Keynotes
Michael Granitzer
Title: TBD.
Date: Monday, September 21
Abstract: TBD.
About: Michael Granitzer is a renowned researcher in data science, machine learning, information retrieval, and natural language processing. As a Professor of Data Science at the University of Passau, his research focuses on intelligent systems for data analysis and utilization, with significant contributions to personalized information retrieval, visual analytics, and user behavior analysis. With over 250 scientific publications, including books, book chapters, and journal articles, Michael Granitzer is widely recognized for his contributions to the field. He also leads major research projects, such as the Horizon Europe project “OpenWebSearch.eu.” His previous roles, including Scientific Director at the Know-Center in Graz and Professor of Media Informatics, demonstrate his expertise in interdisciplinary collaboration and leading large-scale research projects.
Lucie Flek
Title: TBD.
Date: Tuesday, September 22
Abstract: TBD.
About: Lucie Flek is a professor for Data Science & Language Technologies at The University of Bonn. Her research focuses on machine learning for Natural Language Processing, with core expertise in user modeling and stylistic variation. She investigates how individuals and sociodemographic groups differ in their language use, and how this variation can be leveraged to predict in-group behavior. This work has led to a broader engagement with bias in NLP, stereotype exaggeration, ethics, model performance on underrepresented groups, and domain adaptation. Her PhD addressed lexical semantics — specifically, the role of word ambiguity and context in document classification, and whether explicit disambiguation and semantic ontologies remain beneficial in the era of deep learning, particularly under limited training data. She has continued pursuing the low-resource paradigm in industry, leading projects in multilingual and multitask learning and various bootstrapping approaches for scarce labeled data. A strong advocate for cross-disciplinary collaboration, she has published jointly with educational researchers, psychologists, sociologists, physicists, and visual analysts.
Suzan Verberne
Title: Conversational search: A promise fulfilled (or is it?)
Date: Wednesday, September 23
Abstract: In the Information Retrieval (IR) community, we have long seen the promise of conversational search. When I did my PhD, Question Answering was considered the future of IR: interacting with a search engine in natural language was the holy grail. Well, here we are: the search engine of the future is a chatbot, and the IR community is not 100% excited. In my keynote at CLEF, I will discuss the role of IR in the era of generative AI. Is retrieval more than just a component in an AI system’s toolbox? What is the relevance of retrieval in AI research and industrial applications? And which questions should our academic community prioritize? (Spoiler: evaluation is one of them.)
About: Suzan Verberne is a Professor of Natural Language Processing at the Leiden Institute of Advanced Computer Science (LIACS), Leiden University. She obtained her PhD in 2010 on the topic of Question Answering at Radboud University in Nijmegen, and since then she has been working at the intersection of Natural Language Processing (NLP) and Information Retrieval (IR). She has supervised projects across a wide range of application domains and collaborations, from social media to law, and from archaeology to health. Her recent work centres on interactive information access for specific domains and low-resource contexts. She has a strong interest in the interplay between search engines and large language models, and a focus on evaluation beyond accuracy. Suzan is highly active in the NLP and IR communities and holds chairing positions at major international conferences.