Invited Speakers
ESSIR 2027 brings together world-leading researchers to share expertise, inspire new directions, and engage directly with the next generation of IR researchers.
Additional speakers will be announced progressively. All confirmed speakers appear below. If you are interested in contributing a lecture, please contact the programme committee.
University of Amsterdam — Netherlands
Talk
"Dense Retrieval in the Era of Large Language Models: Challenges and Opportunities"
Ana Ríos-Alvarado is a Professor of Information Retrieval at the University of Amsterdam, where she leads the IR Lab. Her research spans neural ranking models, dense retrieval, and conversational search. She has published extensively in SIGIR, ECIR, and CIKM, and is a recipient of the ACM SIGIR Early Career Research Award.
University of Glasgow — United Kingdom
Talk
"Rigorous Evaluation in Information Retrieval: From Cranfield to the LLM Age"
James Winterton is a Professor of Probabilistic Information Retrieval at the University of Glasgow. His work focuses on evaluation methodology, test collections, and the statistical foundations of IR experiments. He co-developed several widely used evaluation frameworks and has contributed foundational work on result diversification and query understanding.
Tsinghua University — China
Talk
"Structured Knowledge and Unstructured Text: Bridging the Gap in Modern IR Systems"
Mei-Chen Liu is a Distinguished Professor at Tsinghua University's Institute for Interdisciplinary Information Sciences. Her research bridges knowledge graphs, entity retrieval, and cross-lingual information access. She is co-editor of the ACM TOIS journal and a frequent keynote speaker at major IR venues including WWW, WSDM, and SIGIR.
More to come
ESSIR 2027 will feature a full week of invited lectures. Further speakers across the core themes of Information Retrieval — including evaluation, multilingual retrieval, user modelling, and retrieval-augmented generation — will be announced as the programme is finalised.
View the Programme