We are excited to have several keynote speakers at SDP 2025. The following speakers have been confirmed.
University of Washington and AI2, USA
Lucy Lu Wang is an Assistant Professor at the University of Washington Information School and Research Scientist at the Allen Institute for AI (Ai2). Her work focuses on the development of AI methods and tools to improve access to scientific literature and support practitioners and the public in decision‑making, especially in high-expertise domains such as healthcare. She has made notable contributions to scientific NLP datasets, and her work on document accessibility and academic publishing trends have been featured in media outlets like Geekwire, VentureBeat, and the New York Times. Prior to joining the UW, she was a Young Investigator at AI2, and received her PhD in Biomedical Informatics and Medical Education from the University of Washington.
Despite growing interest in making scientific research more accessible and actionable, a persistent research-to-practice gap remains. This talk explores translational science communication, inspired by translational research’s “bench to bedside” ethos, aimed at bridging the communication divide between scholarly knowledge and real-world application. Research papers as they are are the wrong medium for such dissemination—too complex in language, too rigid in form, and too static to satisfy diverse stakeholder needs. I will highlight how LLMs and human-centered design can support more effective translation by enabling accessible content, interactive formats, and personalized outputs tailored to specific audiences such as clinicians, patients, designers, and policymakers. I’ll discuss open challenges in evaluating these systems, balancing automation and augmentation, and ensuring that efforts are inclusive, actionable, and grounded in stakeholders’ definitions of relevance.
University of Tübingen
Dr. Mario Krenn is Professor for Machine Learning for Science at the University of Tübingen, where he leads the Artificial Scientist Lab. Previously, he was a research group leader at the Max Planck Institute for the Science of Light in Erlangen, a postdoctoral researcher at the University of Toronto in Canada, and completed his PhD in quantum physics at the University of Vienna under Anton Zeilinger (Nobel Prize 2022). Dr. Krenn’s research develops artificial intelligence to enhance human creativity in scientific discovery, particularly in quantum physics. His AI methods have autonomously designed quantum experiments and hardware, several of which have been realized experimentally. His ERC Starting Grant project ArtDisQ (2024) aims to transform physics simulators with advanced AI, accelerating discoveries in quantum technologies.
Artificial intelligence (AI) is a potentially disruptive tool for physics and science in general. One crucial question is how this technology can contribute at a conceptual level to help acquire new scientific understanding or inspire new surprising ideas. I will talk about how AI can be used as an artificial muse in physics, which suggests surprising and unconventional ideas and techniques that the human scientist can interpret, understand and generalize to its fullest potential.
University of Melbourne, Carnegie Mellon University
Eduard Hovy is currently a Research Professor with the Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA, USA. He is one of the original 17 fellows of the Association for Computational Linguistics (ACL). He has published more than 500 research articles. His researches focus on various topics, including aspects of the computational semantics of human language.,Dr. Hovy is a fellow of the Association for the Advancement of Artificial Intelligence (AAAI). He serves or has served on the editorial boards of several journals, such as the ACM Transactions on Asian Language Information Processing (TALIP) and Language Resources and Evaluation (LRE).
The transfer of scientific knowledge in the time of LLMs is easier than ever before. From Abstract to Citations, LLMs have shown an ‘understanding’ of all facets of scientific paper structure, well enough to author plausible-looking scientific papers by themselves. They have been used to produce paper interpretations and cross-document analyses, and are being investigated for their ability to produce suggestions for future work automatically simply by reading the literature. Is there anything more for SDP to do? What is it that we (still) want to study, and why do we care?