Find Academic Supervisors

• Information Retrieval
• Machine Learning
• Natural Language Processing
• +3 more

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.

Rianne ConijnView profile

Assistant Professor

• Learning Analytics
• Human-Technology Interaction
• Artificial Intelligence
• +6 more

Rianne Conijn is an assistant professor in the Human-Technology Interaction group at Eindhoven University of Technology (TU/e), Netherlands. Her research bridges data-driven methodologies (machine learning, statistical modeling) with human-centered design to enhance learning analytics, explainable AI, and writing process analysis. She holds a joint PhD (cum laude) from Antwerp University and Tilburg University, and an MSc (cum laude) in Human-Technology Interaction from TU/e. Academic Background: MSc (2015, TU/e, cum laude), PhD (2020, Antwerp University & Tilburg University, cum laude). Research Focus: Learning analytics, keystroke logging, explainable AI for education, data dashboards, and self-regulated learning dynamics. Teaching: Courses in Advanced Research Methods, Human-AI Interaction, Behavioral Research Methods, and AI ethics in education. Her recent publications explore parallel language planning in writing, longitudinal self-regulated learning strategies, and generalizability of academic performance prediction models. She leads an NWO Veni project on Human-Centered AI in education, emphasizing tailored explanations for student-AI collaboration. Scientific awards include cum laude distinctions for her MSc and PhD, and the NWO Veni grant. Collaborative work spans institutions in the Netherlands, Norway, and the U.S., with applications in intelligent tutoring systems and ethical AI deployment in exams. Key trends across her work: integration of machine learning with educational theory, leveraging keystroke data for cognitive process insights, and prioritizing actionable, explainable AI systems for student support. Publications span journals like the Journal of Experimental Psychology: General , Computers and Education , and IEEE Transactions on Learning Technologies . Scientific Awards: NWO Veni grant for Human-Centered AI in education Cum laude for MSc and PhD Grants & Collaborations: National Science Foundation grants (2016868, 2302644) for biometric feedback in writing UK Research and Innovation grant (ES/W011832/1) for real-time AI scaffolding TU/e Boost! Program grant for self-regulated learning analysis Labs & Teams: EAISI Foundational (Eindhoven AI Systems Institute) Human Technology Interaction group at TU/e Collaboration with Norwegian Reading National Center (University of Stavanger) Project teams for Waterproof ITS and ProWrite grants

Franziska PannachView profile

Assistant Professor

• Computational Linguistics
• Digital Humanities
• Semantic Web Technology
• +4 more

Dr. Franziska Pannach is an Assistant Professor at the Faculty of Arts of the Rijksuniversiteit Groningen (Netherlands), affiliated with the Centre for Language and Cognition (CLCG) . She specializes in Computational Linguistics , Digital Humanities , and Semantic Web Technology , focusing on modeling narratives and mythological texts using NLP and ontologies. Her work emphasizes Open Science and cross-cultural comparisons of folktales and myths. Education : Dr. rer. nat. (PhD) in Applied Computer Science (Digital Humanities/Computational Linguistics), University of Göttingen, Germany (2024) MSc in Applied Computer Science (Digital Humanities), University of Göttingen (2019) BSc in Applied Computer Science (Computational Neuroscience), University of Göttingen (2013) Research Interests include computational approaches to mythological and folkloristic analysis, shallow ontologies for narrative comparison, and semantic web applications in cross-cultural studies. She leads the GOLEM (Graph Ontologies for Literary Evolution Models) project and contributed to the STRATA (Stratification Analyses for Mythic Narrative Materials) project. Teaching : Semantic Web Technology (MSc), Database-driven Web Technology (BSc), Digital Humanities: Tools and Methods (MSc), Analysing Data (MSc) Supervision : PhD students Pritha Majumdar (RUG) and Kristina Schneider (RUG/University of Mainz) Publications focus on narrative ontologies, mythological event annotation, and NLP for under-resourced languages. Her recent work includes the GOLEM Triple Store and shallow ontologies for myth comparison. Networks & Memberships : Member, Digital Humanities Association of Southern Africa (DHASA) since 2019 Co-founder and editor of the Journal of Digital Humanities Association of Southern Africa (JDHASA) Collaborations with institutions in Germany, South Africa, and Denmark

Tiziano De MatteisView profile

Assistant Professor

• Parallel and Distributed Computing
• High-Performance Computing
• Energy Efficiency in Computing
• +4 more

