018) for geographic question-answering research and an ERC Consolidator Grant (2024) for automatic geoQA systems. He leads the NWO-funded Exposome-NL project (since 2020), simulating urban policy impacts on citizen health. Scheider co-organized AGILE 2023 and COSIT 2024, and serves on editorial boards for journals like Applied Ontology and ISPRS International Journal of Geo-Information. Research interests span spatial semantics, geocomputation, and human-centered AI. Recent work addresses validity in spatio-temporal models, extensive/intensive quantities in geographic data, and integrating LLMs with GIS. His teaching includes Advanced GIS, Spatial Data Analysis (ADS), and National GI Minor courses.

• Geographic Information Science
• Data Modelling
• Geocomputation
• +۵ مورد دیگر

Nikolay Yakovets is an Assistant Professor at the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). His research focuses on databases and data-intensive systems with specialization in graph data management, query processing optimization, and high-performance data engineering. He leads academic courses including Data Modeling, Database Technology, and Engineering Data-Intensive Systems. Education background includes: PhD in Computer Science from York University (2017) MSc and BSc in Computer Science from York University Research expertise spans: Foundational database technologies Graph query optimization algorithms Cardinality estimation methods Knowledge graph construction High-performance data processing architectures Publication trends show consistent focus on graph database systems, with recent works advancing property graph management, cardinality estimation techniques, and ontology-driven knowledge graphs. Research demonstrates strong emphasis on practical system efficiency and theoretical foundations. Active in student supervision with 41 supervised works recorded. No awards mentioned in source materials.

• Databases
• Data Intensive Systems
• Graph Databases
• +۳ مورد دیگر

Ronald de Wolf is a part-time Full Professor at the Institute for Logic, Language and Computation (ILLC), University of Amsterdam, and a researcher/group leader at the Algorithms and Complexity group of CWI (Dutch Centre for Mathematics and Computer Science). He is an active member of QuSoft and the Amsterdam Theoretical Computer Science ecosystem. His PhD was completed at CWI and ILLC, followed by postdoctoral research at UC Berkeley. Research Focus: De Wolf specializes in quantum computing, complexity theory, and algorithm design. His work explores quantum advantages in computation, communication, and learning, with applications in optimization, machine learning, and information theory. Recent investigations include quantum algorithms for linear algebra, error correction, and communication complexity. Publication Trends: His recent articles (2020-2025) predominantly focus on quantum algorithmic advantages, complexity bounds, and practical applications in machine learning and optimization. Key themes include quantum speedups for linear algebra, error-resilient quantum protocols, and theoretical limits of quantum computation. Awards & Honors: ERCIM Cor Baayen Award (2003) STOC Best Paper Award (2012) STOC Test-of-Time Award (2022) Gödel Prize (2023) Academic Leadership: He currently advises PhD student Lynn Engelberts and has graduated 10 doctoral students. As coordinator of the NWO Gravitation program Quantum Software Consortium , he oversees major research initiatives. He secured participation in EU projects (QAIP, RESQ, QAP, QCS, QALGO, QuantAlgo) and leads research teams at CWI and QuSoft.

• Quantum Computing
• Complexity Theory
• Error-Correcting Codes
• +۳ مورد دیگر

J.C. Scholtes is an Extra-ordinary Professor of Text Mining at the Department of Knowledge Engineering, Faculty of Science and Engineering, University of Maastricht. He is also a Senior Research Fellow at the Dutch School for Information and Knowledge Systems (SIKS), a Board Member at IPRally, and a Venture Partner at ENDEIT Capital. M.Sc. in Computer Science from Delft University of Technology Ph.D. in Computational Linguistics from University of Amsterdam His research expertise spans Natural Language Processing, Machine Learning, and Artificial Intelligence, with applications in legal, medical, business, and regulatory domains. He focuses on text mining, machine translation, question-answering systems, and information extraction from unstructured data. Recent publications (2024–2025) emphasize context-aware machine translation, misinformation detection in recommendation systems, healthcare data analysis, and food science applications. Key themes include integrating deep learning architectures (e.g., Transformers), optimizing search and translation efficiency, and leveraging hybrid human-machine approaches. He has collaborated widely in industry and academia, notably deploying e-discovery software for institutions like the UN War Crimes Tribunals and FBI-ENRON. His career history includes leadership roles at ZyLAB (1987–2021) and prior service in the Royal Dutch Navy.

