Seminars - page 3

The DIG team holds a seminar about every two weeks with speakers either from the team, or invited.

You can add the seminars to your calendar with this ics file, and get emails about future seminars by subscribing to our mailing-list.

If you would like to present your work at our seminar, please contact Nils.

Upcoming Seminars

Title TBA

Tuesday, March 31, 2026 11:45, 1D23

Duy Nguyen Ho Minh

Abstract TBA

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Toward Responsible Natural Language Processing : Ideal, Illusion, or Imperative?

Tuesday, April 14, 2026 11:45, 1A312

Antoine Gourru (Télécom Saint-Etienne)

Large language models have profoundly transformed natural language processing and are increasingly reshaping work, knowledge production, and social organization, yet they remain misaligned with societal values and demand substantial computational resources. In this seminar, I will present my research on responsible NLP, structured around two central pillars: fairness and frugality. Through a scientific overview of selected recent and ongoing works, I will discuss methods to assess and mitigate alignment failures, and to develop resource-efficient approaches that promote more sustainable NLP systems.

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Data Integration: Remaining Challenges and Research Paths

Tuesday, May 19, 2026 11:45, 4A301

Robert Wrembel (Poznań University of Technology)

Data integration (DI) has been a cornerstone of computer science research for decades, resulting in a few established reference architectures. They generally fall into three categories: virtual (federated and mediated), physical (data warehouse), and hybrid (data lake, data lakehouse, and data mesh). Regardless of the paradigm, these architectures depend on an integration layer, implemented by means of sophisticated software designed to orchestrate and execute DI processes. The integration layer is responsible for ingesting data from various sources (typically heterogeneous and distributed) and for homogenizing data into formats suitable for future processing and analysis. On the one hand, in all business domains, large volumes of highly heterogeneous data are produced, e.g., medical systems, smart cities, smart agriculture, which require further advancements in the data integration technologies. On the other hand, the widespread adoption of artificial intelligence (AI) solutions is now extending towards DI, offering alternative solutions, opening new research paths, and generating new open problems. Emerging paradigms, such as Data Spaces and the Model Context Protocol, further advance DI.

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