Cuadro de ponentes

Iryna Gurevych

Professor of Ubiquitous Knowledge Processing in the Department of Computer Science at the Technical University of Darmstadt in German

Iryna Gurevych is Professor of Ubiquitous Knowledge Processing in the Department of Computer Science at the Technical University of Darmstadt in Germany. She also is an adjunct professor at MBZUAI in Abu-Dhabi, UAE, and an affiliated professor at INSAIT in Sofia, Bulgaria. She is widely known for fundamental contributions to natural language processing (NLP) and machine learning. Professor Gurevych is a past president of the Association for Computational Linguistics (ACL), the leading professional society in NLP. Her many accolades include being a Fellow of the ACL, an ELLIS Fellow, and the recipient of an ERC Advanced Grant. Most recently, she has received the 2025 Milner award of the British Royal Society for her major contributions to NLP and artificial intelligence that combine deep understanding of human language and cognitive faculty with the latest paradigms in machine learning.

NLP for Mental Health: Promise, Risk, Responsibility

More than a billion people live with a mental health condition, yet care remains scarce and often a poor fit for the person receiving it — so people increasingly turn to unsupervised chatbots to fill the gap. Because language is the richest signal we have into someone’s psychological state, natural language processing is the key to doing this better. In this talk, I argue that NLP can genuinely help — but solving it takes clinical grounding, real data, and responsible evaluation, not just larger models. Drawing on work from my group, I show how large language models can turn therapy transcripts into treatment-relevant structure, how clinically grounded synthetic data eases the field’s privacy bottleneck, and why evaluation must reflect what clinicians actually care about — not just what’s easy to benchmark. I close with an invitation to build mental health AI that is personalized, privacy-aware, and responsibly evaluated: tools that support clinicians, not replace them.
Team’s web site: https://psych.ukp-lab.de/

Dirk Hovy

Professor, Computing Sciences Department, Bocconi University

Dirk Hovy is a professor in the Computing Sciences Department, the scientific director of the Data and Marketing Insights research unit, and the inaugural dean for Digital Transformation and AI of Bocconi University. Previously, he was faculty at the University of Copenhagen, got a PhD from USC’s Information Sciences Institute, and a linguistics master’s from Marburg university in Germany. Dirk is interested in what computers can tell us about language and what language can tell us about society. That involves ethical questions of bias and algorithmic fairness in AI. Dirk has authored over 150 articles on these topics, two textbooks on NLP in Python, and forthcoming title at MIT Press. Dirk has co-founded and organized several workshops (on computational social science, and ethics in NLP), was a local organizer for the EMNLP 2017 conference, and general chair of EMNLP 2025. He was awarded an ERC Starting Grant project 2020 for research on demographic bias in NLP. In his spare time, he enjoys cooking, leather working, and picking up heavy things to put them back down.

Title: Unsolved: Why the Most Interesting Problems in NLP Are Still Ahead

Abstract: Large language models now power everyday writing, search, tutoring, and decision support. It might seem that with all that, natural language processing is essentially solved. However, that ignores the nature of language. While LLMs are remarkably fluent and better than humans at information processing, scale and fluency are not the same as language understanding. Language is not just a vehicle for information transfer: it is shaped by context, norms, relationships, ideology, and communicative goals. In this talk, I will argue that we have solved one set of problems, only to be able to explore a new, even more interesting set of challenges along those dimensions. Drawing on recent work on disagreement, moral judgment, safety evaluation, and socioeconomic differences in AI use, I will show that current models capture linguistic patterns without fully modeling the social aspects of language. I will argue that this is a hopeful moment for the field: we are finally in a position to address richer questions about social reasoning, human diversity, and language in interaction. The central claim is that NLP is far from finished. Its next frontier is not simply better next-token prediction, but deeper engagement with the social nature of language and closer collaboration with the social sciences, opening exciting new research directions.