Tiziano De Matteis is an Assistant Professor in the @Large Research group at Vrije Universiteit Amsterdam's Faculty of Science, Department of Computer Systems. He also holds an affiliation with the Network Institute. His research focuses on overcoming post-Moore architecture challenges through parallel and distributed computing, high-performance systems, energy efficiency, and FPGA applications. Previously, he was a PostDoc at ETH Zurich's SPCL Group and earned his MSc/PhD from the University of Pisa. Education PhD in Computer Science, University of Pisa MSc in Computer Science, University of Pisa Research Interests Post-Moore architectures for distributed ecosystems Energy-aware parallel computing High-level abstractions for parallel software development FPGA-based hardware acceleration Data stream processing and distributed systems Recent Research Trends Recent work emphasizes: Data center risk analysis and sustainability Optimizing microservices and distributed scheduling LLM model offloading to NVMe storage Python-based data-centric programming productivity GPU interconnect performance in supercomputing Grants & Projects Participates in the EU-funded 'Extreme and Sustainable Graph Processing' project (2023-2025), exploring scalable graph algorithms and energy-efficient computing systems. Teaching Accelerator-Centric Computing Ecosystems Computer Organization Distributed Systems Systems Seminar

• Machine Learning
• Neural Networks
• Uncertainty Quantification
• +4 more

Dr. Matias Valdenegro Toro is an Assistant Professor of Machine Learning at the University of Groningen within the Faculty of Science and Engineering and the Artificial Intelligence department of the Bernoulli Institute. He holds a PhD from Heriot-Watt University (2019) and a Master's in Autonomous Systems from Bonn-Rhein-Sieg University of Applied Sciences (2014). His research focuses on trustworthy machine learning models , particularly in uncertainty quantification , medical AI , and robotics , with applications in computer vision and explainable AI. He teaches courses like Introduction to Machine Learning and Deep Learning at the Bachelor and Master levels. His work emphasizes robustness in AI systems, including uncertainty estimation for medical applications, super-resolution techniques, and neuromorphic robotics. He has published widely on topics like Bayesian neural networks, prompt tuning, and sanity checks for explanations. Notable awards include Best Reviewer at ICML (2024) and Highlighted Reviewer at ICLR (2022). He collaborates with institutions like the German Research Center for Artificial Intelligence and actively contributes to open-source datasets (e.g., the Japanese Uncertain Scenes Dataset ). Key grants and activities include organizing the ENLIGHT BIP Course on Deep Learning for Forestry and teaching at the European Summer School on AI . His research also addresses regulatory challenges like the EU AI Act's implications for uncertainty quantification in general-purpose AI.

Annette ten TeijeView profile

Professor

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

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

• Language Acquisition
• Developmental Psychology
• Social Cognition
• +5 more

Dr. Tom St Pierre is an Assistant Professor in the Department of Languages, Literature and Communication at Utrecht University, affiliated with the Institute for Language Sciences. His research investigates the interplay between linguistic knowledge and social cognition in child development, focusing on how children use social information to guide language processing and vice versa. Current institution: Utrecht University Research areas: Language acquisition, processing, and disorders; Dynamics of Youth (DoY) - Early Childhood Teaching: Involved in BA and MA programs in English Language & Culture and Multilingualism & Language Acquisition His recent publications analyze: 2024: Experimenter identity effects in developmental research 2024: Group membership influence on children's lexical choices 2023: Pandemic-related socioeconomic impacts on toddler language development 2022: Cross-generational phonetic alignment and vocabulary processing

Giulia PerugiaView profile

Assistant Professor

• Social Robotics
• Social Psychology
• Ethical and Inclusive HRI
• +3 more

Dr. Giulia Perugia is an Assistant Professor at Eindhoven University of Technology (TU/e), affiliated with the Human Technology Interaction group within the School of Industrial Engineering and Innovation Sciences. She holds a BA in Literature and Linguistics from the University of Roma Tre, an MSc in Cognitive Science (Summa cum Laude) from the University of Siena, and a double PhD in Assistive Technologies from the Polytechnic University of Catalonia and TU/e. Prior to joining TU/e in 2021, she conducted postdoctoral research at Uppsala Social Robotics Lab (2018–2021). Her research focuses on Social Robotics, Social Psychology, and Ethical HRI, investigating societal biases in robot design and their impact on human interactions. She has contributed to projects like the ESDiT initiative exploring ethics in disruptive technologies. Notable achievements include the 2022 Honorable Mention for User Studies at HRI and the 2017 RSJ/KROS Distinguished Interdisciplinary Research Award. She teaches courses on Human-Robot Interaction, Qualitative Methods, and User Experience Design. Research interests include studying perceptions of social robots over time, biases in humanoid robot design, and applications for people with dementia. She has developed engagement models like ENGAGE-DEM to improve quality of life through technology. Her work bridges cognitive science, robotics, and ethics, with over 30 peer-reviewed publications and media engagements discussing gendering robots and societal implications.

