Seminars - page 2
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
CIPHER: Towards Sovereign Blockchain Intelligence for Europe
Tuesday, November 17, 2026 11:45, 4A301
Peter Fratric (European Commission’s Joint Research Centre)
In an era of heightened geopolitical uncertainty, the European Union is pursuing greater strategic autonomy in critical digital technologies. As crypto-assets and DeFi continue to reshape global markets, developing sovereign analytical capabilities is increasingly important for European financial stability, supervisory independence and monetary sovereignty. Initiated by the European Commission’s Joint Research Centre, CIPHER (Crypto Intelligence Platform for a Holistic European Regulation) provides a scalable intelligence architecture designed to translate complex on-chain data into actionable policy insights. By ingesting data directly from blockchain nodes and processing it through dedicated storage, indexing and analytics layers, CIPHER provides the foundation for near-real-time monitoring of crypto-asset markets, transfers and protocol activity. Current development focuses on advancing in-house methodologies for entity resolution and attribution, extending transfer-level monitoring capabilities, and addressing technical challenges related to blockchain interoperability and cross-chain analysis. In parallel, CIPHER is laying the groundwork for longer-term risk-assessment and stress-testing capabilities, while exploring greater data interoperability across EU institutions and strengthening collaboration with academia. The presentation provides an update on these developments and demonstrates how CIPHER is evolving into a sovereign European capability for crypto-market intelligence and evidence-based regulatory decision-making.
Riding the Waves of Change: Towards Streaming Continual Learning
Wednesday, November 18, 2026 11:45, 4A301
Federico Giannini (Politecnico di Milano)
Real-world data streams continuously evolve, requiring Machine Learning models to adapt to changing distributions while preserving previously acquired knowledge. Some streaming scenarios, such as sensor data streams, also exhibit temporal dependencies. This setting is defined as Evolving Streaming Time Series (ESTS).
Scalable Graph Edit Distance with Deep Reinforcement Learning
Tuesday, December 01, 2026 11:45, 4A301
Hector Kohler
In this talk, we will tackle the Graph Edit Distance (GED) problem. Given two labeled graphs, how many edits does one need to get from one graph to the other (e.g. remove edge, add node, relabel nodes…)? This is an NP-hard problem for which exact solvers can’t scale past graphs with ~20 nodes. In recent years, graph machine learning has shown good performances in the supervised learning setting for the GED. GNN models are trained on graph edit distance instances with up to ~20 nodes, for which exact labels can be computed, and then are used at test time to solve larger and larger instances. Unsuprisingly, performances decay with problem size. In parallel, optimal transport approaches were also studied. They formulate GED as a graph matching problem that can be approximated in cubic time and quadratic space and where supervised training labels are not necessary. The OT quadratic space complexity prohibits scaling to 1000s nodes. We propose to use deep reinforcement learning to train policies that can approximate GED in quadratic time and linear space. We show empirically that our approach can compute GED at scale better than all other existing work.
Title TBA
Wednesday, December 09, 2026 11:45, 4A301
Steffen Staab
Abstract TBA
Past Seminars
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.
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.
From Multimodal Foundation Models to Physical AI: Scalable and Structured Learning
Tuesday, March 31, 2026 11:45, 1D23
Duy Nguyen Ho Minh
Multi-modal LLMs have transformed AI’s ability to learn from rich, multi-modal inputs, spanning images, text, structured records, and more. Yet real-world domains such as healthcare and scientific discovery demand not only accuracy, but also scalability, robustness, and efficiency. This talk will present recent advances in scalable multi-modal learning with a focus on hybrid discrete–continuous representations that bridge structured knowledge and high-dimensional signals. We will discuss algorithmic designs that integrate combinatorial structures into neural networks via differentiable relaxations, enabling end-to-end training across heterogeneous modalities.
Neuro-symbolic representation learning
Tuesday, March 10, 2026 11:45, 4A301
Boshko Koloski (Jožef Stefan Institute)
Abstract TBA
Computational analysis of news: methods and applications in keyword extraction, fake-news classification, sentiment and migrations discourse analysis
Tuesday, February 24, 2026 11:45, 4A301
Senja Pollak & Boshko Koloski (Jožef Stefan Institute)
With the growing volume and influence of digital news media, computational methods for analysing news content have become essential. This talk addresses interconnected challenges spanning keyword extraction, misinformation detection, sentiment classification, and the study of migration discourse. For keyword extraction, we propose SEKE, a mixture-of-experts architecture that achieves state-of-the-art performance while offering interpretability through expert specialisation in distinct linguistic components. To combat misinformation, we demonstrate that ensembling heterogeneous representations from bag-of-words to knowledge graph-enriched neural embeddings substantially improves fake news classification. Extending beyond English, we develop zero-shot cross-lingual methods for both offensive language detection and news sentiment analysis, introducing novel training strategies that significantly outperform prior approaches for less-resourced languages. We apply these computational tools to the socially critical domain of migration discourse, analysing dehumanisation patterns and news framing in Slovene media coverage of Syrian and Ukrainian migrants — uncovering that while discourse has grown more negative over time, it is notably less dehumanising toward Ukrainian migrants. These contributions advance NLP methodology for news analysis while demonstrating its power to illuminate media narratives around pressing societal issues.