• Natural Language Processing
• Information Retrieval
• Text Mining
• +۵ مورد دیگر

Chuan Meng is a researcher transitioning to a postdoctoral position at the University of Edinburgh's Natural Language Processing Group in September 2025. He obtained his PhD in Artificial Intelligence from the University of Amsterdam (UvA) in June 2025, supervised by Maarten de Rijke and Mohammad Aliannejadi. His research focuses on Information Retrieval and Natural Language Processing with Large Language Models , particularly in conversational agents, model/data-efficient neural ranking, and automatic evaluation techniques like query performance prediction. Education: PhD in Artificial Intelligence, University of Amsterdam (2025) MS in Computer Science and Technology, Shandong University (2021) BS in Electronic Commerce, Shandong Normal University (2018) His research interests span conversational search optimization, proactive agent development, knowledge-grounded dialogue systems, and query performance prediction using LLM-generated judgments. Recent work includes UniConv (unified retrieval/response generation), SOLID (intent-aware dialog generation), and QPP++ 2025 workshop organization. Key scientific contributions include 440+ Google Scholar citations (H-index: 13) and publications in premier venues like SIGIR , ACL , EMNLP , and TOIS . He has served as program committee member for SIGIR 2025/2024, ACL 2023, and other top conferences. As teaching assistant , he contributed to Information Retrieval and Natural Language Processing courses at UvA and Shandong University. His administrative roles include IRLab LinkedIn manager, webmaster for IRLab website, and seminar chair at UvA.

• Information Retrieval
• Natural Language Processing
• Conversational Search
• +۶ مورد دیگر

Mel Chekol is an Assistant Professor at Utrecht University, affiliated with the Data Intensive Systems group within the Science faculty. He holds a PhD from INRIA Rhône-Alpes and a double MSc from Vienna University of Technology and Free University of Bozen-Bolzano. His research focuses on knowledge graphs, spatio-temporal data integration, probabilistic inference, and scalable machine learning applications. Previously, he worked at institutions including INRIA Nancy Grand Est, University of Mannheim, and the National Institute of Informatics in Tokyo. Key research interests include reasoning in knowledge graphs, temporal data modeling, and applying language models to enhance knowledge representation. He contributes to projects like the Utrecht Platform for Applied Data Science and has collaborated on frameworks such as the EXMO and WAM teams. His work emphasizes practical applications of AI and data science in governance and sustainability. Mel has published extensively in venues like VLDB Journal, ISWC, and AAAI, focusing on topics like rule learning, temporal knowledge graphs, and scalable inference systems. His research bridges theoretical advancements with real-world data challenges.

• Knowledge Graphs
• Artificial Intelligence
• 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

Jerry Spanakis is an Assistant Professor at Maastricht University with dual affiliations: the Department of Advanced Computing Sciences (Faculty of Science and Engineering) and the Maastricht Law+Tech Lab (Faculty of Law). His roles include leading the EU Horizon project VOXReality, researching for NSMD/HumanAds/RegTech4AI initiatives, and serving as a technical expert for the European Commission’s e-enforcement academy. He coordinates MaastrichtNLP (NLP research group) and participates in the Open Science Community Maastricht. Education: PhD in Computational Intelligence (2007–2012) from the National Technical University of Athens, School of Electrical & Computer Engineering. Research Focus: Social Machine Learning: Developing responsible AI systems for societal challenges, including interpretable models for consumer protection and regulatory compliance. Computational Social Media: Analyzing social media data to detect online harms (e.g., misleading ads, content moderation failures) and model user behavior. Structuring Unstructured Data: Semantic organization of legal texts, social media, and multimodal data for applications in law, aviation, and public health. Publication Trends: Jerry's recent work (2023–2025) emphasizes NLP innovations for legal and regulatory domains, multilingual information retrieval, and ethical AI frameworks. Key themes include Large Language Model applications in law, influencer marketing compliance, and cross-lingual neural machine translation. Scientific Awards: None reported. Advising & Grants: Jerry supervises 7 PhD candidates and 100+ Master’s/Bachelor’s students in NLP, machine learning, and social computing. He leads the €2.8M EU project VOXReality (voice-driven XR interactions) and contributes to NWO/Philips grants on mental health analytics. Current grants focus on: AI-driven legal process automation (RegTech4AI) Dark pattern detection in e-commerce (NSMD) Influencer marketing transparency (HumanAds) Labs & Teams: Jerry founded MaastrichtNLP, a university-wide NLP research group, and co-leads the Law+Tech Lab, which develops computational tools for legal compliance. His teams collaborate with Deloitte, the European Commission, and healthcare institutions on applied AI projects.

• Machine Learning
• Natural Language Processing
• Computational Social Media
• +۵ مورد دیگر