Janet HuangView profile

Assistant Professor

• Human-AI Interaction
• Creative AI
• Design of AI Systems
• +3 more

Janet Huang serves as Assistant Professor in the Industrial Design department at Eindhoven University of Technology (TU/e), where she contributes to the Future Everyday group. Her research pioneers human-AI co-learning frameworks that empower designers to harness AI as a creative material for solving complex real-world problems across diverse contexts. Academic Background: PhD in Computer Science, National Taiwan University (2018) MSc in Computer Science, National Taiwan University (2008) Research Focus: Dr. Huang designs novel toolkits bridging data sensemaking and decision-making support, with emphasis on creative AI applications and ethical human-AI collaboration . Her work spans healthcare data visualization, urban play systems, and memory augmentation tools, consistently exploring how AI can enhance human creativity while addressing societal implications. She employs participatory design methods to develop systems that balance technical innovation with user-centered values. Publication Trends: Recent articles (2023-2025) reveal three dominant trajectories: (1) generative AI for domain-specific problem-solving in nursing and biodesign, (2) playful urban exploration systems examining citizen-AI tensions, and (3) memory and decision-making support tools analyzing anthropomorphism effects. These works demonstrate her signature approach of embedding technical AI research within rich human contexts. Awards & Recognition: Ph.D. Thesis Honorable Mention, TAAI 2018 MOST Postdoctoral Scholarship (Taiwan) Mingler Scholarship 2023 Best Poster Honorable Mention, CSCW 2023 Academic Contributions: Dr. Huang teaches specialized courses including Creativity and Aesthetics of Data & AI and Digital Craftsmanship , while supervising 19 student projects. Her research receives support from competitive grants including the MOST scholarship, with applications spanning healthcare innovation and sustainable community development. She actively contributes to UN Sustainable Development Goals through human-centered AI solutions. Research Environment: As core member of TU/e's Future Everyday group, she collaborates with interdisciplinary teams spanning computer science, design, and domain experts. Her lab environment emphasizes iterative prototyping and real-world deployment, particularly in healthcare settings and urban contexts where AI systems interface with complex human practices.

Chuan MengView profile

Researcher

• Information Retrieval
• Natural Language Processing
• Conversational Search
• +6 more

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.

• Synchronous Computer Mediated Communication
• Task-Based Language Teaching
• Telecollaboration
• +4 more

Dr. Silvia Canto is an Assistant Professor of Spanish at Utrecht University, working within the Department of Languages, Literature and Communication in the Faculty of Humanities. She is affiliated with the Institute for Cultural Inquiry and coordinates Spanish language courses while contributing to the Educational Minor (ICT and language education) and the Master of Foreign Language Pedagogy program. Her primary research focuses on the integration of technology in language education, with particular emphasis on telecollaboration, virtual worlds, and synchronous computer-mediated communication. Canto's work explores task design and the added value of virtual exchanges in language curricula to enhance intercultural communication. Analysis of her recent publications (2021-2025) reveals a consistent focus on virtual reality applications in language learning, with increasing emphasis on high-immersion technologies. Her work spans both theoretical frameworks and practical implementations, with numerous publications examining virtual exchange programs, teacher training for digital environments, and the impact of immersive technologies on intercultural communicative competence development. Dr. Canto actively participates in European-funded research projects including E-LIVE (2022-2024), TeCoLa, TILA, NIFLAR, and EUROVERSITY, demonstrating her leadership in the field of technology-enhanced language learning. She serves as an external examiner for the International Baccalaureate and is a member of the Dutch National Expertise Team on Foreign Language Pedagogy. As a dedicated academic, she conducts peer reviews for prominent journals including ReCALL, CALICO, and CALL, and is an active member of professional associations such as Task Based Language Teaching (TBLT) and the European Association for Computer Assisted Language Learning (EuroCALL). Her work bridges theoretical research with practical applications in language teaching methodology, making significant contributions to the evolving landscape of digital language education.