Automatic Evaluation of Human-Written and Machine-Generated Text
Tuesday, January 13, 2026 11:45, 4A301
Yanzhu Guo
With the rapid expansion of digital content, automatic evaluation of textual information has become increasingly crucial. My research addresses the challenge of evaluating and enhancing the quality of both human-written and machine-generated texts. Beginning with human-written texts, we develop an argument extraction system to evaluate the substantiation of scientific peer reviews, providing insights into the quality of peer review in NLP conferences in recent years. Additionally, we assess the factuality of abstractive summarization datasets and propose a data refinement approach that enhances model performance while reducing computational demands. For machine-generated texts, we focus on the underexplored aspects of diversity and naturalness. We introduce a suite of metrics for measuring linguistic diversity and conduct a systematic evaluation across state-of-the-art LLMs, exploring how development and deployment choices influence their output diversity. We also investigate the impact of training LLMs on synthetic data produced by earlier models, demonstrating that recursive training loops lead to diminishing diversity. Finally, we explore the naturalness of multilingual LLMs, uncovering an English-centric bias and proposing an alignment method to mitigate it. These contributions advance evaluation methodologies for natural language generation and provide insights into the interaction between evaluation metrics, dataset quality and language model performance.
Contextual knowledge representation for neurosymbolic Artificial Intelligence reasoning
Thursday, December 11, 2025 12:15, 4A301
Simon Coumes
The field of Knowledge Representation and Reasoning is concerned with the representation of information about reality in a form that is both human-readable and machine-processable. It has been a part of artificial intelligence since its inception, and has produced many important formalisms and systems. One key aspect of knowledge is the context in which it is expressed. This has been identified early on in the field and matches with our common experience: understanding a statement or judging its validity often require to know in what context it was meant. Historically, there has been some work aiming at producing logics implementing a general notion of context. None of them saw a lot of adoption, in part because they lack either sufficient expressive power or because they were not sufficiently usable.
SPECTRA: Faster Large Language Model Inference with Optimized Internal and External Speculation
Tuesday, December 09, 2025 11:45, 1D19
Le-Minh Nguyen (Japan Advanced Institute of Science and Technology)
Inference with modern Large Language Models (LLMs) is both computationally intensive and time-consuming. While speculative decoding has emerged as a promising solution, existing approaches face key limitations. Training-based methods require the development of a draft model, which is often difficult to obtain and lacks generalizability. On the other hand, training-free methods provide only modest speedup improvements. In this work, we introduce SPECTRA — a novel framework designed to accelerate LLM inference without requiring any additional training or modifications to the original LLM. SPECTRA incorporates two new techniques that efficiently leverage both internal and external speculation, each independently outperforming corresponding state-of-the-art (SOTA) methods. When combined, these techniques deliver up to a 4.08× speedup across a variety of benchmarks and LLM architectures, significantly surpassing existing training-free approaches. The implementation of SPECTRA is publicly available. Biography: Le-Minh Nguyen is currently a Professor of the School of Information Science and the director of the Interpretable AI Center at JAIST. He leads the Machine Learning and Natural Language Understanding Laboratory at JAIST. He is currently taking his sabbatical at Imperial College London, UK (Until April 2026). His research interests include machine learning & deep learning, natural language processing, legal text processing, and explainable AI. He serves as an action editor of TACL (a leading journal in NLP), a board member of VLSP (Vietnamese language and speech processing), and an editorial board member of AI &Law, Journal of Natural Language Processing (Cambridge). He is a steering committee of Juris-informatics (Jurisin) in Japan – a research area that studies legal issues from informatics.
Mining Expressive Cross-Table Dependencies in Relational Databases
Tuesday, December 02, 2025 11:45, 4A125
François Amat
This thesis addresses the gap between what relational database schemas declare and the richer set of cross-table rules that actually govern real-world data. It introduces MATILDA, the first deterministic system capable of mining expressive first-order tuple-generating dependencies (FO-TGDs) with multi-atom heads, existential witnesses, and recursion directly from arbitrary relational databases, using principled, database-native definitions of support and confidence. MATILDA uncovers hidden business rules, workflow constraints, and multi-relation regularities that schemas alone cannot capture, while ensuring reproducible results through canonicalized search and tractable pruning guided by a constraint graph. To understand when simpler formalisms suffice, the thesis also presents MAHILDA, a relational Horn-rule baseline equipped with disjoint semantics to prevent self-justifying recursion. Overall, the work shows that expressive rule mining on realistic databases is both feasible and insightful, enabling more systematic, explainable, and schema-grounded analyses of complex relational data.
Entity Linking and Relation Extraction for Historical Italian Texts: Challenges and Potential Solutions
Tuesday, October 28, 2025 11:45, 4A125
Cristian Santini (University of Macerata)
Entity Linking and Relation Extraction enable the automatic identification of named entities mentioned in texts, along with their relationships, by connecting them to external knowledge graphs such as Wikidata. While these techniques work well on modern documents, applying them to historical texts presents significant challenges due to the diachronic evolution of language and limited resources for training computational models. This seminar presents recent work on developing methods and datasets for processing historical Italian texts. It will discuss the creation of a new benchmark dataset extracted from digital scholarly editions covering two centuries of Italian literary and political writing. The talk will then present approaches that enhance entity disambiguation by incorporating temporal and contextual information from external Wikidata. Finally, it will detail a method for automatically constructing knowledge graphs from historical correspondence that combines multiple language models in sequence, demonstrating how these technologies can facilitate the exploration and understanding of historical archives without requiring extensive manual annotation or model training.