There is increasing interest in understanding multilingual training dynamics and shared representations, instead of analysing final model checkpoints. Tracing training dynamics allows us to analyse when linguistic information and shared concept spaces emerge during pre-training and understand model mechanisms, e.g. where alignment breaks down. In this talk, I will discuss why studying training dynamics is useful, particularly from a multilingual lens, and present recent findings on studying model behaviour and representations during pre-training.

Barbara Plank is Professor and co-director of the Center for Information and Language Processing at LMU Munich. She holds the Chair for AI and Computational Linguistics at LMU Munich and is a visiting Professor at the Computer Science department at the IT University of Copenhagen. Her MaiNLP research lab (Munich AI and NLP lab) focuses on robust machine learning for Natural Language Processing with an emphasis on human-inspired and data-centric approaches. Her research has been funded by distinguished grants and awards, including an ERC Consolidator Grant, DFF Sapere Aude Research Leader grant, ELLIS Fellow, and several best paper awards. She regularly serves on international committees, including the Association for Computational Linguistics (ACL), the European Chapter of the ACL, the Northern European Association for Language Technology (NEALT) and Scientific Advisory Boards of Research Centers across Europe.

Neural speech interfaces aim to restore natural communication in individuals who have lost the ability to speak while preserving cognitive function. Over the past decade, this field has undergone a remarkable transformation, moving from slow and cognitively demanding spelling-based brain–computer interfaces to systems capable of decoding continuous speech directly from neural activity. These advances have been driven by the convergence of high-resolution invasive neural recording technologies, improved experimental paradigms for speech production and perception, and powerful deep learning models inspired by modern automatic speech recognition systems. In this talk, I will review the state of the art in neural speech prostheses, with a particular focus on next-generation BCIs that translate cortical activity into text or synthetic speech. I will discuss key design choices, including neural recording techniques (such as ECoG, sEEG, and intracortical microelectrodes), target brain areas, decoding architectures, and evaluation metrics. I will also highlight recent clinical results demonstrating unprecedented levels of accuracy, fluency, and long-term stability in continuous speech decoding. Finally, I will outline current challenges and future directions, including scalability across users, real-time bidirectional feedback, and the path towards clinical and real-world deployment, illustrated with ongoing work from our research group.

Jose A. Gonzalez-Lopez is an Associate Professor at the University of Granada whose research sits at the frontier of artificial intelligence, computational neuroscience, and neural speech prostheses. His work addresses the core challenge of how to translate high-dimensional neural activity into fluent, natural speech, bridging invasive neural recordings with modern deep learning and speech–language models. He leads multiple competitive R&D projects on AI-driven speech restoration for individuals with severe neurological and phonatory impairments, with a strong emphasis on long-term robustness, scalability across users, and real-world clinical deployment. He has published over 100 papers in leading international journals and conferences. His contributions have been recognized with several awards for scientific excellence and technological innovation, and his research is embedded in a strong international collaboration network built through extended research visits to institutions such as the University of Sheffield, the University of Bremen, and Maastricht University.

Low resource NLP is nowadays an umbrella keyword that covers a wide set of research directions. In this talk I will argue that it is important to carefully present the low resource scenario and distinguish when the languages are truly lacking resources, as opposed to simulating lack of labeled data. I propose to follow a more systematic way to represent work in this space and to question how we approach technology development for these languages. I will also present recent work in my group that contributes to improve language representation by exploring efficient approaches to diverse languages.

Thamar Solorio is a professor of NLP at MBZUAI where she also serves as Vice Provost for Faculty Excellence and Advancement. Her research interests include NLP for low-resource settings and multilingual data, including code-switching and information extraction. More recently, she has been exploring language and vision problems, focusing on developing inclusive NLP. She served two terms as an elected board member of the North American Chapter of the Association of Computational Linguistics (NAACL) and was PC co-chair for NAACL 2019, and recently stepped down from being co-Editor in Chief of the ACL Rolling Review Initiative (ARR). She was the general chair of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP).

Despite recent tendencies towards building large "monolithic" neural models, fine-tuned expert models and parameter-efficient specialised modules still offer gains over large monoliths in specific tasks and for specific data distributions (e.g., low-resource languages or specialised domains). Moreover, such modularisation of skills and expertise into dedicated models or modules allows for asynchronous, decentralised, and more efficient continuous model development, as well as module reusability. However, a central question remains: how to combine and compose these modules to enable positive transfer, sample-efficient learning, and improved out-of-domain generalisation. In this talk, after discussing the key advantages of modularisation and modular specialisation, I will provide an overview of prominent module and model composition strategies. I will focus on composition at the parameter level (model merging) and functional level (model MoErging), and then illustrate the usefulness of these techniques across several applications.

Ivan Vulić is currently a Research Scientist at Google DeepMind in Zurich after spending a year there as a Visiting Researcher. Before that he was a Research Professor and a Royal Society University Research Fellow in the Language Technology Lab, University of Cambridge, where he spent 10 years across different research roles. From January 2018 until November 2024 he was also a Senior Scientist at PolyAI in London. Ivan holds a PhD in Computer Science from KU Leuven awarded summa cum laude. In 2021 he was awarded the annual Karen Spärck Jones Award from the British Computing Society for his research contributions to Natural Language Processing and Information Retrieval. His core expertise and research interests span, among others, cross-lingual, multilingual and multi-modal representation learning, modularity and composability of ML models, sample-efficient, parameter-efficient and few-shot ML, conversational AI, data-centric ML.