Mustafa AkpinarView profile

Lecturer

• Cultural Communication
• Health Communication
• Acculturation Studies
• +3 more

Mustafa Akpinar is a PhD candidate at the Behavioural Science Institute and a Lecturer in Communication Science at Radboud University. His research focuses on cultural and health communication within Turkish-Dutch communities, blending academic inquiry with practical teaching. He coordinates courses on statistics and cultural communication practices, leveraging his Turkish-Dutch heritage to inform his pedagogy. Education: PhD candidate at Radboud University (current role) His research interests emphasize the intersection of culture, health messaging, and generational dynamics. He explores how cultural identity shapes responses to public health initiatives and communication strategies in multi-ethnic societies. His work also addresses acculturation processes across three generations of Turkish-Dutch citizens, examining factors like language use, media engagement, and national identification. His articles highlight trends in effective communication across sectors, including public transport webcare strategies and the role of empathy in customer interactions. He advocates for inclusive communication practices, exemplified by his involvement in initiatives recognizing cultural diversity (e.g., Ramadan and Eid celebrations at Radboud University). Awards/Grants: None explicitly mentioned Advising/Teaching: Coordinates statistics courses and cultural communication projects He actively balances academic responsibilities with family life, appreciating Radboud University's flexibility in scheduling, such as accommodating childcare needs.

• Artificial Intelligence
• AI & Law
• Computational Argumentation
• +9 more

Prof. Floris Bex (Utrecht University) is a leading scholar in AI & Law, focusing on explainable AI , computational argumentation , and responsible AI development . He holds a dual appointment at the Molengraaff Institute for Private Law and Department of Information and Computing Sciences , bridging legal theory with technical AI innovation. His work emphasizes transparency in high-risk domains like policing and judicial decision-making. Scientific Director, National Police Lab AI Member, Board of Advisory Scientific Research (WODC), Ministry of Justice Editorial roles in Artificial Intelligence and Law and Argument and Computation His research combines symbolic and subsymbolic AI to create systems that reason with legal arguments, including projects like ALGOPOL and the Formal Explanations with Computational Argumentation framework. He has developed an explainable AI system for crime report assessment that improves citizen trust through transparent rejections. Key publications span 2016–2024 , with recent work on argumentation-based explanations , incomplete evidence handling , and human-AI collaboration in policing . His projects integrate Bayesian networks with argumentation theory to address real-world challenges in digital justice and public safety. Scientific awards include: Editorial board member, Artificial Intelligence and Law Scientific Advisor to Dutch National Police Lead in ICAI network collaborations He teaches Computational Argumentation and Law and Computer Science , and has supervised numerous AI & Law projects involving students like Daphne Odekerken and Anton Borg.

• Natural Language Processing
• Machine Learning
• Computational Linguistics
• +8 more

Marijn Schraagen is an Assistant Professor in Natural Language Processing at Utrecht University's Faculty of Science, specifically within the Information and Computing Sciences department. His academic work bridges computational linguistics with practical applications across multiple domains including healthcare, law, and cultural heritage. His research interests focus on the application of machine learning methods to large collections of text and speech data. Schraagen has developed expertise in Dutch language processing with specific applications in clinical settings, legal contexts, and historical language analysis. Notably, within the Goallab at the department of Social, Health and Organizational Psychology, he contributes to developing machine learning models for detecting and preventing low literacy among Dutch children. His publication record demonstrates consistent research activity from 2010 through 2025, with recent work showing particular strength in legal text analysis, clinical NLP applications, and historical language processing. His articles reveal a pattern of interdisciplinary collaboration, working with researchers from psychology, public health, law, and social sciences to address real-world problems through NLP solutions. Schraagen has received research funding from diverse sources including Anders AI Lab, NWO Ai-NEDXS, the National Police, and the Royal Library. His projects include developing AI tools for literacy detection, analyzing protest events in social media, and creating systems for processing historical Dutch texts. He teaches courses in computational thinking, knowledge-intensive process analysis, and methods in AI research, contributing to both the theoretical and practical education of students in the field of artificial intelligence and natural language processing.

Christina SchneegassView profile

Researcher

• Human-Computer Interaction (HCI)
• Cognitive Science
• Educational Technology
• +3 more

Christina Schneegass is a researcher at Delft University of Technology's Faculty of Industrial Design Engineering, specializing in Human-Centered Design and Perceptual Intelligence. She holds a Ph.D. in Media Informatics and Human-Computer Interaction from Ludwig-Maximilians University Munich, alongside M.Sc. and B.Sc. degrees in Applied Cognition and Media Sciences from the University of Duisburg-Essen. Her work focuses on cognitive processes in Human-Computer Interaction (HCI), particularly in educational technology, cognitive augmentation, and neurotechnology. She has explored topics like AR-enhanced TV experiences, chatbot response delays, and haptic interface design. Her research emphasizes balancing technological innovation with cognitive enhancement, leveraging neurotechnologies to understand user perception. She teaches courses such as Cognitive and Psychological Design and Cognitive Ergonomics . Schneegass collaborates on projects like Dark Haptics (2025) and Broadening the Mind (2024), advancing interactive system design and neurotechnology